[Perf][Diffusion] Add configurable H3 VAE stacked tiling

Signed-off-by: hsliuustc0106 <liuhongsheng4@huawei.com>

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

@@ -1,4 +1,7 @@
 #!/bin/bash
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
+
 # vllm-omni customized version
 # Based on: https://github.com/vllm-project/ci-infra/blob/main/buildkite/bootstrap-amd.sh
 # Last synced: 2025-12-15
@@ -6,6 +9,9 @@
 
 set -euo pipefail
 
+# The bootstrap runs from the repository root; ShellCheck does not follow
+# sourced files unless invoked with -x.
+# shellcheck disable=SC1091
 source .buildkite/common/scripts/resolve_skip_ci.sh
 
 if [[ -z "${RUN_ALL:-}" ]]; then
@@ -63,6 +69,8 @@ upload_pipeline() {
     # Install minijinja
     ls .buildkite || buildkite-agent annotate --style error 'Please merge upstream main branch for buildkite CI'
     curl -sSfL https://github.com/mitsuhiko/minijinja/releases/download/2.3.1/minijinja-cli-installer.sh | sh
+    # Installed by the minijinja bootstrap above and only present on the CI agent.
+    # shellcheck disable=SC1091
     source /var/lib/buildkite-agent/.cargo/env
 
     if [[ $BUILDKITE_PIPELINE_SLUG == "fastcheck" ]]; then
@@ -90,22 +98,82 @@ upload_pipeline() {
     cd .buildkite/amd
 
     # 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).
+    # For debugging, DEBUG_TEST_YAML accepts a comma-separated list containing
+    # "merge" and/or "ready" (case-insensitive). Multiple suites are combined
+    # before rendering so they share one amd-build step.
     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 '$DEBUG_TEST_YAML'" >&2
+        declare -a DEBUG_TEST_SPECS=()
+        declare -A SEEN_DEBUG_TESTS=()
+        IFS=',' read -ra REQUESTED_DEBUG_TESTS <<< "${DEBUG_TEST_YAML,,}"
+
+        for requested_test in "${REQUESTED_DEBUG_TESTS[@]}"; do
+            # Permit readable values such as "ready, merge," and ignore the
+            # empty item produced by a trailing comma.
+            requested_test="${requested_test//[[:space:]]/}"
+            [[ -z "$requested_test" ]] && continue
+
+            if [[ -n "${SEEN_DEBUG_TESTS[$requested_test]:-}" ]]; then
+                echo "ERROR: duplicate DEBUG_TEST_YAML suite '$requested_test'" >&2
                 exit 1
-                ;;
-        esac
-        echo "DEBUG_TEST_YAML override: using $TEST_YAML"
+            fi
+            SEEN_DEBUG_TESTS[$requested_test]=1
+
+            case "$requested_test" in
+                ready)
+                    DEBUG_TEST_SPECS+=("READY_TESTS:test-amd-ready.yml")
+                    ;;
+                merge)
+                    DEBUG_TEST_SPECS+=("MERGE_TESTS:test-amd-merge.yml")
+                    ;;
+                *)
+                    echo "ERROR: DEBUG_TEST_YAML entries must be 'merge' or 'ready', got '$requested_test'" >&2
+                    exit 1
+                    ;;
+            esac
+        done
+
+        if [[ ${#DEBUG_TEST_SPECS[@]} -eq 0 ]]; then
+            echo "ERROR: DEBUG_TEST_YAML did not contain a test suite" >&2
+            exit 1
+        elif [[ ${#DEBUG_TEST_SPECS[@]} -eq 1 ]]; then
+            TEST_YAML="${DEBUG_TEST_SPECS[0]#*:}"
+        else
+            TEST_YAML=$(mktemp "${TMPDIR:-/tmp}/amd-debug-tests.XXXXXX.yml")
+            python - "$TEST_YAML" "${DEBUG_TEST_SPECS[@]}" <<'PY'
+import sys
+
+import yaml
+
+
+output_path, *suite_specs = sys.argv[1:]
+combined = {"env": {}, "steps": []}
+
+for suite_spec in suite_specs:
+    group_name, input_path = suite_spec.split(":", 1)
+    with open(input_path, encoding="utf-8") as test_file:
+        suite = yaml.safe_load(test_file)
+
+    for name, value in (suite.get("env") or {}).items():
+        previous = combined["env"].get(name, value)
+        if previous != value:
+            raise ValueError(
+                f"Conflicting environment value for {name}: {previous!r} != {value!r}"
+            )
+        combined["env"][name] = value
+
+    suite_steps = []
+    for entry in suite.get("steps") or []:
+        if "group" in entry:
+            suite_steps.extend(entry.get("steps") or [])
+        else:
+            suite_steps.append(entry)
+    combined["steps"].append({"group": group_name, "steps": suite_steps})
+
+with open(output_path, "w", encoding="utf-8") as output_file:
+    yaml.safe_dump(combined, output_file, sort_keys=False)
+PY
+        fi
+        echo "DEBUG_TEST_YAML override: using ${DEBUG_TEST_SPECS[*]}"
     elif [[ $BUILDKITE_BRANCH == "main" ]]; then
         TEST_YAML="test-amd-merge.yml"
     else
@@ -130,19 +198,20 @@ upload_pipeline() {
             > pipeline.yaml
     )
     cat pipeline.yaml
+    if [[ "$TEST_YAML" == "${TMPDIR:-/tmp}/amd-debug-tests."*.yml ]]; then
+        rm -f -- "$TEST_YAML"
+    fi
     buildkite-agent artifact upload pipeline.yaml
     buildkite-agent pipeline upload pipeline.yaml
     exit 0
 }
 
 get_diff() {
-    $(git add .)
-    echo $(git diff --name-only --diff-filter=ACMDR $(git merge-base origin/main HEAD))
+    git diff --name-only --diff-filter=ACMDR "$(git merge-base origin/main HEAD)"
 }
 
 get_diff_main() {
-    $(git add .)
-    echo $(git diff --name-only --diff-filter=ACMDR HEAD~1)
+    git diff --name-only --diff-filter=ACMDR HEAD~1
 }
 
 file_diff=$(get_diff)
@@ -181,7 +250,7 @@ for file in $file_diff; do
     # First check if file matches any pattern
     matches_pattern=0
     for pattern in "${patterns[@]}"; do
-        if [[ $file == $pattern* ]] || [[ $file == $pattern ]]; then
+        if [[ $file == "$pattern"* ]] || [[ $file == "$pattern" ]]; then
             matches_pattern=1
             break
         fi
@@ -191,7 +260,7 @@ for file in $file_diff; do
     if [[ $matches_pattern -eq 1 ]]; then
         matches_ignore=0
         for ignore in "${ignore_patterns[@]}"; do
-            if [[ $file == $ignore* ]] || [[ $file == $ignore ]]; then
+            if [[ $file == "$ignore"* ]] || [[ $file == "$ignore" ]]; then
                 matches_ignore=1
                 break
             fi

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

@@ -17,11 +17,11 @@ steps:
         - 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-$$BUILDKITE_PARALLEL_JOB.xml"
+        - "pytest -sv tests/model_executor -m 'core_model and cpu' --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT --shard-id=$$BUILDKITE_PARALLEL_JOB"
 
     - label: "Simple · Diffusion Test · Shard %N/%t"
       agent_pool: mi300_1
-      parallelism: 2
+      parallelism: 4
       depends_on: amd-build
       mirror_hardwares: [amdproduction]
       grade: Blocking
@@ -29,7 +29,7 @@ steps:
         - 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-$$BUILDKITE_PARALLEL_JOB.xml"
+        - "pytest -sv tests/diffusion -m 'core_model and cpu' --ignore=tests/diffusion/cache/test_teacache_extractors.py --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT --shard-id=$$BUILDKITE_PARALLEL_JOB"
 
     - label: "Simple · Engine&Entrypoints Test"
       agent_pool: mi300_1
@@ -38,7 +38,7 @@ steps:
       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"
+        - "pytest -sv tests/entrypoints tests/engine -m 'core_model and cpu'"
 
     - label: "Simple · Other Test"
       agent_pool: mi300_1
@@ -49,7 +49,7 @@ steps:
         - 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"
+        - "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"
 
 - group: ":card_index_dividers: Diffusion Test"
   steps:
@@ -71,26 +71,45 @@ steps:
       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))
-          '
+        - pytest -s -v tests/diffusion/offloader/test_diffusion_cpu_offload.py
+        - >-
+          pytest -s -v tests/diffusion/offloader/test_diffusion_layerwise_offload.py
+          --deselect='tests/diffusion/offloader/test_diffusion_layerwise_offload.py::test_layerwise_offload_diffusion_model[stabilityai/stable-audio-open-1.0]'
 
-    - label: "Diffusion Sequence Parallelism Test (Need 4 GPUs)"
+    # TODO: Diagnose unstable Stable Audio layerwise-offload memory savings on ROCm.
+    - label: "Diffusion Stable Audio Layerwise Offload Test (quarantined)"
+      agent_pool: mi300_1
+      depends_on: amd-build
+      mirror_hardwares: [amdproduction]
+      grade: NonBlocking
+      commands:
+        - export VLLM_LOGGING_LEVEL=DEBUG
+        - export VLLM_WORKER_MULTIPROC_METHOD=spawn
+        - >-
+          pytest -s -v
+          'tests/diffusion/offloader/test_diffusion_layerwise_offload.py::test_layerwise_offload_diffusion_model[stabilityai/stable-audio-open-1.0]'
+
+    - label: "Diffusion Sequence Parallelism Test (Need 4 GPUs) · Shard 1/2"
       agent_pool: mi300_4
       depends_on: amd-build
       mirror_hardwares: [amdproduction]
+      timeout_in_minutes: 40
       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
+        - pytest -s -v tests/diffusion/distributed/test_sequence_parallel.py
+
+    - label: "Diffusion Sequence Parallelism Test (Need 4 GPUs) · Shard 2/2"
+      agent_pool: mi300_4
+      depends_on: amd-build
+      mirror_hardwares: [amdproduction]
+      timeout_in_minutes: 40
+      grade: Blocking
+      commands:
+        - export VLLM_LOGGING_LEVEL=DEBUG
+        - export VLLM_WORKER_MULTIPROC_METHOD=spawn
+        - pytest -s -v tests/diffusion/distributed/test_ulysses_uaa_perf.py
 
     # merge-only tests
     - label: "Diffusion Tensor Parallelism Test"
@@ -171,21 +190,45 @@ steps:
       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'
+        - pytest -s -v tests/e2e/offline_inference/test_qwen3_omni.py tests/e2e/online_serving/test_qwen3_omni.py tests/e2e/offline_inference/test_qwen3_omni_colocate_async.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
+      parallelism: 3
       depends_on: amd-build
       mirror_hardwares: [amdproduction]
       grade: Blocking
+      timeout_in_minutes: 80
       commands:
         - |
-          bash -c '
+          timeout --signal=TERM --kill-after=2m 80m 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
+            TESTS=(
+              "tests/e2e/online_serving/test_qwen3_tts_customvoice.py::test_default_cuda_graph_startup[async_chunk]"
+              "tests/e2e/online_serving/test_qwen3_tts_customvoice.py::test_text_to_audio_002[async_chunk]"
+              "tests/e2e/offline_inference/test_qwen3_tts_customvoice.py::test_text_to_audio_001[no_cuda_graph]"
+            )
+            TEST="$${TESTS[$$BUILDKITE_PARALLEL_JOB]}"
+            pytest -s -v "$$TEST" -m "advanced_model" --run-level "advanced_model"
+          '
+
+    # TODO: Restore this case to the blocking sharded step once its long-running
+    # Whisper validation on AMD CI is resolved.
+    - label: "Qwen3-TTS CustomVoice async_chunk Test (quarantined)"
+      agent_pool: mi300_1
+      depends_on: amd-build
+      mirror_hardwares: [amdproduction]
+      grade: NonBlocking
+      timeout_in_minutes: 80
+      commands:
+        - |
+          timeout --signal=TERM --kill-after=2m 75m 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::test_text_to_audio_001[async_chunk]" -m "advanced_model" --run-level "advanced_model"
           '
 
     - label: "Qwen3-TTS Base E2E Test"
@@ -195,13 +238,10 @@ steps:
       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'
-          '
+        - export VLLM_LOGGING_LEVEL=DEBUG
+        - export VLLM_WORKER_MULTIPROC_METHOD=spawn
+        - export VLLM_ALLOW_LONG_MAX_MODEL_LEN="1"
+        - timeout 40m 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

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

@@ -17,7 +17,7 @@ steps:
         - 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-$$BUILDKITE_PARALLEL_JOB.xml"
+        - "pytest -sv tests/model_executor -m 'core_model and cpu' --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT --shard-id=$$BUILDKITE_PARALLEL_JOB"
 
     - label: "Simple · Diffusion Test"
       agent_pool: mi300_1
@@ -28,7 +28,7 @@ steps:
         - 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"
+        - "pytest -sv tests/diffusion -m 'core_model and cpu'  --ignore=tests/diffusion/cache/test_teacache_extractors.py"
 
     - label: "Simple · Engine&Entrypoints Test"
       agent_pool: mi300_1
@@ -37,7 +37,7 @@ steps:
       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"
+        - "pytest -sv tests/entrypoints tests/engine -m 'core_model and cpu'"
 
     - label: "Simple · Other Test"
       agent_pool: mi300_1
@@ -48,7 +48,7 @@ steps:
         - 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"
+        - "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"
 
 - label: "Custom Pipeline Test"
   agent_pool: mi300_1
@@ -191,10 +191,7 @@ steps:
       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"
-          '
+        - 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

.buildkite/common/ci_mirror_hardwares.yml

@@ -1,19 +1,31 @@
 # 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.
+# ``agents`` (+ optional top-level ``image`` for NPU) + ``plugins`` (+ ``retry``
+# for ``l4_*``), then strips ``mirror_hardwares`` before Buildkite upload.
+#
+# Omit the key: compose {chip}_{n} from pytest -m SKU + cards_n (max of several
+# positives; not cards_1 with no positive cards_* → that chip's highest existing preset).
+# Unset MIRROR_HW matches H100 or L4 in -m (both → H100); no match skips the step.
+# MIRROR_HW=b200 matches B200 in -m (else the step is skipped).
+# MIRROR_HW must be empty or b200 (case-insensitive); unknown values fail the upload.
+# CUDA preset strings (h100_4, l4_1) are omitted when MIRROR_HW=b200.
+# Unset MIRROR_HW keeps string presets. NPU presets ignore MIRROR_HW.
 #
 # CUDA naming: {chip}_{count}
-#   l4_1, l4_4, h100_1 … h100_4
+#   l4_1, l4_2, l4_3, l4_4, h100_1 … h100_4, b200_1 … b200_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
+#   a3_npu_2    — ascend-a3, resource_class npu-2 (A3 CI image tag …:$BUILDKITE_COMMIT-a3)
 #
-# 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.
+# B200 (b200-k8s): matches vLLM device ``b200-k8s`` from ci-infra
+# (AgentQueue.B200_K8S + b200_plugin_template). Queue routes the K8s pool;
+# podSpec uses hostNetwork + /raid|/mnt/shared (no H100-style nodeSelector).
+#
+# The ``l4_*`` presets run on the EKS ``l4-k8s`` queue (agent-stack-k8s),
+# packed onto g6.12xlarge L4 nodes (``vllm.ci/gpu-pool=l4x4``). GPU / CPU /
+# memory / shm requests scale with card count (1 GPU = 10 CPU / 40Gi / 24Gi shm).
+# Retry covers lost agents / killed pods, not pytest or OOM (exit 1 / 137).
 
 x-npu-k8s-plugins: &npu_k8s_plugins
   - kubernetes:
@@ -21,39 +33,224 @@ x-npu-k8s-plugins: &npu_k8s_plugins
         imagePullSecrets:
           - name: swr-secret
 
+# Infra-only retry. Omit exit 1 (pytest failed) and 137 (cgroup OOM / SIGKILL
+# of the test process). ci-infra ``K8S_RETRY`` still retries exit 1.
+x-l4-k8s-retry: &l4_k8s_retry
+  automatic:
+    - exit_status: -1
+      limit: 1
+    - exit_status: 128
+      limit: 1
+    - signal_reason: agent_stop
+      limit: 1
+    - signal_reason: agent_refused
+      limit: 1
+
 
 mirror_hardwares:
   l4_1:
     agents:
-      queue: gpu_1_queue
+      queue: l4-k8s
+    retry: *l4_k8s_retry
     plugins:
-      - docker#v5.2.0:
-          image: public.ecr.aws/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
-          always-pull: true
-          propagate-environment: true
-          mount-buildkite-agent: true
-          shm-size: "8gb"
-          environment:
-            - HF_HOME=/fsx/hf_cache
-            - HF_TOKEN
-          volumes:
-            - /fsx/hf_cache:/fsx/hf_cache
+      - kubernetes:
+          metadata:
+            annotations:
+              cluster-autoscaler.kubernetes.io/safe-to-evict: "false"
+              karpenter.sh/do-not-disrupt: "true"
+          podSpec:
+            priorityClassName: ci
+            serviceAccountName: buildkite-gpu-jobs
+            tolerations:
+              - key: nvidia.com/gpu
+                operator: Exists
+                effect: NoSchedule
+            nodeSelector:
+              vllm.ci/gpu-pool: l4x4
+            containers:
+              - image: public.ecr.aws/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
+                resources:
+                  limits:
+                    nvidia.com/gpu: 1
+                  requests:
+                    nvidia.com/gpu: 1
+                    cpu: "10"
+                    memory: 40Gi
+                volumeMounts:
+                  - name: devshm
+                    mountPath: /dev/shm
+                  - name: hf-cache
+                    mountPath: /fsx/hf_cache
+                env:
+                  - name: HF_HOME
+                    value: /fsx/hf_cache
+                  - name: HF_TOKEN
+                    valueFrom:
+                      secretKeyRef:
+                        name: hf-token-secret
+                        key: token
+            volumes:
+              - name: devshm
+                emptyDir:
+                  medium: Memory
+                  sizeLimit: 24Gi
+              - name: hf-cache
+                hostPath:
+                  path: /fsx/hf_cache
+                  type: DirectoryOrCreate
+
+  l4_2:
+    agents:
+      queue: l4-k8s
+    retry: *l4_k8s_retry
+    plugins:
+      - kubernetes:
+          metadata:
+            annotations:
+              cluster-autoscaler.kubernetes.io/safe-to-evict: "false"
+              karpenter.sh/do-not-disrupt: "true"
+          podSpec:
+            priorityClassName: ci
+            serviceAccountName: buildkite-gpu-jobs
+            tolerations:
+              - key: nvidia.com/gpu
+                operator: Exists
+                effect: NoSchedule
+            nodeSelector:
+              vllm.ci/gpu-pool: l4x4
+            containers:
+              - image: public.ecr.aws/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
+                resources:
+                  limits:
+                    nvidia.com/gpu: 2
+                  requests:
+                    nvidia.com/gpu: 2
+                    cpu: "20"
+                    memory: 80Gi
+                volumeMounts:
+                  - name: devshm
+                    mountPath: /dev/shm
+                  - name: hf-cache
+                    mountPath: /fsx/hf_cache
+                env:
+                  - name: HF_HOME
+                    value: /fsx/hf_cache
+                  - name: HF_TOKEN
+                    valueFrom:
+                      secretKeyRef:
+                        name: hf-token-secret
+                        key: token
+            volumes:
+              - name: devshm
+                emptyDir:
+                  medium: Memory
+                  sizeLimit: 48Gi
+              - name: hf-cache
+                hostPath:
+                  path: /fsx/hf_cache
+                  type: DirectoryOrCreate
+
+  l4_3:
+    agents:
+      queue: l4-k8s
+    retry: *l4_k8s_retry
+    plugins:
+      - kubernetes:
+          metadata:
+            annotations:
+              cluster-autoscaler.kubernetes.io/safe-to-evict: "false"
+              karpenter.sh/do-not-disrupt: "true"
+          podSpec:
+            priorityClassName: ci
+            serviceAccountName: buildkite-gpu-jobs
+            tolerations:
+              - key: nvidia.com/gpu
+                operator: Exists
+                effect: NoSchedule
+            nodeSelector:
+              vllm.ci/gpu-pool: l4x4
+            containers:
+              - image: public.ecr.aws/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
+                resources:
+                  limits:
+                    nvidia.com/gpu: 3
+                  requests:
+                    nvidia.com/gpu: 3
+                    cpu: "30"
+                    memory: 120Gi
+                volumeMounts:
+                  - name: devshm
+                    mountPath: /dev/shm
+                  - name: hf-cache
+                    mountPath: /fsx/hf_cache
+                env:
+                  - name: HF_HOME
+                    value: /fsx/hf_cache
+                  - name: HF_TOKEN
+                    valueFrom:
+                      secretKeyRef:
+                        name: hf-token-secret
+                        key: token
+            volumes:
+              - name: devshm
+                emptyDir:
+                  medium: Memory
+                  sizeLimit: 72Gi
+              - name: hf-cache
+                hostPath:
+                  path: /fsx/hf_cache
+                  type: DirectoryOrCreate
 
   l4_4:
     agents:
-      queue: gpu_4_queue
+      queue: l4-k8s
+    retry: *l4_k8s_retry
     plugins:
-      - docker#v5.2.0:
-          image: public.ecr.aws/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
-          always-pull: true
-          propagate-environment: true
-          mount-buildkite-agent: true
-          shm-size: "8gb"
-          environment:
-            - HF_HOME=/fsx/hf_cache
-            - HF_TOKEN
-          volumes:
-            - /fsx/hf_cache:/fsx/hf_cache
+      - kubernetes:
+          metadata:
+            annotations:
+              cluster-autoscaler.kubernetes.io/safe-to-evict: "false"
+              karpenter.sh/do-not-disrupt: "true"
+          podSpec:
+            priorityClassName: ci
+            serviceAccountName: buildkite-gpu-jobs
+            tolerations:
+              - key: nvidia.com/gpu
+                operator: Exists
+                effect: NoSchedule
+            nodeSelector:
+              vllm.ci/gpu-pool: l4x4
+            containers:
+              - image: public.ecr.aws/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
+                resources:
+                  limits:
+                    nvidia.com/gpu: 4
+                  requests:
+                    nvidia.com/gpu: 4
+                    cpu: "40"
+                    memory: 160Gi
+                volumeMounts:
+                  - name: devshm
+                    mountPath: /dev/shm
+                  - name: hf-cache
+                    mountPath: /fsx/hf_cache
+                env:
+                  - name: HF_HOME
+                    value: /fsx/hf_cache
+                  - name: HF_TOKEN
+                    valueFrom:
+                      secretKeyRef:
+                        name: hf-token-secret
+                        key: token
+            volumes:
+              - name: devshm
+                emptyDir:
+                  medium: Memory
+                  sizeLimit: 96Gi
+              - name: hf-cache
+                hostPath:
+                  path: /fsx/hf_cache
+                  type: DirectoryOrCreate
 
   h100_1:
     agents:
@@ -195,6 +392,211 @@ mirror_hardwares:
                   path: /mnt/hf-cache
                   type: DirectoryOrCreate
 
+  # vLLM device: b200-k8s → agents.queue b200-k8s + Buildkite K8s agent-stack podSpec
+  b200_1:
+    agents:
+      queue: b200-k8s
+    plugins:
+      - kubernetes:
+          podSpec:
+            runtimeClassName: nvidia
+            hostNetwork: true
+            dnsPolicy: ClusterFirstWithHostNet
+            imagePullSecrets:
+              - name: k8s-ecr-login-renew-docker-secret
+            containers:
+              - image: 936637512419.dkr.ecr.us-west-2.amazonaws.com/vllm-ci-pull-through-cache/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
+                resources:
+                  limits:
+                    nvidia.com/gpu: 1
+                securityContext:
+                  capabilities:
+                    add:
+                      - IPC_LOCK
+                      - SYS_RESOURCE
+                volumeMounts:
+                  - name: devshm
+                    mountPath: /dev/shm
+                  - name: raid
+                    mountPath: /raid
+                  - name: shared
+                    mountPath: /mnt/shared
+                env:
+                  - name: NCCL_CUMEM_HOST_ENABLE
+                    value: "0"
+                  - name: HF_HOME
+                    value: /raid/hf_cache
+                  - name: HF_TOKEN
+                    valueFrom:
+                      secretKeyRef:
+                        name: hf-token-secret
+                        key: token
+            volumes:
+              - name: devshm
+                emptyDir:
+                  medium: Memory
+              - name: raid
+                hostPath:
+                  path: /raid
+                  type: DirectoryOrCreate
+              - name: shared
+                hostPath:
+                  path: /mnt/shared
+                  type: DirectoryOrCreate
+
+  b200_2:
+    agents:
+      queue: b200-k8s
+    plugins:
+      - kubernetes:
+          podSpec:
+            runtimeClassName: nvidia
+            hostNetwork: true
+            dnsPolicy: ClusterFirstWithHostNet
+            imagePullSecrets:
+              - name: k8s-ecr-login-renew-docker-secret
+            containers:
+              - image: 936637512419.dkr.ecr.us-west-2.amazonaws.com/vllm-ci-pull-through-cache/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
+                resources:
+                  limits:
+                    nvidia.com/gpu: 2
+                securityContext:
+                  capabilities:
+                    add:
+                      - IPC_LOCK
+                      - SYS_RESOURCE
+                volumeMounts:
+                  - name: devshm
+                    mountPath: /dev/shm
+                  - name: raid
+                    mountPath: /raid
+                  - name: shared
+                    mountPath: /mnt/shared
+                env:
+                  - name: NCCL_CUMEM_HOST_ENABLE
+                    value: "0"
+                  - name: HF_HOME
+                    value: /raid/hf_cache
+                  - name: HF_TOKEN
+                    valueFrom:
+                      secretKeyRef:
+                        name: hf-token-secret
+                        key: token
+            volumes:
+              - name: devshm
+                emptyDir:
+                  medium: Memory
+              - name: raid
+                hostPath:
+                  path: /raid
+                  type: DirectoryOrCreate
+              - name: shared
+                hostPath:
+                  path: /mnt/shared
+                  type: DirectoryOrCreate
+
+  b200_3:
+    agents:
+      queue: b200-k8s
+    plugins:
+      - kubernetes:
+          podSpec:
+            runtimeClassName: nvidia
+            hostNetwork: true
+            dnsPolicy: ClusterFirstWithHostNet
+            imagePullSecrets:
+              - name: k8s-ecr-login-renew-docker-secret
+            containers:
+              - image: 936637512419.dkr.ecr.us-west-2.amazonaws.com/vllm-ci-pull-through-cache/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
+                resources:
+                  limits:
+                    nvidia.com/gpu: 3
+                securityContext:
+                  capabilities:
+                    add:
+                      - IPC_LOCK
+                      - SYS_RESOURCE
+                volumeMounts:
+                  - name: devshm
+                    mountPath: /dev/shm
+                  - name: raid
+                    mountPath: /raid
+                  - name: shared
+                    mountPath: /mnt/shared
+                env:
+                  - name: NCCL_CUMEM_HOST_ENABLE
+                    value: "0"
+                  - name: HF_HOME
+                    value: /raid/hf_cache
+                  - name: HF_TOKEN
+                    valueFrom:
+                      secretKeyRef:
+                        name: hf-token-secret
+                        key: token
+            volumes:
+              - name: devshm
+                emptyDir:
+                  medium: Memory
+              - name: raid
+                hostPath:
+                  path: /raid
+                  type: DirectoryOrCreate
+              - name: shared
+                hostPath:
+                  path: /mnt/shared
+                  type: DirectoryOrCreate
+
+  b200_4:
+    agents:
+      queue: b200-k8s
+    plugins:
+      - kubernetes:
+          podSpec:
+            runtimeClassName: nvidia
+            hostNetwork: true
+            dnsPolicy: ClusterFirstWithHostNet
+            imagePullSecrets:
+              - name: k8s-ecr-login-renew-docker-secret
+            containers:
+              - image: 936637512419.dkr.ecr.us-west-2.amazonaws.com/vllm-ci-pull-through-cache/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
+                resources:
+                  limits:
+                    nvidia.com/gpu: 4
+                securityContext:
+                  capabilities:
+                    add:
+                      - IPC_LOCK
+                      - SYS_RESOURCE
+                volumeMounts:
+                  - name: devshm
+                    mountPath: /dev/shm
+                  - name: raid
+                    mountPath: /raid
+                  - name: shared
+                    mountPath: /mnt/shared
+                env:
+                  - name: NCCL_CUMEM_HOST_ENABLE
+                    value: "0"
+                  - name: HF_HOME
+                    value: /raid/hf_cache
+                  - name: HF_TOKEN
+                    valueFrom:
+                      secretKeyRef:
+                        name: hf-token-secret
+                        key: token
+            volumes:
+              - name: devshm
+                emptyDir:
+                  medium: Memory
+              - name: raid
+                hostPath:
+                  path: /raid
+                  type: DirectoryOrCreate
+              - name: shared
+                hostPath:
+                  path: /mnt/shared
+                  type: DirectoryOrCreate
+
   a2b3_npu_1:
     agents:
       queue: "ascend-a2b3"

.buildkite/common/scripts/run_cov_split.sh

@@ -16,6 +16,11 @@
 # 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.
 #
+# Coverage + artifact upload run only on scheduled weekly E2E sweeps
+# (WEEKLY=1 and BUILDKITE_BRANCH=main — same gate as bootstrap --e2e).
+# All other invocations (PR merge-test, regular main merge, local) run the
+# same combined pytest as the job used before coverage was added.
+#
 # Exits non-zero if any half or the upload failed.
 set -uo pipefail
 
@@ -44,6 +49,17 @@ if ((${#OFFLINE[@]} == 0 && ${#ONLINE[@]} == 0)); then
     exit 2
 fi
 
+_should_run_coverage() {
+    [[ "${WEEKLY:-}" == "1" && "${BUILDKITE_BRANCH:-}" == "main" ]]
+}
+
+if ! _should_run_coverage; then
+    PATHS=("${OFFLINE[@]}" "${ONLINE[@]}")
+    echo "--- pytest (no coverage): ${MODEL_ID}"
+    pytest -s -v "${PATHS[@]}" "${PYTEST_ARGS[@]}"
+    exit $?
+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.

.buildkite/common/scripts/skip_ci.py

@@ -1,5 +1,6 @@
 #!/usr/bin/env python3
 # SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
 """Shared Buildkite skip-ci and CI-YAML level targeting logic.
 
 ``resolve_ci_decision(changed_files, ...)`` buckets explicit file lists (tests, dry-run).
@@ -10,12 +11,6 @@ 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
@@ -258,11 +253,10 @@ class CiDecision:
 
 @dataclass(frozen=True)
 class CiContext:
-    """Skip-ci decision plus git diff inputs used to compute it."""
+    """Skip-ci decision plus changed files used for pipeline filtering."""
 
     decision: CiDecision
     changed_files: list[str] | None
-    diff_range: str | None
 
 
 def _log(message: str) -> None:
@@ -332,7 +326,7 @@ def resolve_ci_context_from_git() -> CiContext:
     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)
+    return CiContext(decision=decision, changed_files=changed_files)
 
 
 # --- Doc / skip-mark helpers ---
@@ -571,23 +565,6 @@ def main() -> int:
     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
 
@@ -600,16 +577,6 @@ def main() -> int:
             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
 
 

.buildkite/common/scripts/upload_pipeline.py

@@ -1,5 +1,6 @@
 #!/usr/bin/env python3
 # SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
 """Render and optionally upload Buildkite pipeline YAML with diff-aware logic.
 
 Bootstrap mode (``bootstrap-upload-steps.yml``):
@@ -11,8 +12,19 @@ Bootstrap mode (``bootstrap-upload-steps.yml``):
 
 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``
+  - Expand uploader-only ``mirror_hardwares`` into ``agents`` (+ optional ``image``
     for NPU) + ``plugins`` (see ci_mirror_hardwares.yml).
+  - Omit ``mirror_hardwares`` to compose ``{chip}_{n}`` from pytest ``-m`` SKU
+    markers plus ``cards_n`` (max of several positives; ``not cards_1`` with no
+    positive ``cards_*`` uses that chip's highest existing preset). An explicit
+    preset string (``h100_2``) is used as-is (marks are ignored). Names that
+    are not keys in ``ci_mirror_hardwares.yml`` fail the upload.
+
+    Inferred chip: unset ``MIRROR_HW`` matches ``H100`` or ``L4`` in ``-m``
+    (both → H100); no match skips the step. ``MIRROR_HW=b200`` matches
+    ``B200`` in ``-m`` (otherwise skipped). ``-m`` is not rewritten.
+    ``MIRROR_HW`` must be empty or ``b200``; unknown values fail the upload.
+    A CUDA preset string such as ``h100_4`` is omitted when ``MIRROR_HW=b200``.
 
 Usage:
   python3 upload_pipeline.py [--upload] [--all | --e2e] <pipeline.yml>
@@ -24,11 +36,13 @@ from __future__ import annotations
 
 import argparse
 import copy
+import os
+import re
 import subprocess
 import sys
 from functools import lru_cache
 from pathlib import Path
-from typing import Any
+from typing import Any, Literal
 
 try:
     import yaml
@@ -44,6 +58,14 @@ from skip_ci import (
     resolve_ci_context_from_git,
 )
 
+if str(ROOT) not in sys.path:
+    sys.path.insert(0, str(ROOT))
+
+from tests.helpers.mark import (  # noqa: E402
+    get_skus_for_platform,
+    get_supported_card_counts,
+)
+
 # --- Constants ---
 
 LOG = "upload_pipeline"
@@ -58,11 +80,12 @@ BOOTSTRAP_UPLOAD_IF_KEYS = {
 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") || '
+# Bootstrap Buildkite ``if`` expressions.
+# ``*_MAIN_IF``: main + env schedule. ``*_LABEL_IF``: PR label (and/or composed with MAIN).
+# ``*_UPLOAD_IF``: full gate for uploading that child pipeline.
+NIGHTLY_MAIN_IF = 'build.branch == "main" && build.env("NIGHTLY") == "1"'
+NIGHTLY_LABEL_IF = (
+    f"({NIGHTLY_MAIN_IF}) || "
     '(build.branch != "main" && ('
     'build.pull_request.labels includes "nightly-test" || '
     'build.pull_request.labels includes "omni-test" || '
@@ -71,6 +94,19 @@ NPU_NIGHTLY_ONLY = (
     'build.pull_request.labels includes "diffusion-x2v-test"'
     "))"
 )
+WEEKLY_E2E_IF = 'build.branch == "main" && build.env("WEEKLY") == "1"'
+WEEKLY_MAIN_IF = 'build.branch == "main" && (build.env("WEEKLY") == "1" || build.env("NON_CRITICAL") == "1")'
+WEEKLY_LABEL_IF = f'({WEEKLY_MAIN_IF}) || (build.branch != "main" && build.pull_request.labels includes "weekly-test")'
+READY_LABEL_IF = 'build.branch != "main" && build.pull_request.labels includes "ready"'
+MERGE_LABEL_IF = 'build.branch != "main" && build.pull_request.labels includes "merge-test"'
+MERGE_MAIN_IF = (
+    'build.branch == "main" && build.env("NIGHTLY") != "1" && '
+    'build.env("WEEKLY") != "1" && build.env("NON_CRITICAL") != "1"'
+)
+READY_UPLOAD_IF = f"({WEEKLY_E2E_IF}) || ({READY_LABEL_IF})"
+MERGE_UPLOAD_IF = f"({WEEKLY_E2E_IF}) || (({MERGE_MAIN_IF}) || ({MERGE_LABEL_IF}))"
+BOOTSTRAP_DISABLED_IF = "false"
+BOOTSTRAP_ENABLED_IF = "true"
 
 
 # --- Logging ---
@@ -102,74 +138,47 @@ def _format_bootstrap_if(expr: str) -> str:
 
 
 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
+        ready_upload = READY_LABEL_IF
+        merge_upload = BOOTSTRAP_DISABLED_IF
+        weekly_label_if = BOOTSTRAP_DISABLED_IF
     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"'
+        ready_upload = READY_UPLOAD_IF
+        merge_upload = MERGE_UPLOAD_IF
+        weekly_label_if = WEEKLY_LABEL_IF
 
     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
+        # NIGHTLY=1 still runs L4; WEEKLY=1 / NON_CRITICAL=1 still run L5.
+        # main+WEEKLY=1 also uploads L2/L3 (those steps then pass --e2e).
+        image_expr = f"({NIGHTLY_MAIN_IF}) || ({WEEKLY_MAIN_IF})" if platform == "cuda" else NIGHTLY_MAIN_IF
+        ready_expr = WEEKLY_E2E_IF if platform == "cuda" else BOOTSTRAP_DISABLED_IF
+        merge_expr = WEEKLY_E2E_IF if platform == "cuda" else BOOTSTRAP_DISABLED_IF
+        nightly_expr = NIGHTLY_MAIN_IF
+        weekly_expr = WEEKLY_MAIN_IF if platform == "cuda" else BOOTSTRAP_DISABLED_IF
     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
+        ready_expr = ready_upload if l2_enabled else BOOTSTRAP_DISABLED_IF
+        merge_expr = merge_upload if l3_enabled else BOOTSTRAP_DISABLED_IF
+        nightly_expr = NIGHTLY_LABEL_IF
+        weekly_expr = weekly_label_if if platform == "cuda" else BOOTSTRAP_DISABLED_IF
 
-        image_parts = [f"({nightly_label_if})"]
+        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})")
+            image_parts.insert(0, f"({ready_upload})")
         if l3_enabled:
-            image_parts.insert(1 if l2_enabled else 0, f"({merge_base})")
+            image_parts.insert(1 if l2_enabled else 0, f"({merge_upload})")
         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
+        image_expr = BOOTSTRAP_ENABLED_IF
+        ready_expr = ready_upload
+        merge_expr = merge_upload if platform == "cuda" else BOOTSTRAP_DISABLED_IF
+        nightly_expr = NIGHTLY_LABEL_IF
+        weekly_expr = weekly_label_if if platform == "cuda" else BOOTSTRAP_DISABLED_IF
 
     return {
         "image": _format_bootstrap_if(image_expr),
@@ -245,32 +254,257 @@ def _load_mirror_hardwares() -> dict[str, dict[str, Any]]:
     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
+@lru_cache(maxsize=1)
+def _cuda_mirror_chips() -> tuple[str, ...]:
+    """Lowercase CUDA SKUs that have at least one ``{chip}_{n}`` preset.
 
-    if not isinstance(hardware, str) or not hardware.strip():
+    Longer tokens first so ``h100`` is not matched as a prefix of ``h200``.
+    """
+    skus = get_skus_for_platform("cuda")
+    known = _load_mirror_hardwares()
+    chips = {
+        sku.lower() for sku in skus if any(name == sku.lower() or name.startswith(f"{sku.lower()}_") for name in known)
+    }
+    return tuple(sorted(chips, key=lambda chip: (-len(chip), chip)))
+
+
+def _read_cards_marks(expr: str) -> tuple[set[int], bool]:
+    """Parse registered ``cards_n`` / ``not cards_n`` from a pytest ``-m`` expr.
+
+    Returns positive card counts and whether any ``not cards_*`` is present.
+    """
+    counts = get_supported_card_counts()
+    alt = "|".join(str(n) for n in sorted(counts, key=lambda n: (-len(str(n)), -n))) if counts else "(?!)"
+    positives: set[int] = set()
+    has_not_cards = False
+    for match in re.finditer(rf"\b(?:(not)\s+)?cards_({alt})\b", expr):
+        if match.group(1):
+            has_not_cards = True
+        else:
+            positives.add(int(match.group(2)))
+    return positives, has_not_cards
+
+
+def _read_hardware_marks(expr: str) -> set[str]:
+    """Return lowercase positive CUDA SKUs that have a mirror preset.
+
+    ``not H100`` is ignored. SKUs with no ``{chip}_*`` preset (e.g. H200) are dropped.
+    """
+    skus = sorted(get_skus_for_platform("cuda"), key=lambda s: (-len(s), s))
+    if not skus:
+        return set()
+    alt = "|".join(re.escape(sku) for sku in skus)
+    mirror = set(_cuda_mirror_chips())
+    chips: set[str] = set()
+    for match in re.finditer(rf"\b(?:(not)\s+)?({alt})\b", expr):
+        if match.group(1):
+            continue
+        chip = match.group(2).lower()
+        if chip in mirror:
+            chips.add(chip)
+    return chips
+
+
+# CI policy: not pytest-mark registry.
+_SUPPORTED_MIRROR_HW_SELECTORS = frozenset({"b200"})
+# Unset MIRROR_HW: match H100 then L4 in -m (skip if neither).
+_DEFAULT_INFER_CHIPS = ("h100", "l4")
+_PYTEST_MARKER_ARG = re.compile(r"-m\s+(?:\"([^\"]*)\"|'([^']*)'|(\S+))")
+_CARDS_CHIP_MAX: Literal["max"] = "max"
+
+
+def _get_mirror_hw_selector() -> str:
+    """Return lowercase ``MIRROR_HW``, or empty to keep string presets / marker chips.
+
+    A set value must be ``b200`` (case-insensitive). Unknown values fail closed
+    so a typo cannot silently drop the CUDA pipeline.
+    """
+    selector = os.environ.get("MIRROR_HW", "").strip().lower()
+    if not selector:
+        return ""
+    if selector not in _SUPPORTED_MIRROR_HW_SELECTORS:
+        allowed = " or ".join(repr(s) for s in sorted(_SUPPORTED_MIRROR_HW_SELECTORS))
+        raise ValueError(f"unsupported MIRROR_HW={selector!r}; expected empty or {allowed}")
+    return selector
+
+
+def _read_pytest_marks(commands: Any) -> tuple[set[str], int | Literal["max"] | None]:
+    """Read pytest ``-m`` from step commands: CUDA SKU chips and cards count.
+
+    *chips* are lowercase SKUs from positive markers (``not H100`` is ignored).
+    *cards* is the max positive ``cards_n``, ``"max"`` when only ``not cards_*``
+    is present, or ``None``.
+    """
+    chunks: list[str] = []
+
+    def _collect(value: Any) -> None:
+        if value is None:
+            return
+        if isinstance(value, str):
+            chunks.append(value)
+        elif isinstance(value, list):
+            for part in value:
+                _collect(part)
+        else:
+            chunks.append(str(value))
+
+    _collect(commands)
+    text = "\n".join(chunks)
+
+    chips: set[str] = set()
+    positives: set[int] = set()
+    has_not_cards = False
+    for match in _PYTEST_MARKER_ARG.finditer(text):
+        expr = match.group(1) or match.group(2) or match.group(3) or ""
+        chips |= _read_hardware_marks(expr)
+        card_counts, not_cards = _read_cards_marks(expr)
+        positives.update(card_counts)
+        has_not_cards = has_not_cards or not_cards
+
+    if positives:
+        cards: int | Literal["max"] | None = max(positives)
+    elif has_not_cards:
+        cards = _CARDS_CHIP_MAX
+    else:
+        cards = None
+    return chips, cards
+
+
+def _compose_mirror_hardware_name(
+    chips: set[str],
+    cards: int | Literal["max"] | None,
+    *,
+    step_label: str,
+) -> str | None:
+    """Assemble ``{chip}_{n}`` from pytest ``-m`` chips/cards, or None to skip.
+
+    Unset ``MIRROR_HW``: ``H100`` if in ``-m``, else ``L4``, else skip.
+    Set ``MIRROR_HW``: that chip if in ``-m``, else skip.
+    ``cards`` is a registered ``cards_n``, ``"max"`` for that chip's highest
+    existing preset, or None (error after a chip matched — no ``cards_*``).
+    """
+    selector = _get_mirror_hw_selector()
+    if selector:
+        chip = selector if selector in chips else None
+        skip_reason = f"MIRROR_HW={selector!r} not in -m SKUs {sorted(chips)}"
+    else:
+        chip = next((c for c in _DEFAULT_INFER_CHIPS if c in chips), None)
+        skip_reason = f"no H100 or L4 in -m SKUs {sorted(chips)}"
+    if chip is None:
+        _log(f"skip {step_label}: {skip_reason}")
+        return None
+    if cards is None:
         raise ValueError(
-            f"mirror_hardwares must be a non-empty string in step {_get_step_label(step)!r}",
+            f"step {step_label!r} has a pytest -m SKU marker but no cards_* "
+            f"(or not cards_*); add cards_n or set mirror_hardwares to force a preset",
         )
 
-    preset = _load_mirror_hardwares().get(hardware)
+    count_source = "not cards_*" if cards == _CARDS_CHIP_MAX else f"cards_{cards}"
+    count = None if cards == _CARDS_CHIP_MAX else cards
+    registered = get_supported_card_counts()
+    if count is not None and count not in registered:
+        supported = ", ".join(f"cards_{n}" for n in sorted(registered)) or "(none)"
+        raise ValueError(
+            f"{count_source} in step {step_label!r} is not a registered cards_* mark; supported: {supported}",
+        )
+    known = _load_mirror_hardwares()
+    if count is None:
+        available = [n for n in sorted(registered) if f"{chip}_{n}" in known]
+        if not available:
+            raise ValueError(
+                f"{count_source} in step {step_label!r} has no CUDA count presets for {chip}",
+            )
+        count = max(available)
+    chosen = f"{chip}_{count}"
+    if chosen not in known:
+        raise ValueError(
+            f"{count_source} in step {step_label!r} has no preset {chosen!r}",
+        )
+    _log(f"{step_label}: {count_source} → {chosen!r}")
+    return chosen
+
+
+def _resolve_mirror_hardware_name(hardware: Any, *, step_label: str) -> str | None:
+    """Map ``mirror_hardwares`` to a ``ci_mirror_hardwares.yml`` preset, or None to skip.
+
+    The value is a preset name (``h100_2``). Unknown names fail later when the
+    registry is loaded. CUDA names are omitted when ``MIRROR_HW`` names a
+    different chip; NPU strings ignore ``MIRROR_HW``.
+    """
+    if isinstance(hardware, bool):
+        raise ValueError(f"mirror_hardwares must not be a boolean in step {step_label!r}")
+    if isinstance(hardware, str):
+        name = hardware.strip()
+    elif isinstance(hardware, int):
+        name = str(hardware)
+    else:
+        raise ValueError(
+            f"mirror_hardwares must be a preset string in step {step_label!r}",
+        )
+    if not name:
+        raise ValueError(f"mirror_hardwares must be a non-empty string in step {step_label!r}")
+
+    selector = _get_mirror_hw_selector()
+    token = name.lower()
+    chip = next(
+        (c for c in _cuda_mirror_chips() if token == c or token.startswith(f"{c}_")),
+        None,
+    )
+    if selector and chip is not None and chip != selector:
+        _log(
+            f"skip {step_label}: preset {name!r} is {chip} hardware; MIRROR_HW={selector!r}",
+        )
+        return None
+    return name
+
+
+def _expand_mirror_hardwares(step: dict[str, Any]) -> dict[str, Any] | None:
+    """Replace ``mirror_hardwares`` (or infer it from ``-m``) with preset agents/plugins.
+
+    Explicit preset string: used as-is; pytest marks are ignored.
+    Omitted key: match H100/L4 (or ``MIRROR_HW``) in ``-m``, then compose
+    ``{chip}_{n}`` from ``cards_n``. No match skips the step. Steps with no
+    SKU/cards and no ``mirror_hardwares`` are left unchanged (CPU jobs).
+
+    Returns None when the current ``MIRROR_HW`` selector does not apply.
+    """
+    step_label = _get_step_label(step)
+    has_pool = step.get("agents") is not None or step.get("plugins") is not None or step.get("image") is not None
+
+    if "mirror_hardwares" not in step:
+        if has_pool:
+            return step
+        chips, cards = _read_pytest_marks(step.get("commands"))
+        if not chips and cards is None:
+            return step
+        preset_name = _compose_mirror_hardware_name(chips, cards, step_label=step_label)
+    else:
+        if has_pool:
+            raise ValueError(
+                f"step {step_label!r} sets mirror_hardwares together with agents/plugins/image; "
+                f"use mirror_hardwares only",
+            )
+        preset_name = _resolve_mirror_hardware_name(
+            step.get("mirror_hardwares"),
+            step_label=step_label,
+        )
+
+    if preset_name is None:
+        return None
+
+    preset = _load_mirror_hardwares().get(preset_name)
     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",
+            f"unknown mirror_hardwares {preset_name!r} in step {step_label!r}; known: {known}",
         )
 
     expanded = copy.deepcopy(preset)
-    return {key: value for key, value in step.items() if key != "mirror_hardwares"} | expanded
+    merged = {key: value for key, value in step.items() if key != "mirror_hardwares"} | expanded
+    # Preset retry (K8S_RETRY on l4_*) must not clobber a step that opted out.
+    if "retry" in step:
+        merged["retry"] = step["retry"]
+    return merged
 
 
 def _match_source_file(changed_files: list[str], prefixes: list[str]) -> bool:
@@ -309,7 +543,7 @@ def _process_test_steps(
         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:
+            if 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"}
@@ -317,14 +551,11 @@ def _process_test_steps(
             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))
+        leaf = {key: value for key, value in step.items() if key != "source_file_dependencies"}
+        expanded = _expand_mirror_hardwares(leaf)
+        if expanded is None:
+            continue
+        processed.append(expanded)
 
     return processed
 
@@ -350,6 +581,10 @@ def _render_test_pipeline(
     e2e_only: bool = False,
 ) -> dict[str, Any]:
     """Filter steps by PR diff and strip uploader-only ``source_file_dependencies`` metadata."""
+    # Validate MIRROR_HW once up front. Per-step skip must not run for typos
+    # (e.g. b20o), or a pipeline can shrink to leftover CPU-only steps and
+    # still upload successfully.
+    _get_mirror_hw_selector()
     steps = doc.get("steps")
     if not isinstance(steps, list):
         return doc
@@ -379,10 +614,7 @@ def _render_pipeline(
 
     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
+    changed_files = None if force_all or e2e_only else ctx.changed_files
 
     doc = yaml.safe_load(text)
     if not isinstance(doc, dict):

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

@@ -8,28 +8,28 @@ steps:
     agents:
       queue: "cpu_queue_premerge"
 
-  # L2 Test — PR ready label (diff-aware) or main+NIGHTLY=1 (--e2e, full E2E group)
+  # L2 Test — PR ready label (diff-aware) or main+WEEKLY=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
+        if [ "${WEEKLY:-}" = "1" ] && [ "${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)
+  # L3 Test — PR merge-test label (diff-aware) or main+WEEKLY=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
+        if [ "${WEEKLY:-}" = "1" ] && [ "${BUILDKITE_BRANCH}" = "main" ]; then
           EXTRA="--e2e"
         fi
         python3 .buildkite/common/scripts/upload_pipeline.py --upload $$EXTRA .buildkite/cuda/test-merge.yml
@@ -45,7 +45,7 @@ steps:
     agents:
       queue: "cpu_queue_premerge"
 
-  # L5 Test — main+WEEKLY=1 (scheduled), or PR with weekly-test label
+  # L5 Test — main+WEEKLY=1 / NON_CRITICAL=1 (scheduled), or PR with weekly-test label
   - label: "Upload Weekly Pipeline"
     depends_on: image-build
     key: upload-weekly-pipeline

.buildkite/cuda/test-merge.yml

@@ -1,3 +1,6 @@
+# CUDA CI L3 (`advanced_model` e2e) plus L1 CPU Simple Test (`core_model and cpu`).
+# Uploaded by Upload Merge Pipeline: PR label `merge-test`, or post-merge on main.
+
 env:
   VLLM_WORKER_MULTIPROC_METHOD: spawn
   VLLM_USE_FLASHINFER_MOE_FP16: 0
@@ -9,23 +12,27 @@ steps:
     depends_on: upload-merge-pipeline
     steps:
       - label: "Simple · Model Executor Test"
+        timeout_in_minutes: 20
         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"
+          - "pytest -sv tests/model_executor -m 'core_model and cpu'"
         mirror_hardwares: l4_1
 
       - label: "Simple · Diffusion Test"
+        timeout_in_minutes: 20
         commands:
-          - "timeout 20m pytest -sv tests/diffusion -m 'core_model and cpu' --cov=vllm_omni --cov-report=term-missing:skip-covered --cov-report=xml"
+          - "pytest -sv tests/diffusion -m 'core_model and cpu'"
         mirror_hardwares: l4_1
 
       - label: "Simple · Engine&Entrypoints Test"
+        timeout_in_minutes: 20
         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"
+          - "pytest -sv tests/entrypoints tests/engine -m 'core_model and cpu'"
         mirror_hardwares: l4_1
 
       - label: "Simple · Other Test"
+        timeout_in_minutes: 20
         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"
+          - "pytest -sv tests/ -m 'core_model and cpu' --ignore=tests/diffusion --ignore=tests/model_executor --ignore=tests/entrypoints --ignore=tests/engine"
         mirror_hardwares: l4_1
 
   - group: ":card_index_dividers: Diffusion Test"
@@ -81,6 +88,9 @@ steps:
           - 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
+          - vllm_omni/entrypoints/openai/serving_speech.py
+          - vllm_omni/entrypoints/openai/tts_adapters/
+          - vllm_omni/engine/stage_pool.py
         commands:
           - |
             timeout 40m bash -c "
@@ -100,6 +110,9 @@ steps:
           - 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
+          - vllm_omni/entrypoints/openai/serving_speech.py
+          - vllm_omni/entrypoints/openai/tts_adapters/
+          - vllm_omni/engine/stage_pool.py
           - .buildkite/common/scripts/run_cov_split.sh
           - pyproject.toml
         # 40 -> 60: the coverage split runs the model twice.
@@ -118,7 +131,10 @@ steps:
       - label: "Omni · Qwen3-Omni Test"
         source_file_dependencies:
           - tests/e2e/offline_inference/test_qwen3_omni.py
+          - tests/e2e/offline_inference/test_qwen3_omni_colocate_async.py
           - tests/e2e/online_serving/test_qwen3_omni.py
+          - tests/helpers/runtime.py
+          - vllm_omni/entrypoints/openai/serving_chat.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
@@ -126,9 +142,10 @@ steps:
           - 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
+          - vllm_omni/deploy/qwen3_omni_moe_thinking.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'
+          - pytest -s -v tests/e2e/offline_inference/test_qwen3_omni.py tests/e2e/online_serving/test_qwen3_omni.py tests/e2e/offline_inference/test_qwen3_omni_colocate_async.py -m 'advanced_model and cuda' --run-level 'advanced_model'
         mirror_hardwares: h100_2
 
       - label: "Omni · MiniCPM-o 4.5 Test"
@@ -160,10 +177,10 @@ steps:
           - 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_server_vad.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/
@@ -211,3 +228,50 @@ steps:
               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
+
+      - label: "Diffusion · HunyuanVideo-1.5 Test"
+        source_file_dependencies:
+          - tests/e2e/online_serving/test_hunyuan_video_15_expansion.py
+          - vllm_omni/diffusion/models/hunyuan_video/
+        commands:
+          - |
+            timeout 40m bash -c "
+              export VLLM_IMAGE_FETCH_TIMEOUT=60
+              pytest -s -v tests/e2e/online_serving/test_hunyuan_video_15_expansion.py -m 'advanced_model and cuda and diffusion' --run-level 'advanced_model'
+            "
+        mirror_hardwares: h100_1
+
+      - label: "Diffusion · MiniMax-H3 L3 DLO DP2 and FastH3"
+        source_file_dependencies:
+          - tests/e2e/online_serving/minimax_h3/
+          - tests/helpers/assertions.py
+          - tests/helpers/mark.py
+          - tests/helpers/media.py
+          - tests/helpers/runtime.py
+          - vllm_omni/diffusion/models/minimax_h3/
+          - vllm_omni/diffusion/lora/
+          - vllm_omni/diffusion/offloader/
+          - vllm_omni/diffusion/distributed/
+          - vllm_omni/entrypoints/openai/
+        # Keep four server lifecycles in one Buildkite job, but never in one
+        # pytest process. DLO uses multiprocessing shared-memory queues; the
+        # parent's resource_tracker can retain a stale /psm_* entry during
+        # teardown and strand the next Ref2VA collective.
+        # A per-file process plus a hard timeout also makes a regression fail
+        # instead of leaving the merge queue stuck indefinitely.
+        timeout_in_minutes: 150
+        commands:
+          - |
+            set -eu
+            export VLLM_IMAGE_FETCH_TIMEOUT=60
+            if [ -f vllm_omni/diffusion/models/minimax_h3/lora.py ]; then
+              export VLLM_TEST_MINIMAX_H3_TURBO_LORA=$(python3 -c 'from huggingface_hub import hf_hub_download; print(hf_hub_download(repo_id="lightx2v/Minimax-h3-Turbo", filename="minimax_h3_fl2v_turbo_4step_v1.0_768p_bf16.safetensors"))')
+            fi
+            if [ -f vllm_omni/diffusion/models/minimax_h3/fasth3.py ]; then
+              export VLLM_TEST_MINIMAX_H3_FASTH3_LORA=$(python3 -c 'from huggingface_hub import hf_hub_download; print(hf_hub_download(repo_id="FastVideo/FastVideo-FastH3-4-step-Preview-v1-LoRA", filename="dense-datafree/adapter_model.safetensors"))')
+            fi
+            timeout 35m pytest -s -v tests/e2e/online_serving/minimax_h3/test_minimax_h3_dlo_dp2_fl2va.py -m 'advanced_model and cuda and diffusion' --run-level 'advanced_model'
+            timeout 35m pytest -s -v tests/e2e/online_serving/minimax_h3/test_minimax_h3_dlo_dp2_ref2va.py -m 'advanced_model and cuda and diffusion' --run-level 'advanced_model'
+            timeout 35m pytest -s -v tests/e2e/online_serving/minimax_h3/test_minimax_h3_turbo_lora.py -m 'advanced_model and cuda and diffusion' --run-level 'advanced_model'
+            timeout 35m pytest -s -v tests/e2e/online_serving/minimax_h3/test_minimax_h3_fasth3_lora.py -m 'advanced_model and cuda and diffusion' --run-level 'advanced_model'
+        mirror_hardwares: h100_2

.buildkite/cuda/test-nightly.yml

@@ -1,3 +1,7 @@
+# CUDA CI L4 (`full_model`): nightly and release share this file.
+# Uploaded by Upload Nightly Pipeline: NIGHTLY=1, or PR labels (nightly-test / omni-test / ...).
+# Hardware: omit mirror_hardwares to compose from -m SKU + cards_n; or set a preset string to force.
+
 env:
   VLLM_WORKER_MULTIPROC_METHOD: spawn
   VLLM_USE_FLASHINFER_MOE_FP16: 0
@@ -12,29 +16,33 @@ steps:
     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"
+      - label: ":full_moon: Omni · Function Test with H100 · Single-GPU"
         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
+          - pytest -sv tests/e2e --run-level "full_model" -m "full_model and H100 and B200 and omni and cards_1" --ignore=tests/e2e/accuracy --ignore=tests/e2e/online_serving/test_minicpmo_4_5_duplex_expansion.py
+
+      - label: ":full_moon: Omni · Function Test with H100 · 2-GPU"
+        timeout_in_minutes: 90
+        commands:
+          - pytest -sv tests/e2e --run-level "full_model" -m "full_model and H100 and B200 and omni and cards_2" --ignore=tests/e2e/accuracy --ignore=tests/e2e/online_serving/test_qwen3_omni_multi_replicas.py
 
       - 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"
+          - pytest -s -v tests/e2e/online_serving/test_minicpmo_4_5_duplex_expansion.py -m "full_model and cuda and H100 and omni and cards_1" --run-level "full_model"
         mirror_hardwares: h100_1
 
-      - label: ":full_moon: Omni · Doc Test with L4"
+      - label: ":full_moon: Omni · Doc Test with L4 · 4-GPU"
         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"
+          - pytest -s -v tests/examples/ -m "full_model and omni and L4 and cards_4" --run-level "full_model"
         mirror_hardwares: l4_4
 
-      - label: ":full_moon: Omni · Doc Test with H100"
+      - label: ":full_moon: Omni · Doc Test with H100 · 2-GPU"
         timeout_in_minutes: 90
         commands:
-          - pytest -s -v tests/examples/ -m "full_model and omni and H100" --run-level "full_model"
+          - pytest -s -v tests/examples/ -m "full_model and omni and H100 and cards_2" --run-level "full_model"
         mirror_hardwares: h100_2
 
       - label: ":full_moon: Omni · Accuracy Test"
@@ -83,38 +91,12 @@ steps:
           - 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
+            pytest -s -v tests/dfx/perf/scripts/run_benchmark.py --test-config-file tests/dfx/perf/tests/test_qwen3_omni_async_chunk.json -m "H100 and full_model"
             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
@@ -162,15 +144,29 @@ steps:
           - 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
+      - label: ":full_moon: TTS · Perf Test · Single-GPU"
+        key: nightly-tts-performance-single
         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
+            pytest -s -v tests/dfx/perf/scripts/run_benchmark.py --test-config-file tests/dfx/perf/tests/test_tts.json -m "H100 and cards_1"
+            EXIT=$$?
+            buildkite-agent artifact upload "tests/dfx/perf/results/*.json"
+            exit $$EXIT
+        mirror_hardwares: h100_1
+
+      - label: ":full_moon: TTS · Perf Test · 2-GPU"
+        key: nightly-tts-performance-2gpu
+        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 -m "H100 and cards_2"
             EXIT=$$?
             buildkite-agent artifact upload "tests/dfx/perf/results/*.json"
             exit $$EXIT
@@ -184,7 +180,7 @@ steps:
       build.pull_request.labels includes "nightly-test"
     steps:
 
-      - label: ":full_moon: Tiny Model Tests · Diffusion (Multi-GPU accelerations)"
+      - label: ":full_moon: Tiny Model Tests · Diffusion (Multi-GPU accelerations) · 2-GPU"
         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.
@@ -194,7 +190,14 @@ steps:
         # 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"
+          - pytest -sv tests/model_tests/diffusion -m 'full_model and cuda and cards_2' --run-level "core_model"
+        mirror_hardwares: l4_2
+
+      - label: ":full_moon: Tiny Model Tests · Diffusion (Multi-GPU accelerations) · 4-GPU"
+        timeout_in_minutes: 30
+        # CFG+TP extra_test_groups request 2×2=4 devices (see get_required_device_count).
+        commands:
+          - pytest -sv tests/model_tests/diffusion -m 'full_model and cuda and cards_4' --run-level "core_model"
         mirror_hardwares: l4_4
 
   - group: ":card_index_dividers: Diffusion X2I(&A&T) Model Test"
@@ -205,7 +208,6 @@ steps:
       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
@@ -213,24 +215,35 @@ steps:
           - >-
             pytest -sv
             tests/e2e/online_serving/test_qwen_image_expansion.py
-            -m "full_model and diffusion and H100 and not distributed_cuda"
+            -m "full_model and diffusion and H100 and cards_1"
             --run-level "full_model"
         mirror_hardwares: h100_1
 
-      - label: ":full_moon: Diffusion X2I(&A&T) · Function Test with H100 · Multi-GPU Qwen-Image"
+      - label: ":full_moon: Diffusion X2I(&A&T) · Function Test with H100 · 2-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 distributed_cuda"
+            -m "full_model and diffusion and H100 and cards_2"
             --run-level "full_model"
         mirror_hardwares: h100_2
 
-      - label: ":full_moon: Diffusion X2I(&A&T) · Function Test with L4"
+      - label: ":full_moon: Diffusion X2I(&A&T) · Boogu Edit CFG · 3-GPU"
         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
+          - >-
+            pytest -sv
+            tests/e2e/online_serving/test_boogu_image_edit_expansion.py
+            -k "test_double_guidance_cfg_parallel and cfg_parallel_3_full"
+            -m "slow and diffusion and cuda and H100 and cards_3"
+            --run-level "full_model"
+        mirror_hardwares: h100_3
+
+      - label: ":full_moon: Diffusion X2I(&A&T) · Function Test with L4 · 4-GPU"
+        timeout_in_minutes: 120
+        commands:
+          - pytest -sv tests/e2e/ -k "not test_wan and not hunyuan" -m "full_model and diffusion and L4 and cards_4" --run-level "full_model" --ignore=tests/e2e/accuracy
         mirror_hardwares: l4_4
 
       - label: ":full_moon: Diffusion X2I(&A&T) · Doc Test"
@@ -255,8 +268,29 @@ steps:
           - 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
+      - label: ":full_moon: HiDream-O1-Image · Accuracy Test"
+        timeout_in_minutes: 180
+        commands:
+          - pytest -s -v tests/e2e/accuracy/test_hidream_o1_image.py -m full_model --run-level full_model
+        mirror_hardwares: h100_2
+
+      - label: ":full_moon: Diffusion X2I(&A&T) · Perf Test · Qwen-Image · Single-GPU"
+        key: nightly-diffusion-x2iat-performance-qwen-image-single
+        timeout_in_minutes: 180
+        commands:
+          - export DIFFUSION_BENCHMARK_DIR=tests/dfx/perf/results
+          - 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 -m "H100 and cards_1"
+            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_1
+
+      - label: ":full_moon: Diffusion X2I(&A&T) · Perf Test · Qwen-Image · 4-GPU"
+        key: nightly-diffusion-x2iat-performance-qwen-image-4gpu
         timeout_in_minutes: 180
         commands:
           - export DIFFUSION_BENCHMARK_DIR=tests/dfx/perf/results
@@ -264,7 +298,7 @@ steps:
           - 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
+            pytest -s -v tests/dfx/perf/scripts/run_diffusion_benchmark.py --test-config-file tests/dfx/perf/tests/test_qwen_image_vllm_omni.json -m "H100 and cards_4"
             EXIT=$$?
             buildkite-agent artifact upload "tests/dfx/perf/results/diffusion_result_*.json"
             buildkite-agent artifact upload "tests/dfx/perf/results/logs/*.log"
@@ -280,16 +314,34 @@ steps:
       build.pull_request.labels includes "diffusion-x2v-test"
     steps:
 
-      - label: ":full_moon: Diffusion X2V · Wan2.2 T2V Function Test"
+      - label: ":full_moon: Diffusion X2V · Wan2.2 T2V Function Test · Single-GPU"
         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"
+          - pytest -s -v tests/e2e/online_serving/test_wan22_expansion.py -k "t2v" -m "full_model and cuda and H100 and cards_1" --run-level "full_model"
+        mirror_hardwares: h100_1
+
+      - label: ":full_moon: Diffusion X2V · Wan2.2 T2V Function Test · 2-GPU"
+        timeout_in_minutes: 90
+        commands:
+          - pytest -s -v tests/e2e/online_serving/test_wan22_expansion.py -k "t2v" -m "full_model and cuda and H100 and cards_2" --run-level "full_model"
         mirror_hardwares: h100_2
 
-      - label: ":full_moon: Diffusion X2V · Wan2.2 I2V/TI2V Function Test"
+      - label: ":full_moon: Diffusion X2V · Wan2.2 I2V/TI2V Function Test · Single-GPU"
         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"
+          - pytest -s -v tests/e2e/online_serving/test_wan22_expansion.py -k "i2v" -m "full_model and cuda and H100 and cards_1" --run-level "full_model"
+        mirror_hardwares: h100_1
+
+      - label: ":full_moon: Diffusion X2V · Wan2.2 I2V/TI2V Function Test · 2-GPU"
+        timeout_in_minutes: 90
+        commands:
+          - pytest -s -v tests/e2e/online_serving/test_wan22_expansion.py -k "i2v" -m "full_model and cuda and H100 and cards_2" --run-level "full_model"
+        mirror_hardwares: h100_2
+
+      - label: ":full_moon: Diffusion X2V · HunyuanVideo-1.5 Function Test"
+        timeout_in_minutes: 90
+        commands:
+          - pytest -s -v tests/e2e/online_serving/test_hunyuan_video_15_expansion.py -m "full_model and cuda" --run-level "full_model"
         mirror_hardwares: h100_2
 
       - label: ":full_moon: Diffusion X2V · Doc Test"
@@ -324,17 +376,48 @@ steps:
           - 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
+      - label: ":full_moon: Diffusion X2V · Perf Test · Single-GPU"
+        key: nightly-diffusion-x2v-performance-single
+        timeout_in_minutes: 180
+        commands:
+          - export DIFFUSION_BENCHMARK_DIR=tests/dfx/perf/results
+          - |
+            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 -m "H100 and cards_1"
+            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_1
+
+      - label: ":full_moon: Diffusion X2V · Perf Test · 2-GPU"
+        key: nightly-diffusion-x2v-performance-2gpu
         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
+            pytest -s -v tests/dfx/perf/scripts/run_diffusion_benchmark.py --test-config-file tests/dfx/perf/tests/test_wan22_i2v_vllm_omni.json -m "H100 and cards_2"
             EXIT1=$$?
-            pytest -s -v tests/dfx/perf/scripts/run_diffusion_benchmark.py --test-config-file tests/dfx/perf/tests/test_cosmos3_vllm_omni.json
+            pytest -s -v tests/dfx/perf/scripts/run_diffusion_benchmark.py --test-config-file tests/dfx/perf/tests/test_cosmos3_vllm_omni.json -m "H100 and cards_2"
+            EXIT2=$$?
+            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 $$EXIT2
+        mirror_hardwares: h100_2
+
+      - label: ":full_moon: Diffusion X2V · Perf Test · HunyuanVideo-1.5"
+        key: nightly-diffusion-x2v-performance-hunyuanvideo15
+        timeout_in_minutes: 180
+        commands:
+          - export DIFFUSION_BENCHMARK_DIR=tests/dfx/perf/results
+          - |
+            set +e
+            pytest -s -v tests/dfx/perf/scripts/run_diffusion_benchmark.py --test-config-file tests/dfx/perf/tests/test_hunyuanvideo15_t2v_vllm_omni.json
+            EXIT1=$$?
+            pytest -s -v tests/dfx/perf/scripts/run_diffusion_benchmark.py --test-config-file tests/dfx/perf/tests/test_hunyuanvideo15_i2v_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"
@@ -347,7 +430,6 @@ steps:
         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
@@ -362,16 +444,34 @@ steps:
     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"
+      - label: ":full_moon: Diffusion · Distributed Test with L4 · 2-GPU"
         timeout_in_minutes: 60
         commands:
-          - pytest -sv tests/diffusion/distributed/ -m 'full_model and cuda and L4' --run-level "full_model"
+          - pytest -sv tests/diffusion/distributed/ -m 'full_model and cuda and L4 and cards_2' --run-level "full_model"
+        mirror_hardwares: l4_2
+
+      - label: ":full_moon: Diffusion · Distributed Test with L4 · 3-GPU"
+        timeout_in_minutes: 60
+        commands:
+          - pytest -sv tests/diffusion/distributed/ -m 'full_model and cuda and L4 and cards_3' --run-level "full_model"
+        mirror_hardwares: l4_3
+
+      - label: ":full_moon: Diffusion · Distributed Test with L4 · 4-GPU"
+        timeout_in_minutes: 60
+        commands:
+          - pytest -sv tests/diffusion/distributed/ -m 'full_model and cuda and L4 and cards_4' --run-level "full_model"
         mirror_hardwares: l4_4
 
-      - label: ":full_moon: Diffusion · Distributed Test with H100"
+      - label: ":full_moon: Diffusion · Distributed Test with H100 · 2-GPU"
         timeout_in_minutes: 60
         commands:
-          - pytest -sv tests/diffusion/distributed/ -m 'full_model and cuda and H100' --run-level "full_model"
+          - pytest -sv tests/diffusion/distributed/ -m 'full_model and cuda and H100 and cards_2' --run-level "full_model"
+        mirror_hardwares: h100_2
+
+      - label: ":full_moon: Diffusion · Distributed Test with H100 · 4-GPU"
+        timeout_in_minutes: 60
+        commands:
+          - pytest -sv tests/diffusion/distributed/ -m 'full_model and cuda and H100 and cards_4' --run-level "full_model"
         mirror_hardwares: h100_4
 
       - label: ":full_moon: Diffusion · Offloader Test"
@@ -395,7 +495,7 @@ steps:
       - label: ":full_moon: Diffusion · Quantization Test with L4"
         timeout_in_minutes: 60
         commands:
-          - pip install "vllm-gguf-plugin>=0.0.3"
+          - pip install "vllm-gguf-plugin==0.0.4"
           - pytest -sv tests/diffusion/quantization -m 'full_model and cuda and L4' --run-level "full_model"
         mirror_hardwares: l4_1
 
@@ -406,23 +506,25 @@ steps:
     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
+      - nightly-tts-performance-single
+      - nightly-tts-performance-2gpu
+      - nightly-diffusion-x2iat-performance-qwen-image-single
+      - nightly-diffusion-x2iat-performance-qwen-image-4gpu
+      - nightly-diffusion-x2v-performance-single
+      - nightly-diffusion-x2v-performance-2gpu
     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-tts-performance-single
+      - buildkite-agent artifact download "tests/dfx/perf/results/*.json" . --step nightly-tts-performance-2gpu
       - 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
+      - buildkite-agent artifact download "tests/dfx/perf/results/*.json" . --step nightly-diffusion-x2iat-performance-qwen-image-single
+      - buildkite-agent artifact download "tests/dfx/perf/results/*.json" . --step nightly-diffusion-x2iat-performance-qwen-image-4gpu
+      - buildkite-agent artifact download "tests/dfx/perf/results/*.json" . --step nightly-diffusion-x2v-performance-single
+      - buildkite-agent artifact download "tests/dfx/perf/results/*.json" . --step nightly-diffusion-x2v-performance-2gpu
       - 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/cuda/test-ready.yml

@@ -1,3 +1,6 @@
+# CUDA CI L2 (`core_model` e2e) plus L1 CPU Simple Test (`core_model and cpu`).
+# Uploaded by Upload Ready Pipeline: PR label `ready`, or main+WEEKLY=1 with --e2e.
+
 env:
   VLLM_WORKER_MULTIPROC_METHOD: spawn
   VLLM_USE_FLASHINFER_MOE_FP16: 0
@@ -9,23 +12,27 @@ steps:
     depends_on: upload-ready-pipeline
     steps:
       - label: "Simple · Model Executor Test"
+        timeout_in_minutes: 20
         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"
+          - "pytest -sv tests/model_executor -m 'core_model and cpu'"
         mirror_hardwares: l4_1
 
       - label: "Simple · Diffusion Test"
+        timeout_in_minutes: 20
         commands:
-          - "timeout 20m pytest -sv tests/diffusion -m 'core_model and cpu' --cov=vllm_omni --cov-report=term-missing:skip-covered --cov-report=xml"
+          - "pytest -sv tests/diffusion -m 'core_model and cpu'"
         mirror_hardwares: l4_1
 
       - label: "Simple · Engine&Entrypoints Test"
+        timeout_in_minutes: 20
         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"
+          - "pytest -sv tests/entrypoints tests/engine -m 'core_model and cpu'"
         mirror_hardwares: l4_1
 
       - label: "Simple · Other Test"
+        timeout_in_minutes: 20
         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"
+          - "pytest -sv tests/ -m 'core_model and cpu' --ignore=tests/diffusion --ignore=tests/model_executor --ignore=tests/entrypoints --ignore=tests/engine"
         mirror_hardwares: l4_1
 
   - group: ":card_index_dividers: Diffusion Test"
@@ -47,17 +54,20 @@ steps:
           - 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/
+      - label: "Diffusion · Distributed Test · Single-GPU"
         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"
+          - timeout 20m pytest -sv tests/diffusion/distributed -m 'core_model and cuda and L4 and cards_1' --run-level "core_model"
+        mirror_hardwares: l4_1
+
+      - label: "Diffusion · Distributed Test · 2-GPU"
+        commands:
+          - timeout 20m pytest -sv tests/diffusion/distributed -m 'core_model and cuda and L4 and cards_2' --run-level "core_model"
+        mirror_hardwares: l4_2
+
+      - label: "Diffusion · Distributed & Attention Test · 4-GPU"
+        commands:
+          - timeout 20m pytest -sv tests/diffusion/distributed -m 'core_model and cuda and L4 and cards_4' --run-level "core_model"
+          - timeout 20m pytest -sv tests/diffusion/attention/test_attention_sp.py -m 'core_model and cuda and L4 and cards_4' --run-level "core_model"
         mirror_hardwares: l4_4
 
       - label: "Diffusion · Model Test"
@@ -76,7 +86,7 @@ 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"
+          - timeout 15m pytest -sv -n 4 tests/model_tests/diffusion -m 'core_model and cuda' --run-level "core_model"
         mirror_hardwares: l4_1
 
   - label: "Engine&Model Executor Test"
@@ -121,32 +131,8 @@ steps:
           - 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"
+        mirror_hardwares: l4_1
+
 
       - label: "RLHF · VeRL-Omni E2E Test"
         source_file_dependencies:
@@ -168,6 +154,8 @@ steps:
       - label: "Omni · Qwen3-Omni Test"
         source_file_dependencies:
           - tests/e2e/online_serving/test_qwen3_omni.py
+          - tests/helpers/runtime.py
+          - vllm_omni/entrypoints/openai/serving_chat.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
@@ -223,10 +211,10 @@ steps:
           - 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_server_vad.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/
@@ -270,6 +258,9 @@ steps:
           - 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
+          - vllm_omni/entrypoints/openai/serving_speech.py
+          - vllm_omni/entrypoints/openai/tts_adapters/
+          - vllm_omni/engine/stage_pool.py
         commands:
           - |
             timeout 20m bash -c "
@@ -278,25 +269,6 @@ steps:
             "
         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
@@ -325,6 +297,24 @@ steps:
             "
         mirror_hardwares: h100_1
 
+      - label: "Diffusion · MiniMax-H3 DLO DP2 T2VA Test"
+        timeout_in_minutes: 90
+        source_file_dependencies:
+          - tests/e2e/online_serving/test_minimax_h3_dlo_dp2_t2va.py
+          - tests/helpers/assertions.py
+          - tests/helpers/runtime.py
+          - vllm_omni/diffusion/models/minimax_h3/
+          - vllm_omni/diffusion/offloader/
+          - vllm_omni/diffusion/distributed/
+          - vllm_omni/entrypoints/openai/
+        commands:
+          - |
+            timeout 80m bash -c "
+              export VLLM_IMAGE_FETCH_TIMEOUT=60
+              pytest -s -v tests/e2e/online_serving/test_minimax_h3_dlo_dp2_t2va.py -m 'core_model and cuda and diffusion' --run-level 'core_model'
+            "
+        mirror_hardwares: h100_2
+
       - label: "Diffusion · Qwen Image Test"
         source_file_dependencies:
           - tests/e2e/online_serving/test_qwen_image.py
@@ -338,3 +328,15 @@ steps:
               pytest -s -v tests/e2e/online_serving/test_qwen_image.py -m 'core_model' --run-level 'core_model'
             "
         mirror_hardwares: h100_1
+
+      - label: "Diffusion · HunyuanVideo-1.5 Test"
+        source_file_dependencies:
+          - tests/e2e/online_serving/test_hunyuan_video_15_expansion.py
+          - vllm_omni/diffusion/models/hunyuan_video/
+        commands:
+          - |
+            timeout 40m bash -c "
+              export VLLM_IMAGE_FETCH_TIMEOUT=60
+              pytest -s -v tests/e2e/online_serving/test_hunyuan_video_15_expansion.py -m 'core_model and diffusion' --run-level 'core_model'
+            "
+        mirror_hardwares: h100_1

.buildkite/cuda/test-weekly.yml

@@ -1,3 +1,6 @@
+# CUDA CI L5 (`slow` / reliability / extra coverage).
+# Uploaded by Upload Weekly Pipeline: WEEKLY=1 / NON_CRITICAL=1, or PR label `weekly-test`.
+
 env:
   VLLM_WORKER_MULTIPROC_METHOD: spawn
   VLLM_USE_FLASHINFER_MOE_FP16: 0
@@ -5,77 +8,153 @@ env:
   HF_HUB_ETAG_TIMEOUT: 60
 
 steps:
-
-  - label: "Reliability Test - qwen3-omni"
-    timeout_in_minutes: 180
+  - label: "Simple · CPU Coverage Test"
+    key: weekly-simple-cpu-coverage
     depends_on: upload-weekly-pipeline
-    if: build.env("WEEKLY") == "1" || build.pull_request.labels includes "weekly-test"
+    if: build.env("WEEKLY") == "1"
+    timeout_in_minutes: 60
     commands:
-      - pytest -s -v tests/dfx/reliability/test_reliability_qwen3_omni.py -m "slow"
-    mirror_hardwares: h100_2
+      - |
+        set +e
+        REPORT="coverage-core-model-cpu-$${BUILDKITE_STEP_ID:-local}.xml"
+        pytest -sv tests/ -m 'core_model and cpu' --cov=vllm_omni --cov-report=term-missing:skip-covered --cov-report=xml:$${REPORT}
+        EXIT=$$?
+        gzip -9 -f "$${REPORT}"
+        buildkite-agent artifact upload "$${REPORT}.gz"
+        exit $$EXIT
+    mirror_hardwares: l4_1
 
-  - 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"
+  - group: "Reliability 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
+      - label: "Reliability Test · qwen3-omni"
+        timeout_in_minutes: 180
         commands:
-          - pytest -s -v tests/dfx/reliability/invalid_param_test/ -m "slow and H100"
+          - pytest -s -v tests/dfx/reliability/test_reliability_qwen3_omni.py -m "slow"
         mirror_hardwares: h100_2
 
-      - label: "Invalid parameters Test · L4"
+      - label: "Reliability Test · wan22"
+        timeout_in_minutes: 180
+        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
+        commands:
+          - pytest -s -v tests/dfx/reliability/test_reliability_hunyuan_image.py -m "slow"
+        mirror_hardwares: h100_4
+
+      - label: "Reliability Test · Invalid parameters · H100 · Single-GPU"
+        timeout_in_minutes: 90
+        commands:
+          - pytest -s -v tests/dfx/reliability/invalid_param_test/ -m "slow and H100 and cards_1"
+        mirror_hardwares: h100_1
+
+      - label: "Reliability Test · Invalid parameters · H100 · 2-GPU"
+        timeout_in_minutes: 90
+        commands:
+          - pytest -s -v tests/dfx/reliability/invalid_param_test/ -m "slow and H100 and cards_2"
+        mirror_hardwares: h100_2
+
+      - label: "Reliability Test · Invalid parameters · 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"
+  - group: "Perf Test"
     depends_on: upload-weekly-pipeline
     if: build.env("WEEKLY") == "1" || build.pull_request.labels includes "weekly-test"
     steps:
 
-      - label: "Omni · L4"
+      - label: ":full_moon: Omni · Perf Test · vLLM Text"
+        key: weekly-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 · Async Chunk · Random"
+        key: weekly-omni-performance-async-chunk-random
+        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 -m "H100 and slow"
+            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: weekly-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
+
+  - group: ":card_index_dividers: E2E Tests"
+    depends_on: upload-weekly-pipeline
+    if: build.env("NON_CRITICAL") == "1"
+    steps:
+
+      # Online Qwen2.5 expansion is 2-card; offline expansion + AutoRound are 4-card.
+      - label: "Omni · L4 · 2-GPU"
         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/online_serving/test_qwen2_5_omni_expansion.py -m "slow and L4 and omni and cards_2" --run-level "core_model"
+        mirror_hardwares: l4_2
+
+      - label: "Omni · L4 · 4-GPU"
+        timeout_in_minutes: 90
+        commands:
+          - export VLLM_ALLOW_LONG_MAX_MODEL_LEN="1"
+          - pytest -s -v tests/e2e/offline_inference/test_qwen2_5_omni_expansion.py -m "slow and L4 and omni and cards_4" --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"
+            -m "slow and L4 and omni and cards_4"
             --run-level "full_model"
         mirror_hardwares: l4_4
 
-      - label: "Omni · H100"
+      - label: "Omni · H100 · Single-GPU"
         timeout_in_minutes: 90
         commands:
           - >-
             pytest -sv
-            tests/e2e/ -m "slow and H100 and omni"
+            tests/e2e/ -m "slow and H100 and omni and cards_1"
             --run-level "full_model"
-        mirror_hardwares: h100_2
+        mirror_hardwares: h100_1
+
+      - label: "Omni · H100 · 4-GPU"
+        timeout_in_minutes: 90
+        commands:
+          - >-
+            pytest -sv
+            tests/e2e/ -m "slow and H100 and omni and cards_4"
+            --run-level "full_model"
+        mirror_hardwares: h100_4
 
       - label: "TTS · L4"
-        timeout_in_minutes: 90
+        timeout_in_minutes: 120
         commands:
           - export VLLM_ALLOW_LONG_MAX_MODEL_LEN="1"
           - export VLLM_USE_DEEP_GEMM="0"
@@ -116,29 +195,56 @@ steps:
             --run-level "full_model"
         mirror_hardwares: l4_1
 
-      - label: "Diffusion · L4"
+      - label: "Diffusion · L4 · Single-GPU"
         timeout_in_minutes: 60
         commands:
           - >-
             pytest -sv
-            tests/e2e/ -m "slow and diffusion and L4"
+            tests/e2e/ -m "slow and diffusion and L4 and cards_1"
+            --run-level "full_model"
+        mirror_hardwares: l4_1
+
+      - label: "Diffusion · L4 · 2-GPU"
+        timeout_in_minutes: 60
+        commands:
+          - >-
+            pytest -sv
+            tests/e2e/ -m "slow and diffusion and L4 and cards_2"
+            --run-level "full_model"
+        mirror_hardwares: l4_2
+
+      - label: "Diffusion · L4 · 4-GPU"
+        timeout_in_minutes: 60
+        commands:
+          - >-
+            pytest -sv
+            tests/e2e/ -m "slow and diffusion and L4 and cards_4"
             --run-level "full_model"
         mirror_hardwares: l4_4
 
       - label: "Diffusion · H100 · Single-GPU"
-        timeout_in_minutes: 120
+        timeout_in_minutes: 150
         commands:
           - >-
             pytest -sv
-            tests/e2e/ -m "slow and diffusion and H100 and not distributed_cuda"
+            tests/e2e/ -m "slow and diffusion and H100 and cards_1"
             --run-level "full_model"
         mirror_hardwares: h100_1
 
-      - label: "Diffusion · H100 · Multi-GPU"
+      - label: "Diffusion · H100 · 2-GPU"
         timeout_in_minutes: 90
         commands:
           - >-
             pytest -sv
-            tests/e2e/ -m "slow and diffusion and H100 and distributed_cuda"
+            tests/e2e/ -m "slow and diffusion and H100 and cards_2"
+            --run-level "full_model"
+        mirror_hardwares: h100_2
+
+      - label: "Diffusion · H100 · 4-GPU"
+        timeout_in_minutes: 90
+        commands:
+          - >-
+            pytest -sv
+            tests/e2e/ -m "slow and diffusion and H100 and cards_4"
             --run-level "full_model"
         mirror_hardwares: h100_4

.buildkite/intel/pipeline-intel.yml

@@ -10,7 +10,7 @@ steps:
           DOCKER_BUILDKIT: "1"
           # Buildkite will automatically replace this with the actual commit hash
           VLLM_IMAGE_TAG: "${BUILDKITE_COMMIT}"
-          VLLM_VERSION: "v0.27.0"
+          VLLM_VERSION: "v0.28.0"
         priority: 100
         timeout_in_minutes: 60
         soft_fail: false

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

@@ -5,7 +5,12 @@ set -ex
 
 omni_source_dir=$(git rev-parse --show-toplevel)
 
-base_image_name="xpu/vllm-omni-ci-base:${VLLM_VERSION:?VLLM_VERSION must be set}"
+: "${VLLM_VERSION:?VLLM_VERSION must be set}"
+# Official upstream XPU image. It is published from vllm's own
+# docker/Dockerfile.xpu, which is exactly what build_vllm_base() clones and
+# builds below, so pulling it is equivalent to building the fallback base.
+upstream_base_image="vllm/vllm-openai-xpu:${VLLM_VERSION}"
+local_base_image="xpu/vllm-omni-ci-base:${VLLM_VERSION}"
 image_name="xpu/vllm-omni-ci:${BUILDKITE_COMMIT:?BUILDKITE_COMMIT must be set}"
 container_name="xpu_${BUILDKITE_COMMIT}_$(
     tr -dc A-Za-z0-9 </dev/urandom | head -c 10
@@ -49,9 +54,36 @@ build_vllm_base() (
         "${vllm_source_dir}"
 )
 
-if [ -z "$(docker images -q "${base_image_name}")" ]; then
-    build_vllm_base
-fi
+# Resolve the vLLM base image, in priority order:
+#   1. the published upstream image for VLLM_VERSION, pulled fresh (so moving
+#      tags like nightly are refreshed rather than served stale from disk);
+#   2. a local copy of that same image, when the registry is unreachable;
+#   3. VLLM_BASE, if that image is already on disk;
+#   4. otherwise build VLLM_BASE from the matching vLLM source tag (~30min).
+# VLLM_BASE names the fallback base and defaults to ${local_base_image}; set it
+# to reuse or produce a base under a different tag.
+resolve_vllm_base() {
+    if docker pull "${upstream_base_image}"; then
+        base_image_name="${upstream_base_image}"
+        return
+    fi
+
+    # Registry unreachable, but a previous run may have left the image behind.
+    if [ -n "$(docker images -q "${upstream_base_image}")" ]; then
+        echo "WARNING: could not pull ${upstream_base_image}; using local copy" >&2
+        base_image_name="${upstream_base_image}"
+        return
+    fi
+
+    echo "WARNING: ${upstream_base_image} unavailable remotely and locally" >&2
+    base_image_name="${VLLM_BASE:-${local_base_image}}"
+    if [ -z "$(docker images -q "${base_image_name}")" ]; then
+        echo "WARNING: building ${base_image_name} from vLLM ${VLLM_VERSION} source" >&2
+        build_vllm_base
+    fi
+}
+
+resolve_vllm_base
 
 # Try building the docker image
 docker_build "${image_name}" --build-arg "VLLM_BASE=${base_image_name}" --build-arg "VLLM_VERSION=${VLLM_VERSION}"

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

@@ -81,6 +81,55 @@ steps:
             EXIT=$$?
             buildkite-agent artifact upload "tests/e2e/accuracy/minicpmo_4_5/results/*.json"
             exit $$EXIT
+      - label: ":full_moon: Omni · MiniCPM-o 4.5 · Perf Test"
+        key: nightly-omni-performance-minicpmo-4-5-npu
+        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
+          - |
+            set +e
+            pytest -s -v tests/dfx/perf/scripts/run_benchmark.py -m "npu and A3" --test-config-file tests/dfx/perf/tests/test_minicpmo_4_5.json
+            EXIT=$$?
+            buildkite-agent artifact upload "tests/dfx/perf/results/*.json"
+            exit $$EXIT
+      - label: ":full_moon: Omni · MiniCPM-o 4.5 · Perf Test (A2)"
+        key: nightly-omni-performance-minicpmo-4-5-npu-a2
+        timeout_in_minutes: 180
+        mirror_hardwares: a2b3_npu_1
+        env:
+          VLLM_WORKER_MULTIPROC_METHOD: spawn
+          VLLM_ALLOW_LONG_MAX_MODEL_LEN: "1"
+          HF_TOKEN: "${HF_TOKEN}"
+          HF_HUB_OFFLINE: "1"
+        commands:
+          - export BENCHMARK_DIR=tests/dfx/perf/results
+          - |
+            set +e
+            pytest -s -v tests/dfx/perf/scripts/run_benchmark.py -m "npu and A2" --test-config-file tests/dfx/perf/tests/test_minicpmo_4_5.json
+            EXIT=$$?
+            buildkite-agent artifact upload "tests/dfx/perf/results/*.json"
+            exit $$EXIT
+      - label: ":full_moon: Omni · MiniCPM-o 4.5 · Duplex Seed-TTS Perf Test"
+        key: nightly-omni-performance-minicpmo-4-5-duplex-seed-tts-npu
+        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
+          - |
+            set +e
+            pytest -s -v tests/dfx/perf/scripts/run_benchmark.py -m "npu and A3" --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
   - group: ":card_index_dividers: TTS Model Test"
     key: nightly-tts-test-group
     depends_on: upload-nightly-pipeline

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

@@ -10,6 +10,18 @@ steps:
         -m 'core_model and npu and A3 and omni'
         --run-level 'core_model'
 
+  - label: "Diffusion NPU layer tests"
+    key: ready-diffusion-npu-layer-tests
+    depends_on: upload-ready-pipeline
+    mirror_hardwares: a3_npu_2
+    commands:
+      - >-
+        pytest -s -v
+        tests/diffusion/layers/test_activation.py
+        tests/diffusion/layers/test_fused_qk_norm_rope_npu.py
+        -m 'core_model and npu and A3'
+        --run-level 'core_model'
+
   - group: ":card_index_dividers: Omni Model Test"
     key: ready-omni-group
     depends_on: upload-ready-pipeline
@@ -43,7 +55,7 @@ steps:
     depends_on: upload-ready-pipeline
     steps:
       - label: ":sound: TTS · Qwen3-TTS CustomVoice Test"
-        mirror_hardwares: a2b3_npu_1
+        mirror_hardwares: a3_npu_2
         env:
           VLLM_WORKER_MULTIPROC_METHOD: spawn
           VLLM_ALLOW_LONG_MAX_MODEL_LEN: "1"

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

@@ -105,7 +105,7 @@ For custom loop structures, follow `vllm_omni/diffusion/models/progress_bar.py`
 
 ## 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. See [references/custom-model-patterns.md](references/custom-model-patterns.md) for concrete custom-model integration patterns.
+For models without a diffusers pipeline — weights in custom format, model code in a private repo. Real examples: BAGEL, HunyuanImage3. See [references/custom-model-patterns.md](references/custom-model-patterns.md) for concrete custom-model integration patterns.
 
 ### B1. Understand the reference repo
 
@@ -126,9 +126,9 @@ This is the key design decision for custom models. Follow these placement rules:
 | **Pipeline orchestration** (init, forward, denoise loop) | `vllm_omni/diffusion/models/<name>/pipeline_<name>.py` | Always required |
 | **Custom transformer/backbone** (ported and adapted to vllm-omni) | `vllm_omni/diffusion/models/<name>/<name>_transformer.py` or similar | `wan2_2.py`, `fusion.py`, `bagel_transformer.py` |
 | **Custom sub-models** (VAE, fusion, autoencoder) | `vllm_omni/diffusion/models/<name>/` as separate files | `autoencoder.py`, `fusion.py` |
-| **External dependency code** (original repo utilities) | **External repo**, installed via download script or pip | `dreamid_omni` package via git clone |
+| **External dependency code** (original repo utilities) | **External repo**, installed via download script or pip | original-repo package via git clone |
 | **Hardcoded model configs** | Module-level dicts in pipeline file | `VIDEO_CONFIG`, `AUDIO_CONFIG` dicts |
-| **Download/setup script** | `examples/offline_inference/<name>/download_<name>.py` | `download_dreamid_omni.py` |
+| **Download/setup script** | `examples/offline_inference/<name>/download_<name>.py` | `download_<name>.py` |
 | **Custom `model_index.json`** | Generated by download script, placed at model root | Minimal: `{"_class_name": "YourPipeline", ...}` |
 
 ### B3. Handle external dependencies
@@ -157,7 +157,7 @@ Copy the essential model files into `vllm_omni/diffusion/models/<name>/` and ada
 
 Custom models have two common patterns for weight loading:
 
-**Pattern 1: Bypass standard loader** (DreamID-Omni style)
+**Pattern 1: Bypass standard loader** (eager custom init)
 
 When the original model has complex custom init functions that load weights in `__init__`:
 
@@ -338,7 +338,7 @@ Classify the model's **CI priority** first:
 
 **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 (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 (`cards_1` vs `not cards_1`).
 
 **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.
 
@@ -610,6 +610,24 @@ See the [Profiling Single-Stage Diffusion](../../../docs/contributing/profiling.
 
 ---
 
+## Pre-commit conventions
+
+New library files must pass the local gates in
+[docs/contributing/README.md](../../../docs/contributing/README.md#linting).
+That page is the full hook list (SPDX, forbidden imports including Hugging Face
+Hub / Triton / pickle, `torch.cuda`, mypy, test marks, markdownlint, Buildkite,
+shellcheck). In particular:
+
+- SPDX copyright is `vLLM-Omni project` (stale `vLLM project` is rewritten).
+- Use `import regex as re` and `pybase64` in `vllm_omni/`; do not import stdlib
+  `re` or `base64`. Hugging Face Hub downloads go through
+  `vllm.transformers_utils.repo_utils`.
+- Do not add `torch.cuda.*` call sites; use `current_omni_platform`.
+- New `tests/**/test_*.py` files need a CI level mark and a hardware mark.
+- Do not expand `CHECK_IMPORTS[*].allowed_files` or `ALLOWED_FILES` without review.
+- GitHub Actions skips SPDX/shellcheck/mypy-3.10/test-marks/markdownlint; run
+  `pre-commit` locally.
+
 ## Iterative Development Tips
 
 1. **Start minimal**: Basic generation first, no parallelism/caching

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

@@ -16,20 +16,19 @@ vllm_omni/diffusion/models/wan2_2/
 
 The transformer is loaded separately via `weights_sources` + `load_weights()`. Non-transformer components (VAE, text encoder) are loaded in `__init__` via `from_pretrained()`.
 
-### Custom model with external deps (e.g., DreamID-Omni)
+### Custom model with external deps
 
 ```
-vllm_omni/diffusion/models/dreamid_omni/
+vllm_omni/diffusion/models/<name>/
 ├── __init__.py                    # Exports pipeline only
-├── pipeline_dreamid_omni.py       # Pipeline: loads ALL weights in __init__ via custom helpers
-├── fusion.py                      # Custom fusion architecture (video + audio cross-attention)
-└── wan2_2.py                      # Re-implemented Wan backbone with split API
+├── pipeline_<name>.py             # Pipeline: loads ALL weights in __init__ via custom helpers
+└── <backbone>.py                  # Custom transformer / fusion architecture
 
-examples/offline_inference/x_to_video_audio/
-└── download_dreamid_omni.py       # Downloads weights from 3 HF repos + clones code repo
+examples/offline_inference/<task>/
+└── download_<name>.py             # Downloads weights and/or clones code repo
 ```
 
-All weights loaded eagerly in `__init__`. `load_weights()` is a no-op. External dependency (`dreamid_omni` package) imported with try/except.
+All weights loaded eagerly in `__init__`. `load_weights()` is a no-op. External dependency imported with try/except.
 
 ### Custom model with ported code (e.g., BAGEL)
 
@@ -76,7 +75,7 @@ self.weights_sources = [
 ]
 ```
 
-### Pattern 3: Fully custom loading (DreamID-Omni)
+### Pattern 3: Fully custom loading
 
 ```
 init → load ALL weights eagerly via custom helpers → load_weights() = no-op
@@ -108,8 +107,8 @@ Standard diffusers `model_index.json`:
 Custom model `model_index.json` (minimal):
 ```json
 {
-    "_class_name": "DreamIDOmniPipeline",
-    "fusion": "DreamID-Omni/dreamid_omni.safetensors"
+    "_class_name": "YourCustomPipeline",
+    "fusion": "weights/model.safetensors"
 }
 ```
 
@@ -117,7 +116,7 @@ The only **required** field is `_class_name` — it must match a key in `_DIFFUS
 
 ## External Dependency Management
 
-### Git clone + .pth injection (DreamID-Omni pattern)
+### Git clone + .pth injection
 
 ```python
 def download_dependency():
@@ -163,7 +162,7 @@ The engine checks `isinstance(pipeline, SupportImageInput)` at startup to config
 
 Diffusers models use `config.json` in each subfolder. Custom models often use:
 
-**Module-level config dicts** (DreamID-Omni):
+**Module-level config dicts**:
 ```python
 VIDEO_CONFIG = {
     "patch_size": [1, 2, 2], "model_type": "ti2v",
@@ -181,7 +180,7 @@ vae_cfg = bagel_cfg.get("vae_config", {})
 
 ## Custom Architecture Patterns
 
-### Split forward API (DreamID-Omni)
+### Split forward API
 
 When a fusion model needs to interleave blocks from two backbones:
 

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

@@ -138,9 +138,12 @@ A shared buffer silently corrupts audio across concurrent requests — the sympt
    - Load codec weights (may need lazy loading from separate checkpoint)
    - Implement `forward()`: codec codes -> audio waveform
    - Return `OmniOutput` with `multimodal_outputs`
-5. **Create stage config YAML** defining both stages, memory allocation, and model paths
-6. **Create stage input processor** for prompt building
-7. **Write end2end.py** test script
+5. **Define and register the pipeline topology** in
+   `vllm_omni/model_executor/models/<model>/pipeline.py`
+6. **Create the deploy YAML** under `vllm_omni/deploy/` for placement,
+   memory sizing, connectors, and runtime overrides
+7. **Create stage input processor** for prompt building and inter-stage handoff
+8. **Write end2end.py** test script
 
 ### Critical Parameters to Get Right
 
@@ -174,7 +177,7 @@ These bugs appear in almost every new TTS PR. Check all before the first push. S
 - **All return paths must emit `model_outputs`** — if any early-return branch skips setting `model_outputs`, the serving layer silently drops that step's audio (fish speech: `fix: ensure ALL return paths emit model_outputs`)
 - **Per-request state isolation** — for batched concurrent requests, key all state by request ID; a shared buffer corrupts audio across requests (fish speech: `fix: per-request vocode + delta emission`)
 - **Codec tensor device** — move codec codes to the codec decoder's device before calling decode; mismatches cause silent CPU fallback or crashes (fish speech: `fix: use model device for CUDA stream`)
-- **AR stage `max_num_seqs`** — set to at least 4 in production configs; for single-stage models this is the only stage. For two-stage models, Stage 0 (AR) needs `max_num_seqs ≥ 4` to pipeline concurrent requests; Stage 1 (codec decoder) typically uses `max_num_seqs: 1` intentionally. Default of 1 everywhere causes audio gaps under concurrency because the codec window round-robins across requests (RFC #2568)
+- **AR stage `max_num_seqs`** — set to at least 4 in production deploy configs; for single-stage models this is the only stage. For two-stage models, Stage 0 (AR) needs `max_num_seqs ≥ 4` to pipeline concurrent requests; Stage 1 (codec decoder) is model-specific and may intentionally use `max_num_seqs: 1`. Defaulting the AR stage to 1 causes audio gaps under concurrency because the codec window round-robins across requests (RFC #2568)
 
 ### Optional Dependency Handling
 
@@ -190,18 +193,18 @@ pattern, signature constraints, and MOSS-TTS-Nano reference.
 When the upstream model cannot be cleanly split into an AR stage and a
 separate decoder, run the full pipeline inside a single AR worker and
 stream audio through a per-request `inference_stream()` generator keyed by
-`_omni_req_id`. Stage config must set `worker_type: ar`,
-`engine_output_type: audio`, `final_output: true`, `is_comprehension: true`,
-and `async_chunk: false` at the top level. Only extract params from
+`_omni_req_id`. Define one `StagePipelineConfig` with
+`execution_type=StageExecutionType.LLM_AR`, `engine_output_type="audio"`,
+`final_output=True`, and `owns_tokenizer=True`. Set `async_chunk: false` in
+the deploy YAML. Only extract params from
 `additional_information` that you actually forward, or pre-commit fails
 `ruff F841`.
 
 Full walkthrough with the complete `forward()` / `_create_stream_gen()`
-skeleton and stage-config fields:
+skeleton plus pipeline/deploy definitions:
 [references/single-stage-ar.md](references/single-stage-ar.md). For an
 in-tree reference, look for any single-stage AR model under
-`vllm_omni/model_executor/models/` — e.g. the MOSS-TTS-Nano integration when
-it lands.
+`vllm_omni/model_executor/models/`, such as MOSS-TTS-Nano.
 
 **VoxCPM2 is a different pattern** and should not reuse this skeleton — it
 runs the base LM under vLLM PagedAttention with external side-computation.
@@ -210,7 +213,8 @@ See `plan/voxcpm2_native_ar_design.md`.
 ### Deliverables
 
 - Model files in `vllm_omni/model_executor/models/<model_name>/`
-- Stage config YAML
+- Registered `pipeline.py` topology
+- Deploy YAML under `vllm_omni/deploy/`
 - 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>/`.
 
@@ -341,8 +345,8 @@ Heavier scenarios in the same file use **`advanced_model` only** (streaming, ext
 @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({...})
+def test_voice_clone_en_non_streaming_001(omni_server, online_client) -> None:
+    online_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)).
@@ -359,8 +363,8 @@ def test_voice_clone_en_non_streaming_001(omni_server, openai_client) -> None:
 
 | 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)` |
+| Online `/v1/*` | `online_client` | `online_client.send_*_request(request_config)` |
+| Offline inference | `offline_client` | `offline_client.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.
@@ -392,7 +396,7 @@ See [vllm-omni-test skill](../vllm-omni-test/SKILL.md) § **Runtime send helpers
   `/health` return non-200 until you're actually ready.
 - **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
+- **Online and offline e2e go through `runtime.py`** — `online_client.send_audio_speech_request` (online); `offline_client.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)
 
@@ -400,21 +404,29 @@ See [vllm-omni-test skill](../vllm-omni-test/SKILL.md) § **Runtime send helpers
 
 ### Steps
 
-1. **Update stage config YAML**:
+1. **Update the pipeline topology** so the producing stage declares its async
+   handoff processor with `async_chunk_process_next_stage_input_func`.
+2. **Update the deploy YAML** to enable async chunk and configure the connector:
    ```yaml
    async_chunk: true
-   codec_chunk_frames: 25      # frames per chunk
-   codec_left_context_frames: 25  # overlap for smooth boundaries
+
+   connectors:
+     connector_of_shared_memory:
+       name: SharedMemoryConnector
+       extra:
+         codec_streaming: true
+         codec_chunk_frames: 25
+         codec_left_context_frames: 25
    ```
-2. **Implement chunk handling in Stage 1**:
+3. **Implement chunk handling in Stage 1**:
    - Accept partial input (chunk of codec codes)
    - Handle left context for smooth audio boundaries
    - Return partial audio in `OmniOutput`
-3. **Test streaming**:
+4. **Test streaming**:
    - Verify audio quality matches non-streaming output
    - Check for artifacts at chunk boundaries
    - Measure TTFA (time to first audio)
-4. **Update online serving** to support `stream=true` with PCM output
+5. **Update online serving** to support `stream=true` with PCM output
 
 ### Streaming Architecture
 
@@ -435,7 +447,8 @@ Stage 0 (AR)                    Stage 1 (Decoder)
 
 ### Deliverables
 
-- Updated stage config with async_chunk enabled
+- Updated pipeline async handoff processor and deploy config with
+  `async_chunk: true`
 - Smooth streaming audio without boundary artifacts
 - TTFA metrics
 
@@ -507,14 +520,15 @@ Use this checklist when integrating a new TTS model:
 - [ ] Config classes created with `model_type` registration
 - [ ] Stage 0 (AR) implemented and generates correct tokens
 - [ ] Stage 1 (Decoder) produces correct audio from tokens — dtype float32 for codec decoder
-- [ ] Stage 1 `max_num_seqs` ≥ 4 in production config (default 1 causes gaps under concurrency)
+- [ ] AR stage `max_num_seqs` ≥ 4 in the production deploy config unless the model has a tested lower limit
 - [ ] Optional dependency fallbacks handled at `load_weights()` time (torchaudio/soundfile/etc.)
 - [ ] Streaming: codec codes accumulated across AR steps (not reset per step)
 - [ ] Streaming: delta audio emitted per chunk, not full re-decoded waveform
 - [ ] Streaming: all `forward()` return paths emit `model_outputs`
 - [ ] Streaming: per-request state keyed by request ID (not shared across requests)
 - [ ] Streaming: codec tensors moved to codec decoder device before decode
-- [ ] Stage config YAML created
+- [ ] Pipeline topology defined in `pipeline.py` and registered in `OMNI_PIPELINES`
+- [ ] Deploy YAML created under `vllm_omni/deploy/`
 - [ ] `end2end.py` produces audio matching reference quality
 - [ ] README.md written
 
@@ -531,7 +545,8 @@ Use this checklist when integrating a new TTS model:
 - [ ] Documentation added (offline + online docs, nav, supported models)
 
 ### Phase 4: Async Chunk
-- [ ] Stage config updated with `async_chunk: true`
+- [ ] Pipeline declares the async handoff processor
+- [ ] Deploy config sets `async_chunk: true` and connector chunk parameters
 - [ ] Stage 1 handles partial chunks correctly
 - [ ] No audio artifacts at chunk boundaries
 - [ ] Streaming via API (`stream=true`) works
@@ -548,7 +563,7 @@ Use this checklist when integrating a new TTS model:
 - [ ] `pre-commit run --files <changed>` passes before every push
 - [ ] Every commit has `Signed-off-by` matching the author email (`git commit -s`)
 - [ ] `git config user.email` matches the email registered on your GitHub account
-- [ ] Details and failure-recovery commands: [references/precommit-dco.md](references/precommit-dco.md)
+- [ ] Hook list, failure table, and DCO recovery: [references/precommit-dco.md](references/precommit-dco.md)
 
 ## References
 

.claude/skills/add-tts-model/references/precommit-dco.md

@@ -30,6 +30,18 @@ but the changes are correct — stage the modified files and re-commit.
 | `ruff F841` | Variable extracted but never forwarded to model call | Remove the extraction or wire it through |
 | `ruff E402` | Import added below function definitions | Move to top-level import block |
 | `ruff format` | Line length, spacing, quote style | Accept auto-fix, stage, re-commit |
+| `check-spdx-header` | Missing header, or copyright still says `vLLM project` | Two-line Omni header (`vLLM-Omni project`); restage the rewrite |
+| `check-forbidden-imports` | Stdlib `re`/`base64`, pickle, Hugging Face Hub API, or direct Triton/TileLang | `import regex as re` and `pybase64`; use `vllm.transformers_utils.repo_utils`. Do **not** add the file to `allowed_files` without review |
+| `check-torch-cuda-call` | New `torch.cuda.*` call site | Use `current_omni_platform` / `OmniPlatform`; do not grow `ALLOWED_FILES` without review |
+| `check-tts-adapter-migration` | New `self._tts_model_type` branch in `serving_speech.py` | Put per-model logic in `tts_adapters/`. Lower `MAX_MODEL_TYPE_BRANCHES` when removing branches; do not raise it without review |
+| `check-mark` | New `tests/**/test_*.py` missing level/hardware mark, or direct `pytest.mark.H100` | Add `core_model`/`advanced_model`/… plus `cpu`/`cuda`/`hardware_test(`; SKU marks only via helpers |
+| `mypy-3.10` | Type error on changed `vllm_omni/` files | Fix the types; model trees are excluded. Extra versions: `pre-commit run --hook-stage manual mypy-3.12` |
+| `markdownlint-cli2` | Docs/README markdown lint | Accept auto-fix under `docs/` / `recipes/` / root README |
+| `check-buildkite` | Invalid `.buildkite/*.yml` | Fix the YAML; do not grow `SKIP_FILES` without review |
+| `shellcheck` | No `shellcheck` on PATH, or script warning | `apt-get`/`dnf`/`brew install shellcheck`, or `shellcheck.exe` on Windows PATH |
+
+Canonical policy, CI `SKIP` list, and allowlist locations:
+[docs/contributing/README.md](../../../docs/contributing/README.md#linting).
 
 ## DCO sign-off
 

.claude/skills/add-tts-model/references/single-stage-ar.md

@@ -86,12 +86,51 @@ class YourModelForCausalLM(nn.Module):
 - Handle both `"audio"` (incremental) and `"result"` (final combined) event
   types from upstream models.
 
-## Stage config
+## Pipeline and deploy config
 
-Single stage with `worker_type: ar`, `engine_output_type: audio`,
-`final_output: true`, `is_comprehension: true`, and `async_chunk: false` at
-the top level. Omitting any of these causes silent misclassification in the
-serving layer.
+Define the fixed single-stage topology in `pipeline.py`:
+
+```python
+from vllm_omni.config.stage_config import (
+    PipelineConfig,
+    StageExecutionType,
+    StagePipelineConfig,
+)
+
+YOUR_TTS_PIPELINE = PipelineConfig(
+    model_type="your_model_name",
+    default_deploy_config_name="your_model_name.yaml",
+    model_arch="YourModelForCausalLM",
+    stages=(
+        StagePipelineConfig(
+            stage_id=0,
+            model_stage="your_model_name",
+            execution_type=StageExecutionType.LLM_AR,
+            input_sources=(),
+            owns_tokenizer=True,
+            engine_output_type="audio",
+            final_output=True,
+            final_output_type="audio",
+        ),
+    ),
+)
+```
+
+Register it in `vllm_omni/config/pipeline_registry.py`. Keep placement and
+runtime sizing in `vllm_omni/deploy/your_model_name.yaml`:
+
+```yaml
+async_chunk: false
+
+stages:
+  - stage_id: 0
+    devices: "0"
+    max_num_seqs: 4
+    gpu_memory_utilization: 0.8
+```
+
+Do not move `execution_type`, output ownership, tokenizer ownership, or model
+architecture into the deploy YAML; those are pipeline topology.
 
 ## Lint discipline
 
@@ -101,8 +140,5 @@ pre-commit.
 
 ## Reference implementation
 
-Look for any single-stage AR model under
-`vllm_omni/model_executor/models/` — e.g. `moss_tts_nano/` when its
-integration lands. If none is in tree yet, follow the skeleton above and
-cross-check against the `is_comprehension: true` / `async_chunk: false`
-dispatch in `vllm_omni/entrypoints/openai/serving_speech.py`.
+Use `vllm_omni/model_executor/models/moss_tts_nano/pipeline.py` and
+`vllm_omni/deploy/moss_tts_nano.yaml` as the in-tree reference.

.claude/skills/find-simplifications/SKILL.md

@@ -0,0 +1,135 @@
+---
+name: find-simplifications
+description: Find evidence-backed simplification candidates in vLLM-Omni and, when requested, turn them into focused proposals or code changes. Use for audits of dead, duplicated, speculative, over-generalized, unnecessarily defensive, or hand-rolled code. Use review-pr for ordinary correctness review and diffusion-perf-opt for performance-first optimization.
+---
+
+# Find vLLM-Omni Simplifications
+
+Find a small number of well-proven ways to reduce code, state, APIs, or maintenance cost without weakening supported behavior. Treat this as an architecture and ownership audit, not a dead-code search or style cleanup.
+
+The default deliverable is a read-only report. Do not add TODOs, design documents, issues, or code unless the user asks for those changes.
+
+## Establish the Contract
+
+Before judging a candidate:
+
+1. Read the repository instructions supplied by the host.
+2. Read [`docs/design/architecture_overview.md`](../../../docs/design/architecture_overview.md).
+3. Select only the relevant active module and feature documents from [`docs/design/index.md`](../../../docs/design/index.md). Archived design pages are historical context, not current contracts.
+4. Identify the comparison base with `git merge-base HEAD origin/main`; do not assume the current branch represents the intended baseline.
+5. If tests or CI coverage may change, use [`vllm-omni-test`](../vllm-omni-test/SKILL.md) for test placement, markers, and commands.
+
+Separate an implementation accident from an intentional boundary. In particular:
+
+- vLLM-Omni extends and sometimes overrides upstream vLLM. A similar upstream implementation is evidence for reuse only after comparing API, lifecycle, and multi-stage semantics.
+- Registries, import strings, model configuration, deployment YAML, plugins, and optional dependencies create dynamic consumers that a static reference count can miss.
+- Hardware backends, model families, execution modes, and connector implementations may be intentionally parallel. Do not propose collapsing one only because it is inactive in the local environment.
+- Stage, replica, request, cache, and transport state can look redundant while representing different owners. Prove that two fields or barriers encode the same fact before merging them.
+- A shorter hot path is not a simplification if it adds synchronization, tensor copies, host materialization, recompilation, or a measurable quality/performance regression.
+
+## Strong Candidates
+
+A strong candidate removes a meaningful surface and has a clear replacement or deletion boundary. Examples include:
+
+- A public method, configuration option, registry entry, route, message field, or event has no production consumer and no compatibility obligation.
+- Tests or documentation are the only consumers of behavior that is not part of a supported contract.
+- Multiple layers mirror the same request, lifecycle, cache-validity, readiness, or topology state.
+- AR, diffusion, platform, or model-specific implementations duplicate a stable common operation and can share it without hiding performance-critical differences.
+- An inherited upstream vLLM primitive is reimplemented locally without an Omni-specific semantic difference.
+- Compatibility or fallback branches protect environments the project no longer supports.
+- Serialization, IPC, output formatting, or connector code materializes the same payload more than once or converts between equivalent representations.
+- A helper, abstraction, package split, or extension point has only one real implementation and adds more indirection than policy.
+- Hand-written infrastructure can be replaced by an existing project dependency, Python standard-library feature, PyTorch primitive, or upstream vLLM facility with net deletion and equal observability.
+- Tests maintain large mocks, snapshots, or expected data solely for an unused API or representation.
+
+Do not elevate typo fixes, formatting, isolated renames, or vague complexity complaints into simplification candidates. Bundle small cleanups only when they support one substantive removal.
+
+## Survey by Ownership Boundary
+
+For broad audits, cover the largest or riskiest production deltas first. Useful domains are:
+
+- `vllm_omni/engine/`: orchestration, stage pools, request state, output convergence, cancellation, and replica lifecycle.
+- `vllm_omni/worker/` and AR model execution: upstream vLLM overlays, scheduler/cache state, collective RPC, adapters, and weight loading.
+- `vllm_omni/diffusion/`: request/step execution, batching, output materialization, IPC, offload, parallelism, and model pipelines.
+- `vllm_omni/entrypoints/`: duplicated validation/defaulting, offline/online parity, streaming, and route compatibility.
+- Connectors and distributed code: ownership, copies, barriers, handles, cleanup, and failure propagation across process/device/node boundaries.
+- Platform backends: genuine vendor differences versus copied control flow that has drifted.
+- Configuration, registries, recipes, and examples: duplicate sources of truth and options that never reach a runtime consumer.
+- Tests and CI: redundant fixtures, mocks of obsolete contracts, and expensive coverage that can move to a cheaper level without losing the guarded behavior.
+
+When the user explicitly requests a repository-wide or many-candidate audit and parallel agent work is available and authorized, divide these domains among agents and require the same evidence format from each. Otherwise survey them sequentially.
+
+## Prove or Reject Each Candidate
+
+Use `rg` first, then read every relevant caller and implementation. Search exact Python symbols plus serialized names, route paths, configuration keys, registry strings, YAML values, and CLI spellings.
+
+Classify evidence:
+
+- **Production:** `vllm_omni/`, runtime configuration, registries, serving entrypoints, connectors, recipes used for supported deployments, and executable loader paths.
+- **Validation/support:** `tests/`, benchmarks, examples, docs, CI, and developer tooling.
+- **External/dynamic:** upstream vLLM contracts, third-party plugins, model repositories, optional backends, and string-loaded objects. State what was checked and what remains unknown.
+
+For each candidate, answer:
+
+1. What exact surface would disappear or merge?
+2. Who owns the state or behavior today?
+3. Which production consumers exist, including dynamic consumers?
+4. What supported behavior, compatibility, performance, memory, or observability could change?
+5. What is the smallest validation that would catch a bad simplification?
+6. Is the result net deletion after tests, adapters, migration glue, and documentation are counted?
+
+Reject or downgrade the candidate when a live consumer exists and removal is actually a feature decision; when an active design contract explains the separation; when the change only moves complexity; or when hardware/model evidence is unavailable for a performance-sensitive claim.
+
+## Lifecycle and Data-Movement Audits
+
+For asynchronous or distributed code, map the ownership graph before recommending deletion:
+
+```text
+request admission -> orchestrator -> stage/replica -> worker/device
+  -> connector or IPC boundary -> output aggregation -> terminal cleanup
+```
+
+Associate every lock, event, sentinel, queue, readiness flag, timeout, abort path, and cache marker with an owner and transition. Coalesce mechanisms only when they represent the same state and have the same failure boundary. Preserve separate mechanisms when they protect publication versus rollback, local versus remote ownership, first-terminal-outcome arbitration, or cleanup after partial failure.
+
+For tensors and media, also trace device, process, and ownership transitions. Count GPU-to-CPU copies, shared-memory/object-store handles, serialization, decode/encode steps, and unlink/release responsibility. Prefer eliminating a transfer or representation over merely hiding it behind a helper.
+
+## Dependency Replacements
+
+Prefer facilities already available in the repository before adding a dependency. For a proposed replacement:
+
+- Name the exact local behavior it covers and any semantics that remain in glue code.
+- Check maintenance, license, optional-dependency boundaries, platform support, and transitive footprint.
+- Compare net deletion, test burden, import/startup cost, and runtime behavior.
+- Benchmark when the replaced code is on a model, scheduler, connector, serialization, or serving hot path.
+
+A wrapper that retains the same state machine or conversion logic is not a simplification.
+
+## Deliver the Result
+
+Report only candidates supported by concrete evidence. For each one include:
+
+- **Surface:** files and symbols involved.
+- **Evidence:** production consumers found, dynamic paths checked, and relevant design contract.
+- **Simplification:** exactly what to remove, merge, inline, or reuse.
+- **Benefit:** deleted API/state/code, reduced data movement, or reduced maintenance/test burden.
+- **Risk and validation:** behavior that could change and the narrowest useful tests or measurements.
+- **Confidence:** high or medium, with the unresolved evidence named for medium-confidence items.
+
+Also list representative ideas that were rejected when that prevents repeated investigation. Prefer three high-confidence candidates over a long speculative inventory.
+
+If the user asks for a durable design proposal, place it under `docs/design/feature/` and register it in `docs/design/index.md`; use current design-document conventions and keep implementation details proportional to the decision. If the user asks for implementation, make the smallest coherent change, update affected tests/docs, and preserve public compatibility unless the user explicitly accepts a breaking change.
+
+## Folding Work from Another Branch
+
+Diff the source branch against its own merge base with `origin/main`, not against the current feature branch. Port only independently justified candidates. Consolidate overlaps into the candidate with stronger evidence, and do not preserve a candidate count by carrying weaker duplicates.
+
+## Validation
+
+Match validation to the outgoing diff:
+
+- Documentation or skill changes: run the repository skill validator when available, check links/paths, search for stale source-specific terminology, and run `git diff --check`.
+- Python changes: run the narrowest relevant pytest coverage through the repository environment, then lint/format the changed files.
+- Distributed, model, hardware, performance, or accuracy changes: run available CPU/static checks and state the exact GPU/NPU/model gap; never present simulated evidence as device validation.
+- Before submission, use [`precheck-pr`](../precheck-pr/SKILL.md) for the branch-level self-review.
+
+When summarizing, distinguish checks that passed from checks that were not runnable, and state the snapshot or branch against which the audit was performed.

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

@@ -67,6 +67,10 @@ Ask: "Quick mode or full mode?" Then walk the checklist for the detected PR type
 
 **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.
 
+**Also run the Simplification pass on every PR** using [find-simplifications](../find-simplifications/SKILL.md). Keep it diff-scoped: inspect added or modified public APIs, state, abstractions, fallback and compatibility paths, data movement, and only the adjacent ownership needed to prove a candidate. Do not expand the pass into a repository-wide audit. Zero candidates is a valid pass; report only evidence-backed opportunities. Mark a candidate as blocking only when it proves a correctness or merge-readiness problem, such as newly unreachable code. Otherwise report it as a warning.
+
+**Also confirm local pre-commit gates** from [docs/contributing/README.md](../../../docs/contributing/README.md#linting). GitHub Actions `SKIP` does not prove they passed. Full new-hook list: Omni SPDX (`vLLM-Omni project`); forbidden imports (stdlib `re`/`base64`, pickle, Hugging Face Hub API, Triton/TileLang); no new `torch.cuda` call sites; shellcheck (macOS/Windows must install the binary); mypy-3.10; test files have CI level + hardware marks; TTS `_tts_model_type` branches in `serving_speech.py` must not increase; markdownlint on `docs/`; Buildkite YAML schema. ✗ for growing `allowed_files` / `ALLOWED_FILES` or raising `MAX_MODEL_TYPE_BRANCHES` without review.
+
 ### Step 5: Print Report
 
 ```
@@ -80,6 +84,7 @@ Pre-check report for <branch>
   PR title format    ✓
   Code quality       ⚠ 1 broad except, 2 Any hints
   Examples policy    ✗ new model-specific Python example
+  Simplification     ⚠ helper duplicates an existing owner
   PR desc integrity  ✓
   Registry/config    ✓
   Dead code          ⚠ 2 warnings

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

@@ -164,6 +164,7 @@ Diffusion models live under `vllm_omni/diffusion/`. In addition to the New Model
 - [ ] **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
+- [ ] **Local pre-commit gates:** full list in [Linting](../../../../docs/contributing/README.md#linting). Blocking when the diff introduces: stale `vLLM project` SPDX; stdlib `re`/`base64` or pickle in library code; new `torch.cuda` call sites; `tests/**/test_*.py` without level+hardware marks; new `_tts_model_type` branches in `serving_speech.py`; invalid `.buildkite` YAML. macOS/Windows need a native `shellcheck`. ✗ for growing `CHECK_IMPORTS[*].allowed_files`, `ALLOWED_FILES`, or `MAX_MODEL_TYPE_BRANCHES` without review.
 
 ### Full (adds)
 

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

@@ -0,0 +1,458 @@
+---
+name: production-add-diffusion-model
+description: Productionize a vLLM-Omni diffusion model after its Day-0 vertical slice works. Use when work requires official API parity and input limits, feature-combination evidence, online FP8, distributed layerwise offload, per-request Cache-DiT, CUDA/ROCm/NPU/XPU recipes, faster or sparse attention, fused operators, USP or disaggregation analysis, continuous batching and abort handling, long-running RPS stability, or accuracy/performance/reliability CI. For initial architecture porting, registry wiring, and basic weight loading, use add-diffusion-model first.
+---
+
+# Productionizing a Diffusion Model
+
+## Scope
+
+Use this skill to move a working model integration from Day-0 support to a
+measured, fail-closed production deployment. It is deliberately separate from
+[`add-diffusion-model`](../add-diffusion-model/SKILL.md); do not modify or copy
+that skill when productionizing a model. If registry, pipeline, or basic loader
+work is incomplete, read that skill first and finish the Day-0 vertical slice.
+
+MiniMax-H3 [PR #5691](https://github.com/vllm-project/vllm-omni/pull/5691)
+is the merged Day-0 case study. Its open optimization
+[issue #5700](https://github.com/vllm-project/vllm-omni/issues/5700) is an
+actively maintained roadmap and evidence map, not a list of already supported
+features. It can lag merged implementation: read its current revision, cited
+PRs, target source, and maintained recipes, then leave every unverified row
+`not tested` until the target revision has scoped evidence.
+
+Read these references before the corresponding gate:
+
+- [API and hardware recipes](references/api-and-recipes.md)
+- [Production feature patterns](references/feature-patterns.md)
+- [Performance patterns and code examples](references/performance-patterns.md)
+- [Serving, soak, and CI validation](references/production-validation.md)
+
+## Non-negotiable evidence contract
+
+Create a matrix whose row key is at least:
+
+```text
+task x API mode x execution mode/capacity x shape/schedule x attention backend x
+packed layout x cache policy x quantization x offload mode x topology x
+hardware x dtype x output representation/transport
+```
+
+Use exactly these states:
+
+| State | Meaning |
+|---|---|
+| `validated` | The exact row passed correctness and measured deployment tests, with reproducible artifacts. |
+| `limited` | A bounded subset passed; the limitation and rejection/fallback behavior are explicit. |
+| `unsupported` | A stable architectural/platform restriction is proven and fails early with an actionable error. |
+| `not tested` | No adequate evidence exists. This is the default. |
+
+Never infer compatibility from import success, server startup, another task,
+another card, or another topology. Record the model/checkpoint revision,
+vLLM-Omni commit, commands, request assets and hashes, seed/schedule/shape,
+raw output artifacts, environment, and result for every `validated` row.
+
+## Workflow
+
+Follow the gates in order. Keep a dense BF16 single-device path as the
+correctness oracle until every advertised fast path has parity evidence.
+
+### Gate 0: Freeze the official contract
+
+Pin the official implementation and checkpoint revision. Build a matrix for:
+
+- constructor/call arguments, defaults, scheduler and sigma schedule;
+- task-to-source modality/count/order rules;
+- prompt, MIME, bytes, pixels, frames, duration, FPS, steps, seed, guidance,
+  output count, and output format limits;
+- offline API and every public sync/async serving endpoint;
+- checkpoint partitions and task selection.
+
+Normalize offline, sync, and async requests through one validation contract.
+Reject an invalid task/source combination before downloads, upload persistence,
+temporary files, async job creation, or engine submission. Use 400 for invalid
+semantics and 413 for payload limits; clean partial resources on every failure.
+See [API and hardware recipes](references/api-and-recipes.md).
+
+### Gate 1: Re-prove correctness under production shapes
+
+1. Make loading strict: track expected, loaded, unexpected, and intentionally
+   ignored tensors. A fused destination is complete only after every source
+   shard arrives. Missing retained parameters abort startup.
+2. Fix reference revision, asset hashes, seed, schedule, and shape. Compare
+   component outputs, intermediate denoise latents, and final artifacts.
+3. Test unequal packed samples. Verify `cu_seqlens`, padding exclusion,
+   modality/CFG ownership, split/gather boundaries, and one output per request.
+4. Test partial process groups with non-member ranks and group size smaller
+   than world size. Do not construct a private group per component or request.
+5. Run negative API cases before positive E2E tests to prove side-effect-free
+   rejection and cleanup.
+
+### Gate 2: Wire shared fast primitives
+
+Prefer explicit, testable vLLM-Omni/vLLM operators with native fallbacks.
+`torch.compile` remains a separately validated regional optimization; it is
+not a substitute for correct operator selection or lifecycle-safe boundaries.
+
+#### RMSNorm + RoPE baseline
+
+Use the shared cross-platform operators, preserving the official Q/K norm and
+RoPE order:
+
+```python
+from vllm_omni.diffusion.layers.norm import RMSNorm
+from vllm_omni.diffusion.layers.rope import (
+    RotaryEmbedding,
+    apply_rope_to_qk,
+)
+
+self.norm_q = RMSNorm(head_dim, eps=eps)
+self.norm_k = RMSNorm(head_dim, eps=eps)
+self.rope = RotaryEmbedding(is_neox_style=False)
+
+query = self.norm_q(query)  # [B, S, Hq, D] or packed [T, Hq, D]
+key = self.norm_k(key)      # [B, S, Hkv, D] or packed [T, Hkv, D]
+query, key = apply_rope_to_qk(
+    self.rope,
+    query,
+    key,
+    (cos, sin),
+)
+```
+
+Verify `is_neox_style`, `half_head_dim`, partial rotary dimensions, cos/sin
+layout, and packed row ownership against the official model. `False` means
+interleaved/GPT-J style; do not assume the default is NeoX. Shared RMSNorm and
+RoPE are two dispatched operators, not automatically one fused kernel.
+
+For packed non-interleaved RoPE, use the shared fused boundary when its contract
+matches the model:
+
+```python
+from vllm_omni.diffusion.layers.fused_qk_norm_rope import fused_qk_norm_rope
+
+query, key = fused_qk_norm_rope(
+    query,                    # [T, Hq, D]
+    key,                      # [T, Hkv, D]
+    self.norm_q.weight,       # [D]
+    self.norm_k.weight,       # [D]
+    rope_table,               # [T, rotary_dim] = [cos | sin]
+    self.norm_q.variance_epsilon,
+)
+```
+
+The current CUDA fast path specializes BF16 `head_dim=128`, `rotary_dim=96`;
+the public function keeps an eager fallback for unsupported inputs. Verify the
+packed row/frequency contract and trace the actual fast path. Do not reshape an
+incompatible official RoPE layout merely to enter this kernel.
+
+MiniMax-H3-style partial NeoX RoPE rotates a prefix and passes the suffix
+through:
+
+```python
+self.q_norm = RMSNorm(head_dim, eps=eps, dtype=torch.bfloat16)
+self.k_norm = RMSNorm(head_dim, eps=eps, dtype=torch.bfloat16)
+self.rope = RotaryEmbedding(is_neox_style=True, half_head_dim=False)
+
+def apply_partial_rope(self, x, freqs, rot_dim):
+    x_rot, x_pass = x[..., :rot_dim], x[..., rot_dim:]
+    cos, sin = torch.cos(freqs).to(x.dtype), torch.sin(freqs).to(x.dtype)
+    return torch.cat((self.rope(x_rot, cos, sin), x_pass), dim=-1)
+```
+
+The partial-RoPE snippet assumes `import torch` and lives on the owning
+attention module; adapt names and validated frequency shapes rather than
+copying it at module scope.
+
+Call the modules normally so `CustomOp` selects CUDA/HIP/NPU/XPU/native
+implementations. Do not call `forward_cuda()` directly. Compare the shared
+path with native BF16 at operator, block, denoise-trajectory, and artifact
+levels before claiming either accuracy or speed.
+
+#### Faster BF16 attention and fused projections
+
+Use local TP head counts and role-aware shared attention:
+
+```python
+from vllm.model_executor.layers.linear import QKVParallelLinear
+from vllm_omni.diffusion.attention.layer import Attention
+
+self.qkv_proj = QKVParallelLinear(
+    hidden_size=hidden_size,
+    head_size=head_dim,
+    total_num_heads=num_heads,
+    total_num_kv_heads=num_kv_heads,
+    bias=False,
+    quant_config=quant_config,
+    prefix=f"{prefix}.qkv_proj",
+)
+self.attn = Attention(
+    num_heads=self.qkv_proj.num_heads,
+    num_kv_heads=self.qkv_proj.num_kv_heads,
+    head_size=head_dim,
+    softmax_scale=head_dim**-0.5,
+    causal=False,
+    qkv_layout="BSND",
+    role="self",
+    prefix=prefix,
+)
+```
+
+Prove the selected backend from runtime logs/trace and compare it with dense
+SDPA. Packed metadata must express real sample boundaries; do not build masks
+that the selected backend ignores. A backend that is faster for one shape or
+platform is not a global default. See [performance patterns](references/performance-patterns.md)
+for QKV, SwiGLU, AdaLN, sparse attention, redundancy, and fusion examples.
+
+### Gate 3: Add online FP8 without breaking the loader
+
+Route the active config to each component, then pass a stable runtime prefix
+and config into every quantizable vLLM linear:
+
+```python
+def resolve_component_quant_config(quant_config, component):
+    return quant_config.resolve(component) if hasattr(quant_config, "resolve") else quant_config
+
+transformer_quant_config = resolve_component_quant_config(
+    od_config.quantization_config,
+    "transformer",
+)
+self.transformer = MyDiT(
+    od_config,
+    quant_config=transformer_quant_config,
+    prefix="transformer",
+)
+
+self.qkv_proj = QKVParallelLinear(
+    hidden_size=hidden_size,
+    head_size=head_dim,
+    total_num_heads=num_heads,
+    total_num_kv_heads=num_kv_heads,
+    quant_config=quant_config,
+    prefix=f"{prefix}.qkv_proj",
+)
+```
+
+Use `build_quant_config()` for direct construction/testing:
+
+```python
+from vllm_omni.quantization import build_quant_config
+
+quant_config = build_quant_config({
+    "transformer": {"method": "fp8"},
+    "text_encoder": None,
+    "vae": None,
+    "default": None,
+})
+```
+
+Preserve each parameter's vLLM `weight_loader`; perform checkpoint layout
+conversion before invoking it. Account for every QKV/gate-up source shard.
+Keep precision-sensitive norm/modulation/embedders in BF16 unless separately
+proven, using the current `safe_quant_config` pattern where applicable.
+
+Validate resident DiT FP8 first: prove which modules are quantized, HBM saved,
+BF16-vs-FP8 trajectory/artifact quality, latency, and throughput. Text encoder,
+VAE, other hardware, TP/cache/DLO combinations are separate rows.
+Pre-quantized, pruned, rotated, or adapter-modified checkpoints are separate
+model/loading lanes; compare them with the released BF16 model without calling
+the result a pure quantization ablation. Read
+[`../quantization/SKILL.md`](../quantization/SKILL.md) if present and prefer the
+target revision's implementation/docs when sibling guidance differs.
+
+### Gate 4: Make every component offloadable
+
+Declare topology instead of relying on heuristic discovery:
+
+```python
+from typing import ClassVar
+
+import torch.nn as nn
+
+from vllm_omni.diffusion.models.interface import SupportsComponentDiscovery
+from vllm_omni.diffusion.offloader import OffloadPlan
+
+class MyPipeline(nn.Module, SupportsComponentDiscovery):
+    _dit_modules: ClassVar[list[str]] = ["transformer"]
+    _encoder_modules: ClassVar[list[str]] = ["text_encoder"]
+    _vae_modules: ClassVar[list[str]] = ["vae"]
+    _resident_modules: ClassVar[list[str]] = []
+    _offload_plan: ClassVar[OffloadPlan] = OffloadPlan(
+        on_demand_component_paths=frozenset({"text_encoder", "vae"}),
+        block_attrs={"transformer": ("blocks",)},
+        offload_submodules={"token_refiner": "blocks"},
+        resident_dit_paths=frozenset({"transformer"}),
+        encoder_block_attrs={"text_encoder": ("encoder.layers",)},
+    )
+```
+
+Add a test that resolves every dotted component/block path and fails if it is
+missing or is not the expected module/container; discovery may otherwise warn
+and skip. Validate ordinary layerwise offload and distributed layerwise
+offload (DLO) independently.
+
+Keep component lifecycle and host-weight storage separate. `OffloadPlan`
+declares what can be streamed; the diffusion loader owns any `HostWeightPlan`
+and hands the exact prevalidated plan to DLO. Do not make the offloader rescan
+checkpoint files or duplicate loader name/shape/dtype decisions. Direct mmap is
+a fail-closed optimization: current preflight requires TP1, no HSDP, no online
+quantization, complete bindings, and represented transforms. Otherwise the
+ordinary loader remains authoritative.
+
+If the target revision supports Host Weight Runtime (HWR), treat it as a
+separate, exact-identity startup/cache path rather than generic offload or
+zero-copy execution. Qualify `preferred` population/fallback and `required`
+consume-only failure independently; verify immutable manifests, final-layout
+restore, lease cleanup, corruption/quarantine, and node-local sharing. Current
+model contracts may be BF16 no-AllGather-only, so reject AllGather, online
+quantization, HSDP, LoRA/adapted weights, and unrecognized load formats unless
+the exact target revision explicitly adds them.
+
+On small-HBM cards, record load/materialization peak, resident peak, encode,
+each denoise wave, decode, transient per-rank HBM, host PSS, H2D, and collective
+time. Test DLO AllGather and `--dlo-no-use-allgather` as different deployments.
+Sweep the supported resident-block policies and publish the non-dominated
+latency-HBM frontier; a single minimum-memory or fastest point is not the DLO
+trade-off. Keep request-specific preparation replica-local. Only weight
+materialization may enter the declared DLO collective group; never use WORLD
+for per-request work in a DP deployment.
+Direct checkpoint mmap preflight is limited to TP1 without HSDP or online
+quantization; TP>1 falls back to the ordinary TP-aware loader and can still feed
+DLO AllGather or no-AllGather after scoped E2E. HSDP remains incompatible with
+DLO AllGather. A target revision may allow finalized per-tensor online FP8
+through the ordinary loader and AllGather; keep every other online quantizer
+fail-closed, and require model/card/topology E2E before promotion.
+Use deploy YAML for DP under `--omni`; vLLM DP CLI flags are rejected.
+
+### Gate 5: Add request-scoped acceleration safely
+
+Server-wide Cache-DiT support does not imply a per-request quality API. Only
+add request policy after defining calibrated quality tiers and a safe hook
+lifecycle using `SupportsRequestScopedCacheDiT`, `CacheDiTRequestSpec`, and
+`RequestScopedCacheDiTRuntime`. Validate alternating and concurrent quality
+tiers, real cache hits, refresh/reset, success, error, disconnect, abort, and
+the next uncached request. Do not batch different quality policies unless the
+batch compatibility key and hook ownership make that safe.
+
+Then test Cache-DiT against FP8, DLO, TP/SP, compile, and batching one
+combination at a time. See [production feature patterns](references/feature-patterns.md)
+for the lifecycle skeleton and compatibility matrix.
+
+### Gate 6: Optimize only from profiles
+
+Freeze one canonical workload manifest before profiling. Keep lossless runtime
+A/Bs, accelerated paths with numerical/quality trade-offs, and production
+topology studies in separate result lanes. Reconcile the client boundary as
+queue + encode + denoise + video/audio decode + output transport/codec +
+residual; do not hide a material residual in another stage.
+
+Profile stage time, GPU kernels, CPU/GPU synchronization, allocations, H2D,
+output payload/copies, and collectives before choosing work. Apply and A/B one
+change at a time:
+
+1. Remove synchronization and redundant denoise-loop work: hoist static prompt
+   conditioning/token refinement, RoPE tables, masks/metadata, bounded AdaLN
+   schedule projections, and reference scans; remove per-layer `.item()` and
+   unused masks/allocations.
+2. Select the fastest correct BF16 attention backend for each role and shape.
+3. Prefer explicit fused QKV, gate-up + `SiluAndMul`, RMSNorm/RoPE, AdaLN, and
+   layout/residual operators with guarded native fallbacks. Preserve every
+   materialized dtype/rounding boundary consumed by the unfused graph.
+4. Enable sparse attention only when platform, shape, topology, realized
+   sparsity, quality, fallback, and end-to-end speedup are all proven.
+5. Profile USP all-to-all count/bytes, packing, contiguous copies, overlap, and
+   scaling efficiency. Audit replicated cross-attention before using
+   `skip_sequence_parallel`. Qualify regular and accelerated Ulysses transport
+   independently; activation logs, JIT/readiness time, workspace growth,
+   stream ownership, maximum-shape warmup, and numerical drift are gates.
+   Preserve the checkpoint's Q:KV head ratio when padding GQA for Ulysses, and
+   validate strided QKV staging rather than assuming packed contiguity.
+6. Evaluate text-encoder or VAE disaggregation only if stage share, transfer
+   volume, reuse/concurrency, and independent scaling justify it. Tiling,
+   offload, or patch parallelism is not disaggregation.
+
+Do not combine independent optimizations into one performance claim. Preserve
+raw before/after runs with fixed workload and warmup policy.
+
+### Gate 7: Make serving interruptible and load-stable
+
+Treat request-mode batching and step-wise execution as separate capabilities:
+
+- request batching requires `supports_request_batch = True` and
+  `DiffusionRequestBatch -> list[DiffusionOutput]`, exactly one output per
+  request;
+- step execution requires the complete `SupportsStepExecution` contract:
+  `prepare_encode`, `denoise_step`, `step_scheduler`, and `post_decode`.
+
+Keep all mutable scheduler, RNG, latent, mask, cache, and step state request
+scoped. First validate `--step-execution --max-num-seqs 1` and step-boundary
+abort; only then test heterogeneous multi-request waves. Step continuous
+batching is experimental and is not automatically compatible with Cache-DiT.
+Before recommending it, predeclare a useful case and success threshold, then
+A/B it against request mode under the same arrival process. Structural support
+or a generic bridge is not latency, throughput, or cancellation evidence for a
+model.
+
+Test queued and in-flight abort, client disconnect, timeout, OOM, worker error,
+and restart. Require one terminal result, idempotent cleanup, no post-decode
+after abort, no cache/temp/VRAM/request-ID leak, and a successful next request.
+Inject late worker results/exceptions after cancellation and prove the result
+pump stays alive instead of raising `InvalidStateError`. For large batched or
+long-window video outputs, freeze dtype, range, layout, contiguity, ownership,
+payload size, serializer/codec limits, and offline-versus-HTTP semantics.
+Validate device-side output preparation, D2H/IPC or shared handles, remote
+encoding, event-loop responsiveness, and client materialization as separate
+stages, including abort and consumer failure cleanup.
+A model/VAE callback that publishes ordered chunks is only a producer contract;
+it is not transport, backpressure, cancellation, public streaming, or
+time-to-first-frame evidence. Distinguish complete-response faster-than-playback
+(`client E2E / output duration <= 1`) from streaming and report first-chunk,
+steady cadence, finalization, and complete-artifact latency separately.
+Run below-, near-, and above-saturation mixed-RPS soaks and report success/error
+rate, throughput, queue time, memory slope, temp growth, and worker health.
+Report p50/p95/p99 only from a declared arrival model with enough measured
+requests for the claimed percentile. See
+[serving and CI validation](references/production-validation.md).
+
+### Gate 8: Publish recipes and four independent CI tracks
+
+For every target vendor/card/topology, publish exact environment, serve/deploy
+command, one complete JSON or multipart curl per supported task, expected
+output checks, benchmark command, warmup/repeat/concurrency policy, raw metrics,
+per-rank HBM/host PSS, accuracy artifact, and limitations. Mark each
+CUDA/ROCm/NPU/XPU row `validated`, `unsupported`, or `not tested`; never inherit
+support from the platform abstraction or another card. Re-run DLO on each
+small-HBM recipe.
+
+Maintain separate CI gates:
+
+1. Function: strict load, API/offline positive and negative contracts.
+2. Accuracy: fixed reference revision/assets/seed/schedule and scoped tolerance.
+3. Performance: fixed best deployment plus memory-constrained/DLO row, raw JSON,
+   explicit warmup, enough samples for each claimed statistic, and owned
+   regression thresholds.
+4. Reliability: long mixed-RPS soak, abort/disconnect/fault cleanup, memory and
+   temporary-resource trend.
+
+## Production Definition of Done
+
+A model is production-ready only when:
+
+- every official advertised task passes API parity, offline/online parity,
+  strict loading, fixed-reference quality, and negative validation;
+- the recommended deployment row passes correctness, isolation, abort/error
+  cleanup, benchmark, and soak gates on named hardware;
+- online FP8 and DLO are each implemented and either validated in scoped rows
+  or explicitly limited/unsupported with fail-fast behavior;
+- per-request cache quality tiers have measured speed/quality frontiers and
+  request isolation, if advertised;
+- CUDA/ROCm/NPU/XPU recipes describe evidence rather than inferred support;
+- Function, Accuracy, Performance, and Reliability CI have owners, artifacts,
+  and actionable failure output;
+- accelerated paths identify every precision/quality trade-off, and the raw
+  offline plus online encoded output contracts are versioned and tested;
+- all unverified task/backend/cache/quant/offload/topology/hardware combinations
+  remain `not tested` and are not presented as supported.
+
+Day-0 support is still a valid milestone, but title, docs, and matrix must say
+which representative task passed and which production gates remain.

.claude/skills/production-add-diffusion-model/agents/openai.yaml

@@ -0,0 +1,4 @@
+interface:
+  display_name: "Production Add Diffusion Model"
+  short_description: "Productionize vLLM-Omni diffusion models"
+  default_prompt: "Use $production-add-diffusion-model to take this integrated diffusion model from Day-0 support to evidence-backed production readiness."

.claude/skills/production-add-diffusion-model/references/api-and-recipes.md

@@ -0,0 +1,315 @@
+# API Contract and Hardware Recipes
+
+## Contents
+
+1. [Freeze the upstream contract](#freeze-the-upstream-contract)
+2. [Validate before side effects](#validate-before-side-effects)
+3. [Task request templates](#task-request-templates)
+4. [Serving benchmark template](#serving-benchmark-template)
+5. [Hardware recipe contract](#hardware-recipe-contract)
+6. [Best deployment selection](#best-deployment-selection)
+
+## Freeze the upstream contract
+
+Pin three revisions independently:
+
+- official source implementation commit or package version;
+- checkpoint/model-card revision and partition;
+- vLLM-Omni target commit.
+
+Build a table before editing code. One row is one official task/partition.
+
+| Field | Required content |
+|---|---|
+| Task and partition | Official task name, checkpoint subdirectory, task selection rules |
+| Call contract | Constructor and call arguments, types, required/optional, official defaults |
+| Prompt/input | Prompt count/length, modality, MIME, order, count, decoded shape |
+| Size/time | Width/height alignment, pixels, frames, duration, FPS, start offset |
+| Denoise | Scheduler, sigma schedule, steps, CFG/guidance, seed behavior |
+| Output | Count, format, codec/sample rate, return type |
+| API | Offline mapping, sync endpoint, async endpoint/job lifecycle |
+| Rejection | Status code and stable error class/message for every invalid boundary |
+
+Official API parity includes limits and defaults, not just names. Do not add a
+generic default that changes a distilled checkpoint's fixed schedule, or accept
+an input combination the official task rejects.
+
+For every accepted boundary, add the neighboring rejected values. Examples:
+`max_images` and `max_images + 1`, smallest/largest legal duration, aligned and
+misaligned dimensions, omitted and illegal steps, one and too many outputs.
+
+## Validate before side effects
+
+Use one normalization/validation path for offline, sync, and async requests.
+The ordering must be:
+
+```text
+parse -> normalize aliases -> validate task/source matrix -> validate declared
+limits -> stream with byte caps -> inspect/decode with frame/pixel/duration caps
+-> persist only if needed -> submit to engine
+```
+
+Fail before download, persistence, job creation, or engine submit whenever the
+available metadata is sufficient. For streamed uploads, enforce:
+
+- total reference count and per-modality count before reading bodies;
+- per-file bytes while streaming;
+- aggregate bytes across all files;
+- decoded frames, duration, pixels, and media type before engine submission.
+
+On rejection, close streams and remove every partial temporary resource. Use
+HTTP 400 for invalid task/field combinations and 413 for payload limits. Test
+both sync and async paths; an invalid async request must not create a job ID.
+
+Do not trust client MIME alone. Sniff/validate content and keep ordered repeated
+multipart fields ordered. Avoid logging prompts, raw references, signed URLs,
+or base64 payloads.
+
+## Task request templates
+
+These are endpoint-shape templates, not model recipes. Replace placeholders
+with the exact official defaults and validated assets for the target model.
+Every production recipe must include all advertised tasks and an expected
+output check.
+
+### Text to image: JSON
+
+```bash
+curl --fail-with-body -sS -X POST http://127.0.0.1:8091/v1/images/generations \
+  -H 'Content-Type: application/json' \
+  -d '{
+    "model": "<model>",
+    "prompt": "<validated prompt>",
+    "size": "1024x1024",
+    "num_inference_steps": 30,
+    "seed": 42,
+    "response_format": "b64_json"
+  }' | jq -er '.data[0].b64_json' | base64 -d > output.png
+file output.png
+```
+
+### Image edit: multipart
+
+```bash
+curl --fail-with-body -sS -X POST http://127.0.0.1:8091/v1/images/edits \
+  -F 'model=<model>' \
+  -F 'prompt=<validated edit prompt>' \
+  -F 'image=@/path/to/reference.png;type=image/png' \
+  -F 'size=1024x1024' \
+  -F 'num_inference_steps=30' \
+  -F 'seed=42' \
+  -F 'output_format=png' \
+  | jq -er '.data[0].b64_json' | base64 -d > edited.png
+file edited.png
+```
+
+Use repeated `image` fields only if the model's official input matrix allows
+multiple ordered sources. Never describe a file upload as JSON-only.
+
+### Public request fields versus internal sampling fields
+
+Use the field exposed by the target revision's endpoint schema; do not rename
+it from the pipeline's internal attribute. For example, the current video JSON
+and multipart contracts expose `extra_params`, while serving translates that
+object into `DiffusionSamplingParams.extra_args`. Other request paths may
+expose canonical `extra_args` or nest it under `extra_body`. Inspect the exact
+Pydantic model/FastAPI form dependency and exercise the generated curl before
+publishing a recipe. A deprecation warning in a different endpoint is not
+evidence that an unrecognized multipart form field works here.
+
+Current endpoint map (re-check it at the target commit):
+
+| Endpoint | Public model-specific field |
+|---|---|
+| `/v1/videos`, `/v1/videos/sync` | Multipart `extra_params` JSON string |
+| `/v1/images/generations` | JSON `extra_params` object |
+| `/v1/chat/completions` diffusion | `extra_body.extra_args` |
+| `/v1/images/edits` | Declared typed form fields only |
+| `/v1/audio/generate` | Declared typed JSON fields only |
+
+Prefer declared top-level fields. Use a generic model-specific envelope only
+when the endpoint actually exposes it.
+
+### Text to video: multipart
+
+```bash
+curl --fail-with-body -sS -X POST http://127.0.0.1:8091/v1/videos/sync \
+  -H 'Accept: video/mp4' \
+  -F 'model=<model>' \
+  -F 'prompt=<validated prompt>' \
+  -F 'size=1280x720' \
+  -F 'num_frames=81' \
+  -F 'fps=16' \
+  -F 'num_inference_steps=30' \
+  -F 'seed=42' \
+  -F 'extra_params={"task":"<official-task>"}' \
+  -o output.mp4
+ffprobe -v error -show_entries stream=codec_name,width,height,r_frame_rate \
+  -of json output.mp4
+```
+
+### Image/video to video: multipart
+
+```bash
+curl --fail-with-body -sS -X POST http://127.0.0.1:8091/v1/videos/sync \
+  -H 'Accept: video/mp4' \
+  -F 'model=<model>' \
+  -F 'prompt=<validated prompt>' \
+  -F 'num_frames=81' \
+  -F 'fps=16' \
+  -F 'num_inference_steps=30' \
+  -F 'seed=42' \
+  -F 'extra_params={"task":"<official-task>","start_time_seconds":0}' \
+  -F 'input_reference=@/path/to/reference.png;type=image/png' \
+  -o i2v.mp4
+
+curl --fail-with-body -sS -X POST http://127.0.0.1:8091/v1/videos/sync \
+  -H 'Accept: video/mp4' \
+  -F 'model=<model>' \
+  -F 'prompt=<validated prompt>' \
+  -F 'num_frames=81' \
+  -F 'num_inference_steps=30' \
+  -F 'seed=43' \
+  -F 'extra_params={"task":"<official-task>","start_time_seconds":0}' \
+  -F 'input_reference=@/path/to/reference.mp4;type=video/mp4' \
+  -o v2v.mp4
+```
+
+For multiple references, repeat the model's accepted multipart field in
+official order. Include complete downloadable or repository fixtures plus
+SHA256 hashes. A placeholder-only recipe is not runnable evidence.
+
+### Text to audio: JSON
+
+```bash
+curl --fail-with-body -sS -X POST http://127.0.0.1:8091/v1/audio/generate \
+  -H 'Content-Type: application/json' \
+  -d '{
+    "model": "<model>",
+    "input": "<validated audio prompt>",
+    "audio_length": 10.0,
+    "num_inference_steps": 100,
+    "seed": 42,
+    "response_format": "wav"
+  }' -o output.wav
+ffprobe -v error -show_entries stream=codec_name,sample_rate,channels \
+  -of json output.wav
+```
+
+Use `/v1/audio/generate` for general diffusion audio. Do not substitute the
+speech API unless the model is actually served through the speech contract.
+
+### Async video lifecycle
+
+Alongside `/v1/videos/sync`, include `POST /v1/videos`, status polling,
+content retrieval, cancellation/abort if public, and cleanup assertions. The
+same request must normalize to the same engine sampling parameters in both
+modes.
+
+## Serving benchmark template
+
+Run a smoke first, then fixed-concurrency and arrival-rate tests. Video task
+names for the harness are `t2v`, `i2v`, `ti2v`, or `v2v`; model-specific task
+selection belongs in `--extra-body` when required.
+
+```bash
+python benchmarks/diffusion/diffusion_benchmark_serving.py \
+  --base-url http://127.0.0.1:8091 \
+  --endpoint /v1/videos \
+  --model '<model>' \
+  --dataset random \
+  --task t2v \
+  --num-prompts 100 \
+  --width 1280 --height 720 \
+  --num-frames 81 --fps 16 \
+  --num-inference-steps 30 \
+  --request-rate 0.2 \
+  --max-concurrency 4 \
+  --warmup-requests 4 \
+  --warmup-concurrency 4 \
+  --extra-body '{"extra_params":"{\"task\":\"<official-task>\"}"}' \
+  --output-file result.json
+```
+
+The video backend currently stringifies each multipart value. Pre-serialize
+the nested `extra_params` object as above; passing it as a nested object would
+produce Python dict syntax rather than valid JSON. If the target revision
+changes the backend to JSON-encode dict/list values, update and smoke-test this
+command against that revision.
+
+For i2v/v2v, use the harness's validated dataset/trace path or provide the
+documented media source; do not silently benchmark t2v and label it i2v. Keep
+prompt, assets, seed, dimensions, frames, steps, guidance, warmup, concurrency,
+and arrival process identical across A/B runs.
+
+Keep three benchmark lanes distinct:
+
+1. lossless runtime A/B with the released schedule/precision/attention policy;
+2. accelerated-path A/B that changes one adapter, quantization, cache, or
+   sparsity policy and adds same-seed quality evidence;
+3. production-topology studies that vary placement or parallelism and report a
+   latency/throughput/memory frontier rather than a kernel speedup.
+
+For a fixed single-request A/B, retain every measured run and report median or
+mean with range after an explicit warmup; do not infer p95/p99 from a few
+repetitions. Tail latency requires a declared arrival process, request count,
+concurrency, and enough successful samples. Reconcile client E2E with queue,
+encoder, denoise, video/audio decode, D2H/IPC, codec, and residual timings.
+
+For generated media, define `real-time` rather than relying on the label. A
+complete-response claim uses `client E2E / validated output duration <= 1` and
+does not imply streaming or low time-to-first-frame. Report first chunk/fragment
+and cadence separately when a public streaming route exists. Never add gains
+across benchmark lanes with different schedules, prompts, seeds, artifacts, or
+topologies.
+
+The diffusion serving harness currently covers image/video task families, not
+`/v1/audio/generate`. For text-to-audio, use the target model's task-specific
+audio benchmark if one exists; otherwise add a serving harness that records
+request rate, concurrency, latency, generated-audio seconds per second, error
+rate, and output validation. Do not relabel the TTS harness or a video workload
+as diffusion-audio evidence.
+
+## Hardware recipe contract
+
+Create a separate row and runnable section for each vendor/card/topology:
+
+| Evidence | Required detail |
+|---|---|
+| Identity | Vendor, exact SKU, device count, HBM, interconnect |
+| Software | Driver, CUDA/ROCm/CANN/oneAPI, framework/container, dependency versions |
+| Source | vLLM-Omni commit, official/checkpoint revision and hashes |
+| Deployment | dtype, attention backend per role, TP/SP/CFG/HSDP/DP, cache, quant, offload |
+| Commands | Complete deploy YAML/serve command, every task curl, benchmark command |
+| Method | Warmup exclusion, repetitions or request count, concurrency, arrival distribution, duration, statistic definitions |
+| Results | Success rate, p50/p95/p99, RPS/throughput per device, stage latency |
+| Memory | Load/materialize, resident, encode, denoise, decode, transient per-rank HBM and host PSS |
+| Quality | Metric/tolerance, artifact links/hashes, temporal/audio checks |
+| Output | Raw/offline dtype, range, layout and ownership; online codec, payload bytes, transport route and client boundary |
+| Limits | `validated`/`limited`/`unsupported`/`not tested`, fallback/rejection behavior |
+
+Use current paths such as `recipes/<vendor>/`, `recipes/README.md`, supported
+models, and feature compatibility tables. Top-level documentation must link to
+the scoped matrix; it must not use an unqualified checkmark that hides a ROCm,
+NPU, XPU, task, topology, or dtype limitation.
+
+DLO evidence is per card, especially on small-HBM devices. A DLO result on one
+CUDA card does not establish another CUDA SKU, ROCm, NPU, or XPU support.
+
+## Best deployment selection
+
+Benchmark a correctness-approved candidate matrix, not every theoretical
+combination. Choose the recommended row by the production objective—latency,
+throughput, cost, or small-HBM fit—and document that objective.
+
+Do not recommend a row until it passes:
+
+- all advertised task/API contracts;
+- fixed-reference quality;
+- request isolation and abort/error cleanup;
+- sustained arrival-rate tests;
+- per-rank HBM and host PSS limits;
+- hardware-specific accuracy and performance CI.
+
+Keep the most memory-constrained DLO row even when it is not the fastest; it is
+a separate production recipe, not a failed latency candidate.

.claude/skills/production-add-diffusion-model/references/feature-patterns.md

@@ -0,0 +1,462 @@
+# Production Feature Patterns
+
+## Contents
+
+1. [Compatibility matrix](#compatibility-matrix)
+2. [Online FP8](#online-fp8)
+3. [Strict fused-weight loading](#strict-fused-weight-loading)
+4. [Distributed layerwise offload](#distributed-layerwise-offload)
+5. [Per-request Cache-DiT](#per-request-cache-dit)
+6. [Combination gates](#combination-gates)
+
+## Compatibility matrix
+
+Create rows at the granularity below. Add evidence links and a reason for every
+non-validated state.
+
+| Task/shape | Execution mode/capacity | Backend/layout | Quant | Cache policy | Offload | Topology | Hardware/dtype | Output contract/transport | State | Evidence |
+|---|---|---|---|---|---|---|---|---|---|---|
+| `<task>/<shape>/<schedule>` | serial request / 1 | SDPA, padded | BF16 | none | resident | 1 device | `<card>` BF16 | raw float / local | `not tested` | — |
+| `<task>/<shape>/<schedule>` | step / N | `<fast>`, packed | BF16 | none | resident | `<TP/SP>` | `<card>` BF16 | encoded / subprocess | `not tested` | — |
+| `<task>/<shape>/<schedule>` | step / N | `<fast>`, packed | FP8 | high | DLO no-AG | `<TP/SP>` | `<card>` | device uint8 / SHM | `not tested` | — |
+
+Initialize every row as `not tested`; the table is a work queue, not evidence.
+Promote the dense BF16 row to the oracle only after its scoped parity passes.
+Add one axis at a time. If the runtime cannot
+enforce a known-incompatible combination, add construction/admission
+validation before writing a recipe.
+
+## Online FP8
+
+### Component routing
+
+The public builder accepts one method or a longest-prefix component map:
+
+```python
+from vllm_omni.quantization import build_quant_config
+
+config = build_quant_config({
+    "transformer": {
+        "method": "fp8",
+        "ignored_layers": ["transformer.final_layer"],
+    },
+    "text_encoder": None,
+    "vae": None,
+    "default": None,
+})
+
+assert config.resolve("transformer.blocks.0.attn.qkv_proj") is not None
+assert config.resolve("text_encoder.layers.0.mlp") is None
+assert config.resolve("vae.decoder.conv_out") is None
+```
+
+Use actual runtime prefixes. Multi-DiT or nested pipelines may not use the
+generic names `transformer` and `vae`. A prefix that falls through to a default
+is not component validation. Add tests for every included and ignored prefix.
+
+For CLI serving, first validate resident DiT-only FP8:
+
+```bash
+vllm serve '<model>' --omni --quantization fp8 --max-num-seqs 1
+```
+
+For scoped routing:
+
+```bash
+vllm serve '<model>' --omni \
+  --diffusion-quantization-config \
+  '{"<dit-runtime-prefix>":{"method":"fp8","ignored_layers":["<exact-linear-prefix>"]},"<vae-runtime-prefix>":null,"default":null}' \
+  --max-num-seqs 1
+```
+
+Do not publish these placeholders. A recipe must contain the target model's
+verified prefixes.
+
+### Quantizable module wiring
+
+Pass `quant_config` and a stable prefix through every vLLM linear. Keep
+precision-sensitive modulation/norm layers safe unless proven otherwise.
+
+```python
+from vllm.model_executor.layers.activation import SiluAndMul
+from vllm.model_executor.layers.linear import (
+    MergedColumnParallelLinear,
+    QKVParallelLinear,
+    RowParallelLinear,
+)
+
+self.qkv_proj = QKVParallelLinear(
+    hidden_size=hidden_size,
+    head_size=head_dim,
+    total_num_heads=num_heads,
+    total_num_kv_heads=num_kv_heads,
+    bias=False,
+    quant_config=quant_config,
+    prefix=f"{prefix}.attn.qkv_proj",
+)
+self.gate_up_proj = MergedColumnParallelLinear(
+    hidden_size,
+    [intermediate_size, intermediate_size],
+    bias=False,
+    quant_config=quant_config,
+    prefix=f"{prefix}.mlp.gate_up_proj",
+)
+self.act_fn = SiluAndMul()
+self.down_proj = RowParallelLinear(
+    intermediate_size,
+    hidden_size,
+    bias=False,
+    input_is_parallel=True,
+    quant_config=quant_config,
+    prefix=f"{prefix}.mlp.down_proj",
+)
+```
+
+Set required checkpoint parameters to fail rather than silently initialize if
+the current loader supports `missing_param_init = "error"`.
+
+### FP8 evidence
+
+For each task/hardware row, capture:
+
+- startup proves the FP8 quant method is attached to intended linear modules;
+- no unexpected BF16 fallback and no meta/uninitialized parameters;
+- same-seed component, trajectory, and final-artifact comparison with BF16;
+- HBM at load, resident, encode, denoise, decode, and peak;
+- cold load time; fixed-work warm-latency raw runs; and serving p50/p95/p99 plus
+  throughput only from a declared arrival load with enough samples;
+- named ignored layers and why their BF16 retention is required.
+
+DiT FP8 evidence does not cover the text encoder or VAE. CUDA evidence does not
+cover ROCm/NPU/XPU. A pre-quantized checkpoint is a different loading path from
+online FP8 and needs its own state. If it is also pruned, rotated, distilled, or
+adapter-modified, report that model delta explicitly; BF16-versus-artifact
+quality and speed are deployment comparisons, not pure quantization ablations.
+
+## Strict fused-weight loading
+
+Never use `param.data.copy_()` for quantizable vLLM parameters. Transform the
+checkpoint layout before invoking the parameter loader so the online quant
+loader remains outermost:
+
+```python
+from vllm.model_executor.model_loader.weight_utils import default_weight_loader
+
+param = params[target_name]
+weight_loader = getattr(param, "weight_loader", default_weight_loader)
+
+if target_name.endswith(".qkv_proj.weight"):
+    packed_qkv = reorder_checkpoint_qkv(checkpoint_weight)
+    weight_loader(param, packed_qkv)
+elif target_name.endswith(".gate_up_proj.weight"):
+    gate, up = checkpoint_weight.chunk(2, dim=0)
+    weight_loader(param, gate, 0)
+    weight_loader(param, up, 1)
+else:
+    weight_loader(param, checkpoint_weight)
+```
+
+Track source shards per fused destination:
+
+```python
+expected = {
+    "blocks.0.attn.qkv_proj.weight": {"q", "k", "v"},
+    "blocks.0.mlp.gate_up_proj.weight": {0, 1},
+}
+seen = {name: set() for name in expected}
+
+# After mapping/loading each source:
+seen[target_name].add(shard_id)
+
+incomplete = {
+    name: sorted(shards - seen[name], key=str)
+    for name, shards in expected.items()
+    if shards - seen[name]
+}
+if incomplete:
+    raise RuntimeError(f"incomplete fused checkpoint parameters: {incomplete}")
+```
+
+Also compare retained model parameters with the returned loaded set. Explicit
+checkpoint ignores need a reason and test; an unknown or missing weight is not
+an ignore policy.
+
+Load-time adapter fusion creates a dedicated model, not a request-switchable
+LoRA. Pin the base and adapter revisions, validate every low-rank and full-rank
+delta target, freeze the adapter-declared schedule/task, and fuse before
+sharding only when the loader contract proves identical rank-local weights.
+Reject offload or host-weight paths that bypass that fusion rather than serving
+the unfused base model silently.
+
+## Distributed layerwise offload
+
+### Declare component topology
+
+Use `SupportsComponentDiscovery` and `OffloadPlan`. Validate all dotted paths
+at construction/test time because discovery may otherwise warn and skip.
+
+Treat a future/requested `--layerwise-offload-components` selector as a public
+API only if it exists in the target revision. Today, express component topology
+with `OffloadPlan` (`on_demand_component_paths`, `block_attrs`,
+`encoder_block_attrs`, `resident_dit_paths`, and `offload_submodules`). Do not
+invent a CLI flag or claim component-selective DLO from discovery declarations
+alone; validate the selector separately when it lands.
+
+```python
+from operator import attrgetter
+
+import torch
+
+def assert_offload_path(root, path):
+    value = attrgetter(path)(root)
+    if not isinstance(value, torch.nn.Module):
+        raise TypeError(f"offload path {path!r} is not an nn.Module")
+    return value
+
+for path in (
+    *pipeline._dit_modules,
+    *pipeline._encoder_modules,
+    *pipeline._vae_modules,
+    *pipeline._offload_plan.on_demand_component_paths,
+):
+    assert_offload_path(pipeline, path)
+```
+
+For `block_attrs`, resolve the DiT first and then each declared block attribute.
+Require an indexable block container with at least one `nn.Module`. Validate
+`offload_submodules`, `resident_dit_paths`, and `encoder_block_attrs` similarly.
+
+### Keep host storage loader-owned
+
+Current DLO accepts a loader-produced `HostWeightPlan`. Pipeline authors should
+not instantiate this plan or add ad hoc mmap capability flags. The loader must
+preflight complete runtime/checkpoint bindings, names, shapes, dtypes, persistent
+buffers, and represented transforms before it skips ordinary materialization.
+The exact accepted plan is consumed once by DLO; a rejected plan records an
+observable fallback reason and continues through the ordinary loader.
+
+Direct checkpoint mmap currently requires TP1 without HSDP or online
+quantization. TP>1 falls back to the ordinary TP-aware loader and may feed DLO
+AllGather or no-AllGather; the current design records a bounded DP2xTP2
+AllGather smoke, but that path does not receive direct-mmap host-page sharing.
+Per-tensor online FP8 must use the ordinary loader. Current revisions can admit
+its finalized weight/scale layout to DLO AllGather, including transposed runtime
+weights, while unsupported online quantizers still fail closed. Direct mmap
+remains unavailable, so capture the temporary full-model host materialization
+before rank sharding.
+
+Host Weight Runtime is a distinct opt-in path. Current final-layout consumers
+use no-AllGather DLO and exact model-declared BF16 representations. In
+`preferred` mode, consume an exact local hit or canonically load and publish for
+a future startup; in `required` mode, consume only and fail on a miss or unusable
+artifact. HWR must not interact with DLO AllGather, online quantization, HSDP,
+LoRA/adapted weights, or an unrecognized load format unless the target revision
+adds an exact producer/restore contract. It is immutable node-local CPU backing,
+not zero-copy GPU execution; the DLO transport still owns staging or direct H2D.
+Qualify identity inputs, cold population, warm reuse, atomic concurrent
+publication, source digests, corruption/quarantine, lease cleanup, capacity,
+node-local PSS, H2D, and E2E latency. A host-memory saving that materially
+regresses transfer or request latency is a separate memory-first deployment.
+
+### DLO deployment modes
+
+Treat these paths separately:
+
+| Path | Weight/runtime behavior | Required boundaries |
+|---|---|---|
+| DLO AllGather | Rank-sharded pinned host tensors; reconstruct layer through collective | TP>1 uses ordinary TP-aware loader output; HSDP and unsupported online quantizers are rejected; finalized per-tensor online FP8 is eligible only when recognized by the target revision; concurrent ranks must participate consistently |
+| DLO no-AllGather | Loader-approved checkpoint mmap or ordinary runtime tensors; each rank streams a complete block | Direct mmap is TP1/non-HSDP/non-online-quant only; TP/HSDP/online quant use ordinary runtime tensors and require scoped E2E |
+| SP + DLO | SP group can supply the DLO collective group | Packed boundaries and collective order require E2E |
+
+Single-stage examples:
+
+```bash
+# SP + DLO AllGather candidate; validate exact model/card before publishing.
+vllm serve '<model>' --omni \
+  --enable-distributed-layerwise-offload \
+  --usp 4 \
+  --max-num-seqs 1
+
+# TP + DLO rank-local candidate; not a support statement.
+vllm serve '<model>' --omni \
+  --tensor-parallel-size 2 \
+  --enable-distributed-layerwise-offload \
+  --dlo-no-use-allgather \
+  --max-num-seqs 1
+```
+
+Under `--omni`, configure DP in a deploy YAML rather than passing vLLM DP CLI
+flags:
+
+```yaml
+pipeline: <registered-pipeline>
+async_chunk: false
+data_parallel_size: 4
+stages:
+  - stage_id: 0
+    devices: "0,1,2,3"
+    max_num_seqs: 1
+    enable_distributed_layerwise_offload: true
+    dlo_use_allgather: true
+    parallel_config:
+      tensor_parallel_size: 1
+      sequence_parallel_size: 1
+```
+
+```bash
+vllm serve '<model>' --omni --deploy-config /path/to/dlo_dp4.yaml
+```
+
+Keep the YAML next to the validated recipe. Match its schema to the target
+revision; do not assume a model accepts another pipeline's deploy config.
+
+### DLO validation sequence
+
+1. Resident BF16 reference.
+2. Ordinary layerwise offload parity.
+3. DLO enable, warmup, same-seed generation, disable/restart.
+4. AllGather and no-AllGather separately.
+5. Concurrent requests with different prompts and identical collective-affecting
+   parameters; then reject or schedule incompatible steps/shapes safely.
+6. Queued/in-flight abort, error, idle rank, worker exit, and next request.
+7. TP, SP, cache, compile, online FP8 one axis at a time.
+8. Loader-plan selection/fallback reason, cold/warm storage lifecycle, and
+   corruption/stale-entry recovery when mmap/cache backing is advertised.
+9. Replica-local request preparation plus DLO-group-only weight collectives in
+   DP; exercise concurrent replicas to catch accidental WORLD rendezvous.
+10. Sweep resident block counts; publish raw latency/HBM pairs, dominated
+    points, and the non-dominated Pareto frontier.
+11. Per-rank HBM, host PSS for the full process tree, H2D and collective trace.
+
+On a small-HBM card, measure transient encode/VAE peaks as well as resident DiT
+weights. If a non-DiT component still exceeds HBM, combine only independently
+validated on-demand component staging, VAE tiling/patching, or another topology;
+do not hide the remaining peak behind the phrase “DLO enabled.”
+
+## Per-request Cache-DiT
+
+### Policy and lifecycle skeleton
+
+First verify that the target revision contains the shared protocol/runtime.
+If not, follow the nearest current model's lifecycle without inventing a new
+public API.
+
+The following is a **serial/exclusive request-mode sketch only**. Pipeline-wide
+hooks are mutable; concurrent requests need an admission/batching key and
+runner-owned transition serialization before this shape is safe.
+
+```python
+import torch.nn as nn
+
+from vllm_omni.diffusion.cache.cachedit import (
+    CacheDiTBackend,
+    CacheDiTRequestSpec,
+    RequestScopedCacheDiTRuntime,
+)
+
+class MyPipeline(nn.Module):
+    def __init__(self, *args, **kwargs):
+        super().__init__()
+        # ... components ...
+        self._cache_dit_runtime = RequestScopedCacheDiTRuntime(self)
+
+    def adopt_cache_dit_backend(self, backend: CacheDiTBackend) -> None:
+        self._cache_dit_runtime.adopt(
+            backend,
+            installation_key="my_model.generic",
+        )
+
+    def is_cache_dit_enabled(self) -> bool:
+        return self._cache_dit_runtime.is_enabled
+
+    def _cache_spec(self, quality, steps):
+        if quality == "lossless":
+            return None
+        if quality == "high":
+            return CacheDiTRequestSpec(
+                installation_key="my_model.high.v1",
+                cache_config=self._validated_high_config,
+                num_inference_steps=steps,
+            )
+        raise ValueError(f"unsupported quality tier: {quality!r}")
+
+    def forward(self, req):
+        quality, steps = validate_and_resolve_request(req)
+        self._cache_dit_runtime.prepare(self._cache_spec(quality, steps))
+        return self._run_validated_request(req)
+```
+
+The snippet shows the model-owned transition for exclusive execution. Put
+disable/restore handling in the runtime/runner lifecycle that owns success,
+exception, disconnect, and asynchronous abort; a `try/finally` inside
+`forward()` alone cannot observe every server cancellation boundary.
+
+The `installation_key` must include every policy dimension that changes hook
+installation; changes in step count can use runtime refresh. `None` disables
+installed hooks. Do not mutate the global startup cache config for a request.
+
+Centralize cleanup in the owning request/runner boundary. An exception, abort,
+disconnect, or admission failure must not leave a changed hook policy visible
+to the next request. The exact owner depends on the target revision; add a
+failure-injection test rather than relying on `forward()` alone to observe
+asynchronous abort.
+
+Example model-specific request form after calibration:
+
+```bash
+curl --fail-with-body -sS -X POST http://127.0.0.1:8091/v1/videos/sync \
+  -F 'model=<model>' \
+  -F 'prompt=<prompt>' \
+  -F 'quality=lossless' \
+  -F 'num_inference_steps=<validated-steps>' \
+  -o lossless.mp4
+
+curl --fail-with-body -sS -X POST http://127.0.0.1:8091/v1/videos/sync \
+  -F 'model=<model>' \
+  -F 'prompt=<prompt>' \
+  -F 'quality=high' \
+  -F 'num_inference_steps=<validated-steps>' \
+  -o high.mp4
+```
+
+Do not expose these tier names merely because another model uses them.
+
+### Cache-DiT evidence
+
+For every tier/task/shape/schedule:
+
+- show real cache hits/skipped blocks, not only “backend enabled” logs;
+- measure fixed-work speed and quality against lossless/native;
+- publish a speed/quality frontier and chosen tier mapping;
+- alternate lossless/high/lossless and vary steps to prove restore/refresh;
+- interleave tasks and shapes; run concurrent requests only when scheduling
+  guarantees exclusive compatible hook state;
+- inject validation error, denoise error, disconnect, and abort, then prove the
+  next request starts with its requested policy;
+- bound any cached tensors and clear request-local state.
+
+## Combination gates
+
+Start every combination as `not tested`.
+
+| Combination | Default production posture |
+|---|---|
+| Direct checkpoint mmap + TP>1/HSDP/online quant | Preflight falls back to the ordinary loader; do not claim direct-mmap savings |
+| Per-tensor online FP8 + DLO AllGather | Generic compatibility exists for finalized weights/scales; each model/card/topology remains a candidate until E2E, and other online quantizers fail closed |
+| Online FP8 + DLO no-AllGather | Candidate only after model/card/topology E2E |
+| TP>1 + DLO AllGather | Ordinary TP-aware loader only; bounded DP2xTP2 smoke exists, but each new model/card/topology still needs E2E |
+| TP>1 + DLO no-AllGather | Ordinary TP-aware loader only; candidate with limited generic coverage |
+| HSDP + DLO AllGather | Reject; it would double-shard HSDP-managed parameters |
+| HSDP + DLO no-AllGather | Configuration-compatible candidate with limited E2E coverage |
+| Cache-DiT + online FP8 | Separate trajectory, hit-rate, HBM, latency test |
+| Cache-DiT + DLO | Verify hooks, streamed blocks, memory peaks, abort cleanup |
+| Cache-DiT + step execution | Do not advertise until lifecycle/batching support is explicit |
+| Sparse attention + Ring | Verify backend execution; reject silently ignored sparse settings |
+| Load-time fused adapter + offload/HWR | Reject unless the selected loader path applies and validates the same fusion before sharding/storage |
+| Packed attention + any topology | Unequal-sample boundary parity is mandatory |
+| Device-side uint8 preparation + offline caller | Version the dtype/range/layout change; HTTP MP4 parity does not preserve the raw tensor contract |
+| Device-side preparation + remote codec | Validate route selection, strided/contiguous layouts, payload bytes, ordering, fallback, and encoded-media parity |
+
+A startup-only test never upgrades a combination. Require request output,
+parity/quality, actual fast-path evidence, resource cleanup, and a benchmark on
+the named hardware.

.claude/skills/production-add-diffusion-model/references/performance-patterns.md

@@ -0,0 +1,481 @@
+# Performance Patterns and Code Examples
+
+## Contents
+
+1. [Measurement loop](#measurement-loop)
+2. [Faster BF16 attention](#faster-bf16-attention)
+3. [Sparse attention](#sparse-attention)
+4. [Fusion-first operator patterns](#fusion-first-operator-patterns)
+5. [Remove redundant computation](#remove-redundant-computation)
+6. [USP communication optimization](#usp-communication-optimization)
+7. [Text encoder and VAE disaggregation](#text-encoder-and-vae-disaggregation)
+8. [Performance acceptance](#performance-acceptance)
+
+## Measurement loop
+
+Do not start with a list of optimizations. Start with a fixed correctness and
+performance workload:
+
+```text
+model/checkpoint revision + prompt/media hashes + seed + dimensions/frames +
+scheduler/sigma/steps/guidance + task + dtype + backend + topology + hardware
+```
+
+Capture:
+
+- stage time: load, encode, conditioning, denoise, VAE decode/postprocess;
+- GPU trace: kernel duration/count, launch gaps, allocations, H2D, collectives;
+- CPU trace: preprocessing, per-layer sync, Python dispatch, temporary files;
+- memory: per-rank HBM peaks and process-tree host PSS;
+- output: device preparation, D2H/IPC, payload bytes, codec wall/process CPU,
+  event-loop wait, client materialization, and signed residual;
+- serving: queue time, throughput, errors, request mix, and distribution samples.
+
+For a fixed-work single-request A/B, run one explicit warmup policy and at
+least three measured repetitions, keep every raw run, and report median or mean
+with range. Do not derive p95/p99 from three samples. For tail latency, declare
+the arrival model and collect enough successful requests for the percentile and
+confidence claimed. Implement one optimization at a time, re-run parity, and
+report both local kernel and end-to-end deltas. A kernel win that moves no E2E
+metric is not a production speed claim.
+
+Use the pipeline profiler already exposed by the target revision when
+available, and use PyTorch/CUDA/ROCm/NPU/XPU profiling tools appropriate to the
+platform. Do not force every platform through a CUDA-only workflow.
+
+## Faster BF16 attention
+
+### Shared attention wiring
+
+Use the shared role-aware attention layer and give it the true local head
+counts and QKV layout:
+
+```python
+import torch
+
+from vllm.model_executor.layers.linear import (
+    QKVParallelLinear,
+    RowParallelLinear,
+)
+from vllm_omni.diffusion.attention.layer import Attention
+
+class SelfAttention(torch.nn.Module):
+    def __init__(
+        self,
+        hidden_size,
+        head_dim,
+        num_heads,
+        num_kv_heads,
+        quant_config=None,
+        prefix="",
+    ):
+        super().__init__()
+        self.head_dim = head_dim
+        self.qkv_proj = QKVParallelLinear(
+            hidden_size=hidden_size,
+            head_size=head_dim,
+            total_num_heads=num_heads,
+            total_num_kv_heads=num_kv_heads,
+            bias=False,
+            quant_config=quant_config,
+            prefix=f"{prefix}.qkv_proj",
+        )
+        self.attention = Attention(
+            num_heads=self.qkv_proj.num_heads,
+            num_kv_heads=self.qkv_proj.num_kv_heads,
+            head_size=head_dim,
+            softmax_scale=head_dim**-0.5,
+            causal=False,
+            qkv_layout="BSND",
+            role="self",
+            prefix=prefix,
+        )
+        self.out_proj = RowParallelLinear(
+            num_heads * head_dim,
+            hidden_size,
+            bias=False,
+            input_is_parallel=True,
+            quant_config=quant_config,
+            prefix=f"{prefix}.out_proj",
+        )
+
+    def forward(self, x, attn_metadata=None):
+        qkv, _ = self.qkv_proj(x)
+        q_size = self.qkv_proj.num_heads * self.head_dim
+        kv_size = self.qkv_proj.num_kv_heads * self.head_dim
+        q, k, v = qkv.split([q_size, kv_size, kv_size], dim=-1)
+        q = q.unflatten(-1, (self.qkv_proj.num_heads, self.head_dim))
+        k = k.unflatten(-1, (self.qkv_proj.num_kv_heads, self.head_dim))
+        v = v.unflatten(-1, (self.qkv_proj.num_kv_heads, self.head_dim))
+        out = self.attention(q, k, v, attn_metadata)
+        out = out.flatten(-2)
+        out, _ = self.out_proj(out)
+        return out
+```
+
+Add Q/K norm and RoPE in the official order before the `Attention` call. Keep
+checkpoint mapping for the fused QKV and output projection complete.
+
+### Backend qualification
+
+For each role (`self`, cross-attention category, token refiner, etc.), record:
+
+- actual selected backend from logs/trace;
+- supported dtype, head size, QKV layout, mask/metadata, packed varlen behavior;
+- platform and compute capability;
+- TP/SP/Ring/AllGather-KV restrictions;
+- fallback behavior and whether fallback changes the claimed performance.
+
+Use unequal packed samples and compare with dense SDPA. Verify `cu_seqlens`,
+max sequence length, padding exclusion, modality boundaries, CFG ownership, and
+output slicing. Do not pass an `attn_mask` and assume it is consumed. Inspect
+the backend contract and trace.
+
+TRTLLM, Flash Attention, cuDNN, NPU varlen, and other backends are independent
+qualification rows. The shared `Attention` class does not make every backend
+packed-safe or faster automatically. Ring and hybrid paths require their own
+boundary parity. AllGather-KV and specific backend combinations may fail fast;
+keep that behavior visible in docs.
+
+## Sparse attention
+
+Sparse attention is a backend/model/platform optimization, not a synonym for
+low-precision dense attention. Enable it only after dense BF16 parity.
+
+For every candidate task/shape/topology:
+
+1. State the sparsity policy, selected backend/kernel, block/head dimensions,
+   supported hardware, external kernel/package revision, and fallback.
+2. Prove the sparse kernel executed and report realized sparsity; an enabled
+   flag is insufficient.
+3. Compare same-seed per-block outputs, denoise trajectory, and final artifact
+   with dense attention.
+4. Add modality-specific metrics: temporal consistency and audio/video sync for
+   video, spectral/audio quality for audio, and prompt/image metrics as relevant.
+5. Report attention time and full E2E time. Include preprocessing/index-build
+   overhead, peak memory, and the exact dense control.
+6. Validate each task and shape. Do not extrapolate a T2V result to I2V/Ref2V,
+   or NPU sparse support to CUDA/ROCm/XPU.
+
+If Ring bypasses the selected backend or a sparse setting would be silently
+ignored, reject the combination. Prefer a visible dense fallback only when the
+recipe labels the row `limited` and does not claim sparse speed.
+
+## Fusion-first operator patterns
+
+Prefer current shared/custom operators with explicit guards and native
+fallbacks. Prove parity at operator, block, trajectory, and artifact levels,
+then use a trace to prove fewer launches/bytes or lower latency.
+
+### Q/K RMSNorm + RoPE
+
+```python
+from vllm_omni.diffusion.layers.norm import RMSNorm
+from vllm_omni.diffusion.layers.rope import RotaryEmbedding, apply_rope_to_qk
+
+self.norm_q = RMSNorm(head_dim, eps=eps)
+self.norm_k = RMSNorm(head_dim, eps=eps)
+# Example only: set these booleans from the official checkpoint contract.
+use_neox_layout = False
+cos_sin_are_half_dim = True
+self.rope = RotaryEmbedding(
+    is_neox_style=use_neox_layout,
+    half_head_dim=cos_sin_are_half_dim,
+)
+
+q = self.norm_q(q)
+k = self.norm_k(k)
+q, k = apply_rope_to_qk(self.rope, q, k, (cos, sin))
+```
+
+Replace the illustrative booleans with the official layout in real code.
+Preserve partial rotary dimensions by splitting the rotated prefix and concatenating
+the pass-through suffix. If 3D cos/sin includes a batch axis, the current
+shared path assumes the values are shared across the batch; per-sample
+different positions require a proven representation/path.
+
+For packed `[tokens, heads, head_dim]` tensors with a non-interleaved
+`[cos | sin]` table, the current shared fused boundary is:
+
+```python
+from vllm_omni.diffusion.layers.fused_qk_norm_rope import fused_qk_norm_rope
+
+q, k = fused_qk_norm_rope(
+    q,
+    k,
+    self.norm_q.weight,
+    self.norm_k.weight,
+    rope_table,
+    self.norm_q.variance_epsilon,
+)
+```
+
+Its CUDA fast path currently specializes BF16 `head_dim=128` and
+`rotary_dim=96`; other valid inputs take the eager implementation. The separate
+shared RMSNorm and RotaryEmbedding still provide the general cross-platform
+baseline. Treat NPU/MUSA and other vendor fusions as independent qualification
+rows, and do not generalize a model-specific Triton kernel without compatible
+shape, layout, frequency partition, dtype, and platform evidence.
+
+### Fused QKV projection
+
+Use `QKVParallelLinear` as shown above. Qualification includes:
+
+- `num_heads` and `num_kv_heads` divisibility by TP;
+- local split sizes from the layer, never reusing global counts after TP;
+- Q/K/V checkpoint order conversion before the vLLM loader;
+- all Q, K, and V shards present;
+- quantization scale/loader mapping when FP8 or another method is enabled.
+
+### Gate-up projection + fused SwiGLU
+
+```python
+from vllm.model_executor.layers.activation import SiluAndMul
+from vllm.model_executor.layers.linear import (
+    MergedColumnParallelLinear,
+    RowParallelLinear,
+)
+
+self.gate_up_proj = MergedColumnParallelLinear(
+    input_size=hidden_size,
+    output_sizes=[intermediate_size, intermediate_size],
+    bias=False,
+    quant_config=quant_config,
+    prefix=f"{prefix}.gate_up_proj",
+)
+self.act_fn = SiluAndMul()
+self.down_proj = RowParallelLinear(
+    input_size=intermediate_size,
+    output_size=hidden_size,
+    bias=False,
+    input_is_parallel=True,
+    quant_config=quant_config,
+    prefix=f"{prefix}.down_proj",
+)
+
+gate_up, _ = self.gate_up_proj(x)
+x = self.act_fn(gate_up)
+x, _ = self.down_proj(x)
+```
+
+Map both checkpoint `gate_proj` and `up_proj` shards into the merged parameter
+and require both. A merged projection followed by `F.silu(gate) * up` is not
+the same claim as the `SiluAndMul` fused activation kernel; identify what the
+trace actually uses.
+
+### AdaLN
+
+Use a shared layer only when its mathematical/return contract matches the
+official model:
+
+```python
+from vllm_omni.diffusion.layers.adalayernorm import AdaLayerNorm
+
+self.adaln = AdaLayerNorm(
+    hidden_size,
+    elementwise_affine=False,
+    eps=1e-6,
+)
+x = self.adaln(x, scale[:, None, :], shift[:, None, :])
+```
+
+`AdaLayerNormZero`, `AdaLayerNormZeroSingle`, and continuous variants include
+different projections, broadcasts, and tuple outputs; they are not drop-in
+replacements. A shared AdaLN may have an NPU fast path while using native ops on
+CUDA/HIP/XPU. Do not call it a universal fused AdaLN kernel.
+
+For sigma schedules with a small finite set of timesteps, profile whether
+precomputing the timestep/AdaLN projection once per request is worthwhile.
+Cache keys must include the exact schedule/config/device/dtype and stay bounded.
+
+### Layout, residual, and other candidates
+
+After profiling, inspect:
+
+- bias/dropout/add or gated residual chains;
+- norm + linear or norm + modulation boundaries;
+- repeated transpose/reshape/contiguous around attention;
+- VAE normalization/activation/convolution boundaries;
+- MoE routing and fused experts for models that actually use MoE.
+
+For fused residual-plus-norm or modulation chains, preserve the unfused
+materialization boundary. If the residual was stored in BF16 before RMSNorm,
+round to BF16 and promote again inside the fused kernel; using the unrounded
+FP32 temporary changes the denoising trajectory even when the stored residual
+looks correct.
+
+Keep DLO materialize/release hooks, process-group collectives, request cache
+transitions, and cleanup outside compiled/fused regions unless lifecycle safety
+is explicitly tested. Compile only stable regions and retain eager fallback.
+
+## Remove redundant computation
+
+### Hoist request invariants
+
+Audit the denoise loop for work that depends only on the request rather than
+the current timestep:
+
+```python
+# Before the denoise loop.
+condition = self.condition_proj(text_embeddings)
+refined_condition = self.token_refiner(condition, packed_metadata)
+rope_tables = self.build_rope_tables(position_ids)
+attn_metadata = self.build_attention_metadata(layout)
+
+for step, sigma in enumerate(sigmas):
+    timestep_state = self.prepare_timestep_state(sigma)
+    noise = self.transformer(
+        latents,
+        refined_condition,
+        rope_tables=rope_tables,
+        attn_metadata=attn_metadata,
+        timestep_state=timestep_state,
+    )
+    latents = self.scheduler_step(noise, sigma, latents)
+```
+
+Candidates include prompt conditioning, token-refiner output, reference
+encoding/scans, position IDs/RoPE tables, packed masks/metadata, static CFG
+branches, and finite-schedule AdaLN projections. First prove they are invariant
+for all official tasks, LoRA/adapters, prompt-update features, shapes, and
+schedules. In continuous batching, store request-specific results on request
+state rather than the pipeline singleton.
+
+### Eliminate per-layer synchronization
+
+Avoid device-to-host scalar extraction such as `.item()` inside every layer:
+
+```python
+# Bad: forces a GPU/CPU synchronization in every attention layer.
+max_len = int(cu_seqlens[-1].item())
+
+# Better: compute validated host metadata once during request packing and pass it.
+packed = PackedLayout(
+    cu_seqlens=cu_seqlens,
+    max_seqlen=max(sample_lengths),
+)
+for block in self.blocks:
+    hidden = block(hidden, packed_layout=packed)
+```
+
+Trace to verify the synchronization disappears. Do not replace a correct
+dynamic value with a stale constant merely to remove `.item()`.
+
+### Remove unused masks and allocations
+
+If packed attention consumes `cu_seqlens` and ignores a dense mask, do not
+construct the dense mask. Reuse immutable request metadata and preallocated
+buffers when safe. Prefer direct indexed scatter/gather over constructing large
+temporary tensors only when duplicate-index semantics and gradients (normally
+disabled for inference) remain correct.
+
+Any cross-request cache needs a bounded lifetime and a complete key containing
+shape, layout, device, dtype, schedule, task, adapter/LoRA, and any value that
+changes the tensor. Otherwise keep it request-scoped.
+
+### VAE and reference work
+
+Profile duplicate VAE decode/postprocess, repeated reference decoding/scans,
+unnecessary host-device round trips, and full-frame materialization. Apply VAE
+tiling, slicing, patch parallelism, or on-demand staging only with seam,
+ordering, color/range, and memory parity tests.
+
+## USP communication optimization
+
+“USP works” is not evidence that USP communication is optimized. For Ulysses,
+Ring, hybrid, and AllGather-KV separately, profile:
+
+- collective count, bytes, duration, stream, and overlap with compute;
+- Q/K/V and output all-to-all boundaries;
+- pack/unpack, padding, metadata broadcast, transpose, and contiguous copies;
+- head divisibility or advanced uneven-head support;
+- scaling efficiency from 1 to N devices on the same workload/interconnect;
+- load balance for unequal packed samples.
+
+Declare `_sp_plan` split/gather boundaries around meaningful module outputs,
+not arbitrary Python statements. Keep packed metadata sharded/gathered in the
+same ownership model as tokens.
+
+When the target revision offers accelerated Ulysses transport such as
+`--ulysses-a2a-permute`, qualify it separately from regular Ulysses. Prove the
+strict scatter-heads/gather-sequence layout selected the fast path, record
+one-time JIT/readiness cost, prewarm the maximum workspace shape before CUDA
+graph capture, keep the grow-only workspace on one stream, and exercise cleanup
+before process-group destruction. Compare collective and E2E deltas after the
+dense backend and topology are frozen; keep unsupported layouts on an explicit
+regular-Ulysses fallback.
+
+For GQA, validate `Q_heads % KV_heads == 0`, pad KV heads first, and derive Q
+padding from the original ratio. Exercise both contiguous and row-strided QKV
+layouts; a fast copy path must preserve bitwise values and packed boundaries.
+
+Cross-attention sometimes uses replicated text K/V while only video/audio Q is
+sequence-sharded. In that specific case, `skip_sequence_parallel=True` can
+avoid incorrect or wasteful Q/K/V redistribution:
+
+```python
+self.cross_attention = Attention(
+    num_heads=local_heads,
+    num_kv_heads=local_kv_heads,
+    head_size=head_dim,
+    softmax_scale=head_dim**-0.5,
+    causal=False,
+    role="cross",
+    skip_sequence_parallel=True,
+    prefix=f"{prefix}.cross_attn",
+)
+```
+
+Use it only after proving K/V are replicated and Q/output ownership is correct.
+Otherwise it disables needed communication. Report whether the optimization
+reduces collective bytes/time and improves E2E scaling.
+
+## Text encoder and VAE disaggregation
+
+Disaggregation is a measured architecture decision. First collect:
+
+| Question | Required measurement |
+|---|---|
+| Is the stage large enough? | Encode/decode share at target concurrency and shape |
+| Is transfer economical? | Tensor/media bytes, serialization, connector latency and bandwidth |
+| Is there reuse? | Prompt/reference reuse rate, cacheability, fan-out |
+| Can it scale independently? | Queue saturation and resource utilization per stage |
+| Is the contract stable? | Shape, dtype, mask/tag/position metadata, ordering |
+
+### Text encoder candidate
+
+Define an explicit tensor contract containing hidden states, masks, tags or
+position IDs, dtype, shape, task/checkpoint revision, and request ID. Support
+disabling the local encoder so the DiT cannot accidentally recompute it. Test
+all tasks, prompt lengths, partial TP group membership, retries, backpressure,
+abort, connector cleanup, and independent replica scaling.
+
+### VAE candidate
+
+Separate VAE encode and decode contracts if both exist. Define latent shape,
+scale/shift, dtype/range, tiling/chunk order, output media ordering, and stage
+metadata. Measure latent/media transfer against local decode cost. Test tiling
+seams, RNG/determinism where relevant, chunk ordering, codec/postprocess,
+backpressure, abort, and worker failure.
+
+Do not label VAE tiling, patch parallelism, compile, or offload as VAE
+disaggregation. Likewise, a separate text-encoder TP group is not a separate
+stage. If no reusable generic stage/connector exists in the target revision,
+produce a feasibility result or RFC and keep the capability `not tested` rather
+than inventing a model-local production architecture.
+
+## Performance acceptance
+
+For each accepted optimization, require:
+
+- same fixed workload and correctness artifacts before/after;
+- actual fast-path execution proven by logs/trace/counters;
+- local metric plus E2E latency/throughput and memory;
+- at least three raw fixed-work repetitions after explicit warmup, or a
+  declared arrival-load sample set large enough for every tail percentile;
+- named hardware, backend, topology, dtype, and task scope;
+- fallback/rejection tests for unsupported shapes/platforms;
+- no regression in abort/error cleanup or long-run memory trend.
+
+Update the feature matrix narrowly. Do not convert a profile-driven direction
+into a generic “supported” feature merely because a model-specific PR exists.

.claude/skills/production-add-diffusion-model/references/production-validation.md

@@ -0,0 +1,464 @@
+# Production Serving and Validation
+
+## Contents
+
+1. [Execution-mode decision](#execution-mode-decision)
+2. [Request-level batching](#request-level-batching)
+3. [Step execution and continuous batching](#step-execution-and-continuous-batching)
+4. [Abort and cleanup](#abort-and-cleanup)
+5. [Mixed-RPS soak](#mixed-rps-soak)
+6. [Four CI tracks](#four-ci-tracks)
+7. [PR evidence checklist](#pr-evidence-checklist)
+
+## Execution-mode decision
+
+Request batching and step-wise execution solve different problems:
+
+| Mode | Pipeline contract | Scheduling/abort boundary |
+|---|---|---|
+| Serial request | `forward(DiffusionRequestBatch)` for one request | Whole request; in-denoise preemption is unavailable |
+| Request batching | `supports_request_batch = True`; one forward consumes compatible independent requests | Whole batch/request |
+| Single-request step | Full `SupportsStepExecution`; `max_num_seqs=1` | Between denoise steps |
+| Step continuous batching | Full step contract plus heterogeneous batched-step evidence | Between denoise waves/steps |
+
+Do not expose four stub methods as an opt-out. The current protocol is
+structural; if a native pipeline is not step-capable, do not define the four
+step methods. If it is capable, implement and test all four.
+
+Treat structural support, a generic request-batch bridge, and model benefit as
+three separate claims. Before recommending step execution, name a hypothesis
+that request mode cannot satisfy, define a success threshold, and run a matched
+request-mode control. If throughput or latency regresses, retain step execution
+as functional/limited and keep request mode as the production recommendation.
+
+Choose compatibility keys from every value that changes tensor shapes,
+semantics, hooks, or scheduler behavior: task, output shape/count, frames,
+steps/schedule, guidance/CFG, dtype/backend, LoRA/adapter, cache quality policy,
+and model-specific conditions. Reject or schedule separately rather than
+silently using the first request's settings.
+
+## Request-level batching
+
+A request-batched pipeline returns exactly one `DiffusionOutput` per logical
+request. This is model-owned pseudocode: `validate_batch_compatibility()` and
+`_generate_batch()` are placeholders to implement from the official contract,
+not shared APIs to import.
+
+```python
+import torch
+
+from vllm_omni.diffusion.data import DiffusionOutput
+from vllm_omni.diffusion.worker.request_batch import (
+    DiffusionRequestBatch,
+    split_diffusion_output_by_request,
+)
+
+class MyPipeline(torch.nn.Module):
+    supports_request_batch = True
+
+    def forward(self, req: DiffusionRequestBatch) -> list[DiffusionOutput]:
+        validate_batch_compatibility(req.sampling_params_list, req.prompts)
+        common = req.sampling_params_list[0]
+
+        text_prompts: list[str] | None = [
+            prompt
+            if isinstance(prompt, str)
+            else (prompt.get("prompt") or "")
+            for prompt in req.prompts
+        ]
+
+        prompt_fields = DiffusionRequestBatch.collate_prompt_field_map(
+            req.prompts,
+            {"prompt_embeds": None, "prompt_mask": None},
+        )
+        if prompt_fields["prompt_embeds"] is not None:
+            text_prompts = None
+        generators = req.collate_request_generators(
+            common.num_outputs_per_prompt,
+            default_generator=None,
+        )
+        output = self._generate_batch(
+            prompt=text_prompts,
+            prompt_fields=prompt_fields,
+            generators=generators,
+            sampling=common,
+        )
+        return split_diffusion_output_by_request(
+            DiffusionOutput(output=output),
+            req,
+            num_outputs_per_prompt=common.num_outputs_per_prompt,
+        )
+```
+
+The prompt-field collator only handles tensor fields; extract raw text prompts
+and model-specific negative/alias fields separately, as current in-tree batched
+pipelines do. Set a raw prompt field to `None` when the matching precomputed
+embedding is supplied. The exact aliases and generator device depend on the
+model. Use current collators rather than manual `torch.cat` when they cover the
+contract; they reject mixed present/missing tensors and incompatible
+shape/dtype/device.
+
+Before reading `sampling_params_list[0]`, validate every field that must be
+common. Tests must cover:
+
+- two and N requests, independent seeds/generators, one output each;
+- mixed prompt lengths and packed/padded boundaries;
+- multiple outputs per prompt and correct result slicing;
+- compatible versus incompatible shapes, steps, guidance, LoRA, and quality;
+- one request failure without cross-request output or state corruption;
+- output ordering and request IDs under different completion times.
+
+After the model advertises support, candidate serving is:
+
+```bash
+vllm serve '<model>' --omni \
+  --max-num-seqs 4 \
+  --request-batch-max-wait-ms 20
+```
+
+The wait window trades admission latency for batch formation. Benchmark at 0
+and candidate nonzero values under the target arrival process.
+
+## Step execution and continuous batching
+
+### Full protocol skeleton
+
+Use the current shared state types and make every mutable value request-local:
+
+The helpers beginning with `_` and `validate_step_batch()` below are
+model-owned placeholders; scheduler initialization is scheduler-specific.
+
+```python
+import copy
+from collections.abc import Sequence
+from typing import Any, ClassVar
+
+import torch
+
+from vllm_omni.diffusion.data import DiffusionOutput
+from vllm_omni.diffusion.models.interface import SupportsStepExecution
+from vllm_omni.diffusion.worker.input_batch import InputBatch
+from vllm_omni.diffusion.worker.utils import StepRequestState
+
+class MyPipeline(torch.nn.Module, SupportsStepExecution):
+    supports_step_execution: ClassVar[bool] = True
+
+    def prepare_encode(
+        self,
+        state: StepRequestState,
+        **kwargs: Any,
+    ) -> StepRequestState:
+        prepared = self._prepare_request(state.prompt, state.sampling)
+        state.prompt_embeds = prepared.prompt_embeds
+        state.latents = prepared.latents
+        state.timesteps = prepared.timesteps
+        state.scheduler = copy.deepcopy(self.scheduler)
+        state.scheduler.set_begin_index(0)
+        state.step_index = 0
+        state.extra["task_state"] = prepared.task_state
+        return state
+
+    def denoise_step(
+        self,
+        input_batch: InputBatch,
+        *,
+        states: Sequence[StepRequestState] | None = None,
+        **kwargs: Any,
+    ) -> torch.Tensor | None:
+        validate_step_batch(input_batch, states)
+        return self.transformer(**build_batched_denoise_inputs(input_batch))
+
+    def step_scheduler(
+        self,
+        state: StepRequestState,
+        noise_pred: torch.Tensor,
+        **kwargs: Any,
+    ) -> None:
+        timestep = state.current_timestep
+        if timestep is None:
+            raise RuntimeError("step_scheduler called without a current timestep")
+        state.latents = state.scheduler.step(
+            noise_pred,
+            timestep,
+            state.latents,
+            return_dict=False,
+        )[0].to(state.latents.dtype)
+        state.step_index += 1
+
+    def post_decode(
+        self,
+        state: StepRequestState,
+        **kwargs: Any,
+    ) -> DiffusionOutput:
+        return DiffusionOutput(output=self.decode(state.latents))
+```
+
+This is a shape guide, not drop-in code: verify the target revision's
+`StepRequestState.extra`, scheduler signature, CFG handling, state fields, and
+decode contract. Follow a current native pipeline such as Qwen-Image for the
+exact revision.
+
+Never use the shared `self.scheduler` to advance a request. Store scheduler,
+latents, embeddings, timesteps, masks, RNG/generator, task metadata, cache
+policy, adapter, and output assembly state on `StepRequestState` or its
+request-local extension. Pipeline fields may hold immutable weights/config and
+bounded immutable caches only.
+
+### Qualification sequence
+
+1. Compare request-mode and step-mode at `max_num_seqs=1` for every task using
+   identical seed/schedule/shape and intermediate latents.
+2. Abort queued and in-flight requests at several step boundaries.
+3. Increase to two compatible requests; verify independent scheduler/RNG/state
+   and output order.
+4. Increase to the target capacity and mix prompt lengths.
+5. Exercise mixed shape, frames, steps/schedule, task, guidance, and quality.
+   Batch only compatible rows; otherwise split/reject predictably.
+6. Inject a failure in encode, denoise, scheduler, and decode independently.
+7. Run the production soak and fault matrix.
+
+Candidate commands only after capability validation:
+
+```bash
+# Conservative step execution and step-boundary abort.
+vllm serve '<model>' --omni \
+  --step-execution \
+  --max-num-seqs 1
+
+# Experimental batched-step candidate after multi-request E2E.
+vllm serve '<model>' --omni \
+  --step-execution \
+  --max-num-seqs 4
+```
+
+If an attention backend is only correct for single-request step mode, fail
+early or keep the recipe at `max_num_seqs=1`. Do not describe generic runtime
+support as model support.
+
+## Abort and cleanup
+
+### Required semantics
+
+For queued and in-flight abort/disconnect:
+
+- emit exactly one terminal aborted/error result;
+- do not run `post_decode` after abort unless a documented partial-output mode
+  explicitly requires it;
+- do not increment `step_index` or apply a scheduler result after cancellation;
+- remove scheduler/state cache entries and request IDs;
+- release latents, prompt/reference tensors, cache hooks, DLO buffers, connector
+  messages, uploaded/temp files, and output buffers;
+- keep other requests alive;
+- make cleanup idempotent when disconnect, timeout, and worker error race;
+- drop late results/exceptions for already-resolved or cancelled futures
+  without terminating the shared result-pump thread;
+- complete the next valid request with its requested cache/offload policy.
+
+Test cancellation before engine admission, while queued, after encode, during
+early/middle/final denoise steps, before decode, and while returning/persisting
+output. Include async job cancellation and client socket disconnect.
+
+### Failure injection matrix
+
+| Location | Injection | Assertions |
+|---|---|---|
+| Validation/upload | bad MIME, count/bytes overflow, truncated media | 400/413, no job/engine submit, no temp leak |
+| Load/start | missing fused shard, unsupported topology | startup fails, actionable error, no silent init |
+| Encode | exception/OOM/timeout | one error, request state/temp released |
+| Denoise | exception/OOM/worker exit | peers handled, hooks/buffers released, health behavior explicit |
+| Scheduler | invalid state/shape | no latent/step partial commit |
+| Decode/post | exception/codec error | no corrupt successful result, partial output removed |
+| Result/IPC | late result after cancel, large split tensor, orphan SHM | pump remains alive, one terminal result, exact ownership cleanup |
+| Connector/disagg | timeout/retry/duplicate | idempotency, backpressure, no orphan payload |
+
+After each injection, check server health and issue a known-good request. If
+the process must restart, document and test the restart policy; do not claim
+transparent recovery.
+
+### Large-output transport
+
+Treat worker-to-engine IPC and engine-to-client transport as separate hops.
+For batched video, prove that each logical output does not accidentally retain
+or serialize the complete batch storage. Exercise the target revision's shared
+memory or artifact-handle path above and below its threshold, including
+non-contiguous views, multiple outputs, consumer failure, timeout, abort, and
+worker exit. Verify every shared segment/file is released exactly once.
+
+Freeze the media contract at each hop: dtype, value range, shape/layout,
+contiguity, request ownership, encoding/color model, payload bytes, and pending
+consumers. Device-side float-to-uint8 preparation, D2H/IPC, planar/interleaved
+conversion, CPU encode/mux, and delivery off the asyncio event loop are
+different optimizations. Validate their route selection and fallbacks
+independently. Byte-identical HTTP MP4 output does not prove that a raw offline
+tensor kept its dtype or range.
+
+For long-window output, validate segment ordering and audio/video continuity,
+then exercise payloads around serializer, shared-memory, HTTP, and codec size
+limits. Chunking must reassemble exactly, reject malformed/missing chunks, and
+release partial state after abort, timeout, or peer failure.
+
+Qualify chunk production, transport, and public streaming as separate layers.
+A VAE callback must define owned representation, frame offsets, final marker,
+rank ownership, source/config gate, and collective-safe failure, but it does not
+prove SHM/ZMQ delivery, bounded backpressure, cancellation, codec reuse, or
+client streaming. Measure time to first owned chunk, first encoded fragment,
+steady cadence, mux finalization, and complete client artifact independently.
+
+Measure remote MP4 encoding, serialization/copies, network transfer, and client
+materialization separately from denoise/decode. An inference-kernel speedup does
+not establish serving throughput if output transport dominates or leaks.
+
+## Mixed-RPS soak
+
+Use a realistic request mix and arrival distribution. Include small/large
+shapes, short/long prompts, all advertised tasks, cache tiers, and a small rate
+of abort/disconnect/error injections. Run phases below, near, and above measured
+saturation so queue/backpressure behavior is visible.
+
+Example serving benchmark phase:
+
+```bash
+python benchmarks/diffusion/diffusion_benchmark_serving.py \
+  --base-url http://127.0.0.1:8091 \
+  --endpoint /v1/videos \
+  --model '<model>' \
+  --dataset trace \
+  --dataset-path /path/to/versioned-production-trace.jsonl \
+  --task t2v \
+  --num-prompts 500 \
+  --request-rate 0.5 \
+  --max-concurrency 8 \
+  --warmup-requests 8 \
+  --warmup-concurrency 8 \
+  --output-file soak-near-saturation.json
+```
+
+Use a task-appropriate trace/media dataset. Run the same phase at rates selected
+from an initial saturation curve; a literal `0.5` is illustrative only.
+
+On supported GPU hosts, wrap a test or soak with the repository monitor:
+
+```bash
+bash tests/dfx/stability/scripts/resource_monitor.sh run --backend gpu -- \
+  pytest -s -v tests/dfx/stability/scripts/test_stability_<model>.py -m slow
+```
+
+Check the target revision's monitor backends. Do not claim CPU/NPU monitoring
+from a reserved CLI choice unless collection is implemented and verified.
+
+Report:
+
+- offered and achieved RPS, success/error/abort rates;
+- p50/p95/p99 and queue/service/stage times, with the successful sample count
+  and arrival process used for each percentile;
+- throughput per device and batch/wave utilization;
+- per-rank HBM and process-tree PSS trend/slope;
+- cache size/hit rate, temp-file/disk growth, open FDs/handles;
+- outstanding shared-memory/artifact handles and encoded/network bytes;
+- worker/engine health, restarts, OOM/timeouts, backpressure response;
+- total duration, request count/mix, raw logs/JSON/monitor artifact hashes.
+
+A short burst or repeated serial curl loop is not a stability test.
+
+## Four CI tracks
+
+Keep the tracks independent so a failure answers a specific question. These
+four readiness tracks are not the repository's numbered CI levels. Map them
+onto the current L1-L5 taxonomy:
+
+| Readiness track | Repository CI placement |
+|---|---|
+| Function | L1 unit/logic plus L2 basic online/offline E2E; extend real-model scenarios in L3/L4 |
+| Accuracy | L3 real-model gates for important cases; deeper and broader nightly coverage in L4 |
+| Performance | Important thresholds in L3 where they fit the time budget; full baselines/regression jobs in nightly L4 |
+| Reliability | Cheap rejection/recovery assertions can run earlier, but long stability, fault injection, and reliability suites belong to weekly L5 |
+
+Distributed topology is a test case within the appropriate level, not a
+replacement definition for a level.
+
+### 1. Function CI
+
+Cover:
+
+- strict unquantized and advertised quantized loading;
+- fused source-shard completeness and prefix routing;
+- official task positive boundaries and neighboring negative cases;
+- offline, sync, async normalization/parity;
+- single-device reference plus every advertised topology smoke;
+- output type/count/order and request ID isolation;
+- feature rejection paths that must fail early.
+
+### 2. Accuracy CI
+
+Pin official implementation/checkpoint/vLLM-Omni revisions, prompts and media
+hashes, seed, scheduler/sigma/steps, dimensions/frames/FPS, guidance, dtype,
+backend, and topology. Compare at suitable levels:
+
+- component outputs or selected blocks;
+- one or more intermediate denoise latents;
+- final image/video/audio artifact metrics;
+- temporal consistency and audio/video synchronization where relevant.
+
+Set tolerance per modality/hardware/backend with rationale. Keep failure
+artifacts and metadata. A single visual inspection does not define CI.
+
+For quantization trajectory tooling, inspect the target revision's current
+`vllm_omni/quantization/tools/compare_diffusion_trajectory_similarity.py` and
+reuse it when compatible instead of creating an ad hoc comparator.
+
+### 3. Performance CI
+
+Pin workload and best deployment. Include a resident recommended row and a
+small-HBM/DLO row when both are production targets. Declare:
+
+- one explicit warmup exclusion and at least three measured fixed-work
+  repetitions;
+- raw result JSON and environment identity;
+- median/mean plus range for small fixed-work A/Bs; p50/p95/p99 only for an
+  arrival-load run with enough samples, plus throughput/RPS, stage time,
+  HBM/PSS;
+- regression metric, threshold, baseline owner, and update procedure;
+- correctness/accuracy check linked to the same configuration.
+
+Use the current harness config style, for example:
+
+```bash
+pytest -s -v tests/dfx/perf/scripts/run_diffusion_benchmark.py \
+  --test-config-file tests/dfx/perf/tests/test_<model>_vllm_omni.json
+```
+
+Verify parser options on the target revision. Do not copy a JSON schema from a
+different model without matching its endpoint, task, dataset, and deploy path.
+
+### 4. Reliability CI
+
+Run a bounded CI soak plus a longer scheduled soak. Cover:
+
+- below/near/above-saturation arrival phases;
+- queued/in-flight abort and disconnect;
+- OOM/timeout and worker/process failure appropriate to the platform;
+- DLO/cache/connector cleanup;
+- health, fast-fail/backpressure, restart/recovery policy;
+- memory/temp/FD trend and successful known-good request after each fault.
+
+Keep reliability thresholds independent from latency thresholds. A fast server
+that leaks memory or hangs after cancellation fails production readiness.
+
+## PR evidence checklist
+
+The PR description must include or link:
+
+- official source/checkpoint revisions and API/limit matrix;
+- scoped feature compatibility matrix with the four allowed states;
+- implementation notes for FP8, DLO, request cache, attention/fusion, batching;
+- exact environment, deploy/serve, every task curl, benchmark and soak commands;
+- fixed accuracy inputs and artifacts;
+- raw before/after performance runs and memory traces;
+- output-contract manifests and stage accounting from device preparation
+  through the complete client artifact;
+- abort/disconnect/fault results and cleanup evidence;
+- Function/Accuracy/Performance/Reliability CI files and ownership;
+- limitations/TODOs that remain `not tested` rather than implied support.
+
+Separate the Day-0 milestone from production readiness. If the PR only closes
+some gates, title and description should say which scoped gates it closes.

.claude/skills/readme.md

@@ -31,9 +31,16 @@ self-check.
 - [`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
+- [`find-simplifications`](find-simplifications/SKILL.md): finds evidence-backed
+  opportunities to remove or merge dead, duplicated, speculative,
+  over-generalized, or unnecessarily defensive vLLM-Omni code
 - [`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
+  PR by validating title format, dead code, simplification opportunities,
+  accuracy and performance claims, and merge readiness
+- [`production-add-diffusion-model`](production-add-diffusion-model/SKILL.md):
+  takes a working Day-0 diffusion integration through API/limit parity,
+  feature-combination evidence, quantization/offload, performance, production
+  serving, hardware recipes, and CI readiness
 - [`quantization`](quantization/SKILL.md): guides quantization method selection,
   model integration, checkpoint loading, and quality/performance validation
   for vLLM-Omni

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

@@ -21,7 +21,10 @@ Make every finding:
 - **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.
+missing test that would not protect changed behavior. Do report new allowlist
+or budget entries in `check_forbidden_imports.py` / `check_torch_cuda.py` /
+`check_tts_adapter.py` / `check_buildkite.py` unless the PR justifies them:
+those are policy changes, not lint noise.
 
 ## Select the input and depth
 
@@ -92,12 +95,13 @@ If docs and live code disagree, verify the code/tests and report the drift.
 
 | Reference | Read when |
 | --- | --- |
-| [model-addition-checklist.md](references/checks/model-addition-checklist.md) | A model, architecture, loader, processor, registry, or stage config is added. |
+| [model-addition-checklist.md](references/checks/model-addition-checklist.md) | A model, architecture, loader, processor, registry, pipeline config, or deploy 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`. |
+| [find-simplifications](../find-simplifications/SKILL.md) | Every review; run a diff-scoped subtraction and simplification pass after correctness blockers. |
 
 ### Delivery and reviewer coordination
 
@@ -180,9 +184,13 @@ 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.
+Read and apply [find-simplifications](../find-simplifications/SKILL.md) on every
+review. Constrain it to the diff and the adjacent ownership, callers, or
+consumers needed to prove a candidate. Check whether added or expanded helpers,
+classes, state, fallback and compatibility branches, data movement, or public
+behavior can be deleted, merged, moved, or inlined. Zero candidates is a valid
+result. Do not widen the review into repository backlog or report speculative
+style preferences as simplification findings.
 
 ### 6. Verify the changed path
 

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

@@ -1,7 +1,7 @@
 # Model Addition Checklist
 
 Read this reference when a PR adds or expands a model architecture, pipeline,
-loader, processor, registry entry, or stage configuration.
+loader, processor, registry entry, pipeline config, or deploy config.
 
 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/),

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

@@ -3,7 +3,7 @@
 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
+aliases, CLI projection, pipeline 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.
 

.claude/skills/review-pr/references/process/review-execution.md

@@ -111,6 +111,17 @@ Mark early findings as preliminary and continue. This is not a GitHub comment.
   but do not publish review events without separate authorization.
 - Record DCO, pre-commit, required CI, and mergeability. Pending or unknown gates
   do not block source review.
+- GitHub Actions `SKIP` omits SPDX, shellcheck, markdownlint, mypy-3.10, and
+  test-mark coverage. A green GHA pre-commit job does not prove those local
+  gates passed. New files still need the full Linting list: Omni SPDX
+  (`vLLM-Omni project`), no stdlib `re`/`base64` in `vllm_omni/`, no new
+  pickle / Hugging Face Hub API / `torch.cuda` call sites, test marks, TTS
+  adapter ratchet, Buildkite schema, and native shellcheck on macOS/Windows.
+  See [Linting](https://vllm-omni.readthedocs.io/en/latest/contributing/#linting).
+- Expanding `CHECK_IMPORTS[*].allowed_files`, `ALLOWED_FILES`,
+  `MAX_MODEL_TYPE_BRANCHES`, or Buildkite `SKIP_FILES` is a policy change.
+  Do not rubber-stamp allowlist/budget growth; require justification and
+  prefer fixing the call site.
 - Treat a failed gate as its own evidence. Do not restate its formatting/lint
   output as a new code-review finding.
 - Open CI logs only when the first failing step overlaps the frozen diff or

.claude/skills/review-pr/references/process/review-routing.md

@@ -48,7 +48,7 @@ compatibility changes.
 
 | Signal | Read | Optional repo-local skill |
 | --- | --- | --- |
-| New or expanded model, loader, processor, registry, or stage config | [model-addition-checklist.md](../checks/model-addition-checklist.md) | [`add-tts-model`](../../../add-tts-model/SKILL.md) or [`add-diffusion-model`](../../../add-diffusion-model/SKILL.md) |
+| New or expanded model, loader, processor, registry, pipeline config, or deploy config | [model-addition-checklist.md](../checks/model-addition-checklist.md) | [`add-tts-model`](../../../add-tts-model/SKILL.md) or [`add-diffusion-model`](../../../add-diffusion-model/SKILL.md) |
 | Latency, throughput, memory, scaling, precision, or quality claim | [perf-verification.md](../checks/perf-verification.md) | [`diffusion-perf-opt`](../../../diffusion-perf-opt/SKILL.md) for diffusion |
 | Tests changed, absent for risky behavior, or test-only | [test-quality-evaluation.md](../checks/test-quality-evaluation.md) | [`vllm-omni-test`](../../../vllm-omni-test/SKILL.md) |
 | CI, examples, docs, public behavior, or contributor evidence | [tests-docs-checklist.md](../checks/tests-docs-checklist.md) | None |

.claude/skills/vllm-omni-test/SKILL.md

@@ -52,8 +52,8 @@ Always attach markers deliberately:
   - `tts` — speech synthesis / TTS-only models (`/v1/audio/speech`, voice clone, etc.)
   - `diffusion` — generative diffusion models (image / audio / text / video from noise)
 - **Cross-cutting area** (when relevant): `parallel`, `cache`, `example`, `benchmark`
-- **Hardware**: `cpu`, `gpu`, `cuda`, `rocm`, `npu`, `L4`, `H100`, `distributed_cuda`, …
-- Optional: `slow`, distributed markers when multi-card is required
+- **Hardware**: `cpu`, `gpu`, `cuda`, `rocm`, `npu`, …; SKU (`H100`, `L4`, …) and `cards_{n}` via `@hardware_test(...)` / `hardware_marks(...)` only — never `pytest.mark.H100` (`check-mark` rejects it)
+- Optional: `slow`. Multi-card: `num_cards=N` on the helper (filter with `cards_N` / `not cards_1`)
 
 **Baseline smoke (L2 + L3):** The simplest e2e case per model — default deploy, minimal request — should usually carry **both** `@pytest.mark.core_model` and `@pytest.mark.advanced_model` on the **same** test function so `test-ready.yml` and `test-merge.yml` share one test. `send_*_request` picks validation depth from `--run-level`. References: `test_voxcpm2_tts.py::test_text_to_audio_001`, `test_qwen3_tts_customvoice.py::test_text_to_audio_001`. Heavier scenarios use **`advanced_model` only**; L4 expansion uses **`full_model`**.
 
@@ -94,8 +94,8 @@ Existing references: `tests/e2e/offline_inference/test_qwen2_5_omni.py` (L2-styl
 
 | Scenario | Typical location | Fixtures / runner pattern | Baseline markers & level |
 |----------|------------------|---------------------------|---------------------------|
-| **Offline inference e2e** | `tests/e2e/offline_inference/` | **Module (default):** `omni_runner` + `omni_runner_handler`. **Function (isolation only):** `omni_runner_function` + `omni_runner_handler_function`. Diffusion/TTS may use `Omni(...).generate` directly | L2: `core_model` + **one of** `omni` / `tts` / `diffusion`; `@hardware_test(...)` when GPU/NPU is required |
-| **Online serving e2e** | `tests/e2e/online_serving/` | **Module (default):** `omni_server` + `openai_client`. **Function (isolation only):** `omni_server_function` + `openai_client_function`. Clients: `send_omni_request` (omni), `send_audio_speech_request` (tts), `send_diffusion_request` / `send_video_diffusion_request` / `send_images_generations_request` (diffusion) | Baseline smoke: **`core_model` + `advanced_model`**; heavier paths: `advanced_model` only; L4 expansion: `full_model` |
+| **Offline inference e2e** | `tests/e2e/offline_inference/` | **Module (default):** `omni_runner` + `offline_client`. **Function (isolation only):** `omni_runner_function` + `offline_client_function`. Diffusion/TTS may use `Omni(...).generate` directly | L2: `core_model` + **one of** `omni` / `tts` / `diffusion`; `@hardware_test(...)` when GPU/NPU is required |
+| **Online serving e2e** | `tests/e2e/online_serving/` | **Module (default):** `omni_server` + `online_client`. **Function (isolation only):** `omni_server_function` + `online_client_function`. Clients: `send_omni_request` (omni), `send_audio_speech_request` (tts), `send_diffusion_request` / `send_video_diffusion_request` / `send_images_generations_request` (diffusion) | Baseline smoke: **`core_model` + `advanced_model`**; heavier paths: `advanced_model` only; L4 expansion: `full_model` |
 | **Documentation / runnable examples** | `tests/examples/offline_inference/`, `tests/examples/online_serving/` | **Offline docs (preferred):** extract Python/Bash blocks from the doc README (e.g. `ReadmeSnippet.extract_readme_snippets`), `pytest.mark.parametrize` each snippet, run via `example_runner.run` with a stable `output_subfolder`. **Online docs:** copy client/request scripts into dedicated tests and keep them in sync with the doc page. | Usually **L4**: `advanced_model`, often `example` plus hardware marks matching the nightly docs-example job (see `.buildkite/cuda/test-nightly.yml`). Full conventions: [docs/contributing/ci/test_examples/l4_doc_example_tests.inc.md](../../../docs/contributing/ci/test_examples/l4_doc_example_tests.inc.md) (introduced in [PR #1910](https://github.com/vllm-project/vllm-omni/pull/1910): naming, output directory layout, skip rules, avoid trimming `num_inference_steps` without a strong CI reason). |
 | **Performance / benchmark** | `tests/dfx/perf/tests/*.json` + `run_*_benchmark.py` | JSON or script-driven server + load config; assert explicit metrics / baselines | L4 Perf: JSON `mark` with `full_model` + `omni`/`tts`/`diffusion`; wire `test-nightly.yml` Perf steps |
 | **Invalid parameter / negative HTTP validation** | `tests/dfx/reliability/invalid_param_test/` | Live `omni_server` + low-level `send_*_http_request` with `err_code` / `err_message` | `pytest.mark.slow` + `omni` / `tts` / `diffusion` + `@hardware_marks` (`H100` or `L4`); CI in **`test-weekly.yml`** (not ready/merge/nightly) |
@@ -110,10 +110,10 @@ After choosing offline/online/docs/perf, classify the **product under test** and
 |-----------|------------------------------|----------------------------|----------------------------------------|
 | **What it is** | Multimodal LLM pipeline (thinker/talker/stages; text + vision + audio I/O) | Speech synthesis / voice models | Generative diffusion (noise → image, audio, text, or video) |
 | **Examples** | Qwen2.5-Omni, Qwen3-Omni | Qwen3-TTS, VoxCPM2, Higgs-Audio, Step-Audio2 | Qwen-Image, BAGEL, Wan2.2, HunyuanVideo |
-| **Offline runner** | `omni_runner` + `omni_runner_handler`, `generate_multimodal` | `Omni(...).generate` or stage YAML + TTS params | `Omni(...).generate` + `OmniDiffusionSamplingParams` |
-| **Online client** | `openai_client.send_omni_request` (chat completions, modalities) | `openai_client.send_audio_speech_request` (`/v1/audio/speech`) | `send_diffusion_request` (chat/T2I), `send_video_diffusion_request` (`/v1/videos`, X2V), or `send_images_generations_http_request` / `send_images_edits_http_request` (DALL-E routes) |
-| **Typical assertions** | Stage outputs, text/audio keywords via `OmniRunnerHandler` / response handler | WAV bytes, stream chunks, speech endpoint contract | Image/video dimensions, `final_output_type`, `assert_diffusion_response` |
-| **Stage / deploy YAML** | Per-model omni stage configs (`ci/qwen3_omni_moe.yaml`, …) | `qwen3_tts.yaml`, `voxcpm2.yaml`, … | Often default serve; parallel/offload YAML for heavy DiT |
+| **Offline runner** | `omni_runner` + `offline_client`, `generate_multimodal` | `Omni(...).generate` with deploy YAML + TTS params | `Omni(...).generate` + `OmniDiffusionSamplingParams` |
+| **Online client** | `online_client.send_omni_request` (chat completions, modalities) | `online_client.send_audio_speech_request` (`/v1/audio/speech`) | `send_diffusion_request` (chat/T2I), `send_video_diffusion_request` (`/v1/videos`, X2V), or `send_images_generations_http_request` / `send_images_edits_http_request` (DALL-E routes) |
+| **Typical assertions** | Stage outputs, text/audio keywords via `OfflineOmniClient` / response handler | WAV bytes, stream chunks, speech endpoint contract | Image/video dimensions, `final_output_type`, `assert_diffusion_response` |
+| **Deploy YAML** | Per-model deploy configs (`ci/qwen3_omni_moe.yaml`, …) | `qwen3_tts.yaml`, `voxcpm2.yaml`, … | Often default serve; parallel/offload YAML for heavy DiT |
 | **Nightly group** (`test-nightly.yml`) | **Omni Model Test** — `-m "full_model and omni"` | **TTS Model Test** — `-m "full_model and tts"` | **Diffusion X2I(&A&T)** *or* **Diffusion X2V** (see Step 3 table; same `diffusion` marker, different YAML group / file shard) |
 | **L4 pressure** | Expansion per modality/model as needed | Expansion + accuracy/perf in TTS group | X2I: merge feature combos per [#1832](https://github.com/vllm-project/vllm-omni/issues/1832); X2V: separate nightly group |
 
@@ -175,9 +175,9 @@ See **L1 unit test constraints (mocking)** below for the full do/don't list.
 @pytest.mark.omni
 @hardware_test(...)
 @pytest.mark.parametrize("omni_runner", test_params, indirect=True)
-def test_<scenario>(omni_runner, omni_runner_handler) -> None:
+def test_<scenario>(omni_runner, offline_client) -> None:
     request_config = {"prompts": ..., "modalities": [...]}  # optional: images, videos, audios
-    omni_runner_handler.send_omni_request(request_config)
+    offline_client.send_omni_request(request_config)
 ```
 
 *Offline generative e2e — **Diffusion** (representative):*
@@ -187,8 +187,8 @@ def test_<scenario>(omni_runner, omni_runner_handler) -> None:
 @pytest.mark.diffusion
 @hardware_test(...)
 @pytest.mark.parametrize("omni_runner", test_params, indirect=True)
-def test_text_to_image_001(omni_runner_handler) -> None:
-    omni_runner_handler.send_diffusion_request({"prompt": "...", "extra_body": {"num_inference_steps": 4, ...}})
+def test_text_to_image_001(offline_client) -> None:
+    offline_client.send_diffusion_request({"prompt": "...", "extra_body": {"num_inference_steps": 4, ...}})
 ```
 
 *Offline TTS e2e — **Qwen3-TTS** (two-stage; representative):*
@@ -198,8 +198,8 @@ def test_text_to_image_001(omni_runner_handler) -> None:
 @pytest.mark.tts
 @hardware_test(...)
 @pytest.mark.parametrize("omni_runner", tts_server_params, indirect=True)
-def test_text_to_audio_001(omni_runner, omni_runner_handler) -> None:
-    omni_runner_handler.send_audio_speech_request({
+def test_text_to_audio_001(omni_runner, offline_client) -> None:
+    offline_client.send_audio_speech_request({
         "input": "...",
         "task_type": "Base",
         "ref_audio": REF_AUDIO_URL,
@@ -214,8 +214,8 @@ def test_text_to_audio_001(omni_runner, omni_runner_handler) -> None:
 @pytest.mark.tts
 @hardware_test(...)
 @pytest.mark.parametrize("omni_runner", tts_server_params, indirect=True)
-def test_voice_clone_001(omni_runner_handler) -> None:
-    omni_runner_handler.send_single_stage_tts_request({
+def test_voice_clone_001(offline_client) -> None:
+    offline_client.send_single_stage_tts_request({
         "input": "...",
         "language": "en",
         "prompt_audio_path": REF_AUDIO_PATH,
@@ -231,9 +231,9 @@ def test_voice_clone_001(omni_runner_handler) -> None:
 @pytest.mark.omni
 @hardware_test(...)
 @pytest.mark.parametrize("omni_server", test_params, indirect=True)
-def test_text_to_text_001(omni_server, openai_client) -> None:
+def test_text_to_text_001(omni_server, online_client) -> None:
     request_config = {"model": omni_server.model, "messages": ..., "modalities": ["text"]}
-    openai_client.send_omni_request(request_config)
+    online_client.send_omni_request(request_config)
 ```
 
 *Online serving e2e — **TTS** (representative):*
@@ -244,9 +244,9 @@ def test_text_to_text_001(omni_server, openai_client) -> None:
 @pytest.mark.tts
 @hardware_test(...)
 @pytest.mark.parametrize("omni_server", tts_server_params, indirect=True)
-def test_text_to_audio_001(omni_server, openai_client) -> None:
+def test_text_to_audio_001(omni_server, online_client) -> None:
     request_config = {"model": omni_server.model, "input": "...", "response_format": "wav", ...}
-    openai_client.send_audio_speech_request(request_config)
+    online_client.send_audio_speech_request(request_config)
 ```
 
 *Online serving e2e — **Diffusion** X2I (representative):*
@@ -255,8 +255,8 @@ def test_text_to_audio_001(omni_server, openai_client) -> None:
 @pytest.mark.core_model
 @pytest.mark.diffusion
 @pytest.mark.parametrize("omni_server", _get_default_case(MODEL), indirect=True)
-def test_text_to_image_001(omni_server, openai_client) -> None:
-    openai_client.send_diffusion_request({...})  # chat completions + extra_body
+def test_text_to_image_001(omni_server, online_client) -> None:
+    online_client.send_diffusion_request({...})  # chat completions + extra_body
 ```
 
 *Online serving e2e — **Diffusion** X2V (representative):*
@@ -264,8 +264,8 @@ def test_text_to_image_001(omni_server, openai_client) -> None:
 ```python
 @pytest.mark.core_model
 @pytest.mark.diffusion
-def test_text_to_video_001(omni_server, openai_client) -> None:
-    openai_client.send_video_diffusion_request({"model": ..., "form_data": {...}})  # /v1/videos
+def test_text_to_video_001(omni_server, online_client) -> None:
+    online_client.send_video_diffusion_request({"model": ..., "form_data": {...}})  # /v1/videos
 ```
 
 *Documentation example tests:* follow the **Preferred Test Strategy** in [l4_doc_example_tests.inc.md](../../../docs/contributing/ci/test_examples/l4_doc_example_tests.inc.md): dynamic extraction for offline READMEs; explicit copied client code for online pages until extraction is justified; use the documented **naming**, **output directory** (page folder + case id), and **skipping** rules (e.g. Gradio-only scripts).
@@ -274,6 +274,7 @@ def test_text_to_video_001(omni_server, openai_client) -> None:
 
 **3. Cross-cutting rules**
 
+- New `.py` files need the Omni SPDX header (`Copyright contributors to the vLLM-Omni project`, not `vLLM project`). `tests/` may keep stdlib `re`/`base64`; pickle is still banned unless the file is already on the pickle allowlist. Every collected `tests/**/test_*.py` needs a CI **level** mark and a **hardware** platform mark/helper (`check-mark` runs locally; GHA skips it). Do not write `pytest.mark.H100` / `pytest.mark.L4`; use the helpers so `cards_{n}` is attached. Do not grow `allowed_files` to land a test. Policy: [docs/contributing/README.md](../../../docs/contributing/README.md#linting).
 - Reuse existing fixtures for the chosen scenario; do not mix “online client” assumptions into offline `OmniRunner` tests without a clear reason.
 - Avoid external network dependency in assertions unless the scenario is explicitly “online serving” or doc examples that require a model hub (then align with CI secrets/cache).
 - Keep **one test function = one intent** (one modality combo, one endpoint contract, or one acceleration combo).
@@ -323,21 +324,21 @@ vLLM-Omni e2e tests start a real **OmniServer** (online) or **OmniRunner** (offl
 
 | Scope | Online fixtures | Offline fixtures | When to use |
 |-------|-----------------|------------------|-------------|
-| **Module** (default) | `omni_server` → `openai_client` | `omni_runner` → `omni_runner_handler` | **Default for L2/L3/L4 expansion** — amortize model/server init across `test_*` in the same module. Same `OmniServerParams` / runner config can be reused by multiple tests. |
-| **Function** | `omni_server_function` → `openai_client_function` | `omni_runner_function` → `omni_runner_handler_function` | **Only when required** — each `test_*` must get a **clean** server/runner (no shared engine/GPU state). Typical: `tests/dfx/reliability/`, sleep/wakeup, crash/restart, tests that mutate global server state. |
+| **Module** (default) | `omni_server` → `online_client` | `omni_runner` → `offline_client` | **Default for L2/L3/L4 expansion** — amortize model/server init across `test_*` in the same module. Same `OmniServerParams` / runner config can be reused by multiple tests. |
+| **Function** | `omni_server_function` → `online_client_function` | `omni_runner_function` → `offline_client_function` | **Only when required** — each `test_*` must get a **clean** server/runner (no shared engine/GPU state). Typical: `tests/dfx/reliability/`, sleep/wakeup, crash/restart, tests that mutate global server state. |
 
 **Rules:**
 
 1. **Default to module scope** (`omni_server` / `omni_runner`) unless the scenario **explicitly** needs a fresh instance per test function.
-2. **Indirect parametrize name must match the fixture name**: `@pytest.mark.parametrize("omni_server", ...)` with `omni_server` + `openai_client`; `@pytest.mark.parametrize("omni_server_function", ...)` with `omni_server_function` + `openai_client_function`. Do not mix module fixture with function client (or vice versa).
+2. **Indirect parametrize name must match the fixture name**: `@pytest.mark.parametrize("omni_server", ...)` with `omni_server` + `online_client`; `@pytest.mark.parametrize("omni_server_function", ...)` with `omni_server_function` + `online_client_function`. Do not mix module fixture with function client (or vice versa).
 3. Different `OmniServerParams` per `test_*` (e.g. default vs `--enable-cpu-offload`) is still OK with **module** `omni_server` — pytest parametrizes per test node; only switch to `_function` when isolation between tests matters, not merely because `server_args` differ.
 4. One `test_*` with **many** server configs (expansion matrix) → single function + `@pytest.mark.parametrize("omni_server", [...], indirect=True)` + module `omni_server` (see in-tree `test_qwen_image_expansion.py`).
 
 ```python
 # Default — module-scoped server (L4 expansion)
 @pytest.mark.parametrize("omni_server", [pytest.param(OmniServerParams(model=MODEL), marks=H100)], indirect=True)
-def test_foo_images_generations_default_1024(omni_server, openai_client) -> None:
-    openai_client.send_images_generations_request({...})
+def test_foo_images_generations_default_1024(omni_server, online_client) -> None:
+    online_client.send_images_generations_request({...})
 
 # Function-scoped — reliability / per-test clean state only
 @pytest.mark.parametrize(
@@ -345,9 +346,9 @@ def test_foo_images_generations_default_1024(omni_server, openai_client) -> None
     [pytest.param(OmniServerParams(model=MODEL), marks=H100)],
     indirect=True,
 )
-def test_foo_sleep_wakeup_cycle(omni_server_function, openai_client_function) -> None:
-    openai_client_function.send_omni_sleep_http_request({...})
-    openai_client_function.send_omni_wakeup_http_request({...})
+def test_foo_sleep_wakeup_cycle(omni_server_function, online_client_function) -> None:
+    online_client_function.send_omni_sleep_http_request({...})
+    online_client_function.send_omni_wakeup_http_request({...})
 ```
 
 #### Runtime send helpers — online **and** offline (`tests/helpers/runtime.py`)
@@ -356,19 +357,19 @@ def test_foo_sleep_wakeup_cycle(omni_server_function, openai_client_function) ->
 
 | Principle | Action |
 |-----------|--------|
-| **Reuse first** | Grep `runtime.py` for an existing **`send_*_request`** on `OpenAIClientHandler` (online) or `OmniRunnerHandler` (offline) that matches the endpoint / pipeline shape. |
+| **Reuse first** | Grep `runtime.py` for an existing **`send_*_request`** on `OnlineOmniClient` (online) or `OfflineOmniClient` (offline) that matches the endpoint / pipeline shape. |
 | **Extend when close** | If an existing `send_*_request` almost fits (e.g. missing one optional field), extend it in `runtime.py` — do not fork logic in the test file. |
 | **Add when missing** | No suitable helper → add **`send_<feature>_request`** (high-level: call + general `assert_*`) or **`send_<route>_<verb>_http_request`** (low-level HTTP for negative/dfx) in **`runtime.py` first**, then call it from tests. |
 | **Test module owns payload only** | In `test_*.py`: `MODEL`, deploy path, vendored media, `get_prompt()`, and inline **`request_config` dicts** only. **No** `omni.generate(...)`, raw `requests.post`, OpenAI SDK calls, or `_collect_audio()` / `_process_output()` in e2e tests. |
 
-**Online** (`openai_client` from `omni_server`): `OpenAIClientHandler.send_*_request`.
+**Online** (`online_client` from `omni_server`): `OnlineOmniClient.send_*_request`.
 
-**Offline** (`omni_runner_handler` from `omni_runner`): `OmniRunnerHandler.send_*_request`.
+**Offline** (`offline_client` from `omni_runner`): `OfflineOmniClient.send_*_request`.
 
 | Do | Don't |
 |----|-------|
-| Online: `openai_client.send_omni_request`, `send_diffusion_request`, `send_audio_speech_request`, `send_video_diffusion_request`, `send_images_generations_request`, … | `requests.post(f"{base_url}/v1/...", json=…)` or `client.chat.completions.create(...)` inside a test |
-| Offline: `omni_runner_handler.send_omni_request`, `send_diffusion_request`, `send_audio_speech_request`, `send_single_stage_tts_request`, `send_single_stage_tts_batch_request`, … | `omni_runner.omni.generate(...)` + hand-rolled tensor/WAV extraction in `test_*.py` |
+| Online: `online_client.send_omni_request`, `send_diffusion_request`, `send_audio_speech_request`, `send_video_diffusion_request`, `send_images_generations_request`, … | `requests.post(f"{base_url}/v1/...", json=…)` or `client.chat.completions.create(...)` inside a test |
+| Offline: `offline_client.send_omni_request`, `send_diffusion_request`, `send_audio_speech_request`, `send_single_stage_tts_request`, `send_single_stage_tts_batch_request`, … | `omni_runner.omni.generate(...)` + hand-rolled tensor/WAV extraction in `test_*.py` |
 | Add missing `send_*` to **`runtime.py`** first; bundle general `assert_*` inside the send helper | A one-off `def _post_*` / `def _collect_audio` at the bottom of a test module |
 | Mirror naming/style of neighboring `send_*` (docstring, `request_config` dict, optional `run_level`, `err_code` / `err_message` for negative cases) | Different parameter shapes per test file for the same endpoint |
 
@@ -391,7 +392,7 @@ def test_foo_sleep_wakeup_cycle(omni_server_function, openai_client_function) ->
 
 Changing `request_config` fields (`size`, `n`, `seed`, server flags) is **not** special validation — the general `assert_*` should read those from `request_config` inside `send_*`.
 
-**`send_*` ↔ `assert_*` pairing (online `OpenAIClientHandler`):**
+**`send_*` ↔ `assert_*` pairing (online `OnlineOmniClient`):**
 
 | High-level `send_*` (prefer in L2+ e2e) | Assert already invoked inside `runtime.py` | Low-level HTTP-only sibling |
 |----------------------------------------|--------------------------------------------|-----------------------------|
@@ -404,7 +405,7 @@ Changing `request_config` fields (`size`, `n`, `seed`, server flags) is **not**
 
 **Rule:** Tests call high-level `send_*_request` for the general contract. **Never** call the same bundled `assert_*` again. Call an extra `assert_*` in the test **only** for special, case-specific validation.
 
-**Common `OpenAIClientHandler` entry points** (non-exhaustive — grep `runtime.py` before adding):
+**Common `OnlineOmniClient` entry points** (non-exhaustive — grep `runtime.py` before adding):
 
 | Area | High-level (SDK + assert) | Low-level HTTP (`*_http_request`) |
 |------|---------------------------|-----------------------------------|
@@ -415,7 +416,7 @@ Changing `request_config` fields (`size`, `n`, `seed`, server flags) is **not**
 | TTS (online) | `send_audio_speech_request` | `send_audio_speech_http_request`, `send_audio_generate_http_request`, … |
 | Ops / meta | — | `send_health_http_request`, `send_models_http_request`, `send_omni_sleep_http_request`, … |
 
-**Common `OmniRunnerHandler` entry points** (offline — grep `runtime.py` before adding):
+**Common `OfflineOmniClient` entry points** (offline — grep `runtime.py` before adding):
 
 | Area | High-level (`send_*_request` + assert) | Notes |
 |------|----------------------------------------|-------|
@@ -425,7 +426,7 @@ Changing `request_config` fields (`size`, `n`, `seed`, server flags) is **not**
 | Single-stage TTS (Coqui XTTS, MOSS-TTS-Nano, …) | `send_single_stage_tts_request`, `send_single_stage_tts_batch_request` | `prompt` + `additional_information` + `omni.generate` — **do not** duplicate in tests |
 | New model family | **Add** `send_<family>_request` here first | Then call from `tests/e2e/offline_inference/test_*.py` |
 
-L1 tests under `tests/entrypoints/` may use **FastAPI `TestClient`** or direct handler calls with mocks; they do **not** need `OpenAIClientHandler` / `OmniRunnerHandler`. **L2+ online and offline e2e** must use `runtime.py` `send_*` helpers.
+L1 tests under `tests/entrypoints/` may use **FastAPI `TestClient`** or direct handler calls with mocks; they do **not** need `OnlineOmniClient` / `OfflineOmniClient`. **L2+ online and offline e2e** must use `runtime.py` `send_*` helpers.
 
 #### Invalid parameter validation (`tests/dfx/reliability/invalid_param_test/`)
 
@@ -451,7 +452,7 @@ When the user asks for **invalid parameter validation**, **invalid request bodie
 | **Module markers** | `pytestmark = [pytest.mark.slow, pytest.mark.<omni or tts or diffusion>]` |
 | **Server fixture** | `_PARAMS` / `_QWEN3_TTS_SPEECH`-style list of `pytest.param(OmniServerParams(...), id="...", marks=hardware_marks(...))`; `@pytest.mark.parametrize("omni_server", _PARAMS, indirect=True)` |
 | **Hardware** | `hardware_marks(res={"cuda": "H100"})` for heavy diffusion/omni/video; `hardware_marks(res={"cuda": "L4"})` for smaller TTS models (must match weekly `-m "slow and L4"` step) |
-| **HTTP client** | **Low-level** `openai_client.send_*_http_request({..., "err_code": 400, "err_message": (...)} )` — **not** `send_*_request` (success path) |
+| **HTTP client** | **Low-level** `online_client.send_*_http_request({..., "err_code": 400, "err_message": (...)} )` — **not** `send_*_request` (success path) |
 | **Case shape** | Prefer **one `test_*` per route family** + `@pytest.mark.parametrize("body_spec, err_message", [...])` with stable `id=` per case; or dedicated `test_<route>_malformed_json` when not parametrized |
 | **Body helpers** | `_minimal_<endpoint>_json(omni_server)` / `_minimal_*_form_data()`; merge overrides with `body.update(body_spec)` |
 | **Known gaps** | `pytest.mark.skip(reason="…#3649")` as `_SKIP_ISSUE_3649` when server validation is not yet strict (mirror neighboring cases) |
@@ -484,13 +485,13 @@ When the user asks for **invalid parameter validation**, **invalid request bodie
 @pytest.mark.parametrize("omni_server", _PARAMS, indirect=True)
 def test_images_generations_invalid_requests(
     omni_server: OmniServer,
-    openai_client: OpenAIClientHandler,
+    online_client: OnlineOmniClient,
     body_spec: dict[str, object],
     err_message: str | tuple[str, ...],
 ) -> None:
     body = _minimal_images_gen_json(omni_server)
     body.update(body_spec)
-    openai_client.send_images_generations_http_request(
+    online_client.send_images_generations_http_request(
         {"json": body, "timeout": 300, "err_code": 400, "err_message": err_message}
     )
 ```
@@ -541,18 +542,18 @@ def send_images_generations_request(self, request_config: dict[str, Any], ...) -
 
 # tests/e2e/.../test_foo_expansion.py — one case per test_* (no case_id branching)
 @pytest.mark.parametrize("omni_server", [pytest.param(OmniServerParams(model=MODEL), marks=SINGLE_L4)], indirect=True)
-def test_foo_images_generations_default_1024(omni_server, openai_client) -> None:
-    openai_client.send_images_generations_request({"json": body, "timeout": 300})
+def test_foo_images_generations_default_1024(omni_server, online_client) -> None:
+    online_client.send_images_generations_request({"json": body, "timeout": 300})
 
 @pytest.mark.parametrize("omni_server", [pytest.param(OmniServerParams(model=MODEL), marks=SINGLE_L4)], indirect=True)
-def test_foo_images_generations_sizes_256_512_1024(omni_server, openai_client) -> None:
+def test_foo_images_generations_sizes_256_512_1024(omni_server, online_client) -> None:
     for size in ("256x256", "512x512", "1024x1024"):
-        openai_client.send_images_generations_request({"json": {**body, "size": size}, "timeout": 300})
+        online_client.send_images_generations_request({"json": {**body, "size": size}, "timeout": 300})
 
 # chat diffusion — separate test; send_diffusion_request bundles assert_diffusion_response
 @pytest.mark.parametrize("omni_server", [pytest.param(OmniServerParams(model=MODEL), marks=SINGLE_L4)], indirect=True)
-def test_foo_chat_completions_t2i_fallback(omni_server, openai_client) -> None:
-    openai_client.send_diffusion_request({...})
+def test_foo_chat_completions_t2i_fallback(omni_server, online_client) -> None:
+    online_client.send_diffusion_request({...})
 ```
 
 L1 tests under `tests/entrypoints/` may keep **minimal** asserts next to the handler under test when validating a single branch; still prefer `assertions.py` when the same JSON/media check appears in more than one file or level (e.g. L1 protocol test + L4 e2e).

.claude/skills/vllm-omni-test/references/test-routing.md

@@ -79,7 +79,7 @@ E2E (L2+) should not rely on mocks unless documenting a rare exception; prefer r
    - `full_model` — L4 nightly (`test-nightly.yml`); some expansion tests still carry both `advanced_model` and `full_model` during migration
 2. **Model type** (pick one): `omni`, `tts`, or `diffusion`
 3. **Cross-cutting** when relevant: `parallel`, `cache`, `example`, `benchmark`
-4. **Hardware**: `cpu`, `cuda`, `rocm`, `npu`, `L4`, `H100`, `distributed_cuda`, …
+4. **Hardware**: `cpu`, `cuda`, `rocm`, `npu`, `L4`, `H100`, `cards_1`, …
 5. Multi-card: `@hardware_test(...)` in `tests/helpers/mark.py`
 
 ## Command Templates
@@ -244,7 +244,7 @@ pytest -s -v dfx/reliability/invalid_param_test/test_invalid_image_generation.py
 
 ```bash
 cd tests
-pytest -s -v -m "core_model and distributed_cuda and L4" --run-level=core_model
+pytest -s -v -m "core_model and L4 and not cards_1" --run-level=core_model
 ```
 
 ### Concrete e2e paths (common in-tree)
@@ -279,7 +279,7 @@ When adding or modifying tests, do not stop at “where the file lives” — al
 4. **API client + assert placement**: **General** validation → implement in `assertions.py`, call **inside** `send_*_request` in `runtime.py`; tests call **`send_*_request` only**. **Special** case validation → `assert_*` in `assertions.py`, called **in the test** after `send_*_request`. Low-level `send_*_http_request` is for negative/dfx tests (`err_code`), not ordinary L2+ success e2e.
 5. **Shared assertions**: logic in **`tests/helpers/assertions.py`**; general checks inside `send_*_request`; special checks only in the test. No `_assert_*` in `test_*.py`.
 6. **One case → one `test_*`**: function name reflects what is validated; no `if case_id == ...` mega-test merging multiple scenarios.
-7. **Fixture scope**: default **`omni_server` + `openai_client`** / **`omni_runner` + `omni_runner_handler`** (module). Use **`omni_server_function` + `openai_client_function`** / **`omni_runner_function` + `omni_runner_handler_function`** only when each `test_*` must spawn a fresh instance. Parametrize name must match fixture (`omni_server` vs `omni_server_function`).
+7. **Fixture scope**: default **`omni_server` + `online_client`** / **`omni_runner` + `offline_client`** (module). Use **`omni_server_function` + `online_client_function`** / **`omni_runner_function` + `offline_client_function`** only when each `test_*` must spawn a fresh instance. Parametrize name must match fixture (`omni_server` vs `omni_server_function`).
 8. **Type marker**: `omni`, `tts`, or `diffusion` on every model e2e module.
 9. **Diffusion L4 Function**: wire `*_expansion.py` into **X2I(&A&T)** or **X2V** **Function Test** in **`test-nightly.yml` only** — do not add `test-merge.yml` unless the user also requested L3.
 10. **Diffusion L4 Perf** (only when requested): add `tests/dfx/perf/tests/test_<slug>_vllm_omni.json` + **Perf Test · &lt;Model&gt;** step (artifact upload); not part of “L4 functional cases” by default.

.github/CODEOWNERS

@@ -79,8 +79,6 @@
 /vllm_omni/model_executor/models/ @tzhouam @gcanlin @Sy0307 @amy-why-3459 @linyueqian
 /vllm_omni/model_extras/ @tzhouam @gcanlin @princepride
 /vllm_omni/plugins/ @gcanlin @tjtanaa @xuechendi
-# Config override within model_executor — docs/design/module/vllm_omni_config.md
-/vllm_omni/model_executor/stage_configs/ @lishunyang12 @alex-jw-brooks
 
 # Execution platforms (hardware backends) — docs/design/module/execution_platforms.md
 /vllm_omni/platforms/ @gcanlin @tjtanaa @xuechendi

.github/ISSUE_TEMPLATE/400-bug-report.yml

@@ -83,7 +83,7 @@ body:
 
       omni = Omni(
           model="Qwen/Qwen-Image",
-          stage_configs_path="/path/to/stage_configs.yaml",
+          deploy_config="/path/to/deploy_config.yaml",
       )
 
       prompts = [{"prompt": "A scenic watercolor painting of a lighthouse at sunset"}]

.github/mergify.yml

@@ -45,7 +45,7 @@ pull_request_rules:
         > # For mypy (substitute "3.10" with the failing version if needed)
         > pre-commit run --hook-stage manual mypy-3.10
         > # For markdownlint
-        > pre-commit run --hook-stage manual markdownlint
+        > pre-commit run markdownlint-cli2
         > ```
         > </details>
 

.github/workflows/pre-commit.yml

@@ -24,9 +24,7 @@ jobs:
           python-version: "3.12"
           cache: 'pip'
       - uses: pre-commit/action@v3.0.1
-        # Don't run the pytest mark check against everything in the CI
-        # for now since a lot of files are still missing them. This check
-        # is used to help contributors remember to add the marks when they
-        # commit.
+        # Don't run historical-debt hooks against the whole tree in CI yet.
+        # They still run locally on changed files.
         env:
-          SKIP: check-test-ci-coverage
+          SKIP: check-mark,markdownlint-cli2,shellcheck,check-spdx-header,mypy-3.10

.gitignore

@@ -242,9 +242,9 @@ data/
 datasets/
 *.csv
 *.json
+!recipes/cosmos3/negative_prompt.json
 !tests/dfx/perf/tests/*.json
 !apps/ComfyUI-vLLM-Omni/example_workflows/*.json
-!tests/assets/soulxsinger/*.json
 *.jsonl
 *.parquet
 
@@ -278,3 +278,6 @@ tests/.ci_generated/
 
 # Local Lance precision-matching dump scripts (not part of the repo)
 lance_compare/
+
+# Linting binaries downloaded by local pre-commit hooks
+shellcheck*/

.markdownlint.yaml

@@ -0,0 +1,17 @@
+# Keep these rules aligned with vLLM so MkDocs Material syntax is tolerated.
+MD007:
+  indent: 4
+MD013: false
+# GitHub READMEs start with a centered logo, not an H1.
+MD041: false
+MD024:
+  siblings_only: true
+MD025:
+  # Allow front matter title to be different from the first heading in the document.
+  front_matter_title: ""
+MD031:
+  list_items: false
+MD033: false
+MD046: false
+MD052: false
+MD059: false

.pre-commit-config.yaml

@@ -24,7 +24,7 @@ repos:
     - id: ruff-format
 
   - repo: https://github.com/crate-ci/typos
-    rev: typos-dict-v0.13.13
+    rev: v1.49.0
     hooks:
       - id: typos
         # only for staged files
@@ -38,7 +38,19 @@ repos:
       - id: actionlint
         files: ^\.github/workflows/.*\.ya?ml$
 
+  - repo: https://github.com/DavidAnson/markdownlint-cli2
+    rev: v0.23.2
+    hooks:
+      - id: markdownlint-cli2
+        language_version: lts
+        args: [--fix]
+        files: (^docs/|^recipes/|(^|/)README\.md$|^CONTRIBUTING\.md$)
+        # Agent skill docs and CLAUDE.md are not user-facing docs/READMEs.
+        exclude: (^|/)CLAUDE\.md$|^\.claude/|^\.cursor/
 
+  # Keep `local` last so `suggestion` stays the final hook. Autofix repos
+  # (ruff, typos, markdownlint, trailing-whitespace, etc.) must run first;
+  # otherwise their "files were modified" output displaces the SKIP tip.
   - repo: local
     hooks:
     - id: signoff-commit
@@ -54,8 +66,47 @@ repos:
       verbose: true
       stages: [commit-msg]
 
-    - id: check-test-ci-coverage
-      name: Ensure test files have pytestmarks
+    # TODO: Use https://github.com/pre-commit/mirrors-mypy when mypy setup
+    # is less awkward. Local wrapper groups files so follow_imports=skip on
+    # tests does not leak into library code.
+    # mypy-3.10 runs on git commit (changed files). CI SKIPs it because
+    # --all-files is not clean yet. Extra versions are manual-stage only
+    # (not in default `pre-commit run --all-files`):
+    #   pre-commit run --hook-stage manual mypy-3.12
+    - id: mypy-3.10
+      name: Run mypy for Python 3.10
+      entry: python tools/pre_commit/mypy.py "3.10"
+      <<: &mypy_common
+        language: python
+        types_or: [python, pyi]
+        require_serial: true
+        additional_dependencies:
+          - mypy==1.11.1
+          - regex
+          - types-cachetools
+          - types-setuptools
+          - types-PyYAML
+          - types-requests
+          - types-torch
+          - pydantic
+    - id: mypy-3.11
+      name: Run mypy for Python 3.11
+      entry: python tools/pre_commit/mypy.py "3.11"
+      <<: *mypy_common
+      stages: [manual]
+    - id: mypy-3.12
+      name: Run mypy for Python 3.12
+      entry: python tools/pre_commit/mypy.py "3.12"
+      <<: *mypy_common
+      stages: [manual]
+    - id: mypy-3.13
+      name: Run mypy for Python 3.13
+      entry: python tools/pre_commit/mypy.py "3.13"
+      <<: *mypy_common
+      stages: [manual]
+
+    - id: check-mark
+      name: Ensure test files have CI marks (no direct SKU)
       entry: python tools/pre_commit/check_test_marks.py
       language: python
       types: [python]
@@ -71,7 +122,45 @@ repos:
       # pre-commit hides output from passing hooks unless the hook is verbose.
       verbose: true
 
-    # Keep `suggestion` last
+    - id: shellcheck
+      name: Lint shell scripts
+      # language: script breaks on Windows (shebang path loses slashes).
+      # Invoke the vLLM-style wrapper with bash so Linux CI and Win/Git Bash work.
+      entry: bash
+      args: [tools/pre_commit/shellcheck.sh]
+      language: system
+      types: [shell]
+
+    - id: check-spdx-header
+      name: Check SPDX headers
+      entry: python tools/pre_commit/check_spdx_header.py
+      language: python
+      types_or: [python, pyi, rust, proto, shell]
+      files: \.(py|pyi|rs|proto|sh)$
+
+    - id: check-forbidden-imports
+      name: Check forbidden imports
+      entry: python tools/pre_commit/check_forbidden_imports.py
+      language: python
+      types: [python]
+      additional_dependencies: [regex]
+
+    - id: check-torch-cuda-call
+      name: Prevent new torch.cuda API calls
+      entry: python tools/pre_commit/check_torch_cuda.py
+      language: python
+      types: [python]
+      additional_dependencies: [regex]
+
+    - id: check-buildkite
+      name: Validate Buildkite Pipelines
+      entry: python tools/pre_commit/check_buildkite.py
+      language: python
+      files: ^\.buildkite/.*\.ya?ml$
+      additional_dependencies: [pyyaml, check-jsonschema==0.38.0]
+
+    # Keep `suggestion` as the final hook. Run after autofix hooks so the
+    # bypass/SKIP tip is not displaced.
     - id: suggestion
       name: Suggestion
       entry: bash -c 'echo "To bypass all the pre-commit hooks, add --no-verify to git commit. To skip a specific hook, prefix the commit command with SKIP=<hook-id>."'
@@ -79,10 +168,3 @@ repos:
       verbose: true
       pass_filenames: false
     # Insert new entries above the `suggestion` entry
-
-    - id: check-pickle-imports
-      name: Prevent new pickle/cloudpickle imports
-      entry: python tools/pre_commit/check_pickle_imports.py
-      language: python
-      types: [python]
-      additional_dependencies: [regex]

CONTRIBUTING.md

@@ -1,5 +1,11 @@
 # Contributing to vLLM-Omni
 
-You may find information about contributing to vLLM-Omni on [Contributing](https://vllm-omni.readthedocs.io/en/latest/contributing/)
+You may find information about contributing to vLLM-Omni on [Contributing](https://vllm-omni.readthedocs.io/en/latest/contributing/).
+
+Local `pre-commit` gates (SPDX, forbidden imports, `torch.cuda`, shellcheck,
+mypy, test marks, TTS adapter ratchet, markdownlint, Buildkite schema, and
+how to extend allowlists) are documented in the
+[Linting](https://vllm-omni.readthedocs.io/en/latest/contributing/#linting)
+section of that guide.
 
 Before submitting a PR, run the [precheck-pr skill](https://github.com/vllm-project/vllm-omni/blob/main/.claude/skills/precheck-pr/SKILL.md) with the code agent for a self-check against project conventions.

README.md

@@ -12,10 +12,11 @@ Easy, fast, and cheap omni-modality model serving for everyone
 | <a href="https://vllm-omni.readthedocs.io/en/latest/"><b>Documentation</b></a> | <a href="https://deepwiki.com/vllm-project/vllm-omni"><b>DeepWiki</b></a> | <a href="https://discuss.vllm.ai"><b>User Forum</b></a> | <a href="https://slack.vllm.ai"><b>Developer Slack</b></a> | <a href="docs/assets/WeChat.jpg"><b>WeChat</b></a> | <a href="https://arxiv.org/abs/2602.02204"><b>Paper</b></a> | <a href="https://docs.google.com/presentation/d/1aPj0OGl_-ZVoib-Qne5dGDAlrRFB-PdHl6E-EE99g8E/edit?usp=sharing"><b>Slides</b></a> |
 </p>
 
-
 ---
 
 *Latest News* 🔥
+
+- [2026/08] We released [0.28.0](https://github.com/vllm-project/vllm-omni/releases/tag/v0.28.0), featuring production-ready [MiniMax H3](recipes/MiniMaxAI/MiniMax-H3.md) serving on GPU and NPU, a unified AR/DiT paged KV cache runtime, and enhanced realtime full-duplex serving for the [MiniCPM-o series](recipes/OpenBMB/MiniCPM-o-4_5.md).
 - [2026/08] [VeRL-Omni](https://github.com/verl-project/verl-omni) `v0.2.0` is released: faster diffusion RL powered by vLLM-Omni (request-level/step-wise batching with FA3), rebuilt Qwen3-Omni multimodal training (DPO & GSPO), plus LTX-2.3, Qwen-Image-Edit support and more. See the [release notes](https://github.com/verl-project/verl-omni/releases/tag/v0.2.0).
 - [2026/08] We released [0.26.0](https://github.com/vllm-project/vllm-omni/releases/tag/v0.26.0) - aligned with the vLLM 0.26 release line, featuring [MiniMax H3](recipes/MiniMaxAI/MiniMax-H3.md) joint video/audio generation, an experimental full-duplex realtime runtime for [MiniCPM-o 4.5](recipes/OpenBMB/MiniCPM-o-4_5.md), distributed layerwise diffusion offload, and broader model, hardware, streaming, TTS, and quantization support.
 - [2026/07] We released [0.24.0](https://github.com/vllm-project/vllm-omni/releases/tag/v0.24.0) - aligned with the vLLM 0.24 release line, expanding production-ready coverage across TTS, speech, diffusion, image/video generation, and robot-policy serving, with major Omni stage runtime refactoring, diffusion request-level batching, async output materialization, quantization/cache/memory improvements, and broad CUDA/ROCm/XPU/NPU support.
@@ -57,9 +58,9 @@ vLLM-Omni is flexible and easy to use with:
 vLLM-Omni seamlessly supports most popular open-source models on HuggingFace, including:
 
 - **Omni-modality models** (e.g. Qwen3-Omni, MiniCPM-o 4.5, Cosmos3, HunyuanImage, BAGEL)
-- **TTS models** (e.g. Qwen3-TTS, VoxCPM2, Ming-Omni-TTS, CosyVoice3)
-- **Diffusion models** — image, video, and audio generation (e.g. MiniMax H3, Qwen-Image, Wan2.2, FLUX)
-- **Robot-policy and action models** (e.g. GR00T-N1.7, DreamZero-DROID, InternVLA-A1, Cosmos3 action policy)
+- **TTS models** (e.g. Qwen3-TTS, IndexTTS 2.5, dots.tts, CosyVoice3)
+- **Diffusion models** — image, video, and audio generation (e.g. MiniMax H3, LTX-2.5, SANA-Video, Wan2.2)
+- **Robot-policy and action models** (e.g. π0, GR00T-N1.7, DreamZero-DROID, InternVLA-A1)
 
 ## Getting Started
 
@@ -67,6 +68,7 @@ Visit our [documentation](https://vllm-omni.readthedocs.io/en/latest/) to learn
 
 - [Installation](https://vllm-omni.readthedocs.io/en/latest/getting_started/installation/)
 - [Quickstart](https://vllm-omni.readthedocs.io/en/latest/getting_started/quickstart/)
+- [Nightly CUDA Docker images](https://hub.docker.com/r/vllm/vllm-omni/tags?name=nightly) built from the latest `main`
 - [List of Supported Models](https://vllm-omni.readthedocs.io/en/latest/models/supported_models/)
 - [Deployment Recipes](https://recipes.vllm.ai) for vLLM-Omni model serving
 
@@ -89,11 +91,18 @@ If you use vLLM-Omni for your research, please cite our [paper](https://arxiv.or
 ```
 
 ## Join the Community
+
 Feel free to ask questions, provide feedbacks and discuss with fellow users of vLLM-Omni in `#sig-omni` slack channel at [slack.vllm.ai](https://slack.vllm.ai) or vLLM user forum at [discuss.vllm.ai](https://discuss.vllm.ai).
 
 ## Star History
 
-[![Star History Chart](https://api.star-history.com/svg?repos=vllm-project/vllm-omni&type=date&legend=top-left)](https://www.star-history.com/#vllm-project/vllm-omni&type=date&legend=top-left)
+<a href="https://www.star-history.com/?repos=vllm-project%2Fvllm-omni&type=date&legend=top-left">
+  <picture>
+    <source media="(prefers-color-scheme: dark)" srcset="https://api.star-history.com/chart?repos=vllm-project/vllm-omni&type=date&theme=dark&legend=top-left&sealed_token=ExgLDZJoQEg27Zfhhut2LqN0GYO6Fw2PWLwPE6JYBUp2BgM3hmsYlwaIVopnUEfbRXidQ4nisumrTdKYydiKhy1SZXipw47qY2_tiUDhCpsPXeXtPuEVKVzBwKs3pw0tiHsJgtSfwXx5yjHXck0Y2SblzFWeJYCkTe1WLGTbUAOIETjXXQJjyCGZvKz5" />
+    <source media="(prefers-color-scheme: light)" srcset="https://api.star-history.com/chart?repos=vllm-project/vllm-omni&type=date&legend=top-left&sealed_token=ExgLDZJoQEg27Zfhhut2LqN0GYO6Fw2PWLwPE6JYBUp2BgM3hmsYlwaIVopnUEfbRXidQ4nisumrTdKYydiKhy1SZXipw47qY2_tiUDhCpsPXeXtPuEVKVzBwKs3pw0tiHsJgtSfwXx5yjHXck0Y2SblzFWeJYCkTe1WLGTbUAOIETjXXQJjyCGZvKz5" />
+    <img alt="Star History Chart" src="https://api.star-history.com/chart?repos=vllm-project/vllm-omni&type=date&legend=top-left&sealed_token=ExgLDZJoQEg27Zfhhut2LqN0GYO6Fw2PWLwPE6JYBUp2BgM3hmsYlwaIVopnUEfbRXidQ4nisumrTdKYydiKhy1SZXipw47qY2_tiUDhCpsPXeXtPuEVKVzBwKs3pw0tiHsJgtSfwXx5yjHXck0Y2SblzFWeJYCkTe1WLGTbUAOIETjXXQJjyCGZvKz5" />
+  </picture>
+</a>
 
 ## License
 

benchmarks/kernels/benchmark_diffusion_lora_expand.py

@@ -0,0 +1,156 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
+"""Benchmark diffusion LoRA expand-and-accumulate implementations.
+
+This compares the previous functional expand path with direct accumulation into
+the selected output slice. It is a synthetic kernel microbenchmark and does not
+load a diffusion model.
+
+Example:
+    python benchmarks/kernels/benchmark_diffusion_lora_expand.py \
+        --m 1,16,128,512,4096,16384 \
+        --in-dim 4096 --out-dim 4096 --rank 64 \
+        --dtype bfloat16 --packed --warmup 30 \
+        --small-iterations 500 --large-iterations 100
+"""
+
+import argparse
+import math
+import statistics
+from collections.abc import Callable
+
+import torch
+
+MiB = 1024**2
+
+
+def parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument("--m", default="1,16,128,512,4096,16384")
+    parser.add_argument("--in-dim", type=int, default=4096)
+    parser.add_argument("--out-dim", type=int, default=4096)
+    parser.add_argument("--rank", type=int, default=64)
+    parser.add_argument("--dtype", choices=("bfloat16", "float16"), default="bfloat16")
+    parser.add_argument(
+        "--packed",
+        action="store_true",
+        help="Accumulate into the second half of a packed output tensor.",
+    )
+    parser.add_argument("--warmup", type=int, default=30)
+    parser.add_argument("--small-iterations", type=int, default=500)
+    parser.add_argument("--large-iterations", type=int, default=100)
+    parser.add_argument("--large-m-threshold", type=int, default=1024)
+    return parser.parse_args()
+
+
+def percentile(samples: list[float], probability: float) -> float:
+    ordered = sorted(samples)
+    position = probability * (len(ordered) - 1)
+    lower = math.floor(position)
+    upper = math.ceil(position)
+    if lower == upper:
+        return ordered[lower]
+    weight = position - lower
+    return ordered[lower] * (1 - weight) + ordered[upper] * weight
+
+
+def measure_latency_ms(operation: Callable[[], None], warmup: int, iterations: int) -> list[float]:
+    for _ in range(warmup):
+        operation()
+    torch.accelerator.synchronize()
+
+    starts = [torch.cuda.Event(enable_timing=True) for _ in range(iterations)]
+    ends = [torch.cuda.Event(enable_timing=True) for _ in range(iterations)]
+    for start, end in zip(starts, ends, strict=True):
+        start.record()
+        operation()
+        end.record()
+    torch.accelerator.synchronize()
+    return [start.elapsed_time(end) for start, end in zip(starts, ends, strict=True)]
+
+
+def measure_extra_peak_mib(operation: Callable[[], None]) -> float:
+    torch.accelerator.synchronize()
+    torch.accelerator.reset_peak_memory_stats()
+    allocated = torch.accelerator.memory_allocated()
+    operation()
+    torch.accelerator.synchronize()
+    peak = torch.accelerator.max_memory_allocated()
+    return (peak - allocated) / MiB
+
+
+def print_header(args: argparse.Namespace, dtype: torch.dtype) -> None:
+    device_name = torch.cuda.get_device_name()
+    print(f"device={device_name} torch={torch.__version__} dtype={dtype}")
+    print(f"in_dim={args.in_dim} out_dim={args.out_dim} rank={args.rank} packed={args.packed} warmup={args.warmup}")
+    print("M       iters  old_p10  old_p50  old_p90  new_p10  new_p50  new_p90  speedup  old_MiB  new_MiB  max_abs")
+
+
+def benchmark_shape(m: int, args: argparse.Namespace, dtype: torch.dtype) -> None:
+    device = torch.device("cuda")
+    output_width = args.out_dim * (2 if args.packed else 1)
+    offset = args.out_dim if args.packed else 0
+    output_slice = slice(offset, offset + args.out_dim)
+    scale = 1 / math.sqrt(args.in_dim)
+
+    x = torch.randn(m, args.in_dim, device=device, dtype=dtype)
+    lora_a = torch.randn(args.rank, args.in_dim, device=device, dtype=dtype) * scale
+    lora_b = torch.randn(args.out_dim, args.rank, device=device, dtype=dtype) / math.sqrt(args.rank)
+    initial_output = torch.randn(m, output_width, device=device, dtype=dtype)
+
+    def baseline(output: torch.Tensor) -> None:
+        buffer = x @ lora_a.t()
+        delta = buffer @ lora_b.t()
+        selected = output[:, output_slice]
+        selected[:] = selected + delta
+
+    def optimized(output: torch.Tensor) -> None:
+        buffer = x @ lora_a.t()
+        selected = output[:, output_slice]
+        torch.addmm(selected, buffer, lora_b.t(), out=selected)
+
+    reference = initial_output.clone()
+    candidate = initial_output.clone()
+    baseline(reference)
+    optimized(candidate)
+    torch.testing.assert_close(candidate, reference, rtol=1e-2, atol=1e-2)
+    max_abs = (candidate - reference).abs().max().item()
+
+    old_output = initial_output.clone()
+    new_output = initial_output.clone()
+    iterations = args.large_iterations if m >= args.large_m_threshold else args.small_iterations
+
+    old_peak = measure_extra_peak_mib(lambda: baseline(old_output))
+    new_peak = measure_extra_peak_mib(lambda: optimized(new_output))
+    old_samples = measure_latency_ms(lambda: baseline(old_output), args.warmup, iterations)
+    new_samples = measure_latency_ms(lambda: optimized(new_output), args.warmup, iterations)
+
+    old_p50 = statistics.median(old_samples)
+    new_p50 = statistics.median(new_samples)
+    print(
+        f"{m:<7} {iterations:<6} "
+        f"{percentile(old_samples, 0.1):>8.5f} {old_p50:>8.5f} {percentile(old_samples, 0.9):>8.5f} "
+        f"{percentile(new_samples, 0.1):>8.5f} {new_p50:>8.5f} {percentile(new_samples, 0.9):>8.5f} "
+        f"{old_p50 / new_p50:>8.3f} {old_peak:>8.2f} {new_peak:>8.2f} {max_abs:>8.5f}"
+    )
+
+
+def main() -> None:
+    args = parse_args()
+    if not torch.cuda.is_available():
+        raise RuntimeError("This benchmark requires a CUDA GPU")
+    if args.warmup < 0 or args.small_iterations <= 0 or args.large_iterations <= 0:
+        raise ValueError("warmup must be non-negative and iteration counts must be positive")
+
+    dtype = getattr(torch, args.dtype)
+    torch.manual_seed(0)
+    print_header(args, dtype)
+    with torch.inference_mode():
+        for m in (int(value) for value in args.m.split(",")):
+            if m <= 0:
+                raise ValueError(f"M must be positive, got {m}")
+            benchmark_shape(m, args, dtype)
+
+
+if __name__ == "__main__":
+    main()

benchmarks/tts/README.md

@@ -18,8 +18,8 @@ vllm serve Qwen/Qwen3-TTS-12Hz-1.7B-Base --omni --port 8000
 ```
 
 The server auto-loads its Deploy YAML from `vllm_omni/deploy/qwen3_tts.yaml`
-(Pipeline + Deploy schema introduced in #2383). No `--stage-configs-path` or
-`--deploy-config` flag is needed for any registered model.
+(Pipeline + Deploy schema introduced in #2383). No explicit `--deploy-config`
+flag is needed for a registered model.
 
 ### 2. Run the benchmark (`vllm bench serve --omni`)
 

benchmarks/tts/benchmark_cosyvoice3_trt_streams.py

@@ -0,0 +1,151 @@
+"""Benchmark CosyVoice3-style CUDA stream handoff strategies."""
+
+import argparse
+import json
+import statistics
+import time
+
+import torch
+
+from vllm_omni.platforms import current_omni_platform
+
+
+def run_case(
+    *,
+    device: torch.device,
+    steps: int,
+    warmup_steps: int,
+    producer_cycles: int,
+    estimator_cycles: int,
+    use_stream_dependencies: bool,
+) -> dict[str, float | int | str]:
+    caller_stream = torch.cuda.Stream(device=device)
+    estimator_stream = torch.cuda.Stream(device=device)
+    input_tensor = torch.zeros(1024, device=device)
+    output_tensor = torch.empty_like(input_tensor)
+    submission_ms = []
+
+    def step(measure: bool) -> None:
+        with torch.cuda.stream(caller_stream):
+            torch.cuda._sleep(producer_cycles)
+            input_tensor.add_(1)
+
+        start = time.perf_counter_ns()
+        if use_stream_dependencies:
+            estimator_stream.wait_stream(caller_stream)
+        else:
+            caller_stream.synchronize()
+
+        with torch.cuda.stream(estimator_stream):
+            torch.cuda._sleep(estimator_cycles)
+            output_tensor.copy_(input_tensor)
+
+        if use_stream_dependencies:
+            caller_stream.wait_stream(estimator_stream)
+        else:
+            estimator_stream.synchronize()
+
+        if measure:
+            submission_ms.append((time.perf_counter_ns() - start) / 1e6)
+
+    for _ in range(warmup_steps):
+        step(measure=False)
+    caller_stream.synchronize()
+    estimator_stream.synchronize()
+    input_tensor.zero_()
+    torch.accelerator.reset_peak_memory_stats(device)
+    initial_allocated = torch.accelerator.memory_allocated(device)
+
+    start_event = torch.cuda.Event(enable_timing=True)
+    end_event = torch.cuda.Event(enable_timing=True)
+    wall_start = time.perf_counter()
+    with torch.cuda.stream(caller_stream):
+        start_event.record()
+    for _ in range(steps):
+        step(measure=True)
+    with torch.cuda.stream(caller_stream):
+        end_event.record()
+    end_event.synchronize()
+    wall_ms = (time.perf_counter() - wall_start) * 1e3
+    peak_delta_bytes = torch.accelerator.max_memory_allocated(device) - initial_allocated
+
+    expected = torch.full_like(output_tensor, steps)
+    torch.testing.assert_close(output_tensor, expected, rtol=0, atol=0)
+    return {
+        "mode": "wait_stream" if use_stream_dependencies else "synchronize",
+        "steps": steps,
+        "submission_median_ms": statistics.median(submission_ms),
+        "submission_p95_ms": statistics.quantiles(submission_ms, n=20)[18],
+        "submission_mean_ms": statistics.mean(submission_ms),
+        "gpu_elapsed_ms": start_event.elapsed_time(end_event),
+        "wall_elapsed_ms": wall_ms,
+        "peak_allocation_delta_bytes": peak_delta_bytes,
+        "final_value": int(output_tensor[0].item()),
+    }
+
+
+def parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--device", default="cuda:0")
+    parser.add_argument("--steps", type=int, default=100)
+    parser.add_argument("--warmup-steps", type=int, default=10)
+    parser.add_argument("--repeats", type=int, default=7)
+    parser.add_argument("--producer-cycles", type=int, default=1_000_000)
+    parser.add_argument("--estimator-cycles", type=int, default=2_000_000)
+    return parser.parse_args()
+
+
+def main() -> None:
+    args = parse_args()
+    device = torch.device(args.device)
+    current_omni_platform.set_device(device)
+    torch.manual_seed(0)
+
+    old_results = []
+    new_results = []
+    for repeat in range(args.repeats):
+        common_args = {
+            "device": device,
+            "steps": args.steps,
+            "warmup_steps": args.warmup_steps,
+            "producer_cycles": args.producer_cycles,
+            "estimator_cycles": args.estimator_cycles,
+        }
+        if repeat % 2 == 0:
+            old_results.append(run_case(**common_args, use_stream_dependencies=False))
+            new_results.append(run_case(**common_args, use_stream_dependencies=True))
+        else:
+            new_results.append(run_case(**common_args, use_stream_dependencies=True))
+            old_results.append(run_case(**common_args, use_stream_dependencies=False))
+
+    old_submission_ms = statistics.median(result["submission_median_ms"] for result in old_results)
+    new_submission_ms = statistics.median(result["submission_median_ms"] for result in new_results)
+    old_gpu_ms = statistics.median(result["gpu_elapsed_ms"] for result in old_results)
+    new_gpu_ms = statistics.median(result["gpu_elapsed_ms"] for result in new_results)
+    print(
+        json.dumps(
+            {
+                "device": torch.cuda.get_device_name(device),
+                "torch": torch.__version__,
+                "cuda": torch.version.cuda,
+                "repeats": args.repeats,
+                "warmup_steps": args.warmup_steps,
+                "producer_cycles": args.producer_cycles,
+                "estimator_cycles": args.estimator_cycles,
+                "old_submission_median_ms": old_submission_ms,
+                "new_submission_median_ms": new_submission_ms,
+                "submission_median_speedup": old_submission_ms / new_submission_ms,
+                "old_gpu_elapsed_median_ms": old_gpu_ms,
+                "new_gpu_elapsed_median_ms": new_gpu_ms,
+                "gpu_elapsed_change_percent": (new_gpu_ms - old_gpu_ms) / old_gpu_ms * 100,
+                "parity": all(result["final_value"] == args.steps for result in old_results + new_results),
+                "old_runs": old_results,
+                "new_runs": new_results,
+            },
+            indent=2,
+        )
+    )
+
+
+if __name__ == "__main__":
+    main()

benchmarks/tts/model_configs.yaml

@@ -132,3 +132,14 @@ models:
     endpoint: /v1/audio/speech
     task_extra_body:
       voice_design: {}
+
+  # OmniVoice serves plain text-to-speech over /v1/audio/speech; voice cloning
+  # and voice design are offline-only for this model, so default_voice is the
+  # only task the server can answer.
+  k2-fsa/OmniVoice:
+    supported_tasks: [default_voice]
+    backend: openai-audio-speech
+    endpoint: /v1/audio/speech
+    trust_remote_code: true
+    task_extra_body:
+      default_voice: {}

collect_env.py

@@ -1,5 +1,5 @@
 # SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
 
 # ruff: noqa
 # code borrowed from https://github.com/pytorch/pytorch/blob/main/torch/utils/collect_env.py
@@ -494,6 +494,16 @@ def is_xnnpack_available():
 
 
 def get_env_vars():
+    try:
+        from vllm_omni.config.environment_variable_inventory import ENVIRONMENT_VARIABLE_INVENTORY
+    except Exception:
+        # Keep environment collection usable when an incomplete/broken Omni
+        # installation is the reason the user is running this diagnostic.
+        # Importing an Omni submodule executes package initialization, which
+        # may fail with RuntimeError, AssertionError, or OSError as well as
+        # ImportError when vLLM or the accelerator stack is broken.
+        ENVIRONMENT_VARIABLE_INVENTORY = {}
+
     env_vars = ""
     secret_terms = ("secret", "token", "api", "access", "password")
     report_prefix = (
@@ -507,12 +517,17 @@ def get_env_vars():
         "MKL_",
         "NVIDIA",
     )
-    for k, v in os.environ.items():
-        if any(term in k.lower() for term in secret_terms):
+    for k, v in sorted(os.environ.items()):
+        omni_classification = ENVIRONMENT_VARIABLE_INVENTORY.get(k)
+        if (omni_classification is not None and omni_classification.redact_value) or any(
+            term in k.lower() for term in secret_terms
+        ):
             continue
-        if k in environment_variables:
-            env_vars = env_vars + "{}={}".format(k, v) + "\n"
-        if k.startswith(report_prefix):
+        if (
+            k in environment_variables
+            or (omni_classification is not None and omni_classification.is_public_omni)
+            or k.startswith(report_prefix)
+        ):
             env_vars = env_vars + "{}={}".format(k, v) + "\n"
 
     return env_vars

docker/Dockerfile.ci

@@ -1,4 +1,4 @@
-# Target vllm version: v0.27.0 (releases/v0.27.0 branch).
+# Target vllm version: v0.28.0 (tag v0.28.0 @ 2cf0a6915ce5).
 # Keep VLLM_BASE_TAG aligned with the target vLLM release on every rebase.
 #
 # Release-boundary form: there is no precompiled-wheel pin here, because the
@@ -14,11 +14,14 @@
 # core init), registering libnvJitLink.so.13 with ldconfig, and re-pinning numpy
 # under numba's ceiling. All of it was compensation for a base image and a vLLM
 # that disagreed. At a real release boundary they agree, so none of it is
-# needed. If you pin an untagged commit again, restore the reinstall block —
-# agent/tasks/20_update_wheel_url.sh fails the run rather than letting the
-# Dockerfile silently ship the wrong vLLM.
+# needed. The 0.28 rebase repeated the cycle: the v0.28.0rc / final-tag wheels
+# rode on the v0.27.0 base image with the full reinstall block until the
+# official v0.28.0 image shipped, at which point this file dropped back to the
+# form you are reading. If you pin an untagged commit again, restore the
+# reinstall block — agent/tasks/20_update_wheel_url.sh fails the run rather
+# than letting the Dockerfile silently ship the wrong vLLM.
 ARG VLLM_BASE_IMAGE=vllm/vllm-openai
-ARG VLLM_BASE_TAG=v0.27.0
+ARG VLLM_BASE_TAG=v0.28.0
 
 # We use registry-based layer caching for the dependencies,
 # where the tag is determined based on the hash of the contents

docker/Dockerfile.cuda

@@ -1,4 +1,4 @@
-ARG BASE_IMAGE=vllm/vllm-openai:v0.26.0
+ARG BASE_IMAGE=vllm/vllm-openai:v0.28.0
 FROM ${BASE_IMAGE}
 
 ARG COMMON_WORKDIR=/app

docker/Dockerfile.npu

@@ -1,5 +1,5 @@
 ARG VLLM_ASCEND_IMAGE=quay.io/atlas-ci/vllm-ascend
-ARG VLLM_ASCEND_TAG=v0.27.0
+ARG VLLM_ASCEND_TAG=v0.28.0
 FROM ${VLLM_ASCEND_IMAGE}:${VLLM_ASCEND_TAG}
 
 # WORKDIR /vllm-workspace/vllm
@@ -30,6 +30,7 @@ RUN python3 -m pip install -v --no-deps \
       "stepaudio2-minicpmo>=0.1.1" \
       "step-audio2>=1.0.0"
 
+ENV VLLM_TARGET_DEVICE=npu
 ENV VLLM_WORKER_MULTIPROC_METHOD=spawn
 
 ENTRYPOINT []

docker/Dockerfile.npu.a3

@@ -1,5 +1,5 @@
 ARG VLLM_ASCEND_IMAGE=quay.io/atlas-ci/vllm-ascend
-ARG VLLM_ASCEND_TAG=v0.27.0-a3
+ARG VLLM_ASCEND_TAG=v0.28.0-a3
 FROM ${VLLM_ASCEND_IMAGE}:${VLLM_ASCEND_TAG}
 
 # WORKDIR /vllm-workspace/vllm
@@ -30,6 +30,7 @@ RUN python3 -m pip install -v --no-deps \
       "stepaudio2-minicpmo>=0.1.1" \
       "step-audio2>=1.0.0"
 
+ENV VLLM_TARGET_DEVICE=npu
 ENV VLLM_WORKER_MULTIPROC_METHOD=spawn
 
 ENTRYPOINT []

docker/Dockerfile.npu.ci

@@ -1,5 +1,5 @@
 ARG VLLM_ASCEND_IMAGE=quay.io/atlas-ci/vllm-ascend
-ARG VLLM_ASCEND_TAG=v0.27.0
+ARG VLLM_ASCEND_TAG=v0.28.0
 FROM ${VLLM_ASCEND_IMAGE}:${VLLM_ASCEND_TAG}
 
 ARG APP_DIR=/vllm-workspace/vllm-omni
@@ -65,6 +65,7 @@ RUN python3 -m pip install -v --no-deps \
       --trusted-host cache-service.nginx-pypi-cache.svc.cluster.local \
       --default-timeout=60
 
+ENV VLLM_TARGET_DEVICE=npu
 ENV VLLM_WORKER_MULTIPROC_METHOD=spawn
 
 ENTRYPOINT []

docker/Dockerfile.npu.ci.a3

@@ -1,5 +1,5 @@
 ARG VLLM_ASCEND_IMAGE=quay.io/atlas-ci/vllm-ascend
-ARG VLLM_ASCEND_TAG=v0.27.0-a3
+ARG VLLM_ASCEND_TAG=v0.28.0-a3
 FROM ${VLLM_ASCEND_IMAGE}:${VLLM_ASCEND_TAG}
 
 ARG APP_DIR=/vllm-workspace/vllm-omni
@@ -66,6 +66,7 @@ RUN python3 -m pip install -v --no-deps \
       --trusted-host cache-service.nginx-pypi-cache.svc.cluster.local \
       --default-timeout=60
 
+ENV VLLM_TARGET_DEVICE=npu
 ENV VLLM_WORKER_MULTIPROC_METHOD=spawn
 
 ENTRYPOINT []

docker/Dockerfile.rocm

@@ -1,4 +1,4 @@
-ARG BASE_IMAGE=vllm/vllm-openai-rocm:v0.27.0
+ARG BASE_IMAGE=vllm/vllm-openai-rocm:v0.28.0
 FROM ${BASE_IMAGE} AS base
 
 # Declare a variable to know if we want to use the nightly build or the stable build.
@@ -10,7 +10,7 @@ FROM ${BASE_IMAGE} AS base
 #    we should swap over to use stable release ASAP.
 #    We should avoid relying on custom commits.
 ARG USE_NIGHTLY_BUILD=0
-ARG VLLM_VERSION_OR_COMMIT_HASH=v0.27.0
+ARG VLLM_VERSION_OR_COMMIT_HASH=v0.28.0
 ARG ARG_PYTORCH_ROCM_ARCH
 ENV PYTORCH_ROCM_ARCH=${ARG_PYTORCH_ROCM_ARCH:-${PYTORCH_ROCM_ARCH}}
 

docker/Dockerfile.xpu

@@ -1,140 +1,33 @@
-# Argument to configure vllm base image if pre-built
-ARG VLLM_BASE=vllm-base
-
-FROM intel/deep-learning-essentials:2025.3.2-0-devel-ubuntu24.04 AS vllm-base
-
-WORKDIR /workspace/
-
-ARG PYTHON_VERSION=3.12
-ARG PIP_EXTRA_INDEX_URL="https://download.pytorch.org/whl/xpu"
-
-RUN wget -O- https://apt.repos.intel.com/intel-gpg-keys/GPG-PUB-KEY-INTEL-SW-PRODUCTS.PUB | gpg --dearmor | tee /usr/share/keyrings/oneapi-archive-keyring.gpg > /dev/null && \
-    echo "deb [signed-by=/usr/share/keyrings/oneapi-archive-keyring.gpg] https://apt.repos.intel.com/oneapi all main" | tee /etc/apt/sources.list.d/oneAPI.list
-
-RUN apt clean && apt-get update -y && \
-    apt-get install -y --no-install-recommends --fix-missing \
-    curl \
-    ffmpeg \
-    git \
-    libsndfile1 \
-    libsm6 \
-    libxext6 \
-    libgl1 \
-    lsb-release \
-    libaio-dev \
-    numactl \
-    wget \
-    vim \
-    python3.12 \
-    python3.12-dev \
-    python3-pip
-
-# Add oneAPI repo, pin oneAPI to 2025.3, then install pinned packages in one layer.
-RUN wget -O- https://apt.repos.intel.com/intel-gpg-keys/GPG-PUB-KEY-INTEL-SW-PRODUCTS.PUB | gpg --dearmor | tee /usr/share/keyrings/oneapi-archive-keyring.gpg > /dev/null && \
-    echo "deb [signed-by=/usr/share/keyrings/oneapi-archive-keyring.gpg] https://apt.repos.intel.com/oneapi all main" | tee /etc/apt/sources.list.d/oneAPI.list && \
-    printf '%s\n' \
-    'Package: intel-oneapi-* intel-deep-learning-essentials* intel-pti*' \
-    'Pin: version 2025.3*' \
-    'Pin-Priority: 1001' \
-    > /etc/apt/preferences.d/oneapi-2025.3.pref && \
-    apt-get update -y && \
-    apt-get install -y --no-install-recommends \
-    intel-oneapi-compiler-dpcpp-cpp-2025.3 \
-    intel-oneapi-mkl-devel-2025.3 \
-    intel-oneapi-dnnl-devel-2025.3 && \
-    rm -rf /var/lib/apt/lists/*
-
-# Install UMD with fixed version
-RUN mkdir neo && \
-    cd neo && \
-    wget https://github.com/intel/intel-graphics-compiler/releases/download/v2.24.8/intel-igc-core-2_2.24.8+20344_amd64.deb && \
-    wget https://github.com/intel/intel-graphics-compiler/releases/download/v2.24.8/intel-igc-opencl-2_2.24.8+20344_amd64.deb && \
-    wget https://github.com/intel/compute-runtime/releases/download/25.48.36300.8/intel-ocloc_25.48.36300.8-0_amd64.deb && \
-    wget https://github.com/intel/compute-runtime/releases/download/25.48.36300.8/intel-opencl-icd_25.48.36300.8-0_amd64.deb && \
-    wget https://github.com/intel/compute-runtime/releases/download/25.48.36300.8/libigdgmm12_22.8.2_amd64.deb && \
-    wget https://github.com/intel/compute-runtime/releases/download/25.48.36300.8/libze-intel-gpu1_25.48.36300.8-0_amd64.deb && \
-    wget https://github.com/oneapi-src/level-zero/releases/download/v1.26.0/level-zero_1.26.0+u24.04_amd64.deb && \
-    dpkg -i *.deb && \
-    cd .. && \
-    rm -rf neo
-
-ENV PATH="/root/.local/bin:$PATH"
-ENV VIRTUAL_ENV="/opt/venv"
-ENV UV_PYTHON_INSTALL_DIR=/opt/uv/python
-RUN curl -LsSf https://astral.sh/uv/install.sh | sh
-RUN uv venv --python ${PYTHON_VERSION} --seed ${VIRTUAL_ENV}
-ENV PATH="$VIRTUAL_ENV/bin:$PATH"
-
-# This oneccl contains the BMG support which is not the case for default version of oneapi 2025.3.
-ARG ONECCL_INSTALLER="intel-oneccl-2021.15.9.14_offline.sh"
-RUN wget "https://github.com/uxlfoundation/oneCCL/releases/download/2021.15.9/${ONECCL_INSTALLER}" && \
-    bash "${ONECCL_INSTALLER}" -a --silent --eula accept && \
-    rm "${ONECCL_INSTALLER}" && \
-    echo "source /opt/intel/oneapi/setvars.sh --force" >> /root/.bashrc && \
-    echo "source /opt/intel/oneapi/ccl/2021.15/env/vars.sh --force" >> /root/.bashrc
-RUN rm -f /opt/intel/oneapi/ccl/latest && \
-    ln -s /opt/intel/oneapi/ccl/2021.15 /opt/intel/oneapi/ccl/latest
-
-SHELL ["bash", "-c"]
-CMD ["bash", "-c", "source /root/.bashrc && exec bash"]
-
-WORKDIR /workspace/
-
-ENV UV_HTTP_TIMEOUT=500
-
-# Configure package index for XPU
-ENV PIP_EXTRA_INDEX_URL=${PIP_EXTRA_INDEX_URL}
-ENV UV_EXTRA_INDEX_URL=${PIP_EXTRA_INDEX_URL}
-ENV UV_INDEX_STRATEGY="unsafe-best-match"
-ENV UV_LINK_MODE="copy"
-
-ARG VLLM_VERSION=v0.27.0
-RUN git clone -b ${VLLM_VERSION} https://github.com/vllm-project/vllm
-WORKDIR /workspace/vllm
-
-RUN --mount=type=cache,target=/root/.cache/uv \
-    uv pip install --upgrade pip && \
-    uv pip install -r requirements/xpu.txt && \
-    uv pip install setuptools_scm &&\
-    uv pip install grpcio-tools protobuf nanobind && \
-    source /opt/intel/oneapi/setvars.sh --force && \
-    source /opt/intel/oneapi/ccl/2021.15/env/vars.sh --force && \
-    export CMAKE_PREFIX_PATH="$(python3 -c 'import site; print(site.getsitepackages()[0])'):${CMAKE_PREFIX_PATH}"
-
-ENV LD_LIBRARY_PATH="/opt/intel/oneapi/ccl/2021.15/lib:$LD_LIBRARY_PATH:/usr/local/lib/"
-
-ENV VLLM_TARGET_DEVICE=xpu
-ENV VLLM_WORKER_MULTIPROC_METHOD=spawn
-
-RUN --mount=type=cache,target=/root/.cache/uv \
-    uv pip uninstall triton triton-xpu && \
-    uv pip install triton-xpu==3.7.2 && \
-    uv pip install --no-build-isolation --no-deps .
-
-CMD ["/bin/bash"]
-
-FROM vllm-base AS vllm-openai
-
-# install nixl from source code
-RUN uv pip install nixl
-
-# install additional dependencies for openai api server
-RUN --mount=type=cache,target=/root/.cache/uv \
-    uv pip install accelerate hf_transfer pytest pytest_asyncio lm_eval[api] modelscope
-
-# install development dependencies (for testing)
-RUN uv pip install -e tests/vllm_test_utils
-
-# ensure vllm is properly installed
-RUN python -c "import vllm, inspect; print(vllm.__file__)"
-RUN uv pip show vllm
-
-CMD ["/bin/bash"]
-
-ENTRYPOINT []
+# Target vLLM version: v0.28.0. Keep VLLM_VERSION aligned with the target vLLM
+# release on every rebase.
+#
+# This file layers vLLM-Omni on top of the published upstream XPU image, which
+# already ships a self-consistent XPU runtime (torch-xpu, oneCCL/UCX+NIXL, the
+# pinned UMD/compute-runtime stack, and vLLM itself) built from
+# https://github.com/vllm-project/vllm/blob/main/docker/Dockerfile.xpu. Docker
+# pulls that base automatically, so there is nothing to build here beyond the
+# Omni layers. Mirrors how docker/Dockerfile.ci consumes vllm/vllm-openai.
+#
+# If the tag does not exist yet (targeting an unreleased vLLM commit), build the
+# base from upstream's own Dockerfile and point VLLM_BASE at the result:
+#
+#   git clone --depth 1 --branch $VLLM_VERSION https://github.com/vllm-project/vllm /tmp/vllm
+#   docker build -t vllm-openai-xpu:local --target vllm-openai \
+#       -f /tmp/vllm/docker/Dockerfile.xpu /tmp/vllm
+#   docker build -f docker/Dockerfile.xpu --build-arg VLLM_BASE=vllm-openai-xpu:local .
+#
+# .buildkite/intel/scripts/run-xpu-test.sh automates that pull-then-build fallback.
+ARG VLLM_BASE_IMAGE=vllm/vllm-openai-xpu
+ARG VLLM_VERSION=v0.28.0
+ARG VLLM_BASE=${VLLM_BASE_IMAGE}:${VLLM_VERSION}
 
 FROM ${VLLM_BASE} AS vllm-omni
 
+# The base image ships ENTRYPOINT ["vllm", "serve"]; clear it so this stage stays
+# usable as a plain shell/test container regardless of which VLLM_BASE was used.
+ENTRYPOINT []
+CMD ["/bin/bash"]
+
 RUN apt-get update && \
     apt-get install -y espeak-ng ffmpeg git jq && \
     apt-get clean && \

docs/.nav.yml

@@ -5,14 +5,22 @@ nav:
     - getting_started/quickstart.md
     - getting_started/installation/*
   - Serving:
-    - OpenAI-Compatible API:
-      - Chat Completions API: serving/chat_completions_api.md
-      - Image Generation: serving/image_generation_api.md
-      - Image Edit: serving/image_edit_api.md
-      - Text to Speech: serving/speech_api.md
-      - Videos: serving/videos_api.md
-      - Audio Generation: serving/audio_generate_api.md
-      - Streaming Video Input: serving/video_stream_api.md
+    - API Server:
+      - Overview: serving/README.md
+      - Core APIs:
+        - Chat Completions: serving/chat_completions_api.md
+        - Text to Speech: serving/speech_api.md
+        - Audio Generation: serving/audio_generate_api.md
+        - Image Generation: serving/image_generation_api.md
+        - Image Edit: serving/image_edit_api.md
+        - Videos: serving/videos_api.md
+      - Streaming and Realtime:
+        - Realtime Audio: serving/realtime_api.md
+        - Full Duplex: serving/full_duplex_api.md
+        - Streaming Video Input: serving/video_stream_api.md
+        - Streaming Video Output: serving/streaming_video_output_api.md
+        - OpenPI Robot Policy: serving/openpi_api.md
+      - Standalone Experimental Servers: serving/standalone_servers.md
   - Examples:
     - examples/README.md
     - Offline Inference:
@@ -24,7 +32,6 @@ nav:
       - Text-To-Speech: user_guide/examples/offline_inference/text_to_speech.md
       - Text-To-Video: user_guide/examples/offline_inference/text_to_video.md
       - X-To-Text: user_guide/examples/offline_inference/x_to_text.md
-      - X-To-Video-Audio: user_guide/examples/offline_inference/x_to_video_audio.md
     - Online Serving:
       - Image-To-Image: user_guide/examples/online_serving/image_to_image.md
       - Image-To-Video: user_guide/examples/online_serving/image_to_video.md
@@ -54,6 +61,7 @@ nav:
       - BitsAndBytes W4: user_guide/quantization/bitsandbytes.md
       - MXFP8 W8A8: user_guide/quantization/mxfp8.md
       - MXFP4 W4A4: user_guide/quantization/mxfp4.md
+      - SVDQuant W4A4: user_guide/quantization/svdquant.md
       - ModelOpt: user_guide/quantization/modelopt.md
       - AutoRound: user_guide/quantization/autoround.md
       - msModelSlim: user_guide/quantization/msmodelslim.md
@@ -113,6 +121,7 @@ nav:
     - Feature Design:
       - Runtime and Stage Execution:
         - Disaggregated Inference: design/feature/disaggregated_inference.md
+        - Host Weight Runtime: design/feature/host_weight_runtime.md
         - Async Chunk: design/feature/async_chunk.md
         - Async Diffusion Output: design/feature/async_diffusion_output.md
         - Async Omni Output Materialization: design/feature/omni_async_output_materialization.md
@@ -169,6 +178,7 @@ nav:
         - Offloader: design/module/diffusion/offloader.md
       - Execution Platforms: design/module/execution_platforms.md
       - Cache Management: design/module/cache_management.md
+      - Host Weight Runtime: design/module/host_weight_runtime.md
       - Quantization: design/module/quantization.md
       - Observability: design/module/observability.md
       - Profiling: design/module/profiling.md

docs/README.md

@@ -57,9 +57,9 @@ vLLM-Omni is flexible and easy to use with:
 vLLM-Omni seamlessly supports most popular open-source models on HuggingFace, including:
 
 - Omni-modality models (e.g. Qwen3-Omni, MiniCPM-o 4.5, Cosmos3, HunyuanImage, BAGEL)
-- TTS models (e.g. Qwen3-TTS, VoxCPM2, Ming-Omni-TTS, CosyVoice3)
-- Diffusion models — image, video, and audio generation (e.g. MiniMax H3, Qwen-Image, Wan2.2, FLUX)
-- Robot-policy and action models (e.g. GR00T-N1.7, DreamZero-DROID, InternVLA-A1, Cosmos3 action policy)
+- TTS models (e.g. Qwen3-TTS, IndexTTS 2.5, dots.tts, CosyVoice3)
+- Diffusion models — image, video, and audio generation (e.g. MiniMax H3, LTX-2.5, SANA-Video, Wan2.2)
+- Robot-policy and action models (e.g. π0, GR00T-N1.7, DreamZero-DROID, InternVLA-A1)
 
 For more information, checkout the following:
 

docs/cli/README.md

@@ -2,6 +2,11 @@
 
 The CLI for vLLM-Omni inherits from vllm with some additional arguments.
 
+Environment variables are process configuration rather than CLI-only options.
+See the [Environment Variables](../configuration/environment_variables.md)
+reference for ownership, precedence, evaluation time, and per-stage `env`
+scoping.
+
 ## serve
 
 Starts the vLLM-Omni OpenAI Compatible API server.
@@ -39,4 +44,5 @@ vllm bench serve --omni \
     --num-prompts  5
 ```
 
-See [vllm bench serve](./bench/serve.md) for the full reference of all available arguments.
+See [vllm bench serve](./bench/serve.md) for serving benchmark arguments and
+dataset-specific examples, including OmniInteract native-duplex sessions.

docs/cli/bench/serve.md

@@ -2,7 +2,7 @@
 The vllm bench command launches the vLLM-Omni benchmark to evaluate the performance of multimodal models.
 
 ## Notes
-vLLM-Omni registers the `openai-chat-omni`, `openai-audio-speech`, `openai-image-edits-omni`, and `daily-omni` serving benchmark backends.
+vLLM-Omni registers the `openai-chat-omni`, `openai-audio-speech`, `openai-image-edits-omni`, `daily-omni`, and `openai-realtime-duplex` serving benchmark backends. It also adds the `omniinteract` dataset.
 
 ## Basic Parameter Description
 You can use `vllm bench serve --omni --help=all` to get descriptions of all parameters. The commonly used parameters are described below:
@@ -10,7 +10,7 @@ You can use `vllm bench serve --omni --help=all` to get descriptions of all para
   Enable Omni (multimodal) mode, supporting multimodal inputs and outputs such as images, videos, and audio.
 
 - `--backend`
-  Specify the backend adapter. vLLM-Omni adds `openai-chat-omni`, `openai-audio-speech`, `openai-image-edits-omni`, and `daily-omni` to the upstream vLLM backend choices.
+  Specify the backend adapter. vLLM-Omni adds `openai-chat-omni`, `openai-audio-speech`, `openai-image-edits-omni`, `daily-omni`, and `openai-realtime-duplex` to the upstream vLLM backend choices.
 
 - `--model`
   The model identifier to load, filled according to the models supported by vLLM-Omni.
@@ -19,7 +19,7 @@ You can use `vllm bench serve --omni --help=all` to get descriptions of all para
   The API endpoint exposed externally, to which clients send their requests.
 
 - `--dataset-name`
-  The name of the dataset used; random-mm indicates generating random multimodal inputs (images, videos, audio).
+  The name of the dataset used; random-mm indicates generating random multimodal inputs (images, videos, audio), while `omniinteract` replays official OmniInteract videos.
 
 - `--num-prompts`
   The total number of requests to send, an integer.
@@ -269,6 +269,44 @@ We use audio generation time / audio duration to calculate RTF.
 
 </details>
 
+### OmniInteract Realtime Benchmark
+
+OmniInteract runs each video as one native-duplex WebSocket sample in the standard serving-benchmark lifecycle:
+
+```bash
+vllm bench serve --omni \
+  --backend openai-realtime-duplex \
+  --dataset-name omniinteract \
+  --dataset-path /path/to/OmniInteract \
+  --model openbmb/MiniCPM-o-4_5 \
+  --base-url http://127.0.0.1:8000 \
+  --endpoint /v1/realtime \
+  --omniinteract-ref-audio /path/to/reference.wav \
+  --omniinteract-output-dir ./omniinteract-artifacts \
+  --num-prompts 3 \
+  --num-warmups 0
+```
+
+`--dataset-path` accepts an extracted directory, `data.tar[.gz]`, or a Hugging Face dataset ID; omitting it uses
+`lucky-lance/OmniInteract`. `--num-prompts` is the total across subsets and defaults to 3 for OmniInteract; explicit `0`
+selects all and oversize values use all available cases. Reference audio is required, and OmniInteract uses the
+`/v1/realtime` endpoint.
+
+Audio is replayed as 16 kHz PCM16 in 200 ms chunks and video at 1 FPS with real-time pacing. All selected media is decoded
+before timing and remains in client memory for the run, so `--max-concurrency` does not limit media preparation memory; use
+explicit `--num-prompts 0` only when the client has enough RAM for the full dataset. Media commands are bounded by
+`--omniinteract-media-timeout-s`, and concurrency defaults to 1. Standard request-rate, warmup, result-saving, and summary
+options apply. Use `--omniinteract-require-response` only for functional E2E cases; LISTEN is a valid benchmark result.
+
+Each completed case writes `output.wav`, `wav_transcript.json`, `events.json`, `result.json`, and a final `.done` marker under
+`--omniinteract-output-dir`. The root also contains `batch_summary.json` and `official_eval_manifest.jsonl`; failed cases write
+`.failed.json`. Runs sharing one output root are serialized. Completion validates transport, response lifecycle, and artifacts,
+not answer accuracy. Transcript timestamps are serialized playback-queue times. Clipped or cancelled outputs are ineligible and
+omitted from the official manifest; `audio_clipped_bytes` records output beyond the rounded video horizon.
+
+TTFT, TTFP, and RTF start at client receipt of `response.created`. TPOT/ITL use engine stage-0 timing; ITL is emitted only when
+every token interval is present.
+
 ### Multi-Modal Benchmark
 
 <details class="admonition abstract" markdown="1">

docs/configuration/README.md

@@ -2,13 +2,17 @@
 
 This section lists the most common options for running vLLM-Omni.
 
-For options within a vLLM Engine, please refer to the [vLLM 0.26 configuration guide](https://docs.vllm.ai/en/v0.26.0/configuration/index.html).
+For options within a vLLM Engine, please refer to the [vLLM 0.28 configuration guide](https://docs.vllm.ai/en/v0.28.0/configuration/index.html).
 
-Currently, the main options are maintained by stage configs for each model.
+Each model defines fixed topology in a registered `PipelineConfig` and runtime
+overrides in a deploy YAML.
+
+For process-level settings shared by the CLI, deploy YAML, workers, and model
+integrations, see [Environment Variables](./environment_variables.md).
 
 For a specific example, see the [Qwen2.5-Omni deploy config](gh-file:vllm_omni/deploy/qwen2_5_omni.yaml). The matching frozen pipeline topology lives at [vllm_omni/model_executor/models/qwen2_5_omni/pipeline.py](gh-file:vllm_omni/model_executor/models/qwen2_5_omni/pipeline.py).
 
-For introduction, please check [Introduction for stage config](./stage_configs.md)
+For an introduction, see [Pipeline and deploy configurations](./stage_configs.md).
 
 ## Memory Configuration
 

docs/configuration/composable_parallel.md

@@ -1,6 +1,6 @@
 # Composable parallel strategies
 
-In vLLM-Omni, the `composable_parallel` system layers a declarative *parallel strategy* on top of the existing stage configs. A strategy YAML maps each pipeline stage (by its `model_stage` name) to a stack of axis declarations — one per parallelism dimension — that the engine translates into per-stage parallel sizing (`tensor_parallel_size`, `data_parallel_size`, `pipeline_parallel_size`, expert-parallel toggle, replica count) and, when relevant, an orchestrator-wide load-balancer policy.
+In vLLM-Omni, the `composable_parallel` system layers a declarative *parallel strategy* on top of the registered pipeline and deploy config. A strategy YAML maps each pipeline stage (by its `model_stage` name) to a stack of axis declarations — one per parallelism dimension — that the engine translates into per-stage parallel sizing (`tensor_parallel_size`, `data_parallel_size`, `pipeline_parallel_size`, expert-parallel toggle, replica count) and, when relevant, an orchestrator-wide load-balancer policy.
 
 Strategies are intentionally narrow: each one only writes the axes it declares, leaving every other engine arg untouched. This makes a strategy file a small, reusable overlay you can swap independently of the deploy YAML (see `configuration/stage_configs.md`).
 
@@ -195,7 +195,7 @@ Because `omni_lb_policy` is read once at `AsyncOmniEngine` construction — not
 
 Default: no default — required for every entry under `strategies:`.
 
-Strategy keys MUST be `model_stage` *names* (strings), the same labels stage configs use for `engine_args.model_stage` (see [`model_stage` in `stage_configs.md`](stage_configs.md#engine_argsmodel_stage)). Integer `stage_id` values are not accepted; the resolver matches on `model_stage` exactly (`_resolve_stage` in `vllm_omni/config/composable_parallel/apply.py`).
+Strategy keys MUST be `model_stage` *names* (strings), the same labels defined by [`StagePipelineConfig.model_stage`](stage_configs.md#pipeline-configuration). Integer `stage_id` values are not accepted; the resolver matches on `model_stage` exactly (`_resolve_stage` in `vllm_omni/config/composable_parallel/apply.py`).
 
 ## Common pitfalls
 

docs/configuration/environment_variables.md

@@ -0,0 +1,238 @@
+# Environment Variables
+
+Environment variables are process-level configuration shared by the CLI,
+deploy YAML, Python APIs, worker processes, and model integrations. They are
+therefore documented under **User Guide → Configuration**, rather than under
+CLI Reference.
+
+This page describes the public vLLM-Omni environment-variable contract on the
+`latest` documentation branch. Use the documentation for your installed
+vLLM-Omni release when behavior differs.
+
+!!! important "Public contract"
+    Only names in [Public vLLM-Omni variables](#public-vllm-omni-variables) are
+    public Omni configuration. A name found in source code is not automatically
+    supported: model-specific, benchmark, platform, and internal variables have
+    separate ownership and lifecycle rules.
+
+## Naming and implementation boundary
+
+New public variables owned by vLLM-Omni must use the `VLLM_OMNI_` prefix.
+Existing public names with older prefixes remain supported for compatibility,
+but they do not establish naming precedent for new settings. Inherited vLLM
+and third-party names retain the prefix chosen by their owning project.
+
+The source [environment-variable inventory](gh-file:vllm_omni/config/environment_variable_inventory.py)
+records ownership, migration disposition, and redaction metadata. Unlike
+`vllm.envs`, it is not an executable value resolver: existing consumers still
+own parsing, defaults, precedence, and evaluation time. The public tables below
+describe those live consumer contracts. Stable model or stage behavior should
+move to typed configuration, and request-varying behavior should move to a
+request schema instead of adding another environment variable.
+
+## Precedence and evaluation time
+
+There is no repository-wide rule that makes environment variables override (or
+lose to) every CLI, YAML, or request option. The tables below state precedence
+for each public setting. In general:
+
+- request fields should control behavior that can vary per request;
+- typed deploy or stage configuration should control stable model and stage
+  behavior;
+- environment variables are appropriate for process launch, platform
+  integration, emergency diagnostics, and compatibility fallbacks.
+
+An environment variable can be read while a module is imported, when server or
+model configuration is constructed, when a worker is spawned, or whenever a
+property is accessed. Set import- and startup-time variables before invoking
+`vllm serve` or importing vLLM-Omni. Changing the parent shell after workers
+start does not update existing worker environments.
+
+The lifecycle labels used below are:
+
+- **Stable** — reviewed public configuration; changes require compatibility
+  consideration.
+- **Experimental** — public for evaluation, but its contract may still evolve.
+- **Diagnostic** — supported for investigation or temporary mitigation, not as
+  a normal tuning interface.
+- **Deprecated** — compatibility alias; migrate to the named replacement.
+
+## Public vLLM-Omni variables
+
+### Diffusion backend and cache selection
+
+| Name | Type and default | Applies to and read time | Precedence and invalid values | Lifecycle |
+| --- | --- | --- | --- | --- |
+| `DIFFUSION_ATTENTION_BACKEND` | Backend name or `auto`; default `auto` (platform selection) | Diffusion stages; read when `OmniDiffusionConfig` is constructed | `diffusion_attention_config.default` wins. An unknown backend fails during backend resolution. | Stable fallback |
+| `DIFFUSION_CACHE_BACKEND` | `none`, `cache_dit`, `tea_cache`, `mag_cache`, `step_cache`, `stepcache`, or `step_cache_dit`; default `none` | Diffusion runner startup | Explicit `cache_backend` in config wins. Otherwise this name wins over the deprecated alias. Unsupported values raise `ValueError` during runner setup. | Stable fallback |
+| `DIFFUSION_CACHE_ADAPTER` | Same values as `DIFFUSION_CACHE_BACKEND`; default `none` | Diffusion runner startup | Used only when neither explicit `cache_backend` nor `DIFFUSION_CACHE_BACKEND` is set. Unsupported values raise `ValueError`. | Deprecated; use `DIFFUSION_CACHE_BACKEND` |
+| `OMNI_DIFFUSION_PROMPT_EMBED_CACHE` | Boolean: `1`, `true`, `yes`, `on`, `0`, `false`, `no`, or `off`; default disabled | Each diffusion runner; resolved during model setup | A recognized environment value overrides the explicit enable setting. An unrecognized value is ignored. | Experimental |
+| `OMNI_DIFFUSION_PROMPT_EMBED_CACHE_SIZE` | Positive integer; default `32` entries | Each diffusion runner; resolved during model setup | A valid environment value overrides the explicit cache size. A non-integer logs a warning; a non-positive value is ignored. | Experimental |
+| `OMNI_DIFFUSION_SESSION_STATE_MANAGER` | Boolean with the same accepted spellings as the prompt cache; default disabled | Experimental diffusion session manager; model setup | A recognized environment value overrides the explicit enable setting. An unrecognized value is ignored. | Experimental |
+| `OMNI_DIFFUSION_SESSION_STATE_MANAGER_MAX_SESSIONS` | Positive integer; default `64` | Experimental diffusion session manager; model setup | A valid environment value overrides the explicit maximum. A non-integer logs a warning; a non-positive value is ignored. | Experimental |
+
+Backend names for `DIFFUSION_ATTENTION_BACKEND` are the members of
+`DiffusionAttentionBackendEnum`, such as `FLASH_ATTN`, `TORCH_SDPA`,
+`SAGE_ATTN`, `FLASHINFER_ATTN`, and `TRTLLM_ATTN`. Platform support still
+depends on the installed kernels and model path.
+
+### Serving and runtime
+
+| Name | Type and default | Applies to and read time | Precedence and invalid values | Lifecycle |
+| --- | --- | --- | --- | --- |
+| `SPEAKER_SAMPLES_DIR` | Filesystem path; default `~/.cache/vllm-omni/speakers` | Speech server; read when speaker storage initializes | Environment-only setting. The directory is created; filesystem errors propagate. | Stable |
+| `SPEAKER_MAX_UPLOADED` | Integer; default `1000` | Speech server; read when speaker storage initializes | Environment-only setting. A non-integer logs a warning and uses `1000`; range is not otherwise validated. | Stable |
+| `VLLM_OMNI_ASYNC_OUTPUT_TIMEOUT` | Float seconds; default `600` | Diffusion engine async-output wait in `step_streaming`; resolved per call on the request path, not at import | Environment-only setting. A non-float or `<=0` value warns once and uses the default. | Experimental |
+| `VLLM_OMNI_EVENT_DRIVEN_ORCH` | `1`, `true`, `yes` or `on` enables; default `0` (off) | Orchestration loop and the serving-side final-output drain; read once when the `Orchestrator` is constructed | Environment-only setting. Values are stripped and case-normalized; any unrecognized value leaves the legacy poll loop selected. | Experimental |
+| `VLLM_OMNI_INPUT_WAIT_TIMEOUT_S` | Float seconds; default `600`; `<=0` disables | Full-payload input coordinator, not async-chunk transfer; read when the scheduler module imports in each worker | Environment-only setting. A non-float logs a warning and uses `600`. | Stable operational control |
+| `VLLM_OMNI_ORCH_MONITOR_PATH` | Filesystem path; default `<current-working-directory>/vllm_omni_orch_monitor_<timestamp>.json` | Orchestrator monitor enabled by `--enable-orch-monitor`; read when the monitor is created | Environment-only path override. Parent directories are created; write errors are logged. | Diagnostic |
+| `VLLM_OMNI_VIDEO_SYNC_TIMEOUT` | Float seconds; default `600` | Synchronous Videos API; read when the API server module imports | Environment-only setting. A non-float raises `ValueError` during import. | Experimental |
+| `VLLM_VIDEO_ASYNC_CHUNK` | `on` or `off`; default `on` | Streaming video output; read on attribute access | Environment-only setting. Values are trimmed and case-normalized; an invalid value warns once and uses `on`. | Experimental |
+| `VLLM_VIDEO_AUDIO_DELTA_MODE` | `fast` or `slow`; default `fast` | Streaming video audio deltas; read on attribute access | Environment-only setting. Values are trimmed and case-normalized; an invalid value warns once and uses `fast`. | Experimental |
+
+### Server storage
+
+Storage names use Pydantic's nested-settings delimiter (`__`). They are read
+when `ServerSettings` is initialized, normally during server import/startup.
+Invalid integer values raise a Pydantic validation error.
+
+| Name | Type and default | Meaning | Lifecycle |
+| --- | --- | --- | --- |
+| `VLLM_OMNI_SERVER_STORAGE__PATH` | Path; default `/tmp/storage` | Directory for completed server files. | Stable |
+| `VLLM_OMNI_SERVER_STORAGE__FILE_CONCURRENCY` | Integer; default `4` | Maximum concurrent file operations. No additional range validation is currently applied. | Stable |
+| `VLLM_OMNI_SERVER_STORAGE__FILE_TTL` | Integer seconds or unset; default unset | Optional lifetime for locally stored files. | Stable |
+| `VLLM_OMNI_SERVER_STORAGE__TTL_SWEEP_INTERVAL` | Integer seconds or unset | Sweep frequency. If file TTL is set and this is unset, the effective default is `300`. | Stable |
+| `VLLM_OMNI_STORAGE_PATH` | Path | Deprecated alias for `VLLM_OMNI_SERVER_STORAGE__PATH`. The new name wins when both are set. | Deprecated |
+| `VLLM_OMNI_STORAGE_MAX_CONCURRENCY` | Integer | Deprecated alias for `VLLM_OMNI_SERVER_STORAGE__FILE_CONCURRENCY`. The new name wins when both are set. | Deprecated |
+
+The deprecated aliases emit `DeprecationWarning`. They do not affect file TTL
+settings.
+
+### Quantization diagnostics and performance
+
+| Name | Type and default | Applies to and read time | Precedence and invalid values | Lifecycle |
+| --- | --- | --- | --- | --- |
+| `VLLM_OMNI_SKIP_NVFP4_NAN_CLAMP` | Boolean truthy spellings: `1`, `true`, `yes`, `on`; default false | ModelOpt NVFP4 compatibility patch; read when `vllm_omni.patch` imports | Environment-only escape hatch. Any other value means false. Set only to diagnose the upstream NaN-scale issue. | Diagnostic and temporary |
+| `VLLM_OMNI_USE_QUACK_FP8` | Boolean truthy spellings: `1`, `true`, `yes`, `on`; unset means hardware auto-detection | FP8 scaled matrix multiplication; evaluated when quack capability is selected | A set value overrides auto-detection. Any non-truthy value forces quack off. If quack cannot load, vLLM-Omni warns and falls back to FlashInfer. | Experimental performance control |
+
+`QUACK_CACHE_DIR` is owned by the external quack library and is not an
+Omni-owned variable, even though vLLM-Omni supplies a persistent default when
+it is unset.
+
+## Per-stage environment
+
+Deploy configurations can set arbitrary environment keys for one stage:
+
+```yaml
+stages:
+  - stage_id: 0
+    env:
+      HCCL_IF_BASE_PORT: 23000
+      MY_MODEL_CACHE: /mnt/model-cache
+```
+
+The legacy nested form under `runtime.env` is also accepted. vLLM-Omni converts
+keys and values to strings, applies them while the stage engine or child process
+is launched, logs keys but not values, and restores the parent process's prior
+environment afterward. A child process inherits the scoped values. This keeps
+sibling stages from permanently changing one another's parent environment.
+
+This scoping has two consequences:
+
+1. A setting read inside stage/model initialization can see the stage value.
+2. A setting already cached by an import that occurred before stage launch
+   cannot be changed retroactively with stage `env`.
+
+Stage `env` is an escape hatch for process and third-party settings, not a
+replacement for typed stage configuration. Do not put request-varying behavior
+there. Secret values may reach child processes; logs and diagnostics must emit
+their keys only.
+
+## Inherited vLLM variables
+
+vLLM-Omni also reads variables through its aligned vLLM dependency. Refer to
+the [vLLM 0.28 environment-variable reference](https://docs.vllm.ai/en/v0.28.0/configuration/env_vars.html)
+for their definitions. This includes vLLM launch, cache, logging, plugin, ROCm,
+XPU, ModelScope, and FlashInfer workspace settings.
+
+An inherited name does not become Omni-owned because Omni reads or forwards it.
+Where behavior differs, the Omni-specific page should document only that
+difference. For example, device-visibility values can be further narrowed by a
+stage's `devices` setting.
+
+## Platform, external, and secret variables
+
+Hardware runtimes, distributed launchers, and third-party libraries retain
+ownership of their variables. Common groups include:
+
+- device visibility (`ASCEND_RT_VISIBLE_DEVICES`, `HIP_VISIBLE_DEVICES`, and
+  `MUSA_VISIBLE_DEVICES`);
+- distributed launch (`MASTER_ADDR`, `MASTER_PORT`, `RANK`, and `WORLD_SIZE`);
+- Hugging Face cache and authentication (`HF_HOME`, `HF_TOKEN`, and related
+  names);
+- accelerator libraries such as NCCL, FlashInfer, MindIE, MORI, and quack.
+
+Consult the owning project's documentation for accepted values and support.
+`HF_TOKEN`, `HUGGINGFACE_HUB_TOKEN`, and `OPENAI_API_KEY` are explicitly marked
+for redaction in the reviewed inventory. Never print their values in environment
+collection, examples, bug reports, or logs.
+
+## Model-specific variables
+
+The audit found 58 variables read by a single model or pipeline family. They are
+not listed as public usage options here because doing so would turn implementation
+escape hatches into an accidental compatibility contract.
+
+Every audited model-specific name has a migration disposition in the
+[environment-variable inventory](gh-file:vllm_omni/config/environment_variable_inventory.py):
+
+| Disposition | Count | Required outcome |
+| --- | ---: | --- |
+| Promote | 35 | Move a stable setting into typed stage or model configuration. |
+| Request scope | 6 | Move request-varying behavior into a declared request-option schema. |
+| External | 0 | Retain only when a supported third-party library owns the contract. |
+| Internalize | 11 | Keep a debug or diagnostic switch out of public documentation and configuration. |
+| Deprecate/remove | 6 | Remove a compatibility escape hatch that has no continuing contract. |
+
+The disposition is a migration target, not a statement that the existing
+environment switch is stable. Promote or request-scope work should land in
+model-owner-reviewed follow-up changes with validation and model-page updates.
+The family-sized migration packages and their ownership gate are tracked in
+[#6232](https://github.com/vllm-project/vllm-omni/issues/6232).
+
+## Environment collection and troubleshooting
+
+Run the repository collector when preparing a bug report:
+
+```bash
+python collect_env.py
+```
+
+The environment section includes the safe public Omni allowlist, registered
+vLLM variables, and selected platform prefixes. It omits names marked for
+redaction and names containing common secret terms. Review the output before
+sharing it. A key supplied through stage `env` is not collected merely because
+it appears in deploy configuration; the same allowlist and prefix rules apply.
+
+If a setting appears ineffective, first check its read time in the tables
+above. Restart processes after changing import- or startup-time values, verify
+that the variable reaches the intended worker, and check whether an explicit
+typed configuration field takes precedence.
+
+## Benchmark and internal variables
+
+The reviewed inventory marks 20 benchmark variables as transitional while
+benchmark configuration moves to CLI/request schemas in
+[issue #5376](https://github.com/vllm-project/vllm-omni/issues/5376). They are not
+part of the serving/runtime contract.
+
+Internal process-coordination and debug variables are deliberately excluded
+from this page. The CI scanner resolves string literals and module-level string
+constants used by direct `os` access, local wrapper helpers, aliased `os`
+imports, and environment membership checks. Server-storage names generated by
+Pydantic settings are checked separately against their model fields.
+
+Names assembled from cross-module metadata and arbitrary keys supplied through
+stage `env` cannot be inferred reliably. Contributors must classify those names
+explicitly. CI fails when a statically resolved name has no classification or
+when a reviewed public name is absent from this page.

docs/configuration/gpu_memory_utilization.md

@@ -80,19 +80,15 @@ When multiple stages share the same GPU, you must ensure the sum of their `gpu_m
 
 **Example: Two stages on GPU 0**
 ```yaml
-stage_args:
+stages:
   - stage_id: 0
-    runtime:
-      devices: "0"
-    engine_args:
-      gpu_memory_utilization: 0.6  # Uses 60% of GPU 0
+    devices: "0"
+    gpu_memory_utilization: 0.6  # Uses 60% of GPU 0
 
   - stage_id: 1
-    runtime:
-      devices: "0"
-    engine_args:
-      gpu_memory_utilization: 0.3  # Uses 30% of GPU 0
-      # Total: 90% of GPU 0 (safe, leaves 10% buffer)
+    devices: "0"
+    gpu_memory_utilization: 0.3  # Uses 30% of GPU 0
+    # Total: 90% of GPU 0 (safe, leaves 10% buffer)
 ```
 
 **Important:** If stages run on different GPUs, each can use up to 1.0 independently.
@@ -103,14 +99,12 @@ When using `tensor_parallel_size > 1`, the model is split across multiple GPUs,
 
 **Example: 2-way tensor parallelism**
 ```yaml
-stage_args:
+stages:
   - stage_id: 0
-    runtime:
-      devices: "0,1"  # Uses both GPUs
-    engine_args:
-      tensor_parallel_size: 2
-      gpu_memory_utilization: 0.6  # 60% per GPU
-      # Model is split, so each GPU uses ~30% of model memory
+    devices: "0,1"  # Uses both GPUs
+    tensor_parallel_size: 2
+    gpu_memory_utilization: 0.6  # 60% per GPU
+    # Model is split, so each GPU uses ~30% of model memory
 ```
 
 ## Examples
@@ -118,27 +112,25 @@ stage_args:
 ### Qwen3-Omni-MoE on 2x H100-80GB
 
 ```yaml
-stage_args:
+base_config: /path/to/vllm_omni/deploy/qwen3_omni_moe.yaml
+
+stages:
   - stage_id: 0  # Thinker stage with TP=2
-    runtime:
-      devices: "0,1"
-    engine_args:
-      tensor_parallel_size: 2
-      gpu_memory_utilization: 0.6  # 48GB per GPU
+    devices: "0,1"
+    tensor_parallel_size: 2
+    gpu_memory_utilization: 0.6  # 48GB per GPU
 
   - stage_id: 1  # Talker stage
-    runtime:
-      devices: "1"
-    engine_args:
-      gpu_memory_utilization: 0.3  # 24GB on GPU 1
+    devices: "1"
+    gpu_memory_utilization: 0.3  # 24GB on GPU 1
 
   - stage_id: 2  # Code2Wav stage
-    runtime:
-      devices: "0"
-    engine_args:
-      gpu_memory_utilization: 0.1  # 8GB on GPU 0
+    devices: "0"
+    gpu_memory_utilization: 0.1  # 8GB on GPU 0
 ```
-**Note:** In this configuration, stages 0 and 2 share GPU 0, but they run at different times in the pipeline, so their memory usage doesn't overlap.
+**Note:** Stage 0 uses GPUs 0 and 1, so the combined utilization is `0.7` on
+GPU 0 and `0.9` on GPU 1. Keep the sum of all resident stages below `1.0` on
+each device.
 
 ## Troubleshooting
 

docs/configuration/stage_configs.md

@@ -3,53 +3,104 @@
 In vLLM-Omni, a model's `PipelineConfig` defines its fixed stage topology, while a deploy configuration controls how those stages run.
 
 !!! note
-    Default deploy config YAMLs (for example, `vllm_omni/deploy/qwen2_5_omni.yaml`, `vllm_omni/deploy/qwen3_omni_moe.yaml`, and `vllm_omni/deploy/qwen3_tts.yaml`) are bundled and loaded automatically when `--deploy-config` is omitted. The model registry resolves the right pipeline and deploy YAML by `model_type`.
+    Default deploy config YAMLs (for example, `vllm_omni/deploy/qwen2_5_omni.yaml`, `vllm_omni/deploy/qwen3_omni_moe.yaml`, and `vllm_omni/deploy/qwen3_tts.yaml`) are bundled and loaded automatically when `--deploy-config` is omitted. The resolved pipeline selects its default through `default_deploy_config_name`.
+
+## Pipeline configuration
+
+`PipelineConfig` and its `StagePipelineConfig` entries are Python definitions
+owned by the model implementation and registered in `OMNI_PIPELINES`. They
+describe fixed model topology and are not accepted in deploy YAMLs.
+
+Common `PipelineConfig` fields include:
+
+| Field | Description |
+| ------- | ------------- |
+| `model_type` | Pipeline identifier used during model and config resolution. |
+| `default_deploy_config_name` | Bundled deploy YAML loaded when the user does not pass `deploy_config`. |
+| `model_arch` | Default Hugging Face architecture for the pipeline. |
+| `hf_architectures` | Architecture names used to identify checkpoints whose `model_type` is shared. |
+| `hf_config_predicate` | Optional predicate used to select between pipelines with otherwise identical HF metadata. |
+| `diffusers_class_name` | Diffusers `_class_name` used to identify Diffusers-style repositories. |
+| `stages` | Ordered tuple of fixed `StagePipelineConfig` definitions. |
+
+Common `StagePipelineConfig` fields include:
+
+| Field | Description |
+| ------- | ------------- |
+| `stage_id` | Stable stage identifier. |
+| `model_stage` | Logical stage name used by runtime and strategy resolution. |
+| `execution_type` | `LLM_AR`, `LLM_GENERATION`, or `DIFFUSION`. |
+| `input_sources` | Upstream stage IDs that provide this stage's inputs. |
+| `final_output` / `final_output_type` | Whether the stage produces a user-visible output and its modality. |
+| `owns_tokenizer` | Whether this stage owns the pipeline tokenizer. |
+| `model_arch`, `hf_config_name` | Stage-specific model architecture and nested HF config selector. |
+| `engine_output_type` | Runtime output representation such as `text`, `latent`, or `audio`. |
+| `custom_process_input_func` | Processor applied to this stage's incoming payload. |
+| `custom_process_next_stage_input_func` | Processor used for full-payload handoff to the next stage. |
+| `async_chunk_process_next_stage_input_func` | Processor used for async chunk handoff. |
+| `sampling_constraints` | Model-owned sampling constraints that deploy defaults cannot override. |
+
+To add or change topology, define and register a new pipeline variant. Use
+deploy YAML only for runtime placement, resource sizing, connectors, and other
+deployment overrides.
 
 ## Deploy configuration schema
 
 The new deploy schema lives under `vllm_omni/deploy/` and is paired with a frozen `PipelineConfig` registered by the model's `pipeline.py`. Each deploy YAML has these top-level fields:
 
 | Field | Type | Required | Default | Description |
-|-------|------|----------|---------|-------------|
+| ------- | ------ | ---------- | --------- | ------------- |
 | `base_config` | str (path) | optional | — | Overlay parent (relative or absolute). `stages:` / `platforms:` deep-merged by stage_id; other scalars overlay-wins. Intended for user-authored overlays; prod yamls stay flat. |
 | `async_chunk` | bool | optional | `true` | Enable chunked streaming between stages. Pin to `false` if the pipeline runs end-to-end. |
+| `session_mode` | str | optional | `"turn"` | Session behavior. MiniCPM-o 4.5 deploy YAMLs set `"duplex"` so `/v1/realtime` and chat share the same profile. |
+| `active_stream_window` | int | optional | `0` | Number of active downstream stream slots; `0` preserves all-stream cycling. |
+| `duplex_session` | dict | optional | runtime defaults | Full-duplex session lifecycle, buffering, replay, and capacity limits. |
 | `connectors` | dict | optional | `null` | Named connector specs (`{name, extra}`). Referenced by each stage's `input_connectors` / `output_connectors`. See [Connector schema](#connector-schema). |
 | `edges` | list | optional | `null` | Explicit edge list for the KV transfer graph. Auto-derived from stage inputs if omitted. |
-| `stages` | list | required | — | Per-stage engine args + wiring (see [Stage fields](#stage-fields)). |
+| `stages` | list | optional | `[]` | Per-stage runtime overrides matched by `stage_id`. Pipeline stages are still created from `PipelineConfig` when this list is empty. |
 | `platforms` | dict | optional | `null` | Keyed by `npu` / `rocm` / `xpu`, each contains a `stages:` list with per-platform overrides applied on top of the CUDA defaults. |
-| `pipeline` | str | optional | `null` | Override the auto-detected pipeline registry key (used for structural variants like `qwen2_5_omni_thinker_only`). |
-| `trust_remote_code` | bool | optional | `true` | **Pipeline-wide.** Trust HF remote code on model load; applies to every stage. |
+| `pipeline` | str | optional | `null` | Override the auto-detected pipeline registry key (used for structural variants like `qwen2_5_omni_thinker_only` / `qwen3_omni_moe_thinker_only`). |
+| `trust_remote_code` | bool \| null | optional | `null` | **Pipeline-wide.** Trust HF remote code on model load; applies to every stage when specified. |
 | `distributed_executor_backend` | str \| null | optional | `null` | **Pipeline-wide.** Distributed executor backend forwarded to vLLM (`"mp"`, `"ray"`, `"external_launcher"`). If omitted, vLLM auto-selects backend from runtime topology. |
 | `dtype` | str \| null | optional | `null` | **Pipeline-wide.** Model dtype for every stage. |
 | `quantization` | str \| null | optional | `null` | **Pipeline-wide.** Quantization method for every stage. |
-| `enable_prefix_caching` | bool | optional | `false` | **Pipeline-wide.** Prefix cache toggle applied to every stage. |
+| `enable_prefix_caching` | bool \| null | optional | `null` | **Pipeline-wide.** Prefix cache toggle applied to every stage when specified. |
 | `enable_chunked_prefill` | bool \| null | optional | `null` | **Pipeline-wide.** Chunked prefill toggle applied to every stage. |
-| `data_parallel_size` | int | optional | `1` | **Pipeline-wide.** DP degree for every stage. |
-| `pipeline_parallel_size` | int | optional | `1` | **Pipeline-wide.** PP degree for every stage. |
+| `data_parallel_size` | int \| null | optional | `null` | **Pipeline-wide.** DP degree for every stage. |
+| `pipeline_parallel_size` | int \| null | optional | `null` | **Pipeline-wide.** PP degree for every stage. |
+| `custom_voice_dir` | str \| null | optional | `null` | **Pipeline-wide.** Directory containing custom voice profiles for supported TTS models. |
 
-Note: for diffusion path, `distributed_executor_backend` currently defaults to
-`mp`, and `ray` / `external_launcher` are not fully supported yet.
+For fields whose deploy default is `null`, the deploy layer contributes no
+override. The effective value may still come from a platform section, an
+explicit CLI or stage override, or the downstream vLLM engine default.
+
+Note: for the diffusion path, an omitted `distributed_executor_backend` selects
+`uni` on a single GPU (in-process worker, no MessageQueue / `/dev/shm` output
+segments) and `mp` when `num_gpus > 1`. Set `mp` explicitly to keep a worker
+subprocess on one GPU. `ray` / `external_launcher` are not fully supported yet.
 
 ### Stage fields
 
-Each entry under `stages:` accepts any `StageDeployConfig` field directly (no nested `engine_args:`). Only fields whose value legitimately varies across stages live here; pipeline-wide settings (trust_remote_code, distributed_executor_backend, dtype, quantization, prefix/chunked prefill, DP/PP sizes) are declared at the top level and applied to every stage. Unknown keys fall through to `engine_extras:` and are forwarded to the engine.
+Each entry under `stages:` accepts any `StageDeployConfig` field directly (no nested `engine_args:`). Only fields whose value legitimately varies across stages live here; pipeline-wide settings (trust_remote_code, distributed_executor_backend, dtype, quantization, prefix/chunked prefill, DP/PP sizes) are declared at the top level and applied to every stage. Unknown keys fall through to `engine_extras:` and are forwarded to the engine. Frequently used fields are listed below; the source-of-truth schema is `StageDeployConfig` in `vllm_omni/config/stage_config.py`.
 
 | Field | Type | Required | Default | Description |
-|-------|------|----------|---------|-------------|
+| ------- | ------ | ---------- | --------- | ------------- |
 | `stage_id` | int | required | — | Stage identity; matched against `PipelineConfig.stages[*].stage_id`. |
-| `max_num_seqs` | int | optional | `64` | Max concurrent sequences per stage. |
-| `gpu_memory_utilization` | float | optional | `0.9` | Per-stage total memory target; used for automatic KV-cache sizing. |
+| `max_num_seqs` | int \| null | optional | `null` | Max concurrent sequences per stage. |
+| `gpu_memory_utilization` | float \| null | optional | `null` | Per-stage total memory target; used for automatic KV-cache sizing. |
 | `kv_cache_memory_bytes` | int \| null | optional | `null` | Explicit per-rank KV-cache budget in bytes; overrides automatic sizing. |
-| `tensor_parallel_size` | int | optional | `1` | TP degree for this stage. |
-| `enforce_eager` | bool | optional | `false` | Disable CUDA graphs. |
-| `max_num_batched_tokens` | int | optional | `32768` | Per-stage prefill/token budget; also contributes to the native maximum in-flight token limit. |
+| `tensor_parallel_size` | int \| null | optional | `null` | TP degree for this stage. |
+| `enforce_eager` | bool \| null | optional | `null` | Disable CUDA graphs. |
+| `max_num_batched_tokens` | int \| null | optional | `null` | Per-stage prefill/token budget; also contributes to the native maximum in-flight token limit. |
 | `max_model_len` | int \| null | optional | `null` | Per-sequence context or KV length; `-1` enables native cache-capacity auto-fitting, while values above the HF default auto-set `VLLM_ALLOW_LONG_MAX_MODEL_LEN=1`. |
 | `async_scheduling` | bool \| null | optional | `null` | Per-stage async scheduling toggle. |
-| `devices` | str | optional | `"0"` | `CUDA_VISIBLE_DEVICES`-style device list. |
+| `devices` | str \| null | optional | `null` | Device list assigned to this stage. The number of device ids must equal this stage's local world size (`tensor_parallel_size` × local data-parallel size × `pipeline_parallel_size`, or `num_replicas` × that product for a replica pool); a mismatch fails early — see the note below. |
 | `output_connectors` | dict \| null | optional | `null` | Keyed by `to_stage_<n>`; values are names registered under top-level `connectors:`. |
 | `input_connectors` | dict \| null | optional | `null` | Keyed by `from_stage_<n>`; values are names registered under top-level `connectors:`. |
 | `default_sampling_params` | dict \| null | optional | `null` | Baseline sampling params. Deep-merged with pipeline `sampling_constraints` (pipeline wins). |
-| `engine_extras` | dict | optional | `{}` | Catch-all for keys not listed above; deep-merged across overlays. Also carries per-stage overrides of pipeline-wide settings (e.g. stage-specific `dtype`). |
+| `engine_extras` | dict | optional | `{}` | Catch-all for engine fields not listed above; deep-merged across overlays and forwarded to the stage engine. |
+
+**Note:** a stage's `devices` count must equal its local world size (`tensor_parallel_size` × `data_parallel_size_local` × `pipeline_parallel_size`, falling back to global `data_parallel_size` when the local size is unset), or `num_replicas` × that product for a replica pool. A mismatch fails early and names the offending stage. A top-level `--tensor-parallel-size` is broadcast to every stage, so it can make a single-GPU stage violate this contract ([issue #5003](gh-issue:5003)); fix that case with `--stage-overrides` (set `tensor_parallel_size` and `devices` together per stage) or set TP only on the multi-GPU stage.
 
 ### Connector schema
 
@@ -61,12 +112,12 @@ connectors:
     name: <ConnectorClassName>     # required — class registered in vllm_omni.distributed
     extra:                         # optional — forwarded to the connector's __init__
       <key>: <value>
-      ...
+      # Additional connector-specific options
 ```
 
-| Connector class | Use case | `extra` keys |
-|-----------------|----------|--------------|
-| `SharedMemoryConnector` | Same-host KV transfer between stages (default for bundled YAMLs). | None. All payloads use shared memory. |
+| Connector class          | Use case                                                              | `extra` keys                                                                                                      |
+|--------------------------|-----------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------|
+| `SharedMemoryConnector`  | Same-host KV transfer between stages (default for bundled YAMLs).     | None. All payloads use shared memory.                                                                             |
 | `MooncakeStoreConnector` | Cross-host KV transfer over TCP. Required for multi-node deployments. | `host`, `metadata_server`, `master`, `segment` (int bytes), `localbuf` (int bytes), `proto` (`"tcp"` / `"rdma"`). |
 
 A stage references a connector by name in its `input_connectors` / `output_connectors`:
@@ -86,7 +137,7 @@ stages:
 ### CLI flags
 
 | Flag | Description |
-|------|-------------|
+| ------ | ------------- |
 | `--deploy-config PATH` | Load a deploy YAML. **Optional** — when omitted, the bundled `vllm_omni/deploy/<model_type>.yaml` is auto-loaded by the model registry. |
 | `--stage-overrides JSON` | Per-stage JSON overrides, e.g. `'{"0":{"gpu_memory_utilization":0.5}}'`. Per-stage values always win over global flags. |
 | `--async-chunk` / `--no-async-chunk` | Flip the deploy YAML's `async_chunk:` bool. Unset (default) leaves the YAML value in force. |
@@ -200,7 +251,7 @@ CUDA_VISIBLE_DEVICES=0 vllm serve Qwen/Qwen3-Omni-30B-A3B-Instruct --omni \
 Effective config per stage after the merge:
 
 | Stage | Field | Final value | Source |
-|-------|-------|-------------|--------|
+| ------- | ------- | ------------- | -------- |
 | 0 | `gpu_memory_utilization` | `0.9` | base YAML (overlay didn't touch stage 0) |
 | 0 | `max_num_seqs` | `8` | per-stage CLI (`--stage-overrides`) — wins over base `32` |
 | 0 | `max_model_len` | `16384` | global CLI |
@@ -224,300 +275,17 @@ when loading a deploy configuration.
 Examples:
 
 For offline inference (assuming the necessary dependencies have been imported):
+
 ```python
 model_name = "Qwen/Qwen2.5-Omni-7B"
 omni = Omni(model=model_name, deploy_config="/path/to/deploy_config.yaml")
 ```
 
 For online serving:
+
 ```bash
 vllm serve Qwen/Qwen2.5-Omni-7B --omni --port 8091 --deploy-config /path/to/deploy_config.yaml
 ```
 
 !!! important
     We are actively iterating on the definition of deployment configurations, and we welcome feedback from users and developers.
-
-## Legacy stage configuration reference
-
-The remainder of this page documents the deprecated `stage_args` schema for models that have not migrated to deploy configurations. The following historical Qwen2.5-Omni configuration illustrates that schema; current Qwen2.5-Omni deployments should use `vllm_omni/deploy/qwen2_5_omni.yaml` instead.
-
-```python
-# Historical stage configuration for Qwen2.5-Omni.
-stage_args:
-  - stage_id: 0 # mark the unique id for each stage
-    runtime: # The disaggregated configuration
-      process: true  # Run this stage in a separate process
-      devices: "0" # Logical device index for this stage (mapped through CUDA_VISIBLE_DEVICES / ASCEND_RT_VISIBLE_DEVICES if set)
-    engine_args: # Engine arguments for a certain engine
-      model_stage: thinker
-      max_num_seqs: 1
-      model_arch: Qwen2_5OmniForConditionalGeneration # The model implementation registered in model_executor/models/registry.py
-      worker_type: ar # The specific worker used
-      scheduler_cls: vllm_omni.core.sched.omni_ar_scheduler.OmniARScheduler # The specific scehduler used
-      gpu_memory_utilization: 0.8 # The gpu memory allocation for the stage within a single chip
-      enforce_eager: true  # Now we only support eager mode
-      trust_remote_code: true # Needed by huggingface config parsing
-      engine_output_type: latent  # It claims that the stage will input latent hiddenstates besides token ids
-      enable_prefix_caching: false # For request with hiddenstates output, the prefix caching is not supported now
-    is_comprehension: true # If the stage is a text or multimodal comprehension module. If it is, the AsyncOmni will use its tokenizer as default
-    final_output: true # If the stage has output as part of final outputs. If it is false, which means that the stage only works as a intermediate role.
-    final_output_type: text # What is the final output type. It can be text and audio now.
-    default_sampling_params: # sampling parameters for the stage. Their meaning aligns with vLLM.
-      temperature: 0.0
-      top_p: 1.0
-      top_k: -1
-      max_tokens: 2048
-      seed: 42
-      detokenize: True
-      repetition_penalty: 1.1
-  - stage_id: 1
-    runtime:
-      process: true
-      devices: "1"
-    engine_args:
-      model_stage: talker
-      max_num_seqs: 3
-      model_arch: Qwen2_5OmniForConditionalGeneration
-      worker_type: ar
-      scheduler_cls: vllm_omni.core.sched.omni_ar_scheduler.OmniARScheduler
-      gpu_memory_utilization: 0.8
-      enforce_eager: true
-      trust_remote_code: true
-      enable_prefix_caching: false
-      engine_output_type: latent
-    engine_input_source: [0]
-    default_sampling_params:
-      temperature: 0.9
-      top_p: 0.8
-      top_k: 40
-      max_tokens: 2048
-      seed: 42
-      detokenize: True
-      repetition_penalty: 1.05
-      stop_token_ids: [8294]
-  - stage_id: 2
-    runtime:
-      process: true
-      devices: "0"            # Example: use a different GPU than the previous stage; use "0" if single GPU
-    engine_args:
-      model_stage: code2wav
-      max_num_seqs: 1
-      model_arch: Qwen2_5OmniForConditionalGeneration
-      worker_type: generation
-      scheduler_cls: vllm_omni.core.sched.omni_generation_scheduler.OmniGenerationScheduler
-      gpu_memory_utilization: 0.15
-      enforce_eager: true
-      trust_remote_code: true
-      enable_prefix_caching: false
-      engine_output_type: audio
-    engine_input_source: [1]
-    final_output: true
-    final_output_type: audio
-    default_sampling_params:
-      temperature: 0.0
-      top_p: 1.0
-      top_k: -1
-      max_tokens: 2048
-      seed: 42
-      detokenize: True
-      repetition_penalty: 1.1
-
-# Top-level runtime config (concise): default windows and stage edges
-runtime:
-  enabled: true
-
-  edges:
-    - from: 0                   # thinker → talker: trigger only after receiving full input (-1)
-      to: 1
-    - from: 1                   # talker → code2wav: trigger only after receiving full input (-1)
-      to: 2
-
-```
-
-## Legacy stage configuration arguments
-
-Each stage in the `stage_args` list contains the following configuration options:
-
-### `stage_id`
-
-A unique identifier for each stage in the multi-stage pipeline. Stages are numbered sequentially starting from 0, and this ID is used to reference stages in inter-stage dependencies (e.g., `engine_input_source`).
-
-### `prompt_expand_func` (Optional)
-
-A custom Python function hook for the LLM stage (Stage 0) that expands a single incoming prompt object into multiple prompts. This is primarily used for multi-modal Classifier-Free Guidance (CFG), where it generates the necessary companion requests (like a negative text prompt) and tags them with internal roles (e.g., `cfg_text`). This ensures the upstream LLM generates the needed contextual hidden states for both the conditional and unconditional generations simultaneously.
-
-### `cfg_kv_collect_func` (Optional)
-
-A custom Python function hook for downstream diffusion stages (Stage 1+) to collect, map, and process the KV caches transferred from the companion requests fired by `prompt_expand_func`. It aggregates the hidden condition states cleanly (e.g., binding them as `cfg_text_past_key_values` and `cfg_text_kv_metadata`), allowing the diffusion runtime to perform CFG smoothly without redundantly evaluating text paths on the DiT workers.
-
-### `runtime`
-
-Configuration for disaggregated execution of the stage, controlling how the stage is deployed and executed.
-
-#### `runtime.process`
-
-Whether to run this stage in a separate process. When set to `true`, the stage will be executed in an isolated process, enabling better resource isolation and parallel execution across different stages. This is essential for multi-GPU deployments where different stages run on different devices.
-
-Default: `true`
-
-#### `runtime.devices`
-
-Logical device indices for this stage, specified as a string. Values are **logical indices** (`0`, `1`, `2`, ...) — not physical GPU IDs — and are mapped through the platform's visibility env var (`CUDA_VISIBLE_DEVICES` on CUDA, `ASCEND_RT_VISIBLE_DEVICES` on NPU) before being applied via `torch.cuda.set_device()` (or the equivalent).
-
-Example: if `CUDA_VISIBLE_DEVICES=0,2,4` is set in the environment, then `devices: "0"` selects physical GPU 0 (the first visible), `devices: "1"` selects physical GPU 2, and `devices: "0,1"` makes physical GPUs 0 and 2 available to the stage. If no visibility env var is set, logical and physical IDs coincide.
-
-Default: `"0"`
-
-#### `engine_args.max_num_seqs`
-
-The maximum number of sequences for concurrent processing in this stage. For
-LLM stages, this controls the vLLM scheduler's maximum concurrent sequences.
-For diffusion stages, this controls scheduler wave capacity in both request
-batch mode and step batch mode. `max_num_seqs: 1` is the serial/conservative
-path; values above `1` allow compatible requests to batch when the selected
-diffusion pipeline supports that batching mode.
-
-Default: `1`
-
-#### `engine_args.request_batch_max_wait_ms`
-
-The maximum time, in milliseconds, that a diffusion request-mode stage may wait
-before the first `schedule()` of a new scheduler wave so compatible requests can
-accumulate for request-level batching. This only applies to diffusion pipelines
-that support request-level batching with `step_execution` disabled.
-
-Use this together with `max_num_seqs > 1` for bursty serving traffic. `0`
-disables admission waiting and preserves the lowest first-request latency.
-For diffusion execution and batching tuning, see
-[Diffusion Execution Modes](../user_guide/diffusion/execution_modes.md).
-
-Default: `0.0`
-
-### `engine_args`
-
-Engine arguments for configuring the LLM engine, diffusion engine, or other engine types used by this stage.
-
-#### `engine_args.model_stage`
-
-The name identifier for this model stage within the multi-stage architecture. This is used internally to distinguish different stages of the same model (e.g., "thinker", "talker", "code2wav" in Qwen2.5-Omni).
-
-#### `engine_args.model_arch`
-
-The model architecture class name that is registered in `model_executor/models/registry.py`. This specifies which model implementation to use for this stage. The class must be registered in the model registry for vLLM-Omni to locate and instantiate it.
-
-#### `engine_args.worker_cls`
-
-The specific worker class to use for this stage. This determines how the model computations are executed. Examples include `vllm_omni.worker.gpu_ar_worker.GPUARWorker` for autoregressive stages and `vllm_omni.worker.gpu_generation_worker.GPUGenerationWorker` for diffusion-based stages.
-
-#### `engine_args.scheduler_cls`
-
-The scheduler class to use for this stage. The scheduler manages request queuing, batching, and execution order. Examples include `vllm_omni.core.sched.omni_ar_scheduler.OmniARScheduler` for standard stages and `vllm_omni.core.sched.omni_generation_scheduler.OmniGenerationScheduler` for diffusion stages.
-
-#### `engine_args.gpu_memory_utilization`
-
-The fraction of GPU memory to allocate for this stage within a single GPU chip. This is a value between 0.0 and 1.0, where 0.8 means 80% of the GPU memory will be used by this stage. This allows fine-grained control over memory allocation when multiple stages share the same GPU or when reserving memory for other operations.
-
-Default: `0.8`
-
-!!! tip "Memory Configuration Guide"
-    For detailed information on how to calculate memory requirements and properly configure `gpu_memory_utilization`, see the [GPU Memory Calculation and Configuration Guide](./gpu_memory_utilization.md).
-
-#### `engine_args.enforce_eager`
-
-Whether to enforce eager execution mode. When set to `true`, the engine will run in eager mode without using CUDA graphs or other compilation optimizations. Currently, vLLM-Omni only supports eager mode.
-
-Default: `true`
-
-#### `engine_args.trust_remote_code`
-
-Whether to trust remote code when loading models from Hugging Face. This is required for models that use custom code in their configuration files. Set to `true` when loading models that require custom model implementations.
-
-Default: `true`
-
-#### `engine_args.engine_output_type`
-
-Specifies the type of output produced by this stage's engine. This determines what kind of data flows to downstream stages. Possible values include `latent` (hidden states), `text` (tokenized text), and `audio` (audio waveforms). When set to `latent`, the stage outputs latent hidden states in addition to token IDs, which are consumed by downstream stages.
-
-Default: `latent`
-
-#### `engine_args.enable_prefix_caching`
-
-Whether to enable prefix caching for this stage. Prefix caching can improve performance by caching KV cache for common prompt prefixes. However, for requests that output hidden states (when `engine_output_type` is `latent`), prefix caching is not currently supported and should be set to `false`.
-
-Default: `false`
-
-### `is_comprehension`
-
-Whether this stage is a text or multimodal comprehension module. When set to `true`, the stage acts as a comprehension module that processes input text or multimodal content. If this is the first comprehension stage, `AsyncOmni` will use its tokenizer as the default tokenizer for the entire pipeline.
-
-Default: `true`
-
-### `final_output`
-
-Whether this stage produces output that is part of the final outputs returned to the user. When set to `false`, the stage only works as an intermediate stage, processing data that flows to downstream stages but not contributing directly to the final response.
-
-Default: `true`
-
-### `final_output_type`
-
-The type of final output produced by this stage. This specifies what format the output will be in when returned to the user. Currently supported values are `text` (for text generation) and `audio` (for audio generation).
-
-Default: `text`
-
-### `default_sampling_params`
-
-Default sampling parameters for this stage. These parameters control the generation behavior and align with vLLM's sampling parameter semantics. These defaults are used when no explicit sampling parameters are provided in the request.
-
-#### `default_sampling_params.temperature`
-
-Sampling temperature for controlling randomness. Lower values (e.g., 0.0) make the output more deterministic and focused, while higher values increase randomness.
-
-Default: `0.0`
-
-#### `default_sampling_params.top_p`
-
-Nucleus sampling parameter. Only tokens with cumulative probability mass up to `top_p` are considered. This helps filter out low-probability tokens while maintaining diversity.
-
-Default: `1.0`
-
-#### `default_sampling_params.top_k`
-
-Top-k sampling parameter. Only the top `k` most likely tokens are considered. Set to `-1` to disable top-k filtering and consider all tokens.
-
-Default: `-1`
-
-#### `default_sampling_params.max_tokens`
-
-Maximum number of tokens to generate in this stage. This limits the length of the output sequence.
-
-Default: `2048`
-
-#### `default_sampling_params.seed`
-
-Random seed for reproducible generation. When set, the random number generator will be initialized with this seed to ensure consistent outputs across runs.
-
-Default: `42`
-
-#### `default_sampling_params.detokenize`
-
-Whether to detokenize the output tokens into text. When set to `true`, token IDs are converted back to readable text strings.
-
-Default: `True`
-
-#### `default_sampling_params.repetition_penalty`
-
-Penalty applied to tokens that have already appeared in the generated sequence. Values greater than 1.0 discourage repetition, while values less than 1.0 encourage it. A value of 1.0 applies no penalty.
-
-Default: `1.1`
-
-### `tts_args` (TTS stages only)
-
-Configuration for Text-to-Speech specific parameters. This section is only applicable to TTS model stages (e.g., `qwen3_tts`).
-
-#### `tts_args.max_instructions_length`
-
-Maximum character length for voice style/emotion instructions. Instructions exceeding this limit will be rejected with a validation error.
-
-Default: `500`
-
-This value can be overridden at runtime using the `--tts-max-instructions-length` CLI parameter when starting the server.

docs/contributing/DOCS_GUIDE.md

@@ -46,7 +46,7 @@ class Omni:
 
     Args:
         model: Model name or path
-        stage_configs: Optional stage configurations
+        deploy_config: Optional path to a deploy configuration
         **kwargs: Additional arguments passed to the engine
 
     Example:

docs/contributing/README.md

@@ -44,6 +44,151 @@ vLLM-Omni's `pre-commit` hooks will now run automatically every time you commit.
     pre-commit run --show-diff-on-failure --color=always --all-files  # runs on all files (short for --all-files)
     ```
 
+!!! warning
+    GitHub Actions `pre-commit` **skips** several local gates so a whole-tree
+    `--all-files` run stays green while historical debt is cleaned up. The
+    current `SKIP` list in
+    [`.github/workflows/pre-commit.yml`](https://github.com/vllm-project/vllm-omni/blob/main/.github/workflows/pre-commit.yml)
+    is `check-mark`, `markdownlint-cli2`, `shellcheck`,
+    `check-spdx-header`, and `mypy-3.10`. Those hooks still run on **your
+    commit** for changed files. A passing GitHub pre-commit check does not mean
+    they passed locally. Hooks **not** on that list (forbidden imports,
+    `torch.cuda`, TTS adapter ratchet, Buildkite schema, Ruff, typos, …) run in
+    both places.
+
+The hooks below are new relative to the previous Omni config (Ruff, typos,
+actionlint, YAML/whitespace, DCO sign-off, and the pickle-only checker were
+already there). `check-pickle-imports` is gone: pickle is now one rule inside
+`check-forbidden-imports`.
+
+| Hook id | Enforces | GitHub Actions |
+| ------- | -------- | -------------- |
+| `markdownlint-cli2` | Markdown in `docs/`, `recipes/`, `README.md`, `CONTRIBUTING.md` (not `.claude/` / `.cursor/` / `CLAUDE.md`) | skipped |
+| `mypy-3.10` | Type-check changed `vllm_omni/` files (model trees excluded). `tests/` uses `--follow-imports skip` | skipped |
+| `mypy-3.11` / `3.12` / `3.13` | Same checker, extra Python versions | not installed; `pre-commit run --hook-stage manual mypy-3.12` |
+| `check-mark` | Every `tests/**/test_*.py` has a CI level mark and a hardware platform mark or helper; no direct `pytest.mark.H100` / SKU | skipped |
+| `check-tts-adapter-migration` | `self._tts_model_type` branches in `serving_speech.py` must not increase | runs |
+| `shellcheck` | `*.sh` quoting / undefined vars | skipped |
+| `check-spdx-header` | Omni SPDX header on `.py` / `.pyi` / `.sh` / `.rs` / `.proto` | skipped |
+| `check-forbidden-imports` | pickle, stdlib `re`/`base64`, Triton, TileLang, Hugging Face Hub download/API | runs |
+| `check-torch-cuda-call` | Direct `torch.cuda.*` helpers outside platform adapters | runs |
+| `check-buildkite` | `.buildkite/*.yml` against the Buildkite schema | runs |
+
+Related config (not a new hook id): Ruff `TID251` also bans `librosa` and several
+`torch.cuda.*` names; typos is pinned and ignores git-describe hashes (`+g` +
+hex) and NumPy `writeable`. Keep the `suggestion` hook last in
+`.pre-commit-config.yaml` so autofix output does not bury the SKIP tip.
+
+#### SPDX headers
+
+Source files must start with:
+
+```text
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
+```
+
+Rust and proto files use `//` instead of `#`. Empty files are exempt. A stale
+`Copyright contributors to the vLLM project` line is **rewritten in place** to
+the Omni copyright; the hook then exits non-zero so you restage the rewrite.
+
+#### Forbidden imports
+
+Under `vllm_omni/`, do not use:
+
+- stdlib `re` → `import regex as re`
+- stdlib `base64` → `import pybase64` (or `import pybase64 as base64`)
+- `pickle` / `cloudpickle` (also banned in most tests)
+- `from huggingface_hub import snapshot_download` / `hf_hub_download` / `HfApi`
+  / similar → `vllm.transformers_utils.repo_utils`
+- direct `triton` / `tilelang` → `vllm.triton_utils` / `vllm.tilelang_utils`
+
+`examples/`, `tests/`, `benchmarks/`, `apps/`, `docs/`, `tools/`, `scripts/`,
+`.buildkite/`, and `.github/` may keep stdlib `re` and `base64`
+(`_NON_LIBRARY_DIRS`). Pickle is stricter: tests are not exempt unless the
+file is on the pickle allowlist.
+
+#### `torch.cuda` call sites
+
+Prefer `current_omni_platform` / `OmniPlatform`. Platform adapters under
+`vllm_omni/platforms/` are allowed; other files must not add new
+`torch.cuda.*` helpers (`device_count`, `synchronize`, `empty_cache`,
+`device()`, `manual_seed`, and similar). `import torch` is fine.
+
+#### shellcheck
+
+The hook does **not** download a binary. Install `shellcheck` with a signed
+package manager, then re-run:
+
+- Debian/Ubuntu/WSL: `sudo apt-get install shellcheck`
+- Fedora: `sudo dnf install ShellCheck`
+- macOS: `brew install shellcheck`
+- Git Bash (MINGW): `scoop install shellcheck` so `shellcheck.exe` is on PATH.
+  WSL is Linux: use `apt-get`, not a Windows `.exe`.
+
+If no native binary is found, the wrapper prints those commands (and
+[the upstream install page](https://github.com/koalaman/shellcheck?tab=readme-ov-file#installing))
+and exits 1. AMD and other vendor scripts are **not** excluded.
+
+#### mypy
+
+`mypy-3.10` runs on commit for changed files. `[tool.mypy]` uses Python 3.12,
+`ignore_missing_imports = true`, and `follow_imports = "silent"`. Wrapper
+`tools/pre_commit/mypy.py` skips `vllm_omni/model_executor/models/` and
+`vllm_omni/diffusion/models/`, and type-checks `tests/` with
+`--follow-imports skip`. Extra versions:
+
+```bash
+pre-commit run --hook-stage manual mypy-3.12
+```
+
+#### Test CI marks
+
+`check-mark` requires each collected test module under `tests/` to
+have at least one CI **level** mark (`core_model`, `advanced_model`,
+`full_model`, `local_model`, or `slow`) and a **hardware** platform mark
+(`cpu`, `cuda`, …) or helper (`hardware_test(` / `hardware_marks(`). SKU marks
+(`H100`, `L4`, …) must come from those helpers so `cards_{n}` is attached;
+direct `pytest.mark.H100` is rejected. GitHub Actions skips this hook; local
+commit does not. See the
+[test writing guide](./ci/test_writing_guide.md).
+
+#### TTS adapter ratchet
+
+`check-tts-adapter-migration` counts `self._tts_model_type` comparisons in
+`vllm_omni/entrypoints/openai/serving_speech.py`. The count must stay at or
+below `MAX_MODEL_TYPE_BRANCHES` in
+[`tools/pre_commit/check_tts_adapter.py`](https://github.com/vllm-project/vllm-omni/blob/main/tools/pre_commit/check_tts_adapter.py).
+New per-model behavior belongs in
+`vllm_omni/entrypoints/openai/tts_adapters/`. Removing branches **must** lower
+the budget in the same change; raising it is a reviewable policy edit.
+
+#### Markdownlint and Buildkite
+
+`markdownlint-cli2` auto-fixes `docs/`, `recipes/`, and the root README /
+CONTRIBUTING (rules in `.markdownlint.yaml`). Skill files under `.claude/` and
+`.cursor/` are excluded.
+
+`check-buildkite` validates `.buildkite/*.yml` against the official schema
+after the same expansion `upload_pipeline.py` uses. Omni-only keys such as
+`mirror_hardwares` are stripped or rendered first. A few files are skipped in
+`SKIP_FILES` inside `tools/pre_commit/check_buildkite.py`.
+
+#### Extending allowlists and budgets
+
+Prefer fixing the call site. Growing an allowlist or raising a budget is a
+policy change and needs review — do not edit these just to make the hook pass.
+
+| Gate | File | What to edit |
+| ---- | ---- | ------------ |
+| pickle, `re`, `base64`, Hugging Face Hub, Triton/TileLang | [`tools/pre_commit/check_forbidden_imports.py`](https://github.com/vllm-project/vllm-omni/blob/main/tools/pre_commit/check_forbidden_imports.py) | `CHECK_IMPORTS[<rule>].allowed_files`. Use `allowed_dirs` only when a whole tree should stay exempt. |
+| `torch.cuda` | [`tools/pre_commit/check_torch_cuda.py`](https://github.com/vllm-project/vllm-omni/blob/main/tools/pre_commit/check_torch_cuda.py) | `ALLOWED_FILES`, or `ALLOWED_PREFIXES` for a platform adapter tree. |
+| TTS `_tts_model_type` branches | [`tools/pre_commit/check_tts_adapter.py`](https://github.com/vllm-project/vllm-omni/blob/main/tools/pre_commit/check_tts_adapter.py) | `MAX_MODEL_TYPE_BRANCHES` (down only, unless the PR justifies a raise). |
+| Buildkite skip list | [`tools/pre_commit/check_buildkite.py`](https://github.com/vllm-project/vllm-omni/blob/main/tools/pre_commit/check_buildkite.py) | `SKIP_FILES` for pipelines that cannot be expanded. |
+
+To skip one hook for a single commit (discouraged): `SKIP=<hook-id> git commit`.
+To bypass every hook: `git commit --no-verify` (also discouraged).
+
 ### Documentation
 
 MkDocs is a fast, simple and downright gorgeous static site generator that's geared towards building project documentation. Documentation source files are written in Markdown, and configured with a single YAML configuration file, `mkdocs.yml`.
@@ -162,12 +307,14 @@ Only specific types of PRs will be reviewed. The PR title is prefixed appropriat
 Before submitting a PR, run the [precheck-pr skill](https://github.com/vllm-project/vllm-omni/blob/main/.claude/skills/precheck-pr/SKILL.md) with the code agent for a self-review against project conventions:
 
 The skill offers two modes:
+
 - **Quick (~3 min):** catches showstoppers — PR title format, missing benchmark claims, rebase status
 - **Full (~10 min):** thorough maintainer-grade review — dead code scan, copy-paste detection, import hygiene
 
 The precheck covers five PR types: Bug Fix, Performance, New Model, Diffusion Model, and General. Each type has a tailored checklist that validates evidence quality (repro steps, A/B benchmarks, registry entries, etc.). See the [precheck-pr skill](https://github.com/vllm-project/vllm-omni/blob/main/.claude/skills/precheck-pr/SKILL.md) for the full checklist.
 
 ### Local Test
+
 Please run the L1 and L2 test cases locally first and attach the results before contacting us to add the "ready" label. Please refer to the [test instructions](./ci/test_execution_guide.md) for running the test cases.
 
 ### Automatic skip-ci (docs and pytest skip marks)
@@ -189,7 +336,7 @@ These PR shapes all trigger skip-ci:
 Skip-ci does **not** apply when the diff also touches product code (for example `vllm_omni/`), or when test files change assertions, imports, fixtures, or other non-skip logic. If the diff cannot be resolved (non-PR branches outside `main`), CI runs as usual.
 
 !!! note
-    Skipping L2/L3 does **not** disable the Docker image build step. Nightly (L4) upload can still run when the PR has a `nightly-test` label or on scheduled `main` builds with `NIGHTLY=1`. Bootstrap child steps live in `bootstrap-upload-steps.yml`; the hook entry step runs `upload_pipeline.py --upload <platform>/bootstrap-upload-steps.yml`, which injects `if` by step `key` from skip-ci before upload. See [CI Settings — Diff-aware CI](./ci/ci_settings.md#diff-aware-ci) and [Test System Overview](./ci/test_system_overview.md).
+    Skipping L2/L3 does **not** disable the Docker image build step. Nightly (L4) upload can still run when the PR has a `nightly-test` label or on scheduled `main` builds with `NIGHTLY=1`. Weekly (L5) upload can still run with `weekly-test`, `WEEKLY=1`, or `NON_CRITICAL=1`. Bootstrap child steps live in `bootstrap-upload-steps.yml`; the hook entry step runs `upload_pipeline.py --upload <platform>/bootstrap-upload-steps.yml`, which injects `if` by step `key` from skip-ci before upload. See [CI Settings — Diff-aware CI](./ci/ci_settings.md#diff-aware-ci) and [Test System Overview](./ci/test_system_overview.md).
 
 ### Code Quality
 

docs/contributing/ci/ci_settings.md

@@ -8,7 +8,7 @@ For CI levels (L1–L5) and triggers, see [Test System Overview](./test_system_o
 
 Canonical layout (prefer these paths for new changes):
 
-```
+```text
 .buildkite/
 ├── common/                          # Shared across platforms
 │   ├── scripts/
@@ -48,7 +48,7 @@ Canonical layout (prefer these paths for new changes):
     └── scripts/
 ```
 
-**Placement rules**
+### Placement rules
 
 | Rule | Detail |
 | ---- | ------ |
@@ -57,13 +57,11 @@ Canonical layout (prefer these paths for new changes):
 | Bootstrap vs test YAML | **Bootstrap** (`pipeline*.yml`) builds images and uploads **child** test pipelines. **Test** YAML (`test-*.yml`) lists pytest steps only. |
 | Register CI YAML in `skip_ci.py` | If you add a new whitelisted test pipeline file, update `L2_YAML_FILES`, `L3_YAML_FILES`, or `L45_YAML_FILES` in `.buildkite/common/scripts/skip_ci.py` so skip-ci paths stay correct. |
 
-There are still **legacy copies** at `.buildkite/*.yaml` (without the `cuda/` prefix). Treat `.buildkite/cuda/*` as source of truth.
-
 ## Platform comparison
 
 | Platform | Bootstrap entry | Test job files | Upload mechanism | Job hardware in YAML |
 | -------- | ---------------- | -------------- | ---------------- | -------------------- |
-| **CUDA** | `cuda/pipeline.yml` | `test-ready.yml`, `test-merge.yml`, `test-nightly.yml`, `test-weekly.yml` | `upload_pipeline.py --upload` (expands uploader-only keys) | `mirror_hardwares: <preset>` (string) |
+| **CUDA** | `cuda/pipeline.yml` | `test-ready.yml`, `test-merge.yml`, `test-nightly.yml`, `test-weekly.yml` | `upload_pipeline.py --upload` (expands uploader-only keys) | omit `mirror_hardwares` (from `-m`) / preset string + `MIRROR_HW` |
 | **NPU** | `npu/pipeline-npu.yml` | `test-npu-ready.yml`, `test-npu-nightly.yml` | `upload_pipeline.py --upload` | `mirror_hardwares: a2b3_npu_1` / `a2b3_npu_4` / `a3_npu_2` |
 | **AMD** | `amd/scripts/bootstrap-amd-omni.sh` | `test-amd-ready.yml`, `test-amd-merge.yml` | Jinja (`test-template-amd-omni.j2`) → `pipeline upload` | `agent_pool` + `mirror_hardwares: [amdproduction]` (array, template filter) |
 | **Intel** | `intel/scripts/bootstrap-intel-omni.sh` | `intel/pipeline-intel.yml` (steps inline) | Direct `pipeline upload` | Inline `agents.queue` on each step |
@@ -81,11 +79,34 @@ There are still **legacy copies** at `.buildkite/*.yaml` (without the `cuda/` pr
     | L2 | `cuda/test-ready.yml` | `ready` label |
     | L3 | `cuda/test-merge.yml` | `merge-test` label / main merge |
     | L4 | `cuda/test-nightly.yml` | `nightly-test` label or `NIGHTLY=1` |
-    | L5 | `cuda/test-weekly.yml` | `weekly-test` label or `WEEKLY=1` |
+    | L5 | `cuda/test-weekly.yml` | `weekly-test` label, `WEEKLY=1`, or `NON_CRITICAL=1` (see step `if`s below) |
+
+    On scheduled `main` builds, `test-weekly.yml` is uploaded when **`WEEKLY=1` or `NON_CRITICAL=1`**. Inside that file:
+
+    - **Reliability** / **Perf Test** / **Simple · CPU Coverage Test** → `WEEKLY=1` (or PR label `weekly-test` for Reliability/Perf). Pipeline upload for scheduled env vars still requires `main`.
+    - **E2E Tests** group (slow Omni/TTS/Diffusion sweeps) → `NON_CRITICAL=1`
+
+    **`--e2e`:** when uploading ready/merge on `main` with **`WEEKLY=1`**, bootstrap passes `--e2e` so only the **E2E Test** group is kept (same flag `run_cov_split.sh` uses to enable per-model coverage).
 
     **Upload:** `upload_pipeline.py --upload` expands uploader-only keys before Buildkite upload.
 
-    **Hardware in YAML:** `mirror_hardwares: <preset>` (string)—preset names in [`common/ci_mirror_hardwares.yml`](https://github.com/vllm-project/vllm-omni/blob/main/.buildkite/common/ci_mirror_hardwares.yml). Do **not** set `agents` / `plugins` on the same step.
+    **Hardware in YAML:** omit `mirror_hardwares` to compose `{chip}_{n}` from pytest `-m` (SKU + `cards_n`). Several positive `cards_*` take the **max** n (`cards_2 and cards_3` → `{chip}_3`). `not cards_1` with no positive `cards_*` uses that chip's **highest existing** preset in `ci_mirror_hardwares.yml` (L4 → `l4_4`, H100 → `h100_4`, B200 → `b200_4`). Set a **preset string** to force a pool (marks are ignored). Do **not** set `agents` / `plugins` on the same step as `mirror_hardwares`. `MIRROR_HW=b200` remaps inferred jobs whose `-m` includes `B200`, and omits CUDA `h100_*` / `l4_*` strings. Jobs whose `-m` does not match (unset: no H100/L4; set: no B200) are **skipped**.
+
+    ```yaml
+    # inferred: H100+B200 + cards_2 → h100_2, or b200_2 when MIRROR_HW=b200
+    commands:
+      - pytest -sv tests/e2e -m "full_model and H100 and B200 and omni and cards_2"
+    # no mirror_hardwares
+
+    # inferred: not cards_1 (no positive cards_*) → that chip's highest pool (l4_4 / h100_4 / b200_4)
+    commands:
+      - pytest -sv tests/e2e -m "full_model and diffusion and L4 and not cards_1"
+
+    # forced preset (ignores -m SKU/cards for pool selection)
+    mirror_hardwares: h100_1
+    ```
+
+    Inferred form: unset/`empty` `MIRROR_HW` matches `H100` or `L4` in `-m` (both present → H100); no match skips the step. `MIRROR_HW=b200` must appear in `-m` or the step is skipped. The uploader does **not** rewrite pytest `-m`; B200 collection comes from the YAML expression and from tests that declare `B200` in `hardware_test` / `hardware_marks` (for example `res={"cuda": ["H100", "B200"]}`). `MIRROR_HW` must be empty or `b200` (case-insensitive); unknown values (for example `b20o`) **fail the upload**. A CUDA preset string such as `mirror_hardwares: h100_4` is skipped when `MIRROR_HW=b200`. Unset `MIRROR_HW` keeps string presets. NPU presets ignore `MIRROR_HW`. A `mirror_hardwares` name that is not a key in `ci_mirror_hardwares.yml` **fails the upload**.
 
     **Conventions**
 
@@ -96,9 +117,9 @@ There are still **legacy copies** at `.buildkite/*.yaml` (without the `cuda/` pr
 
     **Adding a job**
 
-    1. Pick the level file (ready / merge / nightly / weekly).
+    1. Pick the level file (`test-ready.yml` / `test-merge.yml` / `test-nightly.yml` / `test-weekly.yml`).
     2. Add a step under the right **group** (usually **E2E Test** for model pytest).
-    3. Set `label`, `commands`, `mirror_hardwares`, `depends_on: upload-<level>-pipeline`.
+    3. Set `label`, `commands`, `mirror_hardwares`, `depends_on: upload-ready-pipeline` (or `upload-merge-pipeline` / `upload-nightly-pipeline` / `upload-weekly-pipeline`).
     4. For L2/L3 E2E, add `source_file_dependencies` (pytest + model + deploy YAML prefixes).
     5. Dry-run:
 
@@ -112,6 +133,8 @@ There are still **legacy copies** at `.buildkite/*.yaml` (without the `cuda/` pr
 
     **Test YAML:** `npu/test-npu-ready.yml` (L2), `test-npu-nightly.yml` (L4).
 
+    **L2 trigger:** PR label `ready` (CUDA/AMD L2). PR labels `ready` + `npu-test` trigger NPU CI only.
+
     **Upload:** same as CUDA—`upload_pipeline.py --upload`.
 
     **Hardware in YAML:** `mirror_hardwares` preset (string), expanded to `agents`, top-level `image`, and `plugins`. Presets: `a2b3_npu_1`, `a2b3_npu_4`, `a3_npu_2` in `common/ci_mirror_hardwares.yml`.
@@ -204,15 +227,15 @@ Label triggers (`ready`, `merge-test`) are unchanged—diff-aware logic only red
 | Diff shape | Path | Default L2/L3 |
 | --- | --- | --- |
 | Product code / non-whitelisted paths | normal CI | all on |
-| Docs and/or qualifying skip-mark only | `skip_all` | all off (scheduled `main` NIGHTLY/WEEKLY exceptions below) |
+| Docs and/or qualifying skip-mark only | `skip_all` | all off (scheduled `main` NIGHTLY/WEEKLY/`NON_CRITICAL` exceptions below) |
 | Whitelisted CI YAML only | yaml-gated (`skip_l2_l3`) | per platform/level matrix |
 | Docs/skip-mark **+** whitelisted CI YAML | yaml-gated (`skip_l2_l3`) | same as CI-YAML-only (does **not** widen to normal CI) |
 | Non-qualifying skip-mark and no CI YAML | normal CI | all on |
 | Diff unavailable | normal CI | all on |
 
-**`skip_all` exceptions (CUDA/NPU bootstrap only):** PR labels (`nightly-test`, `merge-test`, `npu-test`, `weekly-test`, …) do **not** revive jobs. On `main`, scheduled `NIGHTLY=1` still builds the image and uploads L4 plus L2/L3 (with `--e2e`); `WEEKLY=1` still builds the image and uploads L5 (CUDA).
+**`skip_all` exceptions (CUDA/NPU bootstrap only):** PR labels (`nightly-test`, `merge-test`, `weekly-test`, `ready`, …) do **not** revive jobs under `skip_all`. On `main`, scheduled `NIGHTLY=1` still builds the image and uploads L4; `WEEKLY=1` or `NON_CRITICAL=1` still builds the image and uploads L5 (CUDA). `WEEKLY=1` on `main` also uploads L2/L3 with `--e2e`.
 
-**Yaml-gated nightly/weekly:** L4/L5 upload steps keep their normal label / `NIGHTLY` / `WEEKLY` conditions (for example PR `nightly-test` still uploads nightly). Only L2/L3 upload steps are matrix-gated.
+**Yaml-gated nightly/weekly:** L4/L5 upload steps keep their normal label / `NIGHTLY` / `WEEKLY` / `NON_CRITICAL` conditions (for example PR `nightly-test` still uploads nightly). Only L2/L3 upload steps are matrix-gated.
 
 ##### Whitelisted CI YAML → platform
 
@@ -285,12 +308,12 @@ CUDA **L2** (`.buildkite/cuda/test-ready.yml`) and **L3** (`.buildkite/cuda/test
 | `source_file_dependencies` | List of path **prefixes**. If any changed file equals a prefix or starts with `prefix/`, keep the step (or group); otherwise omit it. |
 | `mirror_hardwares` | Expand to `agents` + `plugins` (+ optional `image`) from `ci_mirror_hardwares.yml`. |
 
-**Policy**
+### Policy
 
 - **Always uploaded** (no key): groups outside **E2E Test**—Simple Test, Diffusion unit tests, Engine/Model Executor, Distributed, Custom Pipeline, Entrypoints (L2), LoRA / Entrypoints (L3).
 - **Diff-gated**: every **E2E Test** leaf job. List the smallest prefix set per step—pytest file(s), model code under `vllm_omni/model_executor/models/` or `vllm_omni/diffusion/models/`, plus `stage_input_processors/` and `vllm_omni/deploy/*.yaml` when applicable. A **group** may define the key instead; the whole group drops if no prefix matches.
 
-**YAML examples**
+### YAML examples
 
 ```yaml
       - label: "Diffusion · Qwen Image Test"
@@ -312,7 +335,7 @@ CUDA **L2** (`.buildkite/cuda/test-ready.yml`) and **L3** (`.buildkite/cuda/test
         mirror_hardwares: l4_4
 ```
 
-**Local dry-run**
+### Local dry-run
 
 ```bash
 python3 .buildkite/common/scripts/upload_pipeline.py .buildkite/cuda/test-ready.yml
@@ -324,18 +347,19 @@ On PR builds, `upload_pipeline.py` logs `skip '…' (no changes under …)` for
 
 ### Labels and grouping
 
-- Use **groups** for dashboard readability; keep **E2E Test** as the group name CUDA diff filtering expects for `--e2e` nightly runs on main.
+- Use **groups** for dashboard readability; keep **E2E Test** as the group name CUDA diff filtering expects for `--e2e` weekly runs on main (`WEEKLY=1`).
 - Prefix labels by model domain: **Omni ·**, **TTS ·**, **Diffusion ·**, **Simple ·**, etc., matching existing steps.
 
 ### Per-model coverage
 
 Pilot (v1): one Merge-tier (L3) job in `.buildkite/cuda/test-merge.yml` uploads
 per-model, per-entry-mode coverage as Buildkite artifacts — "TTS · Qwen3-TTS Base
-Test". It runs on a single GPU so the pilot is cheap to reproduce. (A second
-diffusion pilot, "Diffusion · Bagel Test", was retired from merge; reintroduce
-another diffusion coverage job if a second pilot is needed again.) This is a
-pilot, not a rollout: no dashboard/visualization lives in this repo, and nothing
-is gated on coverage.
+Test". Coverage + artifact upload run only when bootstrap uploaded that job with
+`--e2e` (`WEEKLY=1` and `BUILDKITE_BRANCH=main`, via `run_cov_split.sh`); otherwise
+the same job runs plain pytest without `--cov`. It runs on a single GPU so the
+pilot is cheap to reproduce.
+
+**L1 package coverage (CPU):** ready/merge Simple Test jobs run without `--cov`. Scheduled weekly on `main` with `WEEKLY=1` runs **Simple · CPU Coverage Test** in `.buildkite/cuda/test-weekly.yml` (`pytest -m 'core_model and cpu'` + Cobertura XML artifact).
 
 **Naming convention**: `coverage-<model_id>-<mode>-<step_id>.xml.gz`, where
 `<model_id>` is the model's directory name under
@@ -354,8 +378,8 @@ imported. A run covering zero lines produces the same 7 MB. gzip takes it to
 roughly 410 KB and changes nothing about the data.
 
 `<step_id>` is what keeps two steps that cover the same model apart. Artifacts are
-scoped to the build, and a nightly build carries the ready, merge and nightly tiers
-at once, so several same-model steps land in one namespace — `qwen3_tts` already has
+scoped to the build, and a weekly build can carry ready/merge (with `--e2e`) and
+weekly tiers at once, so several same-model steps land in one namespace — `qwen3_tts` already has
 both a Base and a CustomVoice job, and `minicpmo_4_5` a base and a duplex one. Two
 uploads on one path do not fail loudly: an exact-name download can report the path
 as ambiguous, and a glob download fetches every match concurrently and renames each
@@ -392,7 +416,7 @@ loads the model once per mode.
 Before splitting, confirm each half actually collects at least one test under the job's forwarded filters — pytest exits with code 5 ("no tests collected") if a half is empty, which the script reports as a failure, red-ing a job that used to pass as one combined invocation.
 
 Also check the tests' `num_cards` against the job's `mirror_hardwares` preset.
-`cuda_marks` turns `num_cards > 1` into `skipif(device_count() < num_cards)`, so a
+`_cuda_marks` turns `num_cards > 1` into `skipif(device_count() < num_cards)`, so a
 test asking for more GPUs than the preset provides is silently skipped and its
 coverage artifact comes back empty. Splitting such a job produces a file that
 looks fine and measures nothing — "Diffusion · Z Image Test" is the current
@@ -404,10 +428,12 @@ settings in `pyproject.toml`: online tests launch the server with
 `subprocess.Popen` and stop it with SIGTERM, and without those settings the XML
 reflects only the pytest parent process, not the server's code paths.
 
-The upload depends on `buildkite-agent` being callable inside the container. The
-`kubernetes` presets (`h100_*`, `*_npu_*`) provide it; the `docker` ones (`l4_*`)
-only do because they set `mount-buildkite-agent: true`. A new docker preset that
-runs a coverage job needs the same.
+The upload depends on `buildkite-agent` being callable inside the container.
+CUDA (`l4_*`, `h100_*`) and NPU (`*_npu_*`) presets all use the `kubernetes`
+plugin, which provides the agent. `l4_*` jobs run on the EKS `l4-k8s` queue
+(agent-stack-k8s); they no longer use the docker plugin or
+`mount-buildkite-agent`. A new **docker** preset that runs a coverage job
+still needs `mount-buildkite-agent: true`.
 
 List both `run_cov_split.sh` and `pyproject.toml` in every opted-in job's
 `source_file_dependencies` — both change what the job measures, so without them a
@@ -425,7 +451,7 @@ just confirming both files exist.
 | Check | Command / location |
 | ----- | ------------------ |
 | CUDA render | `python3 .buildkite/common/scripts/upload_pipeline.py .buildkite/cuda/test-<level>.yml` |
-| No leaked uploader keys | `… \| grep -E 'mirror_hardwares|source_file_dependencies'` → empty |
+| No leaked uploader keys | Pipe the render into `grep -E 'mirror_hardwares\|source_file_dependencies'` — expect empty |
 | Skip-ci / upload unit tests | `pytest tests/buildkite/` |
 | Local job replay (CUDA L2+) | `tools/run_ready_jobs.sh`, `tools/run_merge_jobs.sh`, `tools/nightly/run_nightly_jobs.sh` (read YAML from `cuda/`) |
 

docs/contributing/ci/failures.md

@@ -6,7 +6,7 @@ When you open a PR against vLLM-Omni, several CI checks run automatically:
 
 | Check | Platform | What it does |
 | ----- | -------- | ------------ |
-| **pre-commit** | GitHub Actions | Runs linting (Ruff), formatting, spell-checking (typos), and YAML validation. |
+| **pre-commit** | GitHub Actions | Runs Ruff, formatting, typos, YAML validation, forbidden imports, `torch.cuda`, TTS adapter ratchet, and Buildkite schema. Local-only gates (SPDX, shellcheck, markdownlint, mypy-3.10, test marks) are in the workflow `SKIP` env — see [Linting](../README.md#linting). |
 | **Build Wheel** | GitHub Actions | Builds Python wheels for Python 3.11 and 3.12 on Ubuntu. Skipped for docs-only or Markdown-only changes (controlled by `paths-ignore` in the workflow). |
 | **DCO** | GitHub | Verifies every commit has a `Signed-off-by` line. |
 | **docs/readthedocs.org:vllm-omni** | Read the Docs | Builds the MkDocs documentation site. |
@@ -29,6 +29,13 @@ pre-commit run --all-files
 
 Then commit the fixes and push.
 
+A green GitHub pre-commit job can still miss SPDX, shellcheck, mypy-3.10,
+markdownlint, and test-mark coverage because CI skips those IDs. If your
+**local** commit hook fails, follow [Linting](../README.md#linting) for the
+full hook list (forbidden imports, `torch.cuda`, TTS adapter ratchet, and
+Buildkite schema still run in GHA). Do not grow allowlists or raise
+`MAX_MODEL_TYPE_BRANCHES` without review.
+
 ### DCO failures
 
 Every commit must include a `Signed-off-by` line. If you forgot, amend your commits:

docs/contributing/ci/test_examples/l4_functionality_tests.inc.md

@@ -1,8 +1,8 @@
-**Scope**
+#### Scope
 
 For diffusion models, the L4 functionality test covers all or common *diffusion features* that are supported by this model, including several [parallelism acceleration methods](https://docs.vllm.ai/projects/vllm-omni/en/latest/user_guide/diffusion/parallelism_acceleration/), [CPU offloading](https://docs.vllm.ai/projects/vllm-omni/en/latest/user_guide/diffusion/cpu_offload/), [TeaCache](https://docs.vllm.ai/projects/vllm-omni/en/latest/user_guide/diffusion/teacache/) and [Cache-DiT](https://docs.vllm.ai/projects/vllm-omni/en/latest/user_guide/diffusion/cache_dit_acceleration/) cache backends, [quantization methods](https://docs.vllm.ai/projects/vllm-omni/en/latest/user_guide/quantization/overview/).
 
-**Test Case Design**
+#### Test Case Design
 
 For a *high priority* model (currently listed in [issue #1832](https://github.com/vllm-project/vllm-omni/issues/1832)), we use several test cases, each with multiple features turned on, so that each supported feature is tested in at least one test case. This is to relieve the GPU workload on the CI machine. The suggested test case combination is as follows:
 
@@ -34,12 +34,12 @@ For a *normal priority* model, further reduce the number of test cases.
 
 Currently all the features are available in online serving mode. Hence, only need to add `tests/e2e/online_serving/test_{model}_expansion.py`.
 
-**Code Style**
+#### Code Style
 
-- Validation: test that the multimodal output files of your model have the correct shapes. `OpenAIClientHandler.send_diffusion_request` should have taken care of this.
+- Validation: test that the multimodal output files of your model have the correct shapes. `OnlineOmniClient.send_diffusion_request` should have taken care of this.
 - Test marks: always add `full_model` and `diffusion` for L4 nightly `test_*_expansion.py` cases. Add GPU-related marks if needed. Ref: [Markers for Tests](https://docs.vllm.ai/projects/vllm-omni/en/latest/contributing/ci/test_writing_guide/#markers-for-tests).
 - To maximize code reuse, you may refer to
-    - `tests/conftest.py` for `omni_server` (running server in subprocess) and `openai_client` fixtures (sending requests and validating output), `generate_synthetic_image` and `assert_XXX_valid` helper.
+    - `tests/conftest.py` for `omni_server` (running server in subprocess) and `online_client` fixtures (sending requests and validating output), `generate_synthetic_image` and `assert_XXX_valid` helper.
     - `tests/helpers/mark.py` for `@hardware_test(...)` and `hardware_marks`.
     - [Parametrizing tests (pytest doc)](https://docs.pytest.org/en/stable/example/parametrize.html) to reuse test function implementation for different cases.
 - Doc: add a concise docstring for each test function.

docs/contributing/ci/test_examples/l4_performance_tests.inc.md

@@ -1,14 +1,15 @@
 When you want to add L4-level ***performance test*** cases, add entries to JSON files under `tests/dfx/perf/tests/` and run them via `tests/dfx/perf/scripts/run_benchmark.py` (omni / TTS) or `run_diffusion_benchmark.py` (diffusion).
 
-**Config file layout (in-tree examples)**
+#### Config file layout (in-tree examples)
 
 | Model type | Runner | Example JSON files |
 | ---------- | ------ | ------------------ |
-| Omni | `run_benchmark.py` | `test_qwen3_omni_no_async_chunk.json`, `test_qwen3_omni_async_chunk.json`, `test_qwen3_omni_vllm_text.json`, `test_qwen3_omni_multi_replicas.json` |
+| Omni (nightly) | `run_benchmark.py` | `test_qwen3_omni_no_async_chunk.json`, `test_qwen3_omni_async_chunk.json` (`full_model` without `slow` in `mark`) |
+| Omni (weekly) | `run_benchmark.py` | `test_qwen3_omni_async_chunk.json` (CUDA only), `test_qwen3_omni_vllm_text.json`, `test_qwen3_omni_multi_replicas.json` (`slow` in `mark`; **Perf Test** in `test-weekly.yml`) |
 | TTS | `run_benchmark.py` | `test_tts.json`, `test_voxcpm2.json`, `test_higgs_audio_v3.json` |
 | Diffusion | `run_diffusion_benchmark.py` | `test_qwen_image_vllm_omni.json`, `test_bagel_vllm_omni.json`, `test_wan22_i2v_vllm_omni.json`, `test_cosmos3_vllm_omni.json`, … |
 
-**How runners pick cases**
+#### How runners pick cases
 
 Without **`--test-config-file`**, each runner scans all `*.json` under `tests/dfx/perf/tests/` but only keeps its own model type:
 
@@ -17,9 +18,9 @@ Without **`--test-config-file`**, each runner scans all `*.json` under `tests/df
 
 Diffusion cases are detected when the JSON has `server_type` (typically `"vllm-omni"`) or `"diffusion"` in the `mark` array. Omni / TTS JSON has neither.
 
-**Running perf cases**
+#### Running perf cases
 
-Pass **`--test-config-file`** to run one JSON file as-is, or omit it for the bulk scan above and filter with pytest **`-m`** on each case's JSON `mark` (for example `pytest … run_benchmark.py -m "full_model and tts and H100"`).
+Pass **`--test-config-file`** to load one JSON file, or omit it for the bulk scan above. In either mode, pytest **`-m`** filters each case using its JSON `mark`. A single file can contain nightly, weekly, and platform-specific case objects. For example, the nightly object in `test_qwen3_omni_async_chunk.json` declares both H100 and A3, while its `slow` weekly object is CUDA-only; there is no NPU weekly counterpart. Use `-m "H100 and full_model and not slow"`, `-m "H100 and full_model and slow"`, or `-m "npu"` to select the intended schedule and platform.
 
 ```JSON
 {
@@ -31,7 +32,7 @@ Pass **`--test-config-file`** to run one JSON file as-is, or omit it for the bul
     ],
     "server_params": {
         "model": "Qwen/Qwen3-Omni-30B-A3B-Instruct",
-        "stage_config_name": "qwen3_omni.yaml"
+        "stage_config_name": "qwen3_omni_moe.yaml"
     },
     "benchmark_params": [
         {
@@ -55,31 +56,33 @@ Pass **`--test-config-file`** to run one JSON file as-is, or omit it for the bul
 }
 ```
 
-**Parameter Explanation**
+#### Parameter Explanation
 
-*Overview*
+##### Overview
 
-| Field            | Required | Description                                                     |
-| ---------------- | -------- | --------------------------------------------------------------- |
-| test_name        | Yes      | Unique identifier for the test case                             |
-| mark             | No       | Pytest marks for this case (see **`mark` field** below). Omit only for configs not meant to be filtered by `-m`. |
-| server_params    | Yes      | Server-side configuration parameters                            |
-| benchmark_params | Yes      | Benchmark running parameters (supports multiple configurations) |
-| server_type      | Diffusion only | When set (e.g. `"vllm-omni"`), routes the case to `run_diffusion_benchmark.py`. |
-| benchmark_endpoint | Diffusion only | API path for the benchmark client (e.g. `/v1/videos`, `/v1/images/generations`). |
+| Field              | Required       | Description                                  |
+| ------------------ | -------------- | -------------------------------------------- |
+| test_name          | Yes            | Unique identifier for the test case          |
+| mark               | No             | Pytest marks; see **`mark` field** below     |
+| server_params      | Yes            | Server-side configuration parameters         |
+| benchmark_params   | Yes            | Benchmark running parameters                 |
+| server_type        | Diffusion only | Routes case to run_diffusion_benchmark.py    |
+| benchmark_endpoint | Diffusion only | Benchmark API path                           |
 
-**`mark` field**
+Omit `mark` only for configs not meant to be filtered by `-m`. `server_type` is typically `"vllm-omni"`. `benchmark_endpoint` examples: `/v1/videos`, `/v1/images/generations`.
+
+#### `mark` field
 
 Optional top-level field on each perf JSON **case object** (one per `test_name`). `run_benchmark.py` and `run_diffusion_benchmark.py` read it via `tests.dfx.conftest.resolve_pytest_marks` and attach the marks to the corresponding `pytest.param`, so you can filter locally with `-m`.
 
-When `mark` is present, it must be an **array** with exactly one ``hardware_marks`` object (same shape as `@hardware_test` / `hardware_marks()` in `tests/helpers/mark.py`), followed by registered pytest marker name strings such as `full_model`, `omni`, `tts`, `diffusion`, or `local_model`.
+When `mark` is present, it must be an **array** with exactly one ``hardware_marks`` object (same shape as `@hardware_test` / `hardware_marks()` in `tests/helpers/mark.py`), followed by registered pytest marker name strings such as `full_model`, `slow`, `omni`, `tts`, `diffusion`, or `local_model`.
 
 Supported form:
 
 | Form | Example | Effect |
 | ---- | ------- | ------ |
-| Array (single platform) | `"mark": [{"hardware_marks": {"res": {"cuda": "H100"}, "num_cards": 2}}, "full_model", "diffusion"]` | Hardware marks from `hardware_marks(...)`; `num_cards` > 1 adds `distributed_*` (+ CUDA `skipif_cuda` when applicable). String entries become extra pytest marks. |
-| Array (multi-platform) | `"mark": [{"hardware_marks": {"res": {"cuda": "H100", "rocm": "MI325", "npu": "A2"}, "num_cards": {"cuda": 2, "rocm": 2, "npu": 1}}}, "full_model", "omni"]` | Same as above, but declares **multiple platforms** in one case. Filter examples: `-m "full_model and H100 and cuda"`, `-m "full_model and MI325 and rocm"`. |
+| Array (single platform) | `"mark": [{"hardware_marks": {"res": {"cuda": "H100"}, "num_cards": 2}}, "full_model", "diffusion"]` | Hardware marks from `hardware_marks(...)`; `num_cards` adds `cards_{n}` (including `cards_1` when omitted). String entries become extra pytest marks. |
+| Array (multi-platform) | `"mark": [{"hardware_marks": {"res": {"cuda": "H100", "rocm": "MI325"}, "num_cards": 2}}, "full_model", "omni"]` | Same as above, but declares **multiple platforms** on one item. All platforms must share the same `num_cards` (int or identical dict values). Different counts need `@hardware_test` or one `hardware_marks()` per `pytest.param`. Filter examples: `-m "full_model and H100 and cards_2 and cuda"`, `-m "full_model and MI325 and cards_2 and rocm"`. |
 
 Recommended for L4 perf cases:
 
@@ -119,11 +122,11 @@ Multi-GPU diffusion (example: Cosmos3 with `cfg-parallel-size=2`):
 - Diffusion perf: include `"diffusion"` in the `mark` array
 - HunyuanImage local-weight cases: add `"local_model"` to the `mark` array
 
-**Parametrization IDs**
+#### Parametrization IDs
 
 Each `(server, benchmark index)` pair becomes one `pytest.param` with id `{test_name}-{suffix}`. The suffix comes from `benchmark_params[].name` when set (for example `test_tts-p0`, `test_omni-p1`); otherwise it is derived from fields like `task` / `eval_phase`.
 
-**Benchmark result filenames**
+#### Benchmark result filenames
 
 Result files use the **runtime** hardware label from `get_runtime_resource_label()` (detected GPU/NPU name on the machine that ran the job), **not** `mark.hardware_marks.res`. On the default H100 CI pool, `H100` is **omitted** from filenames (`resource_label_for_filename`).
 
@@ -132,32 +135,40 @@ Examples:
 - Omni/TTS: `result_{test_name}_{optional_hw}_{dataset}_....json` under `BENCHMARK_DIR`
 - Diffusion: one aggregate `diffusion_result_{config_stem}_{optional_hw}_{timestamp}.json` per source JSON file (array of all runs from that file)
 
-**Local commands**
+#### Local commands
 
 ```bash
 # Bulk load + filter by JSON mark
 pytest -s -v tests/dfx/perf/scripts/run_diffusion_benchmark.py -m "full_model and H100 and diffusion"
 pytest -s -v tests/dfx/perf/scripts/run_benchmark.py -m "full_model and omni and H100"
 
-# Single file (same as nightly CI Perf steps)
+# Single file (same selectors as the CI Perf steps)
 pytest -s -v tests/dfx/perf/scripts/run_diffusion_benchmark.py \
   --test-config-file tests/dfx/perf/tests/test_bagel_vllm_omni.json
 pytest -s -v tests/dfx/perf/scripts/run_benchmark.py \
-  --test-config-file tests/dfx/perf/tests/test_qwen3_omni_async_chunk.json
+  --test-config-file tests/dfx/perf/tests/test_qwen3_omni_async_chunk.json \
+  -m "H100 and full_model and not slow"
+pytest -s -v tests/dfx/perf/scripts/run_benchmark.py \
+  --test-config-file tests/dfx/perf/tests/test_qwen3_omni_async_chunk.json \
+  -m "H100 and full_model and slow"
+pytest -s -v tests/dfx/perf/scripts/run_benchmark.py \
+  --test-config-file tests/dfx/perf/tests/test_qwen3_omni_async_chunk.json \
+  -m "npu"
 ```
 
-See also [Markers for Tests](#markers-for-tests) for registered hardware markers (`H100`, `L4`, `cuda`, `distributed_cuda`, …).
+<!-- markdownlint-disable-next-line MD051 -->
+See also [Markers for Tests](#markers-for-tests) for registered hardware markers (`H100`, `L4`, `cuda`, `cards_1`, …).
 
-**`server_params` Configuration**
+#### `server_params` Configuration
 
-*Basic Parameters*
+##### Basic Parameters
 
-| Parameter         | Required | Example                            | Description                   |
-| ----------------- | -------- | ---------------------------------- | ----------------------------- |
-| model             | Yes      | "Qwen/Qwen3-Omni-30B-A3B-Instruct" | Model name or path            |
-| stage_config_name | Yes      | "qwen3_omni.yaml"                  | Stage configuration file name |
+| Parameter         | Required | Example                   | Description                    |
+| ----------------- | -------- | ------------------------- | ------------------------------ |
+| model             | Yes      | Qwen/Qwen3-Omni-30B-A3B…  | Model name or path             |
+| stage_config_name | Yes      | qwen3_omni_moe.yaml       | Deploy configuration file name |
 
-*Dynamic Configuration (update/delete)*
+##### Dynamic Configuration (update/delete)
 
 Supports incremental modifications based on the basic configuration:
 
@@ -166,38 +177,40 @@ Supports incremental modifications based on the basic configuration:
 | update    | Update or add configuration items    |
 | delete    | Delete specified configuration items |
 
-**Example**:
+##### Example
 
-```
+```json
 "update": {
     "async_chunk": true,  // Enable asynchronous chunk processing
-    "stage_args": {
+    "stages": {
         "0": {
-            "engine_args.custom_process_next_stage_input_func": "vllm_omni.model_executor.stage_input_processors.qwen3_omni.thinker2talker_async_chunk"
+            "max_num_seqs": 64
         }
     }
 },
 "delete": {
-    "stage_args": {
-        "2": ["custom_process_input_func"]  // Delete this configuration for stage 2
+    "stages": {
+        "2": ["max_num_batched_tokens"]  // Delete this deploy override for stage 2
     }
 }
 ```
 
-**`benchmark_params` Configuration**
+#### `benchmark_params` Configuration
 
 You can add any benchmark running parameters you need here. For all optional parameters, refer to the [benchmark documentation](https://github.com/vllm-project/vllm-omni/blob/main/docs/cli/bench/serve.md). General modifications are as follows:
 
-1.  Change the --xxx-xx-xx running parameters to xxx_xx_xx format and fill them as keys in the JSON file.
-2.  For boolean variables in the running parameters, modify them to forms such as ignore_eos: true/false and fill them into the JSON file.
-3.  Optionally add a `baseline` object (see **Baseline thresholds** below). If you omit `baseline` or leave it empty, the performance test still runs but does not assert metric thresholds from this field.
-4.  Set `"name"` on each `benchmark_params` entry for stable pytest ids and readable result keys.
-5.  The qps and concurrency modes are recommended to be mutually exclusive. For detailed explanations, see the table below:
+1. Change the --xxx-xx-xx running parameters to xxx_xx_xx format and fill them as keys in the JSON file.
+2. For boolean variables in the running parameters, modify them to forms such as ignore_eos: true/false and fill them into the JSON file.
+3. Optionally add a `baseline` object (see **Baseline thresholds** below). If you omit `baseline` or leave it empty, the performance test still runs but does not assert metric thresholds from this field.
+4. Set `"name"` on each `benchmark_params` entry for stable pytest ids and readable result keys.
+5. The qps and concurrency modes are recommended to be mutually exclusive. For detailed explanations, see the table below:
 
-| Parameter       | Type        | Required | Example/Values  | Description                                                                                                                                                                                                                                                          |
-| --------------- | ----------- | -------- | --------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| name            | string      | No       | `"1024x1024_steps4"` | Stable suffix for pytest param id and logs. |
-| num_prompts     | int / array | Yes      | 10,[10, 20, 30] | Number of requests. Supports single values or arrays. If a single value is used, it will be automatically expanded to match the number of qps or max_concurrency, e.g., [10,10,10]. If an array is used, its length must match the number of qps or max_concurrency. |
-| request_rate    | float / array | No  | 0.5, [0.5, 1, inf] | Queries per second. Supports single values or arrays. If a single value is used, it will be automatically expanded to match the number of num_prompts, e.g., [1,1,1]. If an array is used, its length must match the number of num_prompts.                          |
-| max_concurrency | int / array | No       | 1, [1, 2, 3]    | Maximum concurrent in-flight requests. Same array / expansion rules as `request_rate` (mutually exclusive with QPS mode).                                                                                                                                                                                                             |
-| baseline        | object      | No       | see below       | Optional thresholds. **Hardware-nested only**: top-level keys must be `[hardware-resource]` pytest markers from `pyproject.toml` (`get_hardware_mark_list()`). Under each hardware key, metric names are free-form; values are scalars or sweep-aligned lists. Example: `{"H100": {"throughput_qps": […], "custom_stage_ms": 1.0}, "A3": {…}}`. Runtime filename aliases (`_RUNTIME_DEVICE_ALIASES`) are separate and do not expand this allowlist. |
+| Parameter       | Type          | Required | Example/Values       | Description                          |
+| --------------- | ------------- | -------- | -------------------- | ------------------------------------ |
+| name            | string        | No       | `"1024x1024_steps4"` | Stable suffix for pytest param id    |
+| num_prompts     | int / array   | Yes      | 10, [10, 20, 30]     | Number of requests (scalar or sweep) |
+| request_rate    | float / array | No       | 0.5, [0.5, 1, inf]   | Queries per second (QPS mode)        |
+| max_concurrency | int / array   | No       | 1, [1, 2, 3]         | Max in-flight requests               |
+| baseline        | object        | No       | see below            | Optional hardware-nested thresholds  |
+
+`num_prompts`, `request_rate`, and `max_concurrency` accept scalars or arrays; single values expand to match sweep length. QPS and concurrency modes are mutually exclusive. `baseline` top-level keys must be hardware pytest markers from `pyproject.toml`; metric names and values are free-form (scalars or sweep-aligned lists). Omit `baseline` to run without threshold assertions.

docs/contributing/ci/test_execution_guide.md

@@ -1,13 +1,20 @@
 # Test Execution Guide
+
 ## Setting Up the Test Environment
+
 ### Creating a Container
+
 vLLM-Omni provides an official Docker image for deployment. These images are built upon vLLM Docker images and are available on [Docker Hub](https://hub.docker.com/r/vllm/vllm-omni/tags). The version of vLLM-Omni indicates which vLLM release it is based on.
 For a local test environment, you can follow the steps below to create a container:
+
 ## Installing Dependencies
+
 ### vLLM & vLLM-Omni
+
 vLLM-Omni is built based on vLLM. You can follow [install guide](../../getting_started/installation/README.md) to build your local environment.
 
 ### Test Case Dependencies
+
 When running test cases, you may need to install the following dependencies:
 
 ```bash
@@ -16,6 +23,7 @@ apt-get install -y espeak-ng jq
 ```
 
 ## Running Tests
+
 Our test scripts use the pytest framework. First, please use `git clone https://github.com/vllm-project/vllm-omni.git` to download the vllm-omni source code. Then, in the root directory of vllm-omni, you can run the following commands in your local test environment to execute the corresponding test cases.
 
 ### CI job runners (L2–L4): logs, timing, and timeouts
@@ -25,7 +33,7 @@ Our test scripts use the pytest framework. First, please use `git clone https://
 **Log layout** (default under `logs/ready_jobs`, `logs/merge_jobs`, or `logs/nightly_jobs`; override with `--log-dir`):
 
 | Path | Description |
-|------|-------------|
+| ------ | ------------- |
 | `jobs/*.sh` | Generated per-step bash wrappers |
 | `jobs/.job_timeouts` | Job key → `timeout_in_minutes` from Buildkite YAML (when set) |
 | `<job_key>.log` | Tee output for each job |
@@ -33,7 +41,7 @@ Our test scripts use the pytest framework. First, please use `git clone https://
 
 **Timing:** while each job runs, stderr prints `finished in …` or `failed after …`. When all jobs finish, a summary is printed and written to `timing_summary.log`, for example:
 
-```
+```text
 === Job timing summary ===
   TTS_VoxCPM2_Test  25m 40s  OK
   Omni_Qwen3-Omni_Test  8m 05s  TIMED OUT
@@ -80,7 +88,7 @@ Failed jobs: 1/2
     ```bash
     cd tests
     pytest -s -v test_xxxx.py --run-level=core_model
-    pytest -s -v -m "core_model and distributed_cuda and L4" --run-level=core_model
+    pytest -s -v -m "core_model and L4 and not cards_1" --run-level=core_model
     ```
 
 === "L3 level"
@@ -111,10 +119,9 @@ Failed jobs: 1/2
 
     ```bash
     pytest -s -v test_xxxx.py --run-level=advanced_model
-    pytest -s -v -m "advanced_model and distributed_cuda and L4" --run-level=advanced_model
+    pytest -s -v -m "advanced_model and L4 and not cards_1" --run-level=advanced_model
     ```
 
-
 === "L4 level"
 
     **Recommended:** run CI-aligned nightly jobs from the repo root via [`tools/nightly/run_nightly_jobs.sh`](https://github.com/vllm-project/vllm-omni/blob/main/tools/nightly/run_nightly_jobs.sh). The script reads [`.buildkite/cuda/test-nightly.yml`](https://github.com/vllm-project/vllm-omni/blob/main/.buildkite/cuda/test-nightly.yml), generates per-step bash wrappers, runs pytest (perf jobs first, then aggregates perf JSON into Excel), and tees logs under `logs/nightly_jobs/` (see [CI job runners](#ci-job-runners-l2l4-logs-timing-and-timeouts); `generate_nightly_perf_excel` is included in the timing summary). Requires `bash`, `python3`, and PyYAML (`pip install pyyaml`).
@@ -146,12 +153,12 @@ Failed jobs: 1/2
     ```bash
     cd tests
     pytest -s -v test_xxxx.py --run-level=full_model
-    pytest -s -v -m "full_model and distributed_cuda and L4" --run-level=full_model
+    pytest -s -v -m "full_model and L4 and not cards_1" --run-level=full_model
     pytest -s -v -m "full_model and (omni or tts) and H100" --run-level=full_model
     ```
     If you only want to run specific test cases on a particular platform, you can use:
     ```bash
-    pytest -s -v -m "full_model and distributed_cuda and L4"  --run-level=full_model
+    pytest -s -v -m "full_model and L4 and not cards_1"  --run-level=full_model
     ```
     Note: ``run_benchmark.py`` and ``run_diffusion_benchmark.py`` accept an optional ``--test-config-file``. If omitted, each loads every ``*.json`` under ``tests/dfx/perf/tests/`` (omni/tts vs diffusion split by ``is_diffusion_perf_config``) and pytest ``-m`` filters by each case's JSON ``mark``:
     ```bash
@@ -160,7 +167,7 @@ Failed jobs: 1/2
     pytest -sv tests/dfx/perf/scripts/run_benchmark.py --test-config-file tests/dfx/perf/tests/test_tts.json
     pytest -sv tests/dfx/perf/scripts/run_diffusion_benchmark.py --test-config-file tests/dfx/perf/tests/test_cosmos3_vllm_omni.json
     ```
-    Nightly **Perf Test** jobs in ``test-nightly.yml`` use ``--test-config-file`` only (no ``-m``). E2e L4 function tests still use ``full_model`` + ``--run-level full_model`` (see [test-nightly.yml](https://github.com/vllm-project/vllm-omni/blob/main/.buildkite/test-nightly.yml)). Example:
+    Nightly **Perf Test** jobs in [``test-nightly.yml``](https://github.com/vllm-project/vllm-omni/blob/main/.buildkite/cuda/test-nightly.yml) use ``--test-config-file`` only (no ``-m``). Weekly **Perf Test** in [``test-weekly.yml``](https://github.com/vllm-project/vllm-omni/blob/main/.buildkite/cuda/test-weekly.yml) runs ``test_qwen3_omni_vllm_text.json`` and ``test_qwen3_omni_multi_replicas.json`` (JSON ``mark`` includes ``slow``). E2e L4 function tests use ``full_model`` + ``--run-level full_model``. Example:
 
 === "L5 level"
 
@@ -186,9 +193,13 @@ Failed jobs: 1/2
 
     ```
 
-    The latest L5 CI jobs (reliability + invalid-parameter weekly steps) are in [test-weekly.yml](https://github.com/vllm-project/vllm-omni/blob/main/.buildkite/cuda/test-weekly.yml).
+    The latest L5 CI jobs are in [test-weekly.yml](https://github.com/vllm-project/vllm-omni/blob/main/.buildkite/cuda/test-weekly.yml):
+
+    - **Reliability** (+ invalid-parameter), **Perf Test** (Omni vLLM Text / Multi-Replica), and **Simple · CPU Coverage Test** → `WEEKLY=1` (or PR label `weekly-test` for Reliability/Perf). Scheduled upload of those env-gated steps still requires `main`.
+    - **E2E Tests** (slow Omni / TTS / Diffusion sweeps) → `NON_CRITICAL=1` (pipeline upload also accepts `WEEKLY=1` / `weekly-test`; the group `if` still needs `NON_CRITICAL`)
 
 You can find more information about markers in [Test Writing Guide — Markers for Tests](./test_writing_guide.md#markers-for-tests).
 
 ## Adding New Test Cases
+
 Please refer to the [Test System Overview](./test_system_overview.md) and [Test Writing Guide](./test_writing_guide.md).

docs/contributing/ci/test_system_overview.md

@@ -12,8 +12,7 @@ Through five levels (L1-L5) and common (Common) specifications, the system clari
       <th>Level</th>
       <th>Scope & Focus</th>
       <th>Model Coverage Strategy</th>
-      <th>Feature Coverage Strategy</th>
-      <th>Interface Coverage Strategy</th>
+      <th>Module Strategy</th>
       <th>Tags</th>
       <th>Time Cost</th>
       <th>Test Dir</th>
@@ -31,7 +30,6 @@ Through five levels (L1-L5) and common (Common) specifications, the system clari
       <td>/</td>
       <td>/</td>
       <td>/</td>
-      <td>/</td>
       <td><a href="https://github.com/vllm-project/vllm-omni/blob/main/.github/PULL_REQUEST_TEMPLATE.md">PR Checklist</a></td>
       <td>/</td>
       <td>/</td>
@@ -43,7 +41,6 @@ Through five levels (L1-L5) and common (Common) specifications, the system clari
       <td>/</td>
       <td>/</td>
       <td>/</td>
-      <td>/</td>
       <td><a href="./failures.md">CI Failures</a></td>
       <td>/</td>
       <td>/</td>
@@ -53,7 +50,6 @@ Through five levels (L1-L5) and common (Common) specifications, the system clari
       <td>Unit tests for components like entrypoints, models</td>
       <td>/</td>
       <td>/</td>
-      <td>/</td>
       <td><code>core_model and cpu</code></td>
       <td rowspan="2">&lt;15min</td>
       <td>
@@ -71,9 +67,8 @@ Through five levels (L1-L5) and common (Common) specifications, the system clari
     <tr>
       <td><strong>L2</strong><br>(E2E across models & GPU-required UT)</td>
       <td>Online (basic deployment scenarios):<br>dummy, normal inference function (output format, stream), some instance startup UT</td>
-      <td>High-priority models + online basic scenarios; request success, non-empty output, format match (no Whisper/accuracy)</td>
-      <td>High-priority features and component tests that require launching instances</td>
-      <td>High-priority interfaces (using random lightweight models)</td>
+      <td>Key models + online basic scenarios; request success, non-empty output, format match (no Whisper/accuracy)</td>
+      <td>Key features and modules that require launching instances (prefer random weights)</td>
       <td><code>core_model and hardware_test(H100, L4, etc.) and omni/tts/diffusion</code></td>
       <td>
         <strong>Model E2E:</strong><br>
@@ -95,9 +90,8 @@ Through five levels (L1-L5) and common (Common) specifications, the system clari
     <tr>
       <td><strong>L3</strong><br>(Important Perf & Integration & Accuracy)</td>
       <td>Online & Offline (multiple deployment scenarios):<br>real model, normal inference function, normal accuracy</td>
-      <td>High/medium-priority models + key online/offline scenarios; real weights, Whisper/similarity, preset voice gender, basic accuracy</td>
-      <td>Medium-priority features and component tests that require launching instances</td>
-      <td>Medium-priority interfaces (using random lightweight models)</td>
+      <td>Key models + key online/offline scenarios; real weights, Whisper/similarity, preset voice gender, basic accuracy</td>
+      <td>Key features and modules that require launching instances (using real weights)</td>
       <td><code>advanced_model and hardware_test(H100, L4, etc.) and omni/tts/diffusion</code></td>
       <td>&lt;30min</td>
       <td>
@@ -120,9 +114,8 @@ Through five levels (L1-L5) and common (Common) specifications, the system clari
     <tr>
       <td><strong>L4</strong><br>(Perf & Integration & Accuracy)</td>
       <td>Online: full functional scenarios + performance test + doc test + accuracy test</td>
-      <td>High-priority models: function, performance, accuracy, and doc testing<br>Medium-priority models: function and doc testing</td>
-      <td>Low-priority features and component tests that require launching instances</td>
-      <td>Low-priority interfaces (using real weights)</td>
+      <td>Key models: function, performance, accuracy, and doc testing</td>
+      <td>Other features and modules that require launching instances (using real weights)</td>
       <td><code>full_model and hardware_test(H100, L4, etc.) and omni/tts/diffusion</code></td>
       <td>&lt;3 hour</td>
       <td>
@@ -143,15 +136,14 @@ Through five levels (L1-L5) and common (Common) specifications, the system clari
         <a href="test_writing_guide.md#l4-level-testing-full-functionality-performance-and-documentation-testing">L4</a><br>
         L4: Purpose, Test Content, Directory Location, Example
       </td>
-      <td>Nightly</td>
+      <td>Nightly / Days before Release<br>(additional GPU SKUs)</td>
       <td>GPU</td>
     </tr>
     <tr>
-      <td><strong>L5</strong><br>(Stability & Reliability)</td>
-      <td>Online: long-term stability test + reliability test</td>
-      <td>Long-term stability and reliability testing for high-priority models<br>Low-priority models: function and doc testing</td>
+      <td><strong>L5</strong><br>(Stability & Reliability & selected Perf)</td>
+      <td>Online: long-term stability + reliability; coverage test</td>
+      <td>Long-term stability and reliability testing for key models<br>Non-critical scenario performance and accuracy testing for key models<br>Other models: function and doc testing</td>
       <td>/</td>
-      <td>Invalid-parameter validation for high-priority interfaces</td>
       <td><code>slow and hardware_test(H100, L4, etc.) and omni/tts/diffusion</code></td>
       <td> Depends on reality </td>
       <td>
@@ -178,11 +170,20 @@ For per-level test authoring (markers, examples), see [Test Writing Guide](./tes
 
 Before entering specific testing levels, the project establishes two common specifications aimed at standardizing the development process and quickly locating issues.
 
-1.  ***PR Checklist ([`.github/PULL_REQUEST_TEMPLATE.md`](https://github.com/vllm-project/vllm-omni/blob/main/.github/PULL_REQUEST_TEMPLATE.md))***: This template defines the self-check items that must be completed before submitting a code review (Pull Request). It ensures that each code change meets basic requirements such as code style, dependency updates, and documentation synchronization before entering the automated testing pipeline, serving as the first manual line of defense for quality assurance.
-2.  ***CI Failure Explanation ([CI Failures](./failures.md))***: This document archives and explains common failure patterns in the Continuous Integration (CI) pipeline, error log interpretation, and preliminary troubleshooting steps. It helps developers and testers quickly diagnose the causes of automated test failures, improving problem-solving efficiency.
+1. ***PR Checklist ([`.github/PULL_REQUEST_TEMPLATE.md`](https://github.com/vllm-project/vllm-omni/blob/main/.github/PULL_REQUEST_TEMPLATE.md))***: This template defines the self-check items that must be completed before submitting a code review (Pull Request). It ensures that each code change meets basic requirements such as code style, dependency updates, and documentation synchronization before entering the automated testing pipeline, serving as the first manual line of defense for quality assurance.
+2. ***CI Failure Explanation ([CI Failures](./failures.md))***: This document archives and explains common failure patterns in the Continuous Integration (CI) pipeline, error log interpretation, and preliminary troubleshooting steps. It helps developers and testers quickly diagnose the causes of automated test failures, improving problem-solving efficiency.
 
 ## Notes
 
+### L4 cadence (nightly and pre-release)
+
+CUDA **L4** (`full_model`) jobs are defined once in [`.buildkite/cuda/test-nightly.yml`](https://github.com/vllm-project/vllm-omni/blob/main/.buildkite/cuda/test-nightly.yml). That file is reused at two cadences:
+
+- **Nightly:** scheduled `main` builds with `NIGHTLY=1` (and PR labels such as `nightly-test` / `omni-test`). Default fleet is typically H100.
+- **Pre-release:** the same L4 suite runs again on extra GPU SKUs before a release (for example B200 via `MIRROR_HW=b200`), so `full_model` coverage is not limited to the nightly machine type.
+
+How count-form `mirror_hardwares` and `MIRROR_HW` pick a SKU: [CI Settings](./ci_settings.md).
+
 ### L2 / L3 diff-aware CI (CUDA)
 
 On CUDA **L2** and **L3**, **E2E Test** Buildkite jobs may be omitted at pipeline upload when the PR diff does not touch their path prefixes; other groups still always upload. Full two-layer mechanics and YAML examples: [CI Settings — Diff-aware CI](./ci_settings.md#diff-aware-ci) ([step filtering](./ci_settings.md#step-filtering)).

docs/contributing/ci/test_writing_guide.md

@@ -17,47 +17,43 @@ Summary:
 - **Reliability:** `tests/dfx/reliability/`
 - Do **not** add new top-level `tests/` directories unrelated to a component (or to `e2e` / `dfx` / `helpers` / `examples` / `buildkite`)
 
-Deploy YAMLs: `vllm_omni/deploy/` via `tests.helpers.stage_config.get_deploy_config_path` (see [stage configs](../../configuration/stage_configs.md)).
+Deploy YAMLs: `vllm_omni/deploy/` via `tests.helpers.stage_config.get_deploy_config_path` (see [pipeline and deploy configs](../../configuration/stage_configs.md)).
 
 ## Markers for Tests
 
 By adding markers before test functions, tests can later be executed uniformly by simply declaring the corresponding marker type.
 
 ### Current Markers
+
 Defined in `pyproject.toml`:
 
-| Marker             | Description                                               |
-| ------------------ | --------------------------------------------------------- |
-| `core_model`       | L1&L2 tests (run in each PR)                              |
-| `advanced_model`   | L3 tests (run on each merge to main)                 |
-| `full_model`       | L4 tests (run nightly) |
-| `diffusion`        | Diffusion model tests                                     |
-| `omni`             | Omni multimodal model tests |
-| `tts`              | TTS model tests     |
-| `cache`            | Cache backend tests                                       |
-| `parallel`         | Parallelism/distributed tests                             |
-| `cpu`              | Tests that run on CPU                                     |
-| `gpu`              | Tests that run on GPU *                                   |
-| `cuda`             | Tests that run on CUDA *                                  |
-| `rocm`             | Tests that run on AMD/ROCm *                              |
-| `xpu`              | Tests that run on Intel XPU *                             |
-| `npu`              | Tests that run on NPU/Ascend *                            |
-| `H100`             | Tests that require H100 GPU  *                            |
-| `L4`               | Tests that require L4 GPU *                               |
-| `MI325`            | Tests that require MI325 GPU (AMD/ROCm) *                 |
-| `A2`               | Tests that require A2 NPU *                               |
-| `A3`               | Tests that require A3 NPU *                               |
-| `distributed_cuda` | Tests that require multi cards on CUDA platform *         |
-| `distributed_rocm` | Tests that require multi cards on ROCm platform  *        |
-| `distributed_npu`  | Tests that require multi cards on NPU platform  *         |
-| `skipif_cuda`      | Skip if the num of CUDA cards is less than the required * |
-| `skipif_rocm`      | Skip if the num of ROCm cards is less than the required * |
-| `skipif_npu`       | Skip if the num of NPU cards is less than the required *  |
-| `slow`             | Slow tests (may skip in quick CI)                         |
+| Marker             | Description                                                                                             |
+| ------------------ | ------------------------------------------------------------------------------------------------------- |
+| `core_model`       | L1&L2 tests (run in each PR)                                                                            |
+| `advanced_model`   | L3 tests (run on each merge to main)                                                                    |
+| `full_model`       | L4 tests (run nightly)                                                                                  |
+| `diffusion`        | Diffusion model tests                                                                                   |
+| `omni`             | Omni multimodal model tests                                                                             |
+| `tts`              | TTS model tests                                                                                         |
+| `cache`            | Cache backend tests                                                                                     |
+| `parallel`         | Parallelism/distributed tests                                                                           |
+| `cpu`              | Tests that run on CPU                                                                                   |
+| `gpu`              | Tests that run on GPU *                                                                                 |
+| `cuda`             | Tests that run on CUDA *                                                                                |
+| `rocm`             | Tests that run on AMD/ROCm *                                                                            |
+| `xpu`              | Tests that run on Intel XPU *                                                                           |
+| `npu`              | Tests that run on NPU/Ascend *                                                                          |
+| `H100`             | Tests that require H100 GPU  *                                                                          |
+| `L4`               | Tests that require L4 GPU *                                                                             |
+| `MI325`            | Tests that require MI325 GPU (AMD/ROCm) *                                                               |
+| `A2`               | Tests that require A2 NPU *                                                                             |
+| `A3`               | Tests that require A3 NPU *                                                                             |
+| `cards_{n}`        | Tests that require *n* accelerator cards (`cards_1` … `cards_8`); auto-added from `num_cards` *         |
+| `slow`             | Slow tests (may skip in quick CI)                                                                       |
 | `benchmark`        | Benchmark tests (decorator on runner test functions; perf JSON uses `full_model` + type marker instead) |
-| `local_model`      | Tests requiring local / non-HF-hub model weights          |
+| `local_model`      | Tests requiring local / non-HF-hub model weights                                                        |
 
-\* Means those markers are auto-added by `@hardware_test` (parametrization decorator) or `hardware_marks` (only returning the list of marks for flexibility).
+\* Means those markers are auto-added by `@hardware_test` (parametrization decorator) or `hardware_marks` (only returning the list of marks for flexibility). Do **not** write `pytest.mark.H100` / `pytest.mark.L4` (or other SKU names) by hand: `check-mark` rejects that so `cards_{n}` cannot be skipped.
 
 #### Example usage for markers
 
@@ -80,28 +76,26 @@ def test_video_to_audio()
 This decorator is intended to make hardware-aware, cross-platform test authoring easier and more robust for CI/CD environments. The `hardware_test` decorator in `vllm-omni/tests/helpers/mark.py` performs the following actions:
 
 1. **Applies platform and resource markers**  
-   Adds the appropriate pytest markers for each specified hardware platform (e.g., `cuda`, `rocm`, `xpu`, `npu`) and resource type (e.g., `L4`, `H100`, `MI325`, `B60`, `A2`, `A3`).
-   ```
+   Adds the appropriate pytest markers for each specified hardware platform (e.g., `cuda`, `rocm`, `xpu`, `npu`) and resource type (e.g., `L4`, `H100`, `H200`, `B200`, `MI325`, `B60`, `A2`, `A3`).
+   ```python
    @pytest.mark.cuda
    @pytest.mark.L4
    ```
-2. **Handles multi-card (distributed) scenarios**  
-   For tests requiring multiple cards, it automatically adds distributed markers such as `distributed_cuda`, `distributed_rocm`, or `distributed_npu`.
-   ```
-   @pytest.mark.distributed_cuda(num_cards=num_cards)
-   ```
+2. **Handles card-count filtering**  
+   Adds a filterable marker `cards_{n}` (including `cards_1` when `num_cards` is omitted / 1). Use this with the SKU and platform, e.g. `-m "H100 and cards_4 and cuda"` vs `-m "H100 and cards_2 and cuda"`. Any multi-card CUDA test is `-m "cuda and not cards_1"`.
+
+   When `res` lists more than one platform **and** `num_cards` differs by platform, `@hardware_test` collects **one variant per platform** so those `cards_{n}` marks do not stack. `-m "H100 and cards_4 and cuda"` then selects only the CUDA/H100 4-card variant, not a ROCm 2-card sibling. The same count on every platform stays a single item.
 3. **Supports flexible card requirements**  
    Accepts `num_cards` as either a single integer for all platforms or as a dictionary with per-platform values. If not specified, defaults to 1 card per platform.
 
 4. **Integrates resource validation**  
-   On CUDA, adds a skip marker (`skipif_cuda`) if the system does not have the required number of devices.
-   Support for `skipif_rocm` and `skipif_npu` will be implemented later.
-
+   On CUDA and XPU, when `num_cards > 1`, attaches pytest's built-in `skipif` if the machine has fewer devices than requested. This is not a named `skipif_*` marker and is not used for `-m` filtering.
 
 5. **Works with pytest filtering**  
-   Allows tests to be filtered and selected at runtime using standard pytest marker expressions (e.g., `-m "distributed_cuda and L4"`).
+   Allows tests to be filtered and selected at runtime using standard pytest marker expressions (e.g., `-m "H100 and cards_2"`, `-m "L4 and not cards_1"`).
 
 ##### Example usage for decorator
+
 - Single call for multiple platforms:
     ```python
     @hardware_test(
@@ -116,13 +110,14 @@ This decorator is intended to make hardware-aware, cross-platform test authoring
         num_cards=2,
     )
     ```
-- `res` must be a dict; supported resources: CUDA (L4/H100), ROCm (MI325), XPU (B60), MUSA (S5000), NPU (A2/A3)
+- `res` must be a dict. SKU values come from `pyproject.toml` markers tagged `[hardware-resource]` plus the platform key (`[cuda]`, `[rocm]`, `[xpu]`, `[npu]`, `[musa]`).
 - `num_cards` can be int (all platforms) or dict (per platform); defaults to 1 when missing
-- Distributed markers (`distributed_cuda`, `distributed_rocm`, `distributed_npu`) are auto-added for multi-card cases
 - Filtering examples:
-    - CUDA only: `pytest -m "distributed_cuda and L4"`
-    - ROCm only: `pytest -m "distributed_rocm and MI325"`
-    - NPU only: `pytest -m "distributed_npu"`
+    - CUDA H100 4-card: `pytest -m "H100 and cards_4 and cuda"`
+    - CUDA H100 2-card: `pytest -m "H100 and cards_2 and cuda"`
+    - CUDA multi-card (any count): `pytest -m "L4 and not cards_1"`
+    - ROCm multi-card: `pytest -m "MI325 and not cards_1"`
+    - NPU multi-card: `pytest -m "npu and not cards_1"`
 
 #### Function: `hardware_marks`
 
@@ -178,37 +173,30 @@ If you want to add support for a new platform (e.g., "tpu" for a new accelerator
    [tool.pytest.ini_options]
    markers = [
        # ... existing markers ...
-       "tpu: Tests that require TPU device",
-       "TPU_V3: Tests that require TPU v3 hardware",
-       "distributed_tpu: Tests that require multiple TPU devices",
+       "tpu: [hardware-platform] Tests that require TPU device",
+       # Add [gpu] next to [hardware-platform] if this platform should also get pytest.mark.gpu.
+       "TPU_V3: [hardware-resource] [tpu] Tests that require TPU v3 hardware",
+       "cards_1: [hardware-cards] Tests that require 1 accelerator card",
+       "cards_2: [hardware-cards] Tests that require 2 accelerator cards",
    ]
    ```
+   `check-mark` treats `[hardware-platform]` names (including `cpu`/`gpu`) as allowed `pytest.mark.<name>` values. `hardware_test(res=...)` still rejects `cpu`/`gpu` as `res` keys.
 2. **Implement a marker construction function for your platform** in `vllm-omni/tests/helpers/mark.py`:
    ```python
    # In vllm-omni/tests/helpers/mark.py
 
-   def tpu_marks(*, res: str, num_cards: int):
-       test_platform = pytest.mark.tpu
-       if res == "TPU_V3":
-           test_resource = pytest.mark.TPU_V3
-       else:
-           raise ValueError(
-               f"Invalid TPU resource type: {res}. Supported: TPU_V3")
-
-       if num_cards == 1:
-           return [test_platform, test_resource]
-       else:
-           test_distributed = pytest.mark.distributed_tpu(num_cards=num_cards)
-           # Optionally: add skipif_tpu when implemented
-           return [test_platform, test_resource, test_distributed]
+   def _tpu_marks(*, res: str, num_cards: int):
+       _require_sku("tpu", res)
+       return [pytest.mark.tpu, getattr(pytest.mark, res), _cards_mark(num_cards)]
    ```
-3. **Update `hardware_marks` to recognize your new platform**:
-    In the relevant place (see the `hardware_marks` implementation), add:
+3. **Register the builder** in `_marks_for_platform`:
     ```python
-    if platform == "tpu":
-        marks = tpu_marks(res=resource, num_cards=cards)
+    builders = {
+        # ... existing platforms ...
+        "tpu": _tpu_marks,
+    }
     ```
-    (`hardware_test` calls `hardware_marks` internally, so both will pick up the change.)
+    (`hardware_test` / `hardware_marks` both go through `_marks_for_platform`.)
 4. **(Recommended) Add a test using your new markers**:
    ```python
    @hardware_test(
@@ -220,12 +208,13 @@ If you want to add support for a new platform (e.g., "tpu" for a new accelerator
    ```
 
 **Summary**:  
-- Add pytest markers for your new platform/resources  
-- Implement a marker function (`xxx_marks`)  
-- Plug into `hardware_marks`  
-- You're done: tests using `@hardware_test` or `hardware_marks` with your platform now automatically get the correct markers, distribution, and isolation!
 
-See code in `vllm-omni/tests/helpers/mark.py` for existing examples (`cuda_marks`, `rocm_marks`, `npu_marks`).
+- Add pytest markers for your new platform/resources  
+- Implement a private marker function (`_xxx_marks`)
+- Register it in `_marks_for_platform`
+- You're done: tests using `@hardware_test` or `hardware_marks` with your platform now automatically get the correct markers and `cards_{n}` filtering.
+
+See code in `vllm-omni/tests/helpers/mark.py` for existing examples (`_cuda_marks`, `_rocm_marks`, `_npu_marks`).
 
 ## Test case style
 
@@ -237,47 +226,46 @@ L1 and L2 level testing form the foundation of the quality assurance system. L1
 
 L2 level testing builds upon L1 by introducing GPU resources and verifying that the end-to-end (E2E) process of the model in basic deployment scenarios is smooth. For example, it uses dummy models to confirm that core interfaces like the inference pipeline, output format, and streaming response work properly. The common goal of these two levels is to provide developers with rapid feedback, discovering and fixing issues early in the development cycle.
 
-
-
 #### 1.2 Testing Content and Scope
 
--   ***L1 (Unit & Logic Testing)***:
--   -   ***Scope***: Tests internal functions and methods of core components such as `entrypoints`, `models`.
-    -   ***Focus***: Branch coverage, exception handling, algorithm logic correctness. Does not involve external dependencies or the complete service stack.
-    -   ***Time Cost***: Execution time is controlled within ***15 minutes*** to ensure fast feedback.
--   ***L2 (Basic End-to-End Testing)***:
--   -   ***Scope***: Covers two basic deployment scenarios: `online` (serving) and `offline` (inference).
-    -   ***Focus***: Uses `dummy` weights (via deploy YAML patching at `core_model`) or lightweight real models to verify that the entire chain from request input to result output works normally, including output data structure, streaming (stream) support, and **cheap payload checks** at `--run-level core_model` (see below). Also includes some unit tests that require launching independent service instances.
-    -   ***L2 response validation (`core_model`)***: Implemented in `tests/helpers/assertions.py` and invoked by `OpenAIClientHandler` based on `--run-level`. At L2 we require **request success** plus minimal output sanity—not full accuracy:
-        -   **Speech / TTS** (`assert_audio_speech_response`): decoded audio must be present and non-empty (or exceed `min_audio_bytes` when set in `request_config`); `response_format` must match the returned content-type (e.g. `wav`, `pcm`). Whisper transcript similarity, PCM HNR, and preset-voice gender checks run only at L3+.
-        -   **Diffusion** (`assert_diffusion_response`): at least one non-empty image, video, or audio artifact. Resolution/frame-count parity and other deep checks run only at L3+.
-        -   **Omni multimodal** (`assert_omni_response`): L2 asserts successful completion; keyword, transcript, and cross-modal similarity checks run only at L3+.
-    -   ***Characteristic***: Requires ***GPU*** resources to perform model computations.
+- ***L1 (Unit & Logic Testing)***:
+    - ***Scope***: Tests internal functions and methods of core components such as `entrypoints`, `models`.
+        - ***Focus***: Branch coverage, exception handling, algorithm logic correctness. Does not involve external dependencies or the complete service stack.
+        - ***Time Cost***: Execution time is controlled within ***15 minutes*** to ensure fast feedback.
+- ***L2 (Basic End-to-End Testing)***:
+    - ***Scope***: Covers two basic deployment scenarios: `online` (serving) and `offline` (inference).
+        - ***Focus***: Uses `dummy` weights (via deploy YAML patching at `core_model`) or lightweight real models to verify that the entire chain from request input to result output works normally, including output data structure, streaming (stream) support, and **cheap payload checks** at `--run-level core_model` (see below). Also includes some unit tests that require launching independent service instances.
+        - ***L2 response validation (`core_model`)***: Implemented in `tests/helpers/assertions.py` and invoked by `OpenAIClientHandler` based on `--run-level`. At L2 we require **request success** plus minimal output sanity—not full accuracy:
+            - **Speech / TTS** (`assert_audio_speech_response`): decoded audio must be present and non-empty (or exceed `min_audio_bytes` when set in `request_config`); `response_format` must match the returned content-type (e.g. `wav`, `pcm`). Whisper transcript similarity, PCM HNR, and preset-voice gender checks run only at L3+.
+            - **Diffusion** (`assert_diffusion_response`): at least one non-empty image, video, or audio artifact. Resolution/frame-count parity and other deep checks run only at L3+.
+            - **Omni multimodal** (`assert_omni_response`): L2 asserts successful completion; keyword, transcript, and cross-modal similarity checks run only at L3+.
+        - ***Characteristic***: Requires ***GPU*** resources to perform model computations.
 
 #### 1.3 Test Directory and Execution Files
 
 A clear directory structure is key to managing test cases efficiently. See the **Test Dir** column in [Test System Overview](./test_system_overview.md) for the canonical placement rules.
 
--   ***L1 (component / unit)***: `tests/{component_name}/…` mirroring `vllm_omni/{component_name}/` (e.g. `tests/diffusion/`, `tests/entrypoints/`). Do **not** invent new top-level `tests/` folders unrelated to a component.
--   ***L2 (basic model E2E)***:
-    -   Online serving: `tests/e2e/online_serving/test_{model_name}.py`
-    -   Offline inference: `tests/e2e/offline_inference/test_{model_name}.py`
--   ***Feature-level integration*** (cross-cutting, not a single-model smoke): `tests/e2e/features/<feature>/` (e.g. `fullduplex/`, `custom_pipeline/`, `rlhf_test/`).
+- ***L1 (component / unit)***: `tests/{component_name}/…` mirroring `vllm_omni/{component_name}/` (e.g. `tests/diffusion/`, `tests/entrypoints/`). Do **not** invent new top-level `tests/` folders unrelated to a component.
+- ***L2 (basic model E2E)***:
+    - Online serving: `tests/e2e/online_serving/test_{model_name}.py`
+    - Offline inference: `tests/e2e/offline_inference/test_{model_name}.py`
+- ***Feature-level integration*** (cross-cutting, not a single-model smoke): `tests/e2e/features/<feature>/` (e.g. `fullduplex/`, `custom_pipeline/`, `rlhf_test/`).
 
 #### 1.4 Execution Method and Example
 
--   ***Trigger Timing***: **`PR with ready label`**. That is, when a developer adds a "ready for review" or similar label to a PR on platforms like GitHub, L1 and L2 tests are automatically triggered.
--   ***Diff-aware step skipping***: On L2, **E2E Test** jobs may be omitted at pipeline upload when the PR diff does not touch their [`source_file_dependencies`](ci_settings.md#step-filtering) prefixes; non-E2E groups still always upload. See [CI Settings — Diff-aware CI](ci_settings.md#diff-aware-ci).
--   ***Execution Environment***: L1 uses ***CPU*** environment; L2 requires ***GPU*** environment.
--   ***Script Example***:
+- ***Trigger Timing***: **`PR with ready label`**. That is, when a developer adds a "ready for review" or similar label to a PR on platforms like GitHub, L1 and L2 tests are automatically triggered.
+- ***Diff-aware step skipping***: On L2, **E2E Test** jobs may be omitted at pipeline upload when the PR diff does not touch their [`source_file_dependencies`](ci_settings.md#step-filtering) prefixes; non-E2E groups still always upload. See [CI Settings — Diff-aware CI](ci_settings.md#diff-aware-ci).
+- ***Execution Environment***: L1 uses ***CPU*** environment; L2 requires ***GPU*** environment.
+- ***Script Example***:
 
 <details>
 <summary> L1 Test Examples</summary>
 
 Examples from `tests/model_executor/models/qwen2_5_omni/test_audio_length.py`
+
 ```python
 # SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
 
 import pytest
 
@@ -331,6 +319,7 @@ def test_cap_and_align_mel_length_no_mismatch(repeats, code_len, max_mel_frames)
     if max_mel_frames is not None and int(max_mel_frames) > 0 and int(max_mel_frames) >= repeats:
         assert target_mel_len <= int(max_mel_frames)
 ```
+
 </details>
 
 <details>
@@ -338,7 +327,7 @@ def test_cap_and_align_mel_length_no_mismatch(repeats, code_len, max_mel_frames)
 You can refer to Test Examples in the L3 section to see example test cases that incorporate both L2 and L3 testing logic.
 </details>
 
--   -   ***Run Command***:
+- ***Run Command***:
 
     `pytest -s -v /tests/e2e/online_serving/test_{model_name}.py`
     `pytest -s -v -m 'core_model and cpu' --run-level=core_model`
@@ -351,36 +340,34 @@ L3 level testing executes after code is merged into the main branch. Its core pu
 
 . It acts as the "quality gatekeeper" for the main branch, ensuring that no merge breaks the core capabilities of the model service. Testing needs to provide clear conclusions within a relatively short time (<30min), balancing test depth with feedback speed.
 
-
-
 #### 2.2 Testing Content and Scope
 
--   ***Deployment Scenarios***: Covers richer `online` and `offline` deployment configurations, which may include different hardware configurations, batch sizes, concurrency levels, etc.
--   ***Core Verification***:
--   1.  ***Inference Functionality***: Ensures real models can perform forward computation normally and return results.
-    2.  ***Accuracy Compliance***: Verifies that the model's evaluation metrics (e.g., accuracy) meet the expected baseline, preventing code changes from introducing accuracy issues.
-    3.  ***Important Performance***: Verifies whether performance (e.g., P99 latency, throughput) in core scenarios meets preset thresholds.
--   ***L3 response validation (`advanced_model`)***: At `--run-level advanced_model`, `tests/helpers/assertions.py` adds semantic checks on top of L2 payload gates (see L1 & L2 above):
-    -   **Speech / TTS** (`assert_audio_speech_response`): Whisper transcript of returned audio vs `request_config["input"]` (cosine similarity &gt; 0.9 when input text is set); optional `min_audio_bytes` floor; PCM harmonic-to-noise ratio when `response_format` is `pcm`; **preset voice gender** via `_assert_preset_voice_gender_from_audio` when `voice` matches a known preset in `_PRESET_VOICE_GENDER_MAP` (pitch/F0-based classifier; skipped for unknown voices or `pcm` output).
-    -   **Omni multimodal** (`assert_omni_response`): non-empty text/audio outputs; `key_words` in transcript or text; text–audio similarity / containment; preset **`speaker`** gender check (same helper as TTS).
-    -   **Diffusion** (`assert_*_diffusion_response`): image/video dimension and frame-count parity with request parameters where configured.
-    -   **Accuracy suites**: pixel/video similarity and metric baselines under `/tests/e2e/accuracy/` (separate from inline helper assertions).
+- ***Deployment Scenarios***: Covers richer `online` and `offline` deployment configurations, which may include different hardware configurations, batch sizes, concurrency levels, etc.
+- ***Core Verification***:
+    1. ***Inference Functionality***: Ensures real models can perform forward computation normally and return results.
+    2. ***Accuracy Compliance***: Verifies that the model's evaluation metrics (e.g., accuracy) meet the expected baseline, preventing code changes from introducing accuracy issues.
+    3. ***Important Performance***: Verifies whether performance (e.g., P99 latency, throughput) in core scenarios meets preset thresholds.
+- ***L3 response validation (`advanced_model`)***: At `--run-level advanced_model`, `tests/helpers/assertions.py` adds semantic checks on top of L2 payload gates (see L1 & L2 above):
+    - **Speech / TTS** (`assert_audio_speech_response`): Whisper transcript of returned audio vs `request_config["input"]` (cosine similarity &gt; 0.9 when input text is set); optional `min_audio_bytes` floor; PCM harmonic-to-noise ratio when `response_format` is `pcm`; **preset voice gender** via `_assert_preset_voice_gender_from_audio` when `voice` matches a known preset in `_PRESET_VOICE_GENDER_MAP` (pitch/F0-based classifier; skipped for unknown voices or `pcm` output).
+    - **Omni multimodal** (`assert_omni_response`): non-empty text/audio outputs; `key_words` in transcript or text; text–audio similarity / containment; preset **`speaker`** gender check (same helper as TTS).
+    - **Diffusion** (`assert_*_diffusion_response`): image/video dimension and frame-count parity with request parameters where configured.
+    - **Accuracy suites**: pixel/video similarity and metric baselines under `/tests/e2e/accuracy/` (separate from inline helper assertions).
 
 #### 2.3 Test Directory and Execution Files
 
--   ***Functional / model E2E***:
-    -   Online serving: `tests/e2e/online_serving/test_{model_name}_expansion.py`
-    -   Offline inference: `tests/e2e/offline_inference/test_{model_name}_expansion.py`
-    -   Accuracy / similarity: `tests/e2e/accuracy/`
-    -   (`_expansion.py` holds broader scenario coverage than the L2 smoke file.)
--   ***Feature-level integration***: `tests/e2e/features/<feature>/` (not under a new top-level `tests/` directory).
+- ***Functional / model E2E***:
+    - Online serving: `tests/e2e/online_serving/test_{model_name}_expansion.py`
+    - Offline inference: `tests/e2e/offline_inference/test_{model_name}_expansion.py`
+    - Accuracy / similarity: `tests/e2e/accuracy/`
+    - (`_expansion.py` holds broader scenario coverage than the L2 smoke file.)
+- ***Feature-level integration***: `tests/e2e/features/<feature>/` (not under a new top-level `tests/` directory).
 
 #### 2.4 Execution Method and Example
 
--   ***Trigger Timing***: **`PR Merged`**. Automatically triggered after code review is approved and merged into the main branch (typically via `merge-test` label on the PR before merge).
--   ***Diff-aware step skipping***: Same [`source_file_dependencies`](ci_settings.md#step-filtering) mechanism as L2—**all E2E Test** leaf steps are diff-gated; other L3 groups always upload. See [CI Settings — Diff-aware CI](ci_settings.md#diff-aware-ci).
--   ***Execution Environment***: ***GPU*** servers.
--   ***Script Example***:
+- ***Trigger Timing***: **`PR Merged`**. Automatically triggered after code review is approved and merged into the main branch (typically via `merge-test` label on the PR before merge).
+- ***Diff-aware step skipping***: Same [`source_file_dependencies`](ci_settings.md#step-filtering) mechanism as L2—**all E2E Test** leaf steps are diff-gated; other L3 groups always upload. See [CI Settings — Diff-aware CI](ci_settings.md#diff-aware-ci).
+- ***Execution Environment***: ***GPU*** servers.
+- ***Script Example***:
 
 ???+ example "Test Examples"
 
@@ -496,9 +483,9 @@ L3 level testing executes after code is merged into the main branch. Its core pu
 
     **Audio output debugging**: Deep validation may transcribe returned audio via `convert_audio_bytes_to_text` (Whisper). If an audio keyword or text–audio similarity assertion fails, set `VLLM_OMNI_KEEP_REQUEST_MEDIA=1` before running pytest to keep the intermediate WAV files for inspection (see [Test helper environment variables](test_system_overview.md#test-helper-environment-variables)).
 
--   ***Run Command (L3 merge)***: `pytest -s -v /tests/e2e/online_serving/test_{model_name}.py -m advanced_model --run-level=advanced_model`
+- ***Run Command (L3 merge)***: `pytest -s -v /tests/e2e/online_serving/test_{model_name}.py -m advanced_model --run-level=advanced_model`
 
--   ***Run Command (L4 nightly expansion)***: `pytest -s -v /tests/e2e/online_serving/test_{model_name}_expansion.py -m full_model --run-level=full_model`
+- ***Run Command (L4 nightly expansion)***: `pytest -s -v /tests/e2e/online_serving/test_{model_name}_expansion.py -m full_model --run-level=full_model`
 
 ### L4 Level Testing - Full Functionality, Performance, and Documentation Testing
 
@@ -506,27 +493,25 @@ L3 level testing executes after code is merged into the main branch. Its core pu
 
 L4 level testing is a comprehensive quality audit before a version release. It expands upon L3, executing ***full*** functional scenarios, conducting systematic ***performance stress tests***, and simultaneously verifying the correctness of accompanying ***example documentation***. Its purpose is to perform deep validation of the system during off-peak nighttime hours, providing quality trend reports for daytime development and data support for release decisions.
 
-
-
 #### 3.2 Testing Content and Scope
 
--   ***Full Functionality Testing***: Executes all test cases defined in `test_{model_name}_expansion.py`, covering all implemented features, positive flows, boundary conditions, and exception handling.
--   ***Performance Testing***: Uses `tests/dfx/perf/tests/test_qwen3_omni_*.json` (Omni), `test_tts.json` / `test_voxcpm2.json` / `test_higgs_audio_v3.json` (TTS), and diffusion configs `tests/dfx/perf/tests/test_*_vllm_omni.json` (passed to `run_benchmark.py` or `run_diffusion_benchmark.py` via `--test-config-file` in nightly **Perf Test** steps) to drive throughput, latency, and memory benchmarks. Each JSON **case** may declare an optional top-level **`mark`** array: exactly one ``hardware_marks`` object plus pytest marker name strings (`full_model`, `omni` / `tts` / `diffusion`, …). Runners attach those marks to each parametrized `(server, benchmark index)` pair so **local** bulk runs can filter with `-m` (for example `-m "full_model and H100 and diffusion"`). Nightly CI perf jobs select workloads by **`--test-config-file`**, not `-m`. Details are in the Performance Tests example below.
--   ***Documentation Testing***: Verifies whether the example code provided to users is runnable and its results match the description.
+- ***Full Functionality Testing***: Executes all test cases defined in `test_{model_name}_expansion.py`, covering all implemented features, positive flows, boundary conditions, and exception handling.
+- ***Performance Testing***: JSON configs under `tests/dfx/perf/tests/` drive Omni, TTS, and diffusion serving benchmarks via `run_benchmark.py` or `run_diffusion_benchmark.py`, measuring throughput, latency, and related metrics under configured workloads.
+- ***Documentation Testing***: Verifies whether the example code provided to users is runnable and its results match the description.
 
 #### 3.3 Test Directory and Execution Files
 
--   ***Functional Testing***: Same directories as L3.
--   ***Performance Test Configuration***: `tests/dfx/perf/tests/test_qwen3_omni_*.json`, `test_tts.json`, `test_voxcpm2.json`, `test_higgs_audio_v3.json`, and diffusion configs `tests/dfx/perf/tests/test_*_vllm_omni.json` (e.g. `test_qwen_image_vllm_omni.json`, `test_cosmos3_vllm_omni.json`). Optional per-case `mark` for local `-m` filtering is documented in Section 3.4 Performance Tests.
--   ***Documentation Example Tests***:
-    -   `tests/examples/online_serving/test_{model_name}.py`
-    -   `tests/examples/offline_inference/test_{model_name}.py`
+- ***Functional Testing***: Same directories as L3.
+- ***Performance Test Configuration***: `tests/dfx/perf/tests/test_qwen3_omni_*.json`, `test_tts.json`, `test_voxcpm2.json`, `test_higgs_audio_v3.json`, and diffusion configs `tests/dfx/perf/tests/test_*_vllm_omni.json` (e.g. `test_qwen_image_vllm_omni.json`, `test_cosmos3_vllm_omni.json`). Optional per-case `mark` for local `-m` filtering is documented in Section 3.4 Performance Tests.
+- ***Documentation Example Tests***:
+    - `tests/examples/online_serving/test_{model_name}.py`
+    - `tests/examples/offline_inference/test_{model_name}.py`
 
 #### 3.4 Execution Method and Example
 
--   ***Trigger Timing***: **`Nightly`**, automatically executed every night.
--   ***Execution Environment***: ***GPU*** server clusters to meet the resource demands of performance testing.
--   ***Script Example***:
+- ***Trigger Timing***: **`Nightly`**, automatically executed every night.
+- ***Execution Environment***: ***GPU*** server clusters to meet the resource demands of performance testing.
+- ***Script Example***:
 
 ??? example "Test Examples: Documentation Example Tests"
 
@@ -540,7 +525,7 @@ L4 level testing is a comprehensive quality audit before a version release. It e
 
     --8<-- "docs/contributing/ci/test_examples/l4_functionality_tests.inc.md"
 
--   ***Run Command***: (Specific commands would depend on the performance testing tool and configuration defined in `nightly.json`).
+- ***Run Command***: (Specific commands would depend on the performance testing tool and configuration defined in `nightly.json`).
 
 ### L5 Level Testing - Stability and Reliability Testing
 
@@ -548,40 +533,43 @@ L4 level testing is a comprehensive quality audit before a version release. It e
 
 L5 level testing focuses on the performance of model services under ***long-running*** and ***abnormal fault*** scenarios. It aims to uncover deep-seated issues that only manifest under sustained pressure or extreme conditions, such as memory leaks, resource contention, gradual performance degradation, and lack of fault tolerance mechanisms. This is the final, yet crucial, line of defense for ensuring service high availability and production environment robustness.
 
-
-
 #### 4.2 Testing Content and Scope
 
--   ***Long-term Stability (Stability) Testing***: Uses JSON under `tests/dfx/stability/tests/` (for example `test_qwen3_omni.json` and `test_wan22.json`) to run the service under moderate load for an extended period (e.g., over 12 hours), monitoring whether metrics like memory/VRAM usage, response time, and throughput degrade over time, and whether the service process remains stable.
--   ***Reliability Testing***: Uses pytest suites under `tests/dfx/reliability/` to inject controlled faults against a **live** `vllm_omni serve` instance (same **`omni_server` / `omni_server_function`** fixture style as E2E). Current suites emphasize **GPU memory pressure** (CUDA sidecar “memory hog”), **worker / runtime process kill** (`SIGKILL` on `VLLM::Worker` for Qwen3-Omni, `multiprocessing.spawn` for Wan2.2 video workers, or `vLLM-Omni::` for HunyuanImage DiT workers), **large multimodal chat**, **`/v1/videos`**, **`/v1/images/generations`**, or **`/v1/audio/speech`** jobs under OOM, **`/health` → 503** and **fast-fail / non-hanging concurrent** requests after kill, and **OpenAI-style 5xx error contracts** (e.g. text vs text+audio under OOM). **Post-fault recovery** checks exist where enabled (some cases may be `skip` while issues are tracked). See the Reliability `<details>` block in Section 4.4 for file-level responsibilities and CI markers (`slow`, `hardware_test`, POSIX-only kill).
+- ***Long-term Stability (Stability) Testing***: Uses JSON under `tests/dfx/stability/tests/` (for example `test_qwen3_omni.json` and `test_wan22.json`) to run the service under moderate load for an extended period (e.g., over 12 hours), monitoring whether metrics like memory/VRAM usage, response time, and throughput degrade over time, and whether the service process remains stable.
+- ***Reliability Testing***: Uses pytest suites under `tests/dfx/reliability/` to inject controlled faults against a **live** `vllm_omni serve` instance (same **`omni_server` / `omni_server_function`** fixture style as E2E). Current suites emphasize **GPU memory pressure** (CUDA sidecar “memory hog”), **worker / runtime process kill** (`SIGKILL` on `VLLM::Worker` for Qwen3-Omni, `multiprocessing.spawn` for Wan2.2 video workers, or `vLLM-Omni::` for HunyuanImage DiT workers), **large multimodal chat**, **`/v1/videos`**, **`/v1/images/generations`**, or **`/v1/audio/speech`** jobs under OOM, **`/health` → 503** and **fast-fail / non-hanging concurrent** requests after kill, and **OpenAI-style 5xx error contracts** (e.g. text vs text+audio under OOM). **Post-fault recovery** checks exist where enabled (some cases may be `skip` while issues are tracked). See Section 4.3 for file-level responsibilities and Section 4.4 Test Examples (Reliability) for CI markers (`slow`, `hardware_test`, POSIX-only kill).
 
 #### 4.3 Test Directory and Execution Files
 
--   ***Stability Test Configuration***: `tests/dfx/stability/tests/test_qwen3_omni.json`, `tests/dfx/stability/tests/test_wan22.json` (one JSON per model / runner family)
--   ***Reliability Test Suite*** (`tests/dfx/reliability/`):
-    -   `test_reliability_qwen3_omni.py` — Qwen3-Omni chat / multimodal reliability (GPU OOM, process kill, recovery, error contract under `--async-chunk` vs default).
-    -   `test_reliability_wan22.py` — Wan2.2 I2V video API reliability (`/v1/videos` under OOM and process kill, recovery).
-    -   `test_reliability_hunyuan_image.py` — HunyuanImage-3.0-Instruct DiT-only reliability (`/v1/images/generations` under OOM and process kill; deploy `hunyuan_image3_dit.yaml`, H100 × 4).
-    -   `helpers.py` — Shared primitives used by current suites: raw HTTP probes for `/v1/chat/completions` and `/health`, OpenAI-style error parsing, GPU OOM sidecar (`inject_gpu_oom` / `stop_gpu_oom_hogs`), and `pgrep`-based process-kill injector construction (`make_process_kill_fault_injector`).
-    -   `conftest.py` — `fault_injector` and `omni_server_after_fault` / `omni_server_after_fault_function` fixtures to run a callable **after** the server is ready.
-    -   `README.md` — Short local run commands for this directory.
+- ***Stability Test Configuration***: `tests/dfx/stability/tests/test_qwen3_omni.json`, `tests/dfx/stability/tests/test_wan22.json` (one JSON per model / runner family)
+- ***Reliability Test Suite*** (`tests/dfx/reliability/`):
+    - `test_reliability_qwen3_omni.py` — Qwen3-Omni chat / multimodal reliability (GPU OOM, process kill, recovery, error contract under `--async-chunk` vs default).
+    - `test_reliability_wan22.py` — Wan2.2 I2V video API reliability (`/v1/videos` under OOM and process kill, recovery).
+    - `test_reliability_hunyuan_image.py` — HunyuanImage-3.0-Instruct DiT-only reliability (`/v1/images/generations` under OOM and process kill; deploy `hunyuan_image3_dit.yaml`, H100 × 4).
+    - `helpers.py` — Shared primitives used by current suites: raw HTTP probes for `/v1/chat/completions` and `/health`, OpenAI-style error parsing, GPU OOM sidecar (`inject_gpu_oom` / `stop_gpu_oom_hogs`), and `pgrep`-based process-kill injector construction (`make_process_kill_fault_injector`).
+    - `conftest.py` — `fault_injector` and `omni_server_after_fault` / `omni_server_after_fault_function` fixtures to run a callable **after** the server is ready.
+    - `README.md` — Short local run commands for this directory.
 
 #### 4.4 Execution Method and Example
 
--   ***Trigger Timing***: **`Weekly`** (weekly) or **`Days before Release`** (several days before a major release). Due to long execution times, the frequency is lower.
--   ***Execution Environment***: ***GPU*** servers, requiring a stable and exclusive testing environment.
--   ***Script Example***:
+- ***Trigger Timing***: **`Weekly`** (weekly) or **`Days before Release`** (several days before a major release). Due to long execution times, the frequency is lower.
+- ***Run Command***:
+    - ***Stability***: `pytest -s -v tests/dfx/stability/scripts/test_stability_qwen3_omni.py` or `pytest -s -v tests/dfx/stability/scripts/test_stability_wan22.py` (or add `test_stability_<model>.py` alongside a matching JSON config)
+    - ***Reliability***: `pytest -s -v tests/dfx/reliability/test_reliability_<model>.py -m slow` (current suites: `qwen3_omni`, `wan22`, `hunyuan_image`).
+- ***Script Example***:
+
 <details>
 <summary> Test Examples</summary>
 
-When you want to add L5-level stability test cases, add or extend the appropriate JSON file under `tests/dfx/stability/tests/` (for example `test_qwen3_omni.json` for Omni bench traffic, or `test_wan22.json` for diffusion `/v1/videos` workloads). The following illustrates the Qwen3-Omni shape:
+##### Stability
+
+When you want to add L5-level stability test cases, add or extend the appropriate JSON file under `tests/dfx/stability/tests/` (for example `test_qwen3_omni.json` for Omni bench traffic, or `test_wan22.json` for diffusion `/v1/videos` workloads). Pair the JSON with `tests/dfx/stability/scripts/test_stability_<model>.py`. The following illustrates the Qwen3-Omni shape:
 
 ```json
 {
     "test_name": "test_qwen3_omni_stability",
     "server_params": {
         "model": "Qwen/Qwen3-Omni-30B-A3B-Instruct",
-        "stage_config_name": "qwen3_omni.yaml"
+        "stage_config_name": "qwen3_omni_moe.yaml"
     },
     "benchmark_params": [
         {
@@ -600,108 +588,61 @@ When you want to add L5-level stability test cases, add or extend the appropriat
 }
 ```
 
-##### Parameter Explanation
+###### Parameter Explanation
 
 ***Overview***
 
 | Field            | Required | Description                                                                 |
 | ---------------- | -------- | --------------------------------------------------------------------------- |
 | test_name        | Yes      | Unique identifier for the stability test case                               |
-| server_params    | Yes      | Server-side configuration parameters (model, stage configuration, etc.)     |
+| server_params    | Yes      | Server-side configuration parameters (model, deploy configuration, etc.)    |
 | benchmark_params | Yes      | Stability benchmark running parameters (supports multiple configurations)   |
 
-##### server_params Configuration
+###### server_params Configuration
 
-###### Basic Parameters
+Basic parameters:
 
 | Parameter         | Required | Example                            | Description                         |
 | ----------------- | -------- | ---------------------------------- | ----------------------------------- |
 | model             | Yes      | "Qwen/Qwen3-Omni-30B-A3B-Instruct" | Model name or path                  |
-| stage_config_name | Yes      | "qwen3_omni.yaml"                  | Stage configuration file name       |
+| stage_config_name | Yes      | "qwen3_omni_moe.yaml"              | Deploy configuration file name      |
 
-###### Dynamic Configuration (update/delete)
-
-Supports incremental modifications based on the basic configuration:
+Dynamic configuration (`update` / `delete`) supports incremental modifications based on the basic configuration:
 
 | Operation | Description                          |
 | --------- | ------------------------------------ |
 | update    | Update or add configuration items    |
 | delete    | Delete specified configuration items |
 
-***Example***:
-You can refer to Test Examples in L4 §3.4
+***Example***: You can refer to Test Examples in L4 §3.4.
 
-##### benchmark_params Configuration
+###### benchmark_params Configuration
 
 For stability testing, the key parameters are:
 
--   **duration_sec**: Total duration (in seconds) during which benchmark traffic is sent. The stability benchmark will keep sending batches until this duration is reached.
--   **request_rate** / **max_concurrency**: Exactly one of them must be specified. They control how the traffic is generated for each batch:
-    -   `request_rate`: Number of requests per second. The benchmark will send `num_prompts_per_batch` requests at the given rate.
-    -   `max_concurrency`: Maximum number of concurrent requests. When this is used, `request_rate` is set to `inf` internally.
--   **num_prompts_per_batch**: Number of prompts sent in each batch. Multiple batches will be executed sequentially within `duration_sec`.
+- **duration_sec**: Total duration (in seconds) during which benchmark traffic is sent. The stability benchmark will keep sending batches until this duration is reached.
+- **request_rate** / **max_concurrency**: Exactly one of them must be specified. They control how the traffic is generated for each batch:
+    - `request_rate`: Number of requests per second. The benchmark will send `num_prompts_per_batch` requests at the given rate.
+    - `max_concurrency`: Maximum number of concurrent requests. When this is used, `request_rate` is set to `inf` internally.
+- **num_prompts_per_batch**: Number of prompts sent in each batch. Multiple batches will be executed sequentially within `duration_sec`.
 
 All other optional parameters follow the same rules as in L4 §3.4.
 
-</details>
+##### Reliability
 
-<details>
-<summary> Reliability test suite (<code>tests/dfx/reliability</code>)</summary>
+Add pytest modules under `tests/dfx/reliability/` (for example `test_reliability_qwen3_omni.py`, `test_reliability_wan22.py`, `test_reliability_hunyuan_image.py`). Reuse `helpers.py` and `conftest.py` rather than duplicating OOM / process-kill / raw HTTP plumbing.
 
-##### Purpose and relationship to stability
-
-Reliability tests are **short fault-injection** integration runs (L5 **(b)** in `tests/dfx/reliability/README.md`). They complement **stability** JSON-driven long runs: instead of hours of steady traffic, they **perturb** the server (GPU OOM sidecar, fatal signals on selected processes) and check **failure mode** and **latency bounds** (e.g. chat or `/v1/videos` must not hang under concurrent fault-time load).
-
-##### Directory layout
-
-| Path | Responsibility |
-| ---- | -------------- |
-| `helpers.py` | Shared helpers used by current reliability suites: raw `POST`/`GET` probes (`/v1/chat/completions`, `/health`), OpenAI error parsing (`extract_openai_error_contract_from_bytes`), GPU OOM sidecar lifecycle (`inject_gpu_oom`, `stop_gpu_oom_hogs`), and process-kill injector builder (`make_process_kill_fault_injector`). |
-| `conftest.py` | Pytest fixtures: indirect `fault_injector`, `omni_server_after_fault` / `omni_server_after_fault_function` (run injector after server is ready, then yield server). |
-| `test_reliability_qwen3_omni.py` | Qwen3-Omni: OOM vs **text vs text+audio** error contract, large multimodal chat under OOM, concurrent pressure, **SIGKILL** on `VLLM::Worker`, `/health` → 503 + fast-fail + concurrent chat; optional OOM recovery scenario (may be skipped while tracked in issues). |
-| `test_reliability_wan22.py` | Wan2.2 I2V: large `/v1/videos` under OOM, **SIGKILL** on `multiprocessing.spawn` chain, health / fast-fail / concurrent video requests; optional recovery test (may be skipped). |
-| `test_reliability_hunyuan_image.py` | HunyuanImage DiT-only: large `/v1/images/generations` under OOM, **SIGKILL** on `vLLM-Omni::` workers and serve/tree targets, health / fast-fail / concurrent image requests; some OOM/recovery cases may be skipped while tracked in issues. |
-| `README.md` | Minimal run / collect examples. |
-
-##### Parametrization and markers
+###### Parametrization and markers
 
 - Each test module defines a **`RELIABILITY_SCENARIOS`** list (`test_name`, `server_params`: model, `stage_config_name` or diffusion `server_args`, etc.). **`create_reliability_omni_server_params()`** in `tests/dfx/conftest.py` resolves stage paths (including XPU substitutions where applicable) and builds **`OmniServerParams`** lists consumed by **`@pytest.mark.parametrize(..., indirect=True)`** on `omni_server` or `omni_server_function`.
 - Cases are tagged **`@pytest.mark.slow`** for weekly / selective CI. GPU-heavy suites use **`@hardware_test(res={"cuda": "H100"}, num_cards=...)`** (Qwen3-Omni **2**× H100; Wan2.2 **1**× H100; HunyuanImage DiT **4**× H100).
 - **Process-kill** tests use **`@pytest.mark.skipif(os.name == "nt", ...)`** because injection uses POSIX **`pgrep` / `kill`**.
 
-##### CI trigger
-
-Weekly Buildkite (`.buildkite/cuda/test-weekly.yml`) runs one step per model suite (trigger: `WEEKLY=1` or PR label `weekly-test`), for example:
-
-| Buildkite step | Test file | CI hardware |
-| -------------- | --------- | ----------- |
-| Reliability Test - qwen3-omni | `test_reliability_qwen3_omni.py` | H100 × 2 (`mithril-h100-pool`) |
-| Reliability Test - wan22 | `test_reliability_wan22.py` | H100 × 2 (`mithril-h100-pool`) |
-| Reliability Test - hunyuan-image | `test_reliability_hunyuan_image.py` | H100 × 4 (`mithril-h100-pool`) |
-
-```bash
-pytest -s -v tests/dfx/reliability/test_reliability_qwen3_omni.py -m "slow"
-pytest -s -v tests/dfx/reliability/test_reliability_wan22.py -m "slow"
-pytest -s -v tests/dfx/reliability/test_reliability_hunyuan_image.py -m "slow"
-```
-
-##### Local commands
-
-```bash
-pytest --collect-only tests/dfx/reliability
-pytest -s -v tests/dfx/reliability/test_reliability_qwen3_omni.py -m slow
-pytest -s -v tests/dfx/reliability/test_reliability_wan22.py -m slow
-pytest -s -v tests/dfx/reliability/test_reliability_hunyuan_image.py -m slow
-```
-
-##### Adding a new model suite
+###### Adding a new model suite
 
 1. Add `test_reliability_<model>.py` under `tests/dfx/reliability/`.
-2. Define **`RELIABILITY_SCENARIOS`** and pass them through **`create_reliability_omni_server_params()`** with the correct deploy or e2e stage-config directory (same pattern as existing files).
-3. Reuse **`helpers`** for OOM / kill / raw HTTP; prefer **`assert_fault_exception()`** and **`resolve_oom_device_spec()`** from `tests/dfx/conftest.py` for consistent device selection vs stage YAML.
+2. Define **`RELIABILITY_SCENARIOS`** and pass them through **`create_reliability_omni_server_params()`** with the correct deploy or e2e deploy-config directory (same pattern as existing files).
+3. Reuse **`helpers`** for OOM / kill / raw HTTP; prefer **`assert_fault_exception()`** and **`resolve_oom_device_spec()`** from `tests/dfx/conftest.py` for consistent device selection vs deploy YAML.
 4. Register **`slow`** (and **`hardware_test`** if needed); extend **`.buildkite/cuda/test-weekly.yml`** when the suite should run in weekly L5.
 
 </details>
-
--   -   ***Stability***: `pytest -s -v tests/dfx/stability/scripts/test_stability_qwen3_omni.py` or `pytest -s -v tests/dfx/stability/scripts/test_stability_wan22.py` (or add `test_stability_<model>.py` alongside a matching JSON config)
-    -   ***Reliability***: `pytest -s -v tests/dfx/reliability/test_reliability_<model>.py -m slow` (current suites: `qwen3_omni`, `wan22`, `hunyuan_image`; add `test_reliability_<suite>.py` and a matching step in `.buildkite/cuda/test-weekly.yml` for new models)

docs/contributing/model/adding_omni_model.md

@@ -9,7 +9,7 @@ This guide walks through the process of adding a new multi-stage model to vLLM-O
 3. [Step-by-Step Implementation](#step-by-step-implementation)
 4. [Key Components](#key-components)
 5. [Model Registration](#model-registration)
-6. [Stage Configuration](#stage-configuration)
+6. [Pipeline and Deploy Configuration](#pipeline-and-deploy-configuration)
 7. [Stage Input Processors](#stage-input-processors)
 8. [Testing](#testing)
 9. [Adding a Model Recipe](#adding-a-model-recipe)
@@ -316,18 +316,28 @@ _OMNI_MODELS = {
 
 The registry uses lazy loading, so the model class is imported only when needed.
 
-## Stage Configuration
+## Pipeline and Deploy Configuration
 
-Create a YAML configuration file in `vllm_omni/deploy/`. For a complete example, see the [Qwen3-Omni configuration file](gh-file:vllm_omni/deploy/qwen3_omni_moe.yaml).
+Define fixed topology in `vllm_omni/model_executor/models/<model>/pipeline.py`
+with `PipelineConfig` and `StagePipelineConfig`, then register it in
+`vllm_omni/config/pipeline_registry.py`. For a complete example, see the
+[Qwen3-Omni pipeline](gh-file:vllm_omni/model_executor/models/qwen3_omni/pipeline.py).
 
-### Key Configuration Fields
+### Pipeline topology fields
 
-- **`model_stage`**: Which stage to run ("thinker", "talker", "code2wav", etc.)
-- **`model_arch`**: The model architecture name (must match registry)
-- **`engine_input_source`**: List of stage IDs that provide input to this stage
-- **`custom_process_input_func`**: Function to process inputs from previous stages
-- **`final_output`**: Whether this stage produces the final output (True/False)
-- **`final_output_type`**: Type of final output ("text", "audio", "image", etc.)
+- **`model_stage`**: Logical stage name ("thinker", "talker", "code2wav", etc.)
+- **`execution_type`**: Runtime family (`LLM_AR`, `LLM_GENERATION`, or `DIFFUSION`)
+- **`model_arch`**: Stage architecture name, when different from the pipeline default
+- **`input_sources`**: Upstream stage IDs that provide input to this stage
+- **`custom_process_input_func`**: Function that processes inputs from previous stages
+- **`final_output`**: Whether this stage produces user-facing output
+- **`final_output_type`**: Output modality ("text", "audio", "image", etc.)
+- **`owns_tokenizer`**: Whether this stage owns the pipeline tokenizer
+
+Create the matching runtime defaults in `vllm_omni/deploy/<model>.yaml` and set
+`PipelineConfig.default_deploy_config_name` to that file. The deploy YAML owns
+GPU placement, memory sizing, parallelism, connectors, and sampling defaults;
+it must not redefine topology fields.
 
 ## Stage Input Processors
 
@@ -374,7 +384,7 @@ The stage transition process follows these steps:
 
 2. **Transition Detection**: The orchestrator checks if there's a next stage and calls `process_engine_inputs()` on it
 
-3. **Input Processing**: The stage input processor configured in stage YAML (under `vllm_omni/model_executor/stage_input_processors/`) handles the transition:
+3. **Input Processing**: The stage input processor registered by the pipeline (implemented under `vllm_omni/model_executor/stage_input_processors/`) handles the transition:
    ```python
    def process_engine_inputs(
        self, stage_list: list[Any], prompt: OmniTokensPrompt | TextPrompt = None
@@ -577,7 +587,7 @@ Adding a new model to vLLM-Omni involves:
 1. **Create model directory structure** with stage implementations
 2. **Implement unified model class** that orchestrates stages
 3. **Register model** in `registry.py`
-4. **Create stage configuration** YAML file
+4. **Define pipeline topology and deploy defaults** in `pipeline.py` and `vllm_omni/deploy/`
 5. **Implement stage input processors** for stage transitions
 6. **Write tests** to verify functionality
 7. **Add model recipe** to the [vllm-project/recipes](https://github.com/vllm-project/recipes) repository (see [Adding a Model Recipe](#adding-a-model-recipe) section)
@@ -590,7 +600,8 @@ For a complete reference implementation, see:
 - **Thinker**: `vllm_omni/model_executor/models/qwen3_omni/qwen3_omni_moe_thinker.py`
 - **Talker**: `vllm_omni/model_executor/models/qwen3_omni/qwen3_omni_moe_talker.py`
 - **Code2Wav**: `vllm_omni/model_executor/models/qwen3_omni/qwen3_omni_code2wav.py`
-- **Stage config**: `vllm_omni/deploy/qwen3_omni_moe.yaml`
+- **Pipeline topology**: `vllm_omni/model_executor/models/qwen3_omni/pipeline.py`
+- **Deploy config**: `vllm_omni/deploy/qwen3_omni_moe.yaml`
 - **Input processors**: `vllm_omni/model_executor/stage_input_processors/qwen3_omni.py`
 - **Registry**: `vllm_omni/model_executor/models/registry.py`
 - **Testing**: `vllm_omni/tests/e2e/offline_inference/test_qwen3_omni.py`
@@ -598,4 +609,4 @@ For a complete reference implementation, see:
 For more information, see:
 - [Architecture Overview](../../design/architecture_overview.md)
 - [Supported Models](../../models/supported_models.md)
-- [Stage Configuration Guide](../../configuration/stage_configs.md)
+- [Pipeline and Deploy Configuration Guide](../../configuration/stage_configs.md)

docs/contributing/model/adding_tts_model.md

@@ -20,7 +20,7 @@ pattern, refer to MOSS-TTS-Nano.
 4. [Step-by-Step Implementation](#step-by-step-implementation)
 5. [Key Components](#key-components)
 6. [Model Registration](#model-registration)
-7. [Stage Configuration](#stage-configuration)
+7. [Pipeline and Deploy Configuration](#pipeline-and-deploy-configuration)
 8. [Stage Input Processors](#stage-input-processors)
 9. [Online Serving Integration](#online-serving-integration)
 10. [Single-Stage Models](#single-stage-models)
@@ -78,7 +78,7 @@ Profile before optimizing.
 new TTS model must pass all three levels:
 
 | Layer | Catches | Tool |
-|-------|---------|------|
+| ------- | --------- | ------ |
 | Offline RTF / duration | Throughput regressions, missing audio, wrong sample rate | `end2end.py`, pytest e2e |
 | Browser streaming playback | Delta-vs-cumulative bugs, chunk boundary glitches, TTFP regressions | Gradio demo over `/v1/audio/speech?stream=true` |
 | Concurrent requests | Per-request state leaks, codec window round-robin gaps | `max_num_seqs>1` smoke with 4+ parallel prompts |
@@ -94,10 +94,10 @@ the symptom is crosstalk or truncation under load, nothing in single-request tes
 vLLM-Omni supports TTS models as multi-stage pipelines where each stage runs independently
 and can be placed on different devices. Qwen3-TTS has two stages:
 
-| Stage | Name | Input | Output |
-|-------|------|-------|--------|
-| 0 | Code Predictor (AR) | Text tokens | Discrete RVQ codec codes |
-| 1 | Code2Wav (Decoder) | RVQ codec codes | Audio waveform |
+| Stage | Name                | Input           | Output                   |
+| ----- | ------------------- | --------------- | ------------------------ |
+| 0     | Code Predictor (AR) | Text tokens     | Discrete RVQ codec codes |
+| 1     | Code2Wav (Decoder)  | RVQ codec codes | Audio waveform           |
 
 Each stage is a separate model class configured independently via YAML. The two stages
 are connected by the `async_chunk` framework, which enables inter-stage streaming for
@@ -170,7 +170,7 @@ and Qwen3-Omni).
 
 When adding a new TTS model, create the following structure:
 
-```
+```text
 vllm_omni/model_executor/models/
   your_model_name/
     __init__.py
@@ -201,7 +201,7 @@ it would not be picked up — the hub README is the documented surface.
 **Qwen3-TTS reference files:**
 
 | File | Purpose |
-|------|---------|
+| ------ | --------- |
 | `models/qwen3_tts/qwen3_tts.py` | Unified model class |
 | `models/qwen3_tts/qwen3_tts_code_predictor_vllm.py` | Stage 0 - optimized AR |
 | `models/qwen3_tts/qwen3_tts_code2wav.py` | Stage 1 - decoder |
@@ -427,112 +427,93 @@ _OMNI_MODELS = {
 
 The registry uses lazy loading - model classes are only imported when needed.
 
-## Stage Configuration
+## Pipeline and Deploy Configuration
 
-Each stage has a `worker_type` that determines how it is scheduled:
+Define fixed topology in `models/<model>/pipeline.py`. The execution type
+selects the scheduler and worker family:
 
-- `worker_type: ar` - autoregressive stage, uses `OmniARScheduler` with PagedAttention
-- `worker_type: generation` - non-AR stage (e.g. decoder), uses `OmniGenerationScheduler`
+- `StageExecutionType.LLM_AR` uses the autoregressive runtime with PagedAttention.
+- `StageExecutionType.LLM_GENERATION` uses the generation runtime for stages such as audio decoders.
 
-Key configuration fields:
+Key topology fields belong to `StagePipelineConfig`:
 
 | Field | Description |
-|-------|-------------|
-| `model_stage` | Which stage to initialize (`ar_stage`, `decoder`, etc.) |
-| `model_arch` | Architecture name, must match `registry.py` |
-| `engine_input_source` | List of upstream stage IDs that provide input (e.g. `[0]`) |
-| `engine_output_type` | Output type: `latent` for intermediate, `audio` for final |
-| `custom_process_next_stage_input_func` | Async chunk processor function path (streaming only) |
-| `final_output` | Whether this stage produces the final user-facing output |
-| `final_output_type` | Type of final output (`audio`, `text`, etc.) |
+| ------- | ------------- |
+| `model_stage` | Logical stage name (`ar_stage`, `decoder`, etc.). |
+| `execution_type` | Runtime family for this stage. |
+| `input_sources` | Upstream stage IDs that provide input. |
+| `model_arch` | Stage-specific architecture name, when it differs from the pipeline default. |
+| `engine_output_type` | Output representation such as `latent` or `audio`. |
+| `custom_process_next_stage_input_func` | Processor for full-payload stage handoff. |
+| `async_chunk_process_next_stage_input_func` | Processor for streaming chunk handoff. |
+| `final_output` / `final_output_type` | Whether this stage produces user-facing output and its modality. |
+| `owns_tokenizer` | Whether this stage supplies the pipeline tokenizer. |
 
-!!! note
-    New in-tree models should define frozen topology in `models/<model>/pipeline.py`,
-    register it in `vllm_omni/config/pipeline_registry.py`, and put deployment
-    knobs in `vllm_omni/deploy/<model>.yaml`. Use `--deploy-config` to load a
-    custom deployment YAML.
+For example, a two-stage TTS topology can be defined as:
 
-### Batch mode
+```python
+from vllm_omni.config.stage_config import (
+    PipelineConfig,
+    StageExecutionType,
+    StagePipelineConfig,
+)
 
-```yaml
-# stage_configs/your_model_name.yaml
+_PROC = "vllm_omni.model_executor.stage_input_processors.your_model_name"
 
-stage_args:
-  - stage_id: 0
-    stage_type: llm
-    runtime:
-      devices: "0"
-    engine_args:
-      model_stage: ar_stage
-      max_num_seqs: 64
-      model_arch: YourTTSModelForConditionalGeneration
-      worker_type: ar
-      scheduler_cls: vllm_omni.core.sched.omni_ar_scheduler.OmniARScheduler
-      engine_output_type: latent
-    default_sampling_params:
-      temperature: 0.9
-      top_k: 50
-      max_tokens: 2048
-
-  - stage_id: 1
-    stage_type: llm
-    runtime:
-      devices: "0"
-    engine_args:
-      model_stage: decoder
-      model_arch: YourTTSModelForConditionalGeneration
-      worker_type: generation
-      scheduler_cls: vllm_omni.core.sched.omni_generation_scheduler.OmniGenerationScheduler
-      engine_output_type: audio
-    engine_input_source: [0]
-    final_output: true
-    final_output_type: audio
+YOUR_TTS_PIPELINE = PipelineConfig(
+    model_type="your_model_name",
+    default_deploy_config_name="your_model_name.yaml",
+    model_arch="YourTTSModelForConditionalGeneration",
+    stages=(
+        StagePipelineConfig(
+            stage_id=0,
+            model_stage="ar_stage",
+            execution_type=StageExecutionType.LLM_AR,
+            input_sources=(),
+            owns_tokenizer=True,
+            engine_output_type="latent",
+            custom_process_next_stage_input_func=f"{_PROC}.ar2decoder",
+            async_chunk_process_next_stage_input_func=f"{_PROC}.ar2decoder_async_chunk",
+        ),
+        StagePipelineConfig(
+            stage_id=1,
+            model_stage="decoder",
+            execution_type=StageExecutionType.LLM_GENERATION,
+            input_sources=(0,),
+            model_arch="YourTTSDecoder",
+            engine_output_type="audio",
+            final_output=True,
+            final_output_type="audio",
+        ),
+    ),
+)
 ```
 
-### Streaming mode (async_chunk)
-
-Add `async_chunk: true` at the top level and specify `custom_process_next_stage_input_func`
-on Stage 0 to define how intermediate outputs are chunked and forwarded:
+Register the pipeline in `vllm_omni/config/pipeline_registry.py`. Runtime
+placement and sizing belong in `vllm_omni/deploy/your_model_name.yaml`:
 
 ```yaml
-# stage_configs/your_model_name_async_chunk.yaml
-
 async_chunk: true
 
-stage_args:
+stages:
   - stage_id: 0
-    stage_type: llm
-    runtime:
-      devices: "0"
-    engine_args:
-      model_stage: ar_stage
-      max_num_seqs: 64
-      model_arch: YourTTSModelForConditionalGeneration
-      worker_type: ar
-      scheduler_cls: vllm_omni.core.sched.omni_ar_scheduler.OmniARScheduler
-      engine_output_type: latent
-      custom_process_next_stage_input_func: >
-        vllm_omni.model_executor.stage_input_processors.your_model_name.ar2decoder_async_chunk
+    devices: "0"
+    max_num_seqs: 64
     default_sampling_params:
       temperature: 0.9
       top_k: 50
       max_tokens: 2048
 
   - stage_id: 1
-    stage_type: llm
-    runtime:
-      devices: "0"
-    engine_args:
-      model_stage: decoder
-      model_arch: YourTTSModelForConditionalGeneration
-      worker_type: generation
-      scheduler_cls: vllm_omni.core.sched.omni_generation_scheduler.OmniGenerationScheduler
-      engine_output_type: audio
-    engine_input_source: [0]
-    final_output: true
-    final_output_type: audio
+    devices: "0"
+    max_num_seqs: 1
 ```
 
+Set `async_chunk: false` for end-to-end handoff. The registered pipeline
+selects the full-payload or async chunk processor; processor paths and topology
+must not be moved into the deploy YAML. Use `--deploy-config` to load a custom
+deployment overlay.
+
 ## Stage Input Processors
 
 Stage input processors convert Stage 0 outputs into Stage 1 inputs. Create yours in
@@ -547,9 +528,9 @@ Understanding what's available in stage outputs:
 - `stage_list[source_id].engine_outputs` - list of `EngineCoreOutput` objects
 - Each `EngineCoreOutput` has `outputs` - list of `RequestOutput` objects
 - Each `RequestOutput` has:
-  - `token_ids` - generated token IDs
-  - `multimodal_output` - dict with keys matching your model's `OmniOutput.multimodal_outputs`
-  - `prompt_token_ids` - original prompt token IDs
+    - `token_ids` - generated token IDs
+    - `multimodal_output` - dict with keys matching your model's `OmniOutput.multimodal_outputs`
+    - `prompt_token_ids` - original prompt token IDs
 
 ### Batch mode (non-streaming)
 
@@ -629,6 +610,7 @@ def ar2decoder_async_chunk(
 ```
 
 Key points:
+
 - `transfer_manager` is the `OmniChunkTransferAdapter` that owns the chunk lifecycle
 - Each call appends one AR decode step's output; a chunk is emitted every `chunk_size` steps
 - The final (possibly partial) chunk is flushed when `is_finished` is true
@@ -683,6 +665,7 @@ Create `vllm_omni/entrypoints/openai/tts_adapters/your_model.py`:
 
 ```python
 # SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
 """YourModel serving adapter."""
 
 from typing import TYPE_CHECKING
@@ -821,7 +804,7 @@ directly from the model's own generator.
 
 ### Directory structure
 
-```
+```text
 vllm_omni/model_executor/models/your_model_name/
     __init__.py
     modeling_your_model_name.py    # unified class: load_weights + forward + streaming
@@ -832,32 +815,40 @@ vllm_omni/deploy/your_model_name.yaml
 
 No stage input processor is needed.
 
-### Stage config
+### Pipeline and deploy config
 
-Use a single stage with `worker_type: ar`. The `is_comprehension: true` field and the
-top-level `async_chunk: false` are required — omitting them causes silent
-misclassification in the serving layer. Set `max_num_seqs` to at least 4 for
-concurrent production use.
+Use a single `LLM_AR` stage that owns the tokenizer and produces final audio:
+
+```python
+YOUR_TTS_PIPELINE = PipelineConfig(
+    model_type="your_model_name",
+    default_deploy_config_name="your_model_name.yaml",
+    model_arch="YourModelForCausalLM",
+    stages=(
+        StagePipelineConfig(
+            stage_id=0,
+            model_stage="your_model_stage_key",
+            execution_type=StageExecutionType.LLM_AR,
+            input_sources=(),
+            owns_tokenizer=True,
+            engine_output_type="audio",
+            final_output=True,
+            final_output_type="audio",
+        ),
+    ),
+)
+```
+
+The deploy YAML controls runtime behavior. Set `max_num_seqs` to at least 4
+for concurrent production use:
 
 ```yaml
-# stage_configs/your_model_name.yaml
 async_chunk: false
 
-stage_args:
+stages:
   - stage_id: 0
-    stage_type: llm
-    is_comprehension: true          # required for serving_speech.py dispatch
-    runtime:
-      devices: "0"
-    engine_args:
-      model_stage: your_model_stage_key
-      model_arch: YourModelForCausalLM
-      worker_type: ar
-      scheduler_cls: vllm_omni.core.sched.omni_ar_scheduler.OmniARScheduler
-      engine_output_type: audio
-      max_num_seqs: 4               # min 4 for concurrent requests; default 1 causes gaps
-    final_output: true
-    final_output_type: audio
+    devices: "0"
+    max_num_seqs: 4
 ```
 
 ### Generator-based streaming pattern
@@ -961,10 +952,17 @@ is correct. Stage the modified files and commit again — do not revert the chan
 Common failures and fixes:
 
 | Check | Cause | Fix |
-|-------|-------|-----|
+| ------- | ------- | ----- |
 | `ruff F841` | Local variable assigned but never used | Remove the extraction or forward it to the model call |
 | `ruff E402` | Module-level import not at top of file | Move import to the top-level import block |
 | `ruff format` | Line length, spacing, or quote style | Accept the auto-fix, stage, and re-commit |
+| `check-spdx-header` | Missing header, or copyright still says `vLLM project` | Use `Copyright contributors to the vLLM-Omni project`; restage the rewrite |
+| `check-forbidden-imports` | Stdlib `re`/`base64`, pickle, Hugging Face Hub API, or direct Triton/TileLang | `import regex as re` and `pybase64`; do not grow the allowlist without review |
+| `check-torch-cuda-call` | New `torch.cuda.*` call site | Use `current_omni_platform`; do not grow `ALLOWED_FILES` without review |
+| `check-tts-adapter-migration` | New `_tts_model_type` branch in `serving_speech.py` | Put logic in `tts_adapters/`; lower `MAX_MODEL_TYPE_BRANCHES` when removing branches |
+| `check-mark` | Test file missing level/hardware mark, or direct `pytest.mark.H100` | Add `core_model`/… plus `cpu`/`cuda`/`hardware_test(`; SKU marks only via helpers |
+| `shellcheck` | Native `shellcheck` missing, or script warning | Install via apt/dnf/brew (or `shellcheck.exe` on Windows). See [Linting](../README.md#linting) |
+| `mypy-3.10` / `markdownlint-cli2` / `check-buildkite` | Types, docs markdown, or Buildkite YAML | See [Linting](../README.md#linting) |
 
 ### DCO sign-off
 
@@ -1019,7 +1017,7 @@ Adding a TTS model to vLLM-Omni involves:
 ### Qwen3-TTS Reference Files
 
 | File | Purpose |
-|------|---------|
+| ------ | --------- |
 | `models/qwen3_tts/qwen3_tts.py` | Unified model class |
 | `models/qwen3_tts/qwen3_tts_code_predictor_vllm.py` | AR stage with vLLM fused ops |
 | `models/qwen3_tts/qwen3_tts_code2wav.py` | Decoder stage with `chunked_decode_streaming()` |
@@ -1031,4 +1029,4 @@ For more information, see:
 
 - [Architecture Overview](../../design/architecture_overview.md)
 - [Async Chunk Design](../../design/feature/async_chunk.md)
-- [Stage Configuration Guide](../../configuration/stage_configs.md)
+- [Pipeline and Deploy Configuration Guide](../../configuration/stage_configs.md)

docs/contributing/profiling.md

@@ -322,6 +322,15 @@ Each 1-second window records:
 
 On shutdown the server also logs a short summary (`loop_active_pct`, per-replica queue averages/maxima).
 
+`loop_idle` and `loop_active` count loop iterations, so their absolute scale is
+tied to which orchestration loop is running. Under the default poll loop an idle
+orchestrator records roughly one iteration per millisecond. Under the
+event-driven loop (`VLLM_OMNI_EVENT_DRIVEN_ORCH=1`, see
+[Speech API](../serving/speech_api.md#orchestration-loop-experimental)) an idle
+orchestrator wakes only once per 0.5 s reconcile timeout, while a busy one still
+records one iteration per routed output. Window counts and the `loop_active_pct`
+summary are therefore not comparable across the two modes.
+
 ### Relationship to other diagnostics
 
 This monitor is intentionally separate from the existing profiling tools:

docs/design/architecture_overview.md

@@ -335,8 +335,8 @@ The single resolution boundary is implemented by
 `StageConfigFactory.create_from_model()` and
 `VllmOmniConfig.from_pipeline_config()` in
 [`vllm_omni/config`](https://github.com/vllm-project/vllm-omni/tree/main/vllm_omni/config).
-The legacy `stage_args` YAML path remains only for models that have not yet
-migrated to `PipelineConfig` and `DeployConfig`.
+The legacy `stage_args` YAML path has been removed. Model topology now resolves
+through `PipelineConfig`, with runtime overrides supplied by `DeployConfig`.
 
 In the typed path, each stage configuration derives from
 `BaseVllmOmniStageConfig` and is specialized as

docs/design/feature/async_chunk.md

@@ -211,38 +211,39 @@ In sequential mode, each stage must wait for the previous stage to complete enti
 
 ## Configuration
 
-Enable async_chunk in stage configuration YAML:
+Enable async chunk mode in the deploy YAML:
 
 ```yaml
 async_chunk: true
-stage_args:
-  - stage_id: 0
-    engine_args:
-      custom_process_next_stage_input_func: vllm_omni.model_executor.stage_input_processors.qwen3_omni.thinker2talker_async_chunk
-  - stage_id: 1
-    engine_args:
-      custom_process_next_stage_input_func: vllm_omni.model_executor.stage_input_processors.qwen3_omni.talker2code2wav_async_chunk
 ```
 
-### Stage Configuration
+The registered `PipelineConfig` owns the async handoff processor functions.
+For Qwen3-Omni these are `thinker2talker_async_chunk` and
+`talker2code2wav_async_chunk`; deploy YAML only selects the mode and runtime
+sizing.
+
+### Configuration ownership
 
 - `async_chunk: bool`: Enable/disable async chunk mode
-- `custom_process_next_stage_input_func: str`: Path to custom chunk processing function; receives `(transfer_manager, pooling_output, request)`. For qwen3-omni: `thinker2talker_async_chunk`, `talker2code2wav_async_chunk`
-- `stage_connector_config: dict`: Connector configuration
-- `worker_type: str`: Model type, e.g. `"ar"` or `"generation"` (used by OmniChunkTransferAdapter for mode-specific payload handling)
+- `async_chunk_process_next_stage_input_func: str`: Pipeline-owned chunk processor path
+- `execution_type: StageExecutionType`: Pipeline-owned runtime family
+- `connectors: dict`: Deploy-owned connector definitions
 - `max_num_seqs: int`: Maximum number of sequences for concurrent processing in the stage
 
-
 ### Connector Configuration
 
 ```yaml
 connectors:
-  - from_stage: 0
-    to_stage: 1
-    spec:
-      name: SharedMemoryConnector
-      extra:
-        stage_id: 0
+  connector_of_shared_memory:
+    name: SharedMemoryConnector
+
+stages:
+  - stage_id: 0
+    output_connectors:
+      to_stage_1: connector_of_shared_memory
+  - stage_id: 1
+    input_connectors:
+      from_stage_0: connector_of_shared_memory
 ```
 
 ### Code2Wav Batch Configuration
@@ -250,13 +251,10 @@ connectors:
 For optimal performance with async_chunk, the code2wav stage should be configured with batching:
 
 ```yaml
-stage_args:
+stages:
   - stage_id: 2  # code2wav stage
-    runtime:
-      devices: "1"
-    engine_args:
-      model_stage: code2wav
-      max_num_seqs: 64  # Enables batched audio generation
+    devices: "1"
+    max_num_seqs: 64  # Enables batched audio generation
 ```
 
 ## Related Files

docs/design/feature/async_diffusion_output.md

@@ -124,8 +124,8 @@ When `execute_model_batch` returns a `COMPUTE_DONE` with a single `async_output_
 1. **Drain Before Memory Release (`drain_async_outputs` / `_ASYNC_OUTPUT_DRAIN_TIMEOUT_S`)**:
    When workers transition into sleep states (`handle_sleep_task`) or invoke memory-releasing methods, `WorkerProc.drain_async_outputs()` blocks up to `_ASYNC_OUTPUT_DRAIN_TIMEOUT_S` (10.0s) until all in-flight background D2H/SHM packing tasks finish. This acts as a synchronization barrier preventing worker sleep cycles from releasing device tensors while the side CUDA stream is still actively reading them. Draining typically completes within 10–50 ms under normal operation.
 
-2. **Timeout Diagnostics (`_ASYNC_OUTPUT_TIMEOUT`)**:
-   The engine waits for `wait_output_ready()` with a timeout of `_ASYNC_OUTPUT_TIMEOUT` (30.0s). If a timeout occurs, the executor logs detailed diagnostic bookkeeping (pending futures, cached outputs, and batch split maps) to identify whether the stall occurred in the worker, pump, or waiter.
+2. **Timeout Diagnostics (`_async_output_timeout()`)**:
+   The engine waits for `wait_output_ready()` with a timeout of `_async_output_timeout()` — 600.0s by default, overridable per run with the `VLLM_OMNI_ASYNC_OUTPUT_TIMEOUT` environment variable (a non-numeric or non-positive value is ignored with a warning and the default applies, since this resolves on the request path). If a timeout occurs, the executor logs detailed diagnostic bookkeeping (pending futures, cached outputs, and batch split maps) to identify whether the stall occurred in the worker, pump, or waiter. The packing itself takes milliseconds, but the wait is queued behind the step's GPU work, so its wall-clock duration tracks step time; the bound is generous because a hung engine is surfaced by the worker monitor and `check_health()`, not by this timeout.
 
 3. **Queue Serialization & Atomic Future Resolution**:
    - `result_mq` writes are serialized with `_result_mq_lock` to protect the single-writer `MessageQueue` against concurrent writes from the worker main loop (`COMPUTE_DONE`) and background thread (`OUTPUT_READY`).

docs/design/feature/attention_backend_selection.md

@@ -61,7 +61,8 @@ unstructured keyword dictionaries:
 - `quant` is consumed by FlashInfer and TRTLLM with backend-specific value
   validation;
 - `skip_softmax` is consumed by TRTLLM; and
-- `block_sparse` is consumed by block-sparse backends such as RainFusion.
+- `block_sparse` is consumed by block-sparse backends such as RainFusion; and
+- `fastvideo_vsa_topk` is consumed by FastVideo VSA.
 
 A backend reads only the fields it owns and rejects incompatible values. New
 options should be added to a shared typed spec only when more than one backend
@@ -87,3 +88,19 @@ An implementation change is complete only when it:
    corresponding user guide; and
 6. updates the overview matrix without moving algorithm details back into the
    landing page.
+
+## FastVideo VSA model metadata
+
+FastVideo VSA support is scoped to `FastVideo/FastWan2.2-TI2V-5B-Diffusers`, whose text-to-video and image-to-video modes both use `Wan22Pipeline`. It operates on a flattened DiT sequence but partitions tokens in
+the original latent video grid. Wan integrations therefore attach the
+post-patch `(T, H, W)` shape as `vsa_dit_seq_shape` attention metadata. The
+backend validates that `T * H * W` equals the sequence length, derives the
+runtime block count, and selects the configured top-k key/value blocks for
+each query block.
+
+Checkpoint-specific compensation remains model-owned. Wan layers pass an
+optional learned `gate_compress` projection through attention metadata when
+the checkpoint contains those weights. Checkpoints without the projection do
+not allocate or execute it. When top-k selects every block, native checkpoints
+route to SDPA, while FastVideo DMD checkpoints preserve the VSA all-block path
+to retain checkpoint semantics.

docs/design/feature/cfg_parallel.md

@@ -63,7 +63,7 @@ vLLM-omni provides `CFGParallelMixin` that encapsulates all CFG parallel logic.
 
 ### N-Branch CFG (3+ branches)
 
-Some models require more than 2 CFG branches. For example, Bagel and OmniGen2 use 3 branches, DreamID Omni uses 4 branches.
+Some models require more than 2 CFG branches. For example, Bagel and OmniGen2 use 3 branches.
 
 `predict_noise_with_multi_branch_cfg()` handles these by automatically dispatching N branches across M GPUs using round-robin (rule: branch `i` → rank `i % M`):
 

docs/design/feature/disaggregated_inference.md

@@ -59,19 +59,18 @@ This `metadata` must be passed through the control plane so `get()` can locate t
 
 ## Configuration Model
 
-Define connectors in runtime:
+Define connectors at the top level of the deploy YAML:
 
 ```yaml
-runtime:
-  connectors:
-    connector_of_shared_memory:
-      name: SharedMemoryConnector
+connectors:
+  connector_of_shared_memory:
+    name: SharedMemoryConnector
 ```
 
 Wire stages to connectors:
 
 ```yaml
-stage_args:
+stages:
   - stage_id: 0
     output_connectors:
       to_stage_1: connector_of_shared_memory

docs/design/feature/host_weight_runtime.md

@@ -0,0 +1,386 @@
+# Host Weight Runtime
+
+The Host Weight Runtime is a loader-facing feature for reusing immutable,
+runtime-ready model weights across workers on one host. It lets a model
+integration replace repeated checkpoint loading and transformation with an
+exact lookup of a previously published host representation, while keeping the
+canonical model source authoritative.
+
+This feature is shared infrastructure. Diffusion, autoregressive, speech, and
+future model integrations may depend on the top-level
+`vllm_omni.host_weight_runtime` package without depending on one another's
+loader or execution stack. The internal component and filesystem contracts are
+specified in the [Host Weight Runtime module design](../module/host_weight_runtime.md).
+
+## Status
+
+The implementation provides contracts and a CPU local-filesystem store. The
+diffusion consumer additionally defines typed,
+representation-independent final-layout identity/restoration mechanics plus a
+concrete BF16-with-preserved-FP32 policy for MiniMax H3 and
+`black-forest-labs/FLUX.2-klein-4B`. The opt-in no-AllGather DLO integration
+selects, publishes, restores, and transfers these artifacts.
+
+V1 includes:
+
+- exact runtime-weight identity and immutable manifests;
+- coordinated local lookup and one-producer publication;
+- descriptor-backed safetensors mmap leases;
+- preferred and required resolution policy;
+- explicit, separately reported post-load publication;
+- validation, deny, quarantine, cleanup, and capacity controls; and
+- typed reports for every terminal resolution outcome.
+
+V1 does not include:
+
+- default-on activation or consumers outside no-AllGather DLO;
+- online FP8, quantized, merged-adaptation, or additional model producers;
+- HWR interaction with DLO AllGather;
+- a remote artifact provider or cross-node coordination;
+- automatic eviction; or
+- a change to DLO collective or execution behavior.
+
+## Motivation and use cases
+
+Model loading can create the same final host representation repeatedly. This
+is especially expensive when loading performs checkpoint decoding, tensor
+renaming, TP slicing, quantization, packing, or scale construction before GPU
+transfer. Independent workers may then retain private CPU copies of identical
+runtime weights.
+
+The Host Weight Runtime is useful when:
+
+- multiple same-node workers request semantically identical final weights;
+- constructing those weights is materially more expensive than validating and
+  mapping a local artifact;
+- the runtime layout can be identified exactly and reproduced deterministically;
+- the workers can share immutable file-backed pages through the OS page cache;
+  and
+- canonical loading remains available when policy permits fallback.
+
+Typical consumers include same-node diffusion replicas, DLO host-weight
+sources, and future transformed or quantized model loaders. Request routing and
+replica orchestration remain outside this feature.
+
+This feature is not zero-copy GPU execution. A lease exposes stable CPU tensor
+views and mapped ranges. A separate transport still decides whether to use
+registered mmap, private pinned staging, synchronous copies, or asynchronous
+H2D transfer.
+
+## Resolution behavior
+
+The loader resolves the immutable canonical source and computes the exact
+requested identity before asking the runtime for a representation.
+
+```mermaid
+flowchart TD
+    L["Loader resolves canonical source"] --> I["Compute exact WeightArtifactIdentity"]
+    I --> M{"Runtime mode"}
+    M -->|"disabled"| C["Canonical loader"]
+    M -->|"preferred or required"| A{"Validated local artifact?"}
+    A -->|"yes"| R["Return HostWeightLease"]
+    A -->|"no"| X["Remote exact artifact (future)"]
+    X --> P{"Registered producer available?"}
+    P -->|"yes"| B["Build, validate, and publish atomically"]
+    B --> R
+    P -->|"no or recoverable failure"| F{"Policy permits fallback?"}
+    F -->|"preferred"| C
+    F -->|"required"| E["Fail startup"]
+    P -->|"nonretryable failure"| E
+    C --> W{"Post-load publication enabled?"}
+    W -->|"yes"| B2["Publish final model through POST_LOAD_ONLY producer"]
+    B2 --> CL["Close validated publication lease"]
+    CL --> R2["Return separate publication report"]
+    R2 --> D
+    W -->|"no"| D["Keep canonical model"]
+```
+
+The modes are:
+
+| Mode | Observable behavior |
+| --- | --- |
+| `disabled` | Use canonical loading directly without probing storage, identity, credentials, or topology. |
+| `preferred` | Return an exact lease when available; otherwise use canonical fallback only for a miss, invalid cache entry, or typed retryable failure. |
+| `required` | Fail when an exact lease cannot be acquired. |
+
+During pre-load resolution, an unsupported capability, semantic identity
+collision, producer failure, or publication failure is nonretryable and remains
+visible even in preferred mode. A post-load publication failure is likewise
+visible in its own report, but cannot revise the canonical-fallback outcome.
+Storage policy must never disguise a semantic or configuration error as a cache
+miss.
+
+## Loader and restoration sequence
+
+The model integration owns the end-to-end loading transaction. The runtime
+does not construct a model or invoke the canonical loader.
+
+```mermaid
+sequenceDiagram
+    participant L as Loader adapter
+    participant R as HostWeightRuntime
+    participant S as HostWeightStore
+    participant P as WeightProducer
+    participant X as WeightRestorer
+    participant T as GPU transport
+
+    L->>L: Resolve canonical revision and exact identity
+    L->>R: resolve(identity, producer)
+    R->>S: lookup(exact identity)
+    alt validated hit
+        S-->>R: HostWeightLease
+        R-->>L: lease and LOCAL_HIT report
+        L->>X: plan_restore(model, lease)
+        X-->>L: validation-only restore plan
+        L->>X: commit() once
+    else miss with allowed producer
+        R->>S: get_or_build(identity, producer)
+        S->>P: produce(store-scoped writer)
+        P-->>S: final-layout tensors and metadata
+        S-->>R: validated HostWeightLease
+        R-->>L: lease and LOCAL_PRODUCTION report
+        L->>X: plan_restore(model, lease)
+        X-->>L: validation-only restore plan
+        L->>X: commit() once
+    else policy permits canonical fallback
+        R-->>L: CANONICAL_FALLBACK
+        L->>L: Run canonical loader
+        opt explicit post-load publication enabled
+            L->>R: publish_after_load(identity, POST_LOAD_ONLY producer)
+            R->>S: get_or_build(identity, producer)
+            S-->>R: validated HostWeightLease or typed failure
+            R->>R: close publication lease
+            R-->>L: separate publication report
+        end
+    else required or nonretryable failure
+        R-->>L: FAILED report
+    end
+    L->>T: consume final CPU tensors and any lease
+    T-->>L: transfer teardown complete
+    L->>L: close lease when one was acquired
+```
+
+`plan_restore()` must not mutate the model or lease. `commit() -> None` is the
+sole one-shot model mutation. If planning fails, canonical fallback may reuse
+the untouched model. If commit begins and fails, the partially hydrated model
+must be discarded and canonical fallback must construct a fresh model.
+
+`publish_after_load()` is synchronous in V1 and accepts only a
+`POST_LOAD_ONLY` producer. Its report is separate from the terminal resolution,
+so a publication failure cannot rewrite a successful canonical fallback. A
+successful publication closes the store-returned lease inside the runtime and
+warms only future startups. It does not restore, rebind, or otherwise mutate the
+canonically loaded model serving the current startup.
+`allow_local_build` gates producers during pre-load resolution, while
+`allow_post_load_publish` independently gates this explicit post-load path.
+
+## Exact representation identity
+
+The store performs exact lookup. It never silently converts one representation
+into another or substitutes the first backing that responds.
+
+Identity includes:
+
+- immutable model revision and source fingerprint;
+- model component and ownership boundary;
+- representation name, dtype, and format metadata;
+- final tensor layout and semantic parallel coordinates;
+- static adaptation identity; and
+- producer implementation, manifest, and restorer schema versions.
+
+The requested representation is selected by the loader. The store chooses
+where to obtain that exact representation: validated local artifact, future
+remote materialization, or a registered producer.
+
+### Parallelism
+
+Parallel coordinates are included only when they change bytes, shape, layout,
+or component ownership.
+
+| Dimension | Identity and sharing rule |
+| --- | --- |
+| DP | Exclude DP rank for replicated weights so same-node replicas can acquire the same artifact. Include a coordinate only if DP changes weight ownership. |
+| TP | Include TP size and rank when each rank owns different tensor slices or packed layouts. Each distinct TP shard is a separate exact artifact. |
+| SP | Include SP size and backend when they change the weight layout. Exclude SP rank when all SP ranks consume identical bytes. |
+| PP | Encode component ownership or stage-local layout when PP changes which weights belong to the consumer. |
+| EP | Encode expert ownership and layout whenever ranks own different expert weights. |
+
+The runtime does not infer these rules from process-group topology. The model
+adapter is responsible for constructing a correct identity.
+
+### Quantization and adaptations
+
+Runtime BF16, FP8, packed quantized weights, and future formats are distinct
+representations. Conversion is performed by an explicitly versioned producer,
+never by cache coercion. A producer must publish the final layout consumed by
+its matching restorer.
+
+Dynamic LoRA overlays are not part of a reusable base-weight artifact. A
+statically merged adapter is cacheable only as a separate identity containing
+the adapter fingerprint and merge semantics.
+
+### Initial diffusion final-layout contract
+
+The shared diffusion contract covers complete final-layout DiT parameters and
+persistent buffers. Text encoders, VAEs, non-persistent derived state, and other
+pipeline components remain outside the artifact. One explicit representation
+policy selects allowed dtypes, tensor roles, physical layout identity, producer
+ABI, manifest schema, and restoration schema.
+
+The contract is intentionally separate from loader activation:
+
+- `FinalLayoutRequest` contains typed loader identity/configuration fingerprints,
+  TP coordinate, and conservative SP semantics. It has no open metadata bag,
+  DP coordinate, SP rank, device identity, DLO transfer mode, registration
+  policy, or store path.
+- `FinalLayoutArtifactSpec` binds one `WeightRepresentation` and runtime-layout
+  name to explicit producer/restorer schemas and a canonical, versioned
+  implementation ABI descriptor. Compatibility never depends on reflective
+  source inspection.
+- `PreparedWeightSource` snapshots immutable revisions or exact local file
+  content plus a typed checkpoint-adapter identity before ordinary
+  materialization. Source replacement before or during production fails
+  publication. A hash-looking symlink basename is trusted only for an explicit
+  Hugging Face Hub source whose repository ID and
+  `models--.../snapshots/<revision> -> blobs/<hash>` topology validate; every
+  local or otherwise unverified symlink target is content-hashed.
+- the tensor ownership digest records exact runtime names, kinds, shapes,
+  semantic roles, dtypes, and strides from a CPU or meta model skeleton;
+- `FinalLayoutTensorRestorer` accepts only an exact lease identity, validates
+  complete policy-defined coverage without mutation, and returns a one-shot
+  commit plan;
+- each model declares one dtype-neutral `FinalLayoutModelContract` with an
+  explicit implementation version and a post-commit validator; and
+- `FinalLayoutBF16Producer` accepts only the matching identity context and a
+  finalized CPU model. It is `POST_LOAD_ONLY` and `SINGLE_PROCESS` per exact TP
+  coordinate. Its BF16 policy preserves model-declared FP32 parameters and
+  buffers, revalidates MiniMax H3 mixed-precision invariants, and revalidates
+  FLUX.2-klein's two block stacks, packed QKV mapping, and BF16 base layout.
+
+Other representations reuse source identity, typed parallel identity, tensor
+ownership, and exact restoration only when their policy proves those semantics.
+For example, runtime FP8 needs a separate policy/producer for generated scales,
+quantization metadata, and Cutlass physical layouts; it is not enabled by
+changing a dtype string on the BF16 producer.
+
+This stage makes no startup, sharing, or DLO performance claim. A following
+consumer PR owns disabled/preferred/required precedence, mixed-component loader
+transactions, warm-hit restoration, and transactional lease handoff. A TP2
+prewarm deployment will require a matching TP2 producer cohort to populate both
+TP-coordinate identities even though the store coordinates each artifact
+independently.
+
+## Host sharing and GPU transport
+
+Every process receives its own virtual mappings and `HostWeightLease`, but
+workers mapping the same local artifact can share physical file-backed pages
+through the OS page cache. This avoids independent private tensor allocations;
+it does not guarantee that process PSS reports exactly one model copy.
+
+The ownership boundary is:
+
+- the store owns immutable artifact files, publication, and lifecycle;
+- the kernel owns page-cache residency and placement;
+- the lease owns process-local mappings and the shared artifact lock; and
+- transport owns page registration, page locking, private staging, H2D copies,
+  streams, device buffers, and lease release ordering.
+
+DLO AllGather and no-AllGather remain transport and execution choices. A DLO
+integration may consume a lease, but the Host Weight Runtime does not choose a
+parallel collective or orchestrate DP requests. See the
+[DLO feature design](offloader/distributed_layerwise_offload.md).
+
+## Locality and NUMA policy
+
+The V1 backend is node-local. It accepts an allowlist of known local disk
+filesystems plus tmpfs/ramfs, records the detected kernel filesystem type, and
+rejects known remote or unknown filesystems. NFS, CIFS, Lustre, Ceph, and other
+cross-node mounts cannot silently satisfy the local backend because their
+page-cache and advisory-lock behavior does not provide the required node-local
+contract.
+
+A NUMA-specific root creates a separate storage domain but does not by itself
+place page-cache pages on that NUMA node. A topology-aware integration must
+also pin workers, establish local first touch or prefault, perform transport
+registration in the same domain, and collect residency evidence.
+
+Tmpfs artifacts consume host memory and may consume swap. They must be
+accounted as memory-backed storage rather than ordinary disk capacity.
+
+## Concurrency and timeouts
+
+One process per exact identity owns a build; other workers wait and then acquire
+leases for the published artifact. Publication is invisible until all payloads
+and metadata are validated, hashed, fsynced, and atomically renamed.
+
+`coordination_timeout_seconds` bounds filesystem lock acquisition. It does not
+cancel synchronous validation, a producer that has already started, or atomic
+publication. A hung in-process producer therefore blocks its owning process and
+must be handled by external process supervision. Enforceable producer
+cancellation requires a future process-isolated producer contract.
+
+Leases keep a shared artifact lock for their lifetime. A forked child may close
+its inherited descriptors but cannot unlock the parent process's lease. Cleanup
+uses a nonblocking exclusive lock and reports an active lease instead of
+removing live mappings.
+
+## Failure and fallback contract
+
+| Condition | Preferred mode | Required mode |
+| --- | --- | --- |
+| Exact local hit | Return lease | Return lease |
+| Local miss | Try later backing or canonical fallback | Try later backing or fail |
+| Invalid or denied artifact | Quarantine/rebuild when possible; otherwise canonical fallback | Quarantine/rebuild or fail |
+| Retryable lock, domain, or capacity failure | Canonical fallback | Fail |
+| Unsupported producer or backend | Fail | Fail |
+| Identity collision | Fail | Fail |
+| Pre-load producer or atomic publication failure | Fail | Fail |
+| Post-load publication failure after canonical fallback | Keep canonical model; report publication failure | Not reached through required resolution |
+| Restore planning failure | Canonical fallback may reuse untouched model | Fail |
+| Restore commit failure | Discard model; fallback requires a fresh instance | Fail |
+
+Cache failures should normally reduce performance rather than availability,
+but only when the typed failure explicitly permits fallback. Canonical source,
+authentication, configuration, and semantic errors remain visible.
+
+## Consumer integration requirements
+
+A consumer PR must:
+
+1. Resolve an immutable canonical model source before cache lookup.
+2. Construct the exact final representation identity, including relevant
+   parallel, quantization, and adaptation semantics.
+3. Register a deterministic producer for that identity or support lookup-only
+   operation.
+4. Provide a validation-only restorer with a one-shot commit.
+5. Keep GPU transport outside the producer, store, and restorer contracts.
+6. Handle preferred fallback and required failure without reusing a model after
+   a failed restore commit.
+7. Retain the lease until every transport operation that may access mapped
+   memory has completed.
+8. Emit and test the terminal resolution report and any separate post-load
+   publication report.
+
+Consumer validation must prove:
+
+- output parity with canonical loading;
+- exact warm-hit work avoidance;
+- correct TP/SP/PP/EP ownership and layout identity;
+- correct BF16, FP8, quantized, or adaptation semantics;
+- shared-backing evidence when host-memory savings are claimed;
+- clean unmapping, unregistration, and artifact-lock release; and
+- startup, host-memory, and transport effects for the claimed deployment.
+
+## Rollout
+
+The rollout is intentionally staged:
+
+1. Land the neutral contracts and local filesystem store.
+2. Add one model-specific producer/restorer integration with parity evidence.
+3. Connect eligible DLO or other transport paths without moving transport
+   ownership into the runtime.
+4. Add additional final-layout and quantized producers independently.
+5. Introduce remote materialization only through the same local lease contract.
+
+The architectural decisions and deferred work are tracked in
+[RFC #6414](https://github.com/vllm-project/vllm-omni/issues/6414).

docs/design/feature/offloader/distributed_layerwise_offload.md

@@ -20,12 +20,14 @@ implemented but has less end-to-end coverage than the primary path.
 Legend: ✅ supported, ⚠️ compatibility path or limited validation, ❌ unsupported.
 
 | Feature | DLO + AllGather | DLO without AllGather |
-|---|---|---|
+| --- | --- | --- |
 | **DP** | ✅ Primary path; host weights are sharded across the DP group. | ✅ Each DP rank streams complete rank-local blocks. |
 | **SP** | ✅ When DP=1, DLO uses the SP group for weight sharding. | ✅ SP remains active without a DLO weight collective. |
 | **TP > 1** | ⚠️ Ordinary TP-aware loader only; no direct checkpoint mmap. | ⚠️ Ordinary TP-aware loader only; no direct checkpoint mmap. |
 | **HSDP** | ❌ Rejected to avoid double-sharding parameters. | ⚠️ Limited end-to-end coverage. |
 | **Per-tensor online FP8 linears** | ✅ Ordinary loader finalizes weights and scales before DLO sharding. | ✅ Ordinary loader retains complete rank-local tensors. |
+| **Online INT8 linears** | ✅ Ordinary loader finalizes the int8 weight (contiguous or transposed view) and fp32 scale before DLO sharding. | ✅ Ordinary loader retains complete rank-local tensors. |
+| **Online MXFP8 linears** | ✅ Ordinary loader finalizes the fp8 weight (contiguous) and e8m0 block scale (contiguous or transposed view) before DLO sharding. | ✅ Ordinary loader retains complete rank-local tensors. |
 | **Other online quantization methods** | ❌ Rejected until runtime packing and scale layouts are validated. | ⚠️ Allowed through the ordinary loader; validation is method-specific. |
 | **Model-level or standard layerwise CPU offload** | ❌ Disabled because DLO takes priority. | ❌ Disabled because DLO takes priority. |
 | **Resident leading layers** | ❌ Rejected. | ✅ Requires eligible resident paths in the model's `OffloadPlan`. |
@@ -169,10 +171,242 @@ This mode means:
   copy per rank.
 - The scheduler does not require a synchronized DP request wave for DLO.
 
+### Component allocator-cache retention
+
+MiniMax-H3 DLO keeps one bounded PyTorch allocator cache across its staged
+encoder, DiT, and VAE component boundaries so allocations released by one
+component can be reused by the next. The DiT prefetch path and its two shared
+device buffers are unchanged.
+
+After a component is offloaded, cached-but-unallocated memory is retained only
+while it is at most 25% of device capacity and at least 5% of device capacity
+remains physically free. Crossing either bound releases the allocator cache.
+Missing allocator telemetry also releases it conservatively. Component or
+staging failure forces a release, and an out-of-memory allocation gets one
+retry after release. This is a component-local policy rather than a global
+`empty_cache` override, so the executor's unconditional shutdown cleanup is
+preserved.
+
+Retaining allocator blocks trades higher reserved and peak device memory for
+lower lifecycle latency. The benefit is device- and workload-dependent, and a
+smaller or externally loaded device may cross a bound and return to the
+ordinary per-component cache-release behavior. Current performance validation
+covers MiniMax-H3 no-AllGather DLO on CUDA. Other MiniMax-H3 DLO transfer
+topologies and platforms receive the same bounded policy but are not claimed
+performance targets; other DLO models remain unchanged unless they explicitly
+adopt it.
+
+### Final-layout Host Weight Runtime consumer
+
+> **Status:** PR2 landed in
+> [PR #6486](https://github.com/vllm-project/vllm-omni/pull/6486). PR3 adds
+> registered direct H2D plus generic HWR publication/source-digest
+> optimizations without changing AllGather behavior.
+
+Final-layout Host Weight Runtime (HWR) backing is opt-in and currently applies
+only to no-AllGather DLO. Enable it with
+`--host-weight-runtime-mode preferred` (or `required`) and
+`--host-weight-runtime-root <node-local-root>`.
+
+The modes express operator fallback policy, not different artifact formats:
+
+- `preferred` consumes an exact hit, but on a miss it allows canonical loading
+  followed by post-load publication. It is the normal population path.
+- `required` consumes the same exact artifact but fails startup on a miss or
+  unusable artifact. It never invokes canonical DiT fallback or post-load
+  publication and therefore cannot populate an empty store.
+
+PR2 has no separate prewarm command. Operators populate one matching producer
+cohort per node-local storage domain in `preferred` mode, then restart the same
+model revision and parallel layout with `required`. TP coordinates have distinct
+identities, while equivalent DP replicas share them.
+
+The current producer/consumer boundary is the model-declared final-layout BF16
+contract (MiniMax H3 and `black-forest-labs/FLUX.2-klein-4B`). The FLUX.2-klein
+contract covers both transformer block stacks, constructor-stable packed QKV
+mapping state, BF16 parameters, and any persistent loader-owned `beta`/`eps`
+buffers. The shared HWR machinery supports ordinary-loader final layouts for
+TP1 and TP2 rank identities plus SP layout identities. FLUX.2-klein evidence
+in this change is TP1-only; its TP2/SP layouts rely on the existing
+per-coordinate identity mechanics and remain unmeasured. Online quantization,
+HSDP, LoRA/adapted weights, and non-default load formats remain ineligible.
+HWR mode
+`disabled`, DLO-disabled, and DLO AllGather configurations stop before source
+identity or store construction and retain the existing checkpoint-mmap or
+ordinary-loader path.
+
+Eligibility is decided before constructing HWR, resolving canonical sources,
+hashing identity inputs, probing a filesystem, or emitting an HWR observer
+event:
+
+```text
+DLO disabled / HWR disabled / DLO AllGather
+  -> zero final-layout HWR interaction
+  -> preserve current checkpoint-mmap or ordinary-loader behavior
+```
+
+When final-layout HWR is explicitly enabled for no-AllGather DLO, runtime
+outcomes are authoritative:
+
+- `LOCAL_HIT`: plan and commit an exact restoration, then transfer the lease
+  transactionally to DLO.
+- `CANONICAL_FALLBACK`: bypass checkpoint mmap, canonically materialize and
+  finalize the DiT, then call `publish_after_load()` for a future startup. The
+  current startup retains its canonical tensors.
+- `FAILED`: fail startup. Preferred mode does not reinterpret nonretryable
+  identity, configuration, or compatibility failures as misses.
+- Required mode cannot bootstrap an empty store through a `POST_LOAD_ONLY`
+  producer.
+
+Checkpoint mmap remains an unchanged control path whenever final-layout HWR is
+not selected.
+
+Local source identity and publication avoid repeated full-payload passes while
+preserving the same semantic contract:
+
+- Canonical files without a trusted immutable Hub blob identity are hashed in
+  parallel. Their content IDs are cached per storage domain under the HWR root
+  and reused only when the path, inode, size, timestamps, symlink target, and
+  cache-record checksum still match. Corrupt entries are rebuilt; cache-lock or
+  cache-I/O failure falls back to hashing the canonical file directly.
+- Producers that emit payload bytes in canonical storage-key order may declare
+  `ordered=True`. The filesystem writer then computes file and tensor SHA256
+  values during the write and overlaps payload `fsync` with later producer
+  work. Producers without that guarantee retain unordered writes and use a
+  parallel readback checksum fallback.
+- Manifest and `READY` publication still wait for every payload `fsync` and
+  checksum to complete. These optimizations do not weaken artifact validation
+  or change lease ownership.
+
+#### Registered mmap transport
+
+After DLO takes an HWR lease, the no-AllGather backend may register the lease's
+complete immutable mapped ranges under the existing `pin_cpu_memory` policy.
+Registration is transport state: it does not change artifact identity, the
+store, tensor ownership, or H2D payload. On success, each tensor view copies
+directly into the existing rotating HBM block buffers and the two private host
+staging slots are not allocated.
+
+`--dlo-host-registration-limit-gib` is an optional per-worker preflight ceiling
+over page-aligned registered bytes. Zero adds no ceiling. A disabled pinned
+memory policy, unsupported platform/capability, over-budget mapping, or fully
+rolled-back registration error selects the existing two-slot staging path. A
+partial registration that cannot be rolled back aborts startup because closing
+the lease would unmap memory still owned by the platform.
+
+Direct checkpoint mmap remains unchanged and continues to use staging. It may
+require loader-owned per-block transforms, while the HWR artifact already
+contains final runtime bytes. DLO AllGather never receives an HWR final-layout
+lease and therefore never enters this registration path.
+
+#### Pre-service transaction boundary
+
+The startup transaction does not end at restore commit. A lease-backed model is
+disposable until backend setup and initial prefetch complete:
+
+```text
+UNRESOLVED
+  -> LEASE_OWNED_BY_LOADER
+  -> RESTORE_PLANNED
+  -> COMMIT_STARTED
+  -> CARRIER_OWNED
+  -> BACKEND_OWNED
+  -> BACKEND_READY
+  -> IN_SERVICE
+```
+
+Failure before `COMMIT_STARTED` closes the loader-owned lease. Preferred mode
+may canonically load using the untouched skeleton; required mode fails.
+
+Failure from `COMMIT_STARTED` through `BACKEND_READY` must:
+
+1. synchronize or otherwise quiesce partial backend and initial-prefetch work;
+2. release hook, staging, restore-plan, and model references;
+3. close the lease through its current owner;
+4. discard the restored model; and
+5. in preferred mode, construct a fresh canonical model while bypassing HWR
+   lookup, HWR publication, and checkpoint mmap for that recovery attempt.
+
+Required mode fails instead of constructing the fresh fallback. Once the model
+enters service, failures follow normal runtime handling rather than startup
+fallback.
+
+#### Finalization phases
+
+Cold canonical loading separates byte-changing work from shared runtime-state
+finalization:
+
+```text
+Cold-only byte-changing:
+  casting, reordering, packing, quantization, generated scales,
+  model-specific weight transforms, and calibration that mutates
+  parameters or persistent buffers
+
+Shared cold/warm non-byte:
+  validation, hook installation, eval state, bookkeeping,
+  and non-persistent runtime state
+```
+
+The warm path must snapshot restored tensor bytes and backing pointers before
+shared finalization and prove both are unchanged afterward.
+
+#### Lease ownership
+
+Lease transfer is single-owner and single-take:
+
+```text
+loader owns
+  -> restore plan borrows
+  -> commit succeeds
+  -> runner carrier takes once
+  -> backend takes once before asynchronous work
+  -> backend drains pending H2D work
+  -> backend releases hooks, staging, and model references
+  -> backend unregisters every mapped range
+  -> backend closes lease
+```
+
+The runner carrier is process-local, rejects serialization, and rejects a
+duplicate `take()`. If the carrier still owns the lease, runner cleanup closes
+it. Once the backend takes ownership, only backend abort or teardown may drain
+work and close it.
+
+The implementation keeps the final-layout lease through backend setup and
+initial prefetch. Successful registration copies directly from immutable HWR
+views; otherwise the backend uses the existing two bounded rank-local staging
+slots. Transactional cleanup drains device work, removes source references,
+unregisters mappings, and only then closes the lease. An unregistration failure
+retains both registration and lease for retry/process teardown. Preferred mode
+then uses the runner's fresh canonical retry; required mode propagates the
+failure.
+
+#### PR2 and PR3 promotion gates
+
+- Warm hit performs zero ordinary DiT materialization and zero producer calls.
+- Shared warm finalization changes neither restored bytes nor backing pointers.
+- Preferred `FAILED` outcomes do not silently fall back.
+- Planning failure leaves the skeleton untouched.
+- Any pre-service failure after commit begins discards the restored model;
+  preferred mode uses a fresh canonical model and required mode fails.
+- Mixed components load normally and no required tensor remains on `meta`.
+- Disabled mode and AllGather emit zero final-layout HWR interaction.
+- The lease carrier rejects duplicate take and serialization.
+- Backend setup or prefetch failure drains asynchronous work before lease close.
+- Registration is all-or-nothing; rollback/unregistration failure never closes
+  a mapping still owned by the platform.
+- Registered HWR transport bypasses host staging while preserving output and
+  H2D payload; unsupported registration retains the bounded staging path.
+- Compatible checkpoint mmap remains an unchanged benchmark and control path.
+- Prewarm uses one matching producer cohort per TP/SP topology and storage
+  domain.
+- Benchmarks compare ordinary loading, cold publication, and warm staging with
+  output parity, startup latency, aggregate PSS, page-cache state, HBM, and H2D
+  payload.
+
 ## Parallelism compatibility
 
 | Parallelism | DLO + AllGather | DLO without AllGather |
-|---|---|---|
+| --- | --- | --- |
 | **DP** | Supported primary path. DLO shards host weights across the DP group and can run DP multi-concurrency. | Supported rank-local path. Compatible TP1 replicas can share checkpoint pages on each node; fallback runtime tensors remain private. |
 | **SP** | Supported in the implementation. With DP=1, DLO uses the SP group for host-weight sharding; SP still shards sequence/activation work. | SP remains active, but DLO keeps standard-loader rank-local weights and adds no SP weight collective. |
 | **TP > 1** | Outside the Phase A shared-mmap support scope. The loader falls back before mutation, preserves TP-local layouts, and DLO may apply DP/SP host sharding to those ordinary runtime tensors. | Outside the Phase A shared-mmap support scope. The ordinary TP-aware loader produces rank-local tensors, which DLO streams without an additional weight collective; DP replicas retain private runtime storage. |
@@ -185,7 +419,8 @@ This mode means:
   the SP group becomes DLO's sharding group in AllGather mode.
 - **DP + TP/SP without AllGather:** standard model loading defines the
   rank-local tensor layout. DLO adds no cross-DP, cross-TP, or cross-SP weight
-  collective.
+  collective. When the model declares the final-layout contract, HWR keys the
+  reusable artifact by TP rank/size and SP layout.
 - **HSDP + SP:** the general parallel configuration permits HSDP over SP, but
   DLO must use `--dlo-no-use-allgather`. HSDP remains responsible for weight
   materialization and synchronization.
@@ -205,16 +440,22 @@ requirements.
 
 Direct checkpoint mmap can back either transfer path. It is currently limited
 to proven TP1, non-HSDP, non-online-quantized layouts. Other layouts use the
-ordinary loader. Per-tensor online FP8 linears can use DLO AllGather after the
-ordinary loader finalizes their runtime weights and scales; DLO then shards and
-reconstructs those tensors with their recorded layouts. Other online methods
-must use `--dlo-no-use-allgather` or disable online quantization until their
-runtime layouts are validated.
+ordinary loader. Per-tensor online FP8, online INT8, and online MXFP8 linears
+can use DLO AllGather after the ordinary loader finalizes their runtime
+weights and scales; DLO then shards and reconstructs those tensors with their
+recorded layouts (they all keep plain 1-byte dtypes over ordinary strided
+views: online INT8 has an int8 weight plus fp32 scale, online MXFP8 has an
+fp8 weight plus e8m0 block scale, each with a contiguous or transposed-view
+finalized tensor, all covered by the same physical-order packing as online
+FP8). Other online methods must use `--dlo-no-use-allgather` or disable
+online quantization until their runtime layouts are validated.
 
-A normalized runtime mmap cache, built through the ordinary loader, is the
-proposed general mechanism for sharing transformed TP or quantized layouts.
-That cache and its publication/lifecycle protocol are intentionally outside
-this phase; see [RFC #6195](https://github.com/vllm-project/vllm-omni/issues/6195).
+The Host Weight Runtime representation, publication, and no-AllGather consumer
+contracts are merged; see
+[RFC #6414](https://github.com/vllm-project/vllm-omni/issues/6414),
+[PR #6445](https://github.com/vllm-project/vllm-omni/pull/6445), and
+[PR #6486](https://github.com/vllm-project/vllm-omni/pull/6486). Registered
+direct H2D is an optional transport layer over that merged lease contract.
 
 ## Validation coverage
 
@@ -224,13 +465,19 @@ Current source-level validation includes:
 - HSDP + DLO without AllGather acceptance at configuration level;
 - loader preflight fallback for TP, HSDP, online quantization, unknown custom
   loaders, missing keys, and shape/dtype mismatches;
-- ordinary-loader fallback for per-tensor online FP8 linears followed by DLO
-  sharding of finalized weights and scales;
+- ordinary-loader fallback for per-tensor online FP8, online INT8, and online
+  MXFP8 linears followed by DLO sharding of finalized weights and scales;
 - exact loader-to-backend plan transfer and ordinary-loader fallback;
+- ordered publication hashing, overlapped durability, unordered parallel
+  checksum fallback, and node-local source-digest reuse/invalidation;
 - rank-local mmap source retention, bounded two-slot staging, and adapter
   transforms without parameter-side flags;
+- read-only registration preflight, direct source-to-device copies, safe
+  fallback, partial rollback, retryable unregistration, and lease ordering;
 - resident-layer requests requiring no-AllGather;
 - DP request-wave validation for denoising-step compatibility;
+- bounded component allocator-cache retention, conservative/forced release,
+  OOM retry, and immutable encoder non-block staging;
 - sharding, double-buffer, AllGather-size, and heterogeneous-block regression
   tests.
 
@@ -243,7 +490,7 @@ VAEs at TP1. They validate the ordinary-loader fallback only, not direct mmap
 or shared-mmap host-memory savings.
 
 | Configuration | Result | Warm E2E | Peak device memory | Host PSS |
-|---|---:|---:|---:|---:|
+| --- | ---: | ---: | ---: | ---: |
 | DP4xTP1 AllGather | Passed, 4 concurrent requests | 2.87 s / 4 requests | 13.84 GiB | 211.99 GiB |
 | DP4xTP1 no-AllGather | Passed, 1 request | 15.02 s | 13.23 GiB | 187.77 GiB |
 | DP2xTP2 AllGather | Passed, 2 concurrent requests | 4.16 s / 2 requests | 12.50 GiB | 211.97 GiB |
@@ -275,7 +522,7 @@ pipeline components, so each worker should be compared with the same worker in
 the other storage mode.
 
 | Worker | Ordinary RSS | mmap RSS | Ordinary PSS | mmap PSS | PSS reduction |
-|---|---:|---:|---:|---:|---:|
+| --- | ---: | ---: | ---: | ---: | ---: |
 | DP worker 0 | 168.27 GiB | 132.76 GiB | 167.84 GiB | 101.43 GiB | 66.40 GiB |
 | DP worker 1 | 116.19 GiB | 79.97 GiB | 115.73 GiB | 48.64 GiB | 67.09 GiB |
 | **Two-worker total** | — | — | **283.56 GiB** | **150.08 GiB** | **133.48 GiB (47.1%)** |

docs/design/feature/omni_connectors/mooncake_store_connector.md

@@ -32,26 +32,25 @@ mooncake_master \
 
 ## Configuration
 
-Define the connector in runtime:
+Define the connector at the top level of the deploy YAML:
 
 ```yaml
-runtime:
-  connectors:
-    connector_of_mooncake:
-      name: MooncakeStoreConnector
-      extra:
-        host: "127.0.0.1"
-        metadata_server: "http://<MASTER_IP>:8080/metadata"
-        master: "<MASTER_IP>:50051"
-        segment: 512000000
-        localbuf: 64000000
-        proto: "tcp"
+connectors:
+  connector_of_mooncake:
+    name: MooncakeStoreConnector
+    extra:
+      host: "127.0.0.1"
+      metadata_server: "http://<MASTER_IP>:8080/metadata"
+      master: "<MASTER_IP>:50051"
+      segment: 512000000
+      localbuf: 64000000
+      proto: "tcp"
 ```
 
 Wire stages to the connector:
 
 ```yaml
-stage_args:
+stages:
   - stage_id: 0
     output_connectors:
       to_stage_1: connector_of_mooncake

docs/design/feature/omni_connectors/mooncake_transfer_engine_connector.md

@@ -21,26 +21,25 @@ InfiniBand/RoCE NICs).
 
 ## Configuration
 
-Define the connector in runtime:
+Define the connector at the top level of the deploy YAML:
 
 ```yaml
-runtime:
-  connectors:
-    rdma_connector:
-      name: MooncakeTransferEngineConnector
-      extra:
-        host: "auto"                  # Auto-detect local RDMA IP
-        zmq_port: 50051               # ZMQ base port (see "Port Offset Scheme" below)
-        protocol: "rdma"              # "rdma" or "tcp"
-        device_name: ""               # RDMA device (e.g., "mlx5_0"), empty for auto-detect
-        memory_pool_size: 4294967296  # 4 GB (CPU); use 2147483648 (2 GB) for GPU
-        memory_pool_device: "cpu"     # "cpu" for pinned memory (recommended), "cuda" for GPUDirect RDMA
+connectors:
+  rdma_connector:
+    name: MooncakeTransferEngineConnector
+    extra:
+      host: "auto"                  # Auto-detect local RDMA IP
+      zmq_port: 50051               # ZMQ base port (see "Port Offset Scheme" below)
+      protocol: "rdma"              # "rdma" or "tcp"
+      device_name: ""               # RDMA device (e.g., "mlx5_0"), empty for auto-detect
+      memory_pool_size: 4294967296  # 4 GB (CPU); use 2147483648 (2 GB) for GPU
+      memory_pool_device: "cpu"     # "cpu" for pinned memory (recommended), "cuda" for GPUDirect RDMA
 ```
 
 Wire stages to the connector:
 
 ```yaml
-stage_args:
+stages:
   - stage_id: 0
     output_connectors:
       to_stage_1: rdma_connector
@@ -415,7 +414,7 @@ A receiver connector:
 - allocates receive buffers from its own pool
 - requests metadata or transfer service from the sender
 
-The role is not inferred dynamically. It is injected by the stage configuration layer:
+The role is not inferred dynamically. It is injected from the deploy connector wiring:
 
 - incoming edge for a stage -> `role="receiver"`
 - outgoing edge for a stage -> `role="sender"`

docs/design/feature/omni_connectors/shared_memory_connector.md

@@ -13,10 +13,9 @@ segment name is returned in metadata.
 ## Configuration
 
 ```yaml
-runtime:
-  connectors:
-    connector_of_shared_memory:
-      name: SharedMemoryConnector
+connectors:
+  connector_of_shared_memory:
+    name: SharedMemoryConnector
 ```
 
 ## Notes

docs/design/feature/omni_connectors/yuanrong_connector.md

@@ -65,23 +65,22 @@ dscli stop --worker_address "${WORKER_IP}:31501"
 
 ## Configuration
 
-Define the connector in runtime:
+Define the connector at the top level of the deploy YAML:
 
 ```yaml
-runtime:
-  connectors:
-    connector_of_yuanrong:
-      name: YuanrongConnector
-      extra:
-        host: "127.0.0.1"
-        port: 31501
-        get_sub_timeout_ms: 1000
+connectors:
+  connector_of_yuanrong:
+    name: YuanrongConnector
+    extra:
+      host: "127.0.0.1"
+      port: 31501
+      get_sub_timeout_ms: 1000
 ```
 
 Wire stages to the connector:
 
 ```yaml
-stage_args:
+stages:
   - stage_id: 0
     output_connectors:
       to_stage_1: connector_of_yuanrong

docs/design/feature/omni_connectors/yuanrong_transfer_engine_connector.md

@@ -94,26 +94,23 @@ For Ascend P2P, configure the connector with `protocol: "ascend"` and an NPU
 memory pool:
 
 ```yaml
-runtime:
-  enabled: true
-  defaults:
+connectors:
+  yuanrong_te_connector:
+    name: YuanrongTransferEngineConnector
+    extra:
+      host: "auto"
+      zmq_port: 50051
+      rpc_port: "auto"
+      protocol: "ascend"
+      device_name: "auto"
+      memory_pool_size: 1073741824
+      memory_pool_device: "npu"
+
+edges:
+  - from: 0
+    to: 1
     window_size: -1
     max_inflight: 1
-  connectors:
-    yuanrong_te_connector:
-      name: YuanrongTransferEngineConnector
-      extra:
-        host: "auto"
-        zmq_port: 50051
-        rpc_port: "auto"
-        protocol: "ascend"
-        device_name: "auto"
-        memory_pool_size: 1073741824
-        memory_pool_device: "npu"
-  edges:
-    - from: 0
-      to: 1
-      window_size: -1
 ```
 
 Important fields:
@@ -141,31 +138,27 @@ For AR 4-way TP sending KV cache to DiT 4-way TP, set the stage TP topology in
 both stage `omni_kv_config` blocks:
 
 ```yaml
-stage_args:
+stages:
   - stage_id: 0
-    runtime:
-      devices: "0,1,2,3"
-    engine_args:
-      tensor_parallel_size: 4
-      omni_kv_config:
-        need_send_cache: true
-        rank_mapping:
-          from_tp: 4
-          to_tp: 4
+    devices: "0,1,2,3"
+    tensor_parallel_size: 4
+    omni_kv_config:
+      need_send_cache: true
+      rank_mapping:
+        from_tp: 4
+        to_tp: 4
     output_connectors:
       to_stage_1: yuanrong_te_connector
 
   - stage_id: 1
-    runtime:
-      devices: "4,5,6,7"
-    engine_args:
-      parallel_config:
-        tensor_parallel_size: 4
-      omni_kv_config:
-        need_recv_cache: true
-        rank_mapping:
-          from_tp: 4
-          to_tp: 4
+    devices: "4,5,6,7"
+    parallel_config:
+      tensor_parallel_size: 4
+    omni_kv_config:
+      need_recv_cache: true
+      rank_mapping:
+        from_tp: 4
+        to_tp: 4
     input_connectors:
       from_stage_0: yuanrong_te_connector
 ```

docs/design/feature/prefix_caching.md

@@ -41,7 +41,7 @@ With this in mind, consider the set of blocks in a 2D layout, where the row repr
 
 ### Example
 !!! note "Note 3"
-    Prefix caching in vLLM-Omni currently is only supported on AutoRegressive stages with one kv-cache group. It can be enabled/disabled per-stage via the `enable_prefix_caching` parameter in the model's stage config.
+    Prefix caching in vLLM-Omni currently is only supported on AutoRegressive stages with one kv-cache group. Configure it with the pipeline-wide `enable_prefix_caching` field in the deploy config.
 
 The way in which vLLM-Omni ties into vLLM's prefix caching is best understood by example. Say that we have the following:
 

docs/design/feature/skip_softmax.md

@@ -1,90 +1,197 @@
 # Skip-Softmax
 
-Skip-Softmax is the sparse-attention mode of the `TRTLLM_ATTN` backend. Usage — the config keys
-and how to pick an operating point — is in
-[TRTLLM Attention](../../user_guide/diffusion/attention_backends/trtllm.md#skip-softmax).
-The shared selector contract is documented in
-[Diffusion Attention Backend Selection](attention_backend_selection.md). This
-page explains the algorithm.
+Skip-Softmax is the sparse-attention mode of the `TRTLLM_ATTN` backend. It implements
+[BLASST](https://arxiv.org/abs/2512.12087) (Dynamic BLocked Attention Sparsity via Softmax
+Thresholding), which adds a per-tile skip test to the FlashAttention main loop. This page describes the algorithm, how the user configuration is resolved
+into the value the kernel consumes, and how checkpoint calibration and timestep gating participate
+in that resolution. Configuration keys and operating-point guidance are covered in
+[TRTLLM Attention](../../user_guide/diffusion/attention_backends/trtllm.md#skip-softmax); the
+backend selection contract is covered in
+[Diffusion Attention Backend Selection](attention_backend_selection.md).
 
 ## Motivation
 
 In a long attention row (a video DiT can have tens of thousands of keys), the softmax weight
 concentrates on a small fraction of the keys; the rest receive near-zero weight and barely move the
 output. Computing softmax and the value-weighted sum over those keys is wasted work. Skip-Softmax
-detects, per block of keys, when a block cannot matter and skips its softmax and its value
-multiply.
+detects, per tile of keys, when a tile cannot matter and skips its softmax and its value multiply.
 
-It is approximate: a skipped block still carries a small non-zero contribution, so the mode is
+It is approximate: a skipped tile still carries a small non-zero contribution, so the mode is
 opt-in and off by default.
 
-## The online-softmax pass
+## Algorithm
 
-Attention is computed in a single streaming pass over the keys, in tiles of 128. Per query row the
+![The BLASST algorithm: FlashAttention with a per-tile skip test on the local maximum](../figures/skip_softmax/blasst_algorithm.jpg)
+
+FlashAttention computes attention in a single streaming pass over KV tiles. Per query row the
 kernel maintains three running values:
 
 - `m` — the largest score seen so far,
 - `l` — the running denominator `Σ exp(sⱼ − m)`,
 - `O` — the running numerator `Σ exp(sⱼ − m)·vⱼ`,
 
-and returns `O / l` at the end. For each key tile it computes the tile's scores `Q · K_jᵀ · scale`,
-updates `m`, and accumulates that tile's contribution into `l` and `O`. Rescaling `l` and `O` when
-`m` grows keeps the single pass numerically exact.
+and returns `O / l` at the end. For each KV tile it computes the scores `QK_j^T`, updates `m`, and
+accumulates the tile's contribution into `l` and `O`. Rescaling `l` and `O` when `m` grows keeps the
+dense pass numerically exact.
 
-## The skip test
-
-Once a tile's scores are known, its largest score `tile_max` is compared against the running
-maximum:
+A tile is a block of query rows against a block of keys, and the kernel processes it as a unit.
+BLASST inserts one test between computing a tile's scores and accumulating them. For each query
+row `i` in the tile, let `tile_max[i]` be the row's largest score within the tile, `running_max[i]`
+the row's running maximum, and `λ` the effective threshold:
 
 ```text
-if exp(tile_max − running_max) < threshold:
-    skip this tile          # do not compute its softmax weights or its P·V accumulation
+if all(exp(tile_max[i] - running_max[i]) < λ for every row i in the tile):
+    skip this tile          # do not compute its Softmax or PV contribution
 ```
 
-`exp(tile_max − running_max)` is an upper bound on the softmax weight any key in the tile can
-receive: if even the tile's best key is far below the current maximum, every key in the tile is
-negligible, and both the softmax (the exponentials) and the `P·V` accumulation for that tile can be
-dropped. The tile's contribution to `l` and `O` is simply skipped.
+`exp(tile_max[i] − running_max[i])` is an upper bound on the softmax weight any key in the tile can
+receive in row `i`: if even the row's best key in this tile is far below the row's current maximum,
+the tile is unimportant for that row. Only when this holds for every row in the tile can the
+Softmax and `PV` work for the tile be skipped, in which case its contribution to `l` and `O` is
+simply omitted. A larger `λ` makes the test more aggressive. The figure states the same test in
+log space, `m̃ − m < ln(λ)`, which is how the kernel evaluates it.
 
-## What this bounds
+The test needs the tile's scores, so `QK_j^T` always runs and only the Softmax and `PV` work can
+be skipped. Since the two matmuls have the same FLOP count, skipping every eligible tile removes at
+most roughly half of the attention arithmetic, and the kernel-level speedup stays well under 2×.
 
-Two properties of the test shape the achievable speedup:
+## From configuration to the kernel threshold
 
-- **`Q · K_jᵀ` always runs.** The test needs `tile_max`, which comes from the tile's scores, so the
-  score matmul is never skipped — only the softmax and the `P·V` accumulation are. The score matmul
-  and the value matmul are comparable in cost, so skipping every eligible tile removes at most
-  roughly half the attention work; the kernel-level speedup is bounded well under 2×, not unbounded.
-
-- **The decision is per tile, not per key.** A tile is skipped only when *all* of its keys are
-  collectively negligible; a single important key keeps the whole tile. How many tiles actually
-  qualify depends on the data and rounds down to tile granularity, so `target_sparsity` selects an
-  operating point on a calibrated curve — it is not a promise that a fixed fraction of tiles is
-  skipped.
-
-## The threshold
-
-The per-tile threshold is normalized by sequence length:
+The FlashInfer kernel does not take `λ` directly. It takes a `threshold_scale_factor` and divides
+it by the KV sequence length of the call:
 
 ```text
-threshold = factor / seqlen
+λ = threshold_scale_factor / max_kv_len
 ```
 
-`factor` comes from one of two sources:
+vLLM-Omni resolves the factor from one of two mutually exclusive user controls:
 
-- **Calibrated** — `factor = a · exp(b · target_sparsity)`, where `a` and `b` are fit per model (and
-  per expert, for a multi-expert model) so that a given `target_sparsity` lands near that fraction of
-  skipped tiles on the calibration data.
-- **Direct** — `factor = skip_softmax.threshold · seqlen`, the calibration-free path; the user sets
-  the threshold themselves.
+| Control | Factor passed to the kernel | Resulting `λ` |
+| --- | --- | --- |
+| `threshold` | `threshold * max_kv_len` | `threshold` |
+| `target_sparsity=s` | `a * exp(b * s)` | `a * exp(b * s) / max_kv_len` |
+
+`threshold` is therefore `λ` itself and is independent of sequence length: the same value yields
+the same per-tile test at any resolution or frame count. `threshold=0` skips no tiles. Because the
+left-hand side of the skip test lies in `(0, 1]`, values in `(0, 1)` are the meaningful range; the
+schema accepts any finite non-negative value.
+
+`target_sparsity` selects a point on a curve fitted per model by
+[NVIDIA ModelOpt](https://github.com/NVIDIA/Model-Optimizer/tree/main/examples/diffusers/sparsity),
+so that `s` lands near that fraction of skipped tiles on the calibration data. The coefficients
+`a` and `b` come from the checkpoint; the achieved sparsity on another prompt, shape, or layer can
+differ.
+
+### Skip-Softmax calibration config
+
+ModelOpt stores the calibration in each transformer component's `config.json` under
+`sparse_attention_config`. The following is taken from the `transformer/config.json` of
+[`nvidia/Wan2.2-T2V-A14B-Diffusers-FP8`](https://huggingface.co/nvidia/Wan2.2-T2V-A14B-Diffusers-FP8),
+with the `ignore` list abridged:
+
+```json
+{
+  "sparse_attention_config": {
+    "config_groups": {
+      "group_0": {
+        "algorithm": "skip_softmax",
+        "targets": ["WanAttention"],
+        "ignore": ["blocks.0.attn1", "blocks.0.attn2", "blocks.1.attn1", "..."],
+        "threshold_scale_factor": {
+          "formula": "a * exp(b * target_sparsity)",
+          "coefficients": {"a": 2822.6209952221557, "b": 4.388250623520793}
+        },
+        "target_sparsity": 0.7,
+        "disabled_until_timestep": 0.93
+      }
+    }
+  }
+}
+```
+
+A checkpoint with several transformer components carries one such config per component, each
+with its own coefficients; in this example, `transformer_2/config.json` holds the low-noise
+expert's curve.
+
+vLLM-Omni consumes `formula`, `coefficients`, and `ignore`, and ignores `targets`,
+`target_sparsity`, and `disabled_until_timestep`:
+
+- Only the formula `a * exp(b * target_sparsity)` is accepted; a checkpoint with any other formula
+  is rejected at startup. If a spec requests `target_sparsity` and the checkpoint carries no
+  calibration, startup fails and the error points to `threshold` as the calibration-free
+  alternative.
+- Each component's curve is applied to that component's attention layers. If the config for a
+  secondary component such as `transformer_2` cannot be read, that component stays dense and a
+  warning is logged.
+- `ignore` holds fnmatch patterns matched against both the full module name and the name relative
+  to the component, so `blocks.0.attn1` matches `transformer.blocks.0.attn1` as well as
+  `transformer_2.blocks.0.attn1`. Matching layers receive no coefficients and therefore stay dense
+  regardless of the user configuration.
+
+At each attention call, a layer with a `threshold` uses it directly; a layer with
+`target_sparsity` and stamped coefficients evaluates the curve; a layer with `target_sparsity` but
+no coefficients runs dense. The timestep gate below is applied to the result before it reaches the
+kernel.
 
 ## Timestep gating
 
-`disabled_until_timestep = D` keeps the mode off during the early, high-noise denoise steps and
-turns it on once the normalized timestep `t` drops to `t ≤ D` (`t` runs `1.0` → `0.0` over the
-schedule). The early steps set the global structure of the output and their errors propagate through
-every later step, so keeping them dense costs a few skipped-tile opportunities but protects fidelity.
+The early, high-noise denoising steps set the global structure of the output, and their errors
+propagate through every later step. Keeping just these steps dense markedly improves the quality
+of the generated video at a small cost in skipped work. `disabled_until_timestep` implements this:
+on each attention call the backend compares the current normalized timestep `t` against the
+configured cutoff and passes no skip factor to the kernel while `t` is above it. Skip-Softmax
+becomes active on the first step whose `t` is at or below the cutoff and stays active for the rest
+of the run, since `t` only decreases.
 
-`t` is the scheduler's own timestep divided by `num_train_timesteps`, published by the pipeline via
-`DenoiseProgressMixin.record_denoise_step`. It is deliberately not derived from the step index,
-because schedulers space their steps non-uniformly. A pipeline that does not publish a timestep
-stays dense when `disabled_until_timestep` is set.
+The default `disabled_until_timestep = 0` is a sentinel rather than a cutoff: the gate is off, the
+timestep is never read, and Skip-Softmax runs on every step. Any positive value enables the gate.
+`disabled_until_timestep = 1.0` therefore also yields no dense steps on a pipeline that publishes
+`t`, but on a pipeline that does not publish it the backend stays dense and logs a warning once
+rather than guessing from the step index.
+
+### Where `t` comes from
+
+The pipeline publishes `t` once per denoising step before running the transformer. It is the
+scheduler's own position in `[0, 1]`, decreasing from near `1.0` (pure noise) to `0.0` (clean
+sample): scheduler-based pipelines publish the scheduler timestep divided by `num_train_timesteps`,
+and rectified-flow pipelines publish the current sigma. `t` is deliberately not the step index
+divided by the step count. Schedulers place their steps non-uniformly in `t`, so expressing the
+cutoff in `t` ties it to the same noise level across step counts and schedulers.
+
+### Mapping the cutoff to denoising steps
+
+For a run whose published sequence is `t[0], ..., t[N-1]`, the number of dense steps is
+
+```text
+dense_steps = count(t[i] > disabled_until_timestep)
+skip_softmax_steps = N - dense_steps
+```
+
+This count **depends on the schedule**, and schedules differ from model to model: the
+scheduler family, the number of steps, and any time shift all change where the steps land in `t`.
+The same cutoff can therefore gate a very different fraction of the run on two models. Take
+MiniMax-H3 as an example. Its video branch uses a flow-shifted rectified-flow schedule that applies
+a shift `s` to `N + 1` uniform positions `u` from `1` to `0`,
+
+```text
+t = s * u / (1 + (s - 1) * u)
+```
+
+and a large `s` pushes most positions toward `t ≈ 1`. With the default `s = 12` and
+`num_inference_steps = 50` (50 sigma points, `N = 49` denoiser forwards), the published sequence
+starts `1.000, 0.998, 0.996, 0.995, 0.993, ...` and stays above `0.9` for 28 steps, giving:
+
+| `disabled_until_timestep` | Dense steps | Skip-Softmax steps |
+| :---: | ---: | ---: |
+| `1.00` | 0 | 49 |
+| `0.99` | 6 | 43 |
+| `0.97` | 14 | 35 |
+| `0.95` | 19 | 30 |
+| `0.90` | 28 | 21 |
+| `0.86` | 33 | 16 |
+
+These numbers hold only for that schedule. A model with a smaller shift, a different step count,
+or a scheduler that is not rectified flow produces a different sequence and a different table; a
+request that overrides `flow_shift` or the step count changes it for MiniMax-H3 too.
+Choose the cutoff by counting against the schedule actually served, not by reusing a value from
+another model.

docs/design/feature/vae_parallel.md

@@ -22,59 +22,60 @@ We use **Qwen-Image** as the reference implementation for decode parallel, and *
 
 **VAE Patch Parallelism** is an acceleration technique for both **encoding** and **decoding**. Instead of processing the entire tensor at once, the tensor is:
 
-+ Split into multiple spatial tiles
+- Split into multiple spatial tiles
 
-+ Distributed across multiple ranks
+- Distributed across multiple ranks
 
-+ Encoded/Decoded in parallel
+- Encoded/Decoded in parallel
 
-+ Merged to reconstruct the final output
+- Merged to reconstruct the final output
 
 This approach:
 
-+ Distributes computation across multiple devices
+- Distributes computation across multiple devices
 
-+ Reduces peak memory usage per device
+- Reduces peak memory usage per device
 
-+ Accelerates encoding/decoding latency
+- Accelerates encoding/decoding latency
 
 ### When to Use Encode vs Decode Parallel
 
-| Operation | Use Case | Example |
-|-----------|----------|---------|
-| **Decode Parallel** | Text-to-Image, Text-to-Video | Latent → Image/Video |
-| **Encode Parallel** | Image-to-Video (I2V) | Image → Latent (for conditioning) |
+| Operation           | Use Case                     | Example                           |
+| ------------------- | ---------------------------- | --------------------------------- |
+| **Decode Parallel** | Text-to-Image, Text-to-Video | Latent → Image/Video              |
+| **Encode Parallel** | Image-to-Video (I2V)         | Image → Latent (for conditioning) |
 
 ### Architecture
+
 We introduce **DistributedVaeExecutor** as the core component responsible for distributed VAE encoding/decoding.
 
 The executor is model-agnostic and accepts three function parameters:
 
-+ split – Partition the latent into tiles
+- split – Partition the latent into tiles
 
-+ exec – Decode a single tile
+- exec – Decode a single tile
 
-+ merge – Combine decoded tiles into the final output
+- merge – Combine decoded tiles into the final output
 
 #### Execution Flow
 
-+ Call split(z) to generate a list of TileTask and a GridSpec
+- Call split(z) to generate a list of TileTask and a GridSpec
 
-+ Dispatch tasks across ranks using workload-based balancing
+- Dispatch tasks across ranks using workload-based balancing
 
-+ Each rank executes exec(task) on its assigned tiles
+- Each rank executes exec(task) on its assigned tiles
 
-+ Gather decoded tile results to rank 0
+- Gather decoded tile results to rank 0
 
-+ Rank 0 performs merge(...)
+- Rank 0 performs merge(...)
 
-+ (Optional) Broadcast final result to all ranks
+- (Optional) Broadcast final result to all ranks
 
 This design separates:
 
-+ Distributed execution logic
+- Distributed execution logic
 
-+ Model-specific tiling and merging logic
+- Model-specific tiling and merging logic
 
 #### Why split / exec / merge is necessary?
 
@@ -82,23 +83,23 @@ The latent tensor cannot be arbitrarily partitioned.
 
 During decoding:
 
-+ Each output pixel may depend on neighboring pixels
+- Each output pixel may depend on neighboring pixels
 
-+ The receptive field is model-dependent
+- The receptive field is model-dependent
 
 Therefore:
 
-+ Tiles must include overlap
+- Tiles must include overlap
 
-+ Merge must perform blending to avoid seams
+- Merge must perform blending to avoid seams
 
 ## Step-by-Step Implementation (Decode)
 
 ### Step 1: Implement DistributedAutoencoderKLQwenImage
+
 `QwenImagePipeline` use `AutoencoderKLQwenImage` for vae, so implement a distributed version:
 
-
-```
+```python
 class DistributedAutoencoderKLQwenImage(AutoencoderKLQwenImage, DistributedVaeMixin):
     @classmethod
     def from_pretrained(cls, *args: Any, **kwargs: Any):
@@ -106,28 +107,35 @@ class DistributedAutoencoderKLQwenImage(AutoencoderKLQwenImage, DistributedVaeMi
         model.init_distributed()
         return model
 ```
+
 **Key points**:
-+ Inherit both AutoencoderKLQwenImage and DistributedVaeMixin
-+ Call init_distributed() after loading weights
+
+- Inherit both AutoencoderKLQwenImage and DistributedVaeMixin
+- Call init_distributed() after loading weights
 
 ### Step 2: Implement split/exec/merge
+
 Reuse `AutoencoderKLQwenImage.tiled_decode` logic and divide it into three stages. And we need return tiles with `GridSpec` and `TileTask`:
-```
+
+```python
 class GridSpec:
     split_dims: tuple[int, ...]  # Tensor dimensions being split (e.g., (2, 3) for (B, C, H, W))
     grid_shape: tuple[int, ...]  # Tile grid layout (num_rows, num_cols)
     tile_spec: dict = field(default_factory=dict) # Metadata required for merging
     output_dtype: torch.dtype | None = None # Final output dtype
 ```
-```
+
+```python
 class TileTask:
     tile_id: int # task id
     grid_coord: tuple[int, ...]  # Tile position in grid
     tensor: torch.Tensor | list[torch.Tensor]  # The tile tensor
     workload: int | float = 1 # Used for load balancing (e.g., tile area)
 ```
+
 And tiled base split/exec/merge as follow:
-```
+
+```python
 def tile_split(self, z: torch.Tensor) -> tuple[list[TileTask], GridSpec]:
     # mostly copy from AutoencoderKL
     _, _, num_frames, height, width = z.shape
@@ -210,11 +218,14 @@ def tile_merge(self, coord_tensor_map: dict[tuple[int, ...], torch.Tensor], grid
 ```
 
 ### Step 3: Override tiled_decode
+
 We need to override tiled_decode, the main logic is:
-+ check distributed is enabled
-+ select split/exec/merge
-+ Invoke self.distributed_executor.execute to decode
-```
+
+- check distributed is enabled
+- select split/exec/merge
+- Invoke self.distributed_executor.execute to decode
+
+```python
 def tiled_decode(self, z: torch.Tensor, return_dict: bool = True):
     if not self.is_distributed_enabled():
         return super().tiled_decode(z, return_dict=return_dict)
@@ -230,11 +241,14 @@ def tiled_decode(self, z: torch.Tensor, return_dict: bool = True):
 
     return DecoderOutput(sample=result)
 ```
+
 `broadcast_result` is set to True or False depending on the model; when enabled, the result will be used even on ranks other than 0.
 
 ### Step 4: Modify Pipeline
+
 Change vae model from AutoencoderKLQwenImage to DistributedAutoencoderKLQwenImage
-```
+
+```diff
 class YourModelPipeline(nn.Module):
     def __init__(
         self,
@@ -259,8 +273,8 @@ For models that require VAE encoding (e.g., Image-to-Video), you can also parall
 
 Similar to decode, split the input tensor into tiles. Key considerations:
 
-+ **Patchify handling**: If the model uses `patch_size`, scale tile parameters accordingly
-+ **Temporal chunking**: Video VAEs may have temporal compression (e.g., 4x)
+- **Patchify handling**: If the model uses `patch_size`, scale tile parameters accordingly
+- **Temporal chunking**: Video VAEs may have temporal compression (e.g., 4x)
 
 ```python
 def encode_tile_split(self, x: torch.Tensor) -> tuple[list[TileTask], GridSpec]:
@@ -406,23 +420,28 @@ def tiled_encode(self, x: torch.Tensor) -> torch.Tensor:
 **Key differences from decode parallel:**
 
 | Aspect | Decode Parallel | Encode Parallel |
-|--------|-----------------|-----------------|
+| -------- | ----------------- | ----------------- |
 | `broadcast_result` | Often `False` (only rank 0 needs output) | `True` (all ranks need latents for diffusion) |
 | Patchify | Applied in merge (unpatchify) | Handled by parent `_encode()` before `tiled_encode()` |
 | Temporal chunking | Frame-by-frame | Chunk-based (e.g., 1 + 4n frames) |
 
 ## Testing
-Verify numerical consistency between:
-+ vae_patch_parallel_size = 1
 
-+ vae_patch_parallel_size = N
+Verify numerical consistency between:
+
+- vae_patch_parallel_size = 1
+- vae_patch_parallel_size = N
 
 Example:
+
+```python
 torch.allclose(output_1, output_n, atol=1e-5)
+```
 
 Testing requirements:
-+ Fix random seed
-+ Use identical tiling strategy
+
+- Fix random seed
+- Use identical tiling strategy
 
 ```python
 m = Omni(
@@ -434,6 +453,7 @@ m = Omni(
         ),
     )
 ```
+
 When vae_patch_parallel_size is larger than the DiT world size, it will automatically fall back to using the DiT world size instead.
 
 ## Reference Implementations
@@ -441,7 +461,7 @@ When vae_patch_parallel_size is larger than the DiT world size, it will automati
 Complete examples in the codebase:
 
 | Model | Path | Decode Parallel | Encode Parallel |
-|-------|------|-----------------|-----------------|
+| ------- | ------ | ----------------- | ----------------- |
 | **Z-Image** | `vllm_omni/diffusion/distributed/autoencoders/autoencoder_kl.py` | ✅ | ❌ |
 | **Wan2.2** | `vllm_omni/diffusion/distributed/autoencoders/autoencoder_kl_wan.py` | ✅ | ✅ |
 | **Qwen-Image** | `vllm_omni/diffusion/distributed/autoencoders/autoencoder_kl_qwenimage.py` | ✅ | ❌ |
@@ -456,7 +476,7 @@ The tile-parallel executor above assigns independent spatial **tiles** to ranks.
 ### How it differs from tile parallel
 
 | Aspect | Tile parallel (`"tile"`) | Spatially-sharded (`"spatial_shard_height"`/`"spatial_shard_width"`) |
-|--------|--------------------------|-----------------------------------------------|
+| -------- | -------------------------- | ----------------------------------------------- |
 | Unit of work | Independent overlapping tiles | A single global feature map sharded along H or W |
 | Cross-rank communication | Gather tiles to rank 0, stitch + blend | Per-conv **halo exchange** of boundary rows/cols (P2P) |
 | Output assembly | Blend overlapping tiles | All-gather shards on rank 0, trim padding (matches `broadcast_result=False`) |
@@ -481,7 +501,7 @@ serve.py (--vae-parallel-mode) / OmniEngineArgs
 ### Notes
 
 - The decoder is patched **in place** the first time spatial-shard decode runs and is bound to a single split dimension for the lifetime of the VAE instance.
-- Numerical correctness vs. single-GPU decode is covered by `tests/diffusion/distributed/test_wan_spatial_shard.py::test_spatial_shard_decode_matches_reference` (multi-GPU, nightly `full_model` + `distributed_cuda`).
+- Numerical correctness vs. single-GPU decode is covered by `tests/diffusion/distributed/test_wan_spatial_shard.py::test_spatial_shard_decode_matches_reference` (multi-GPU, nightly `full_model` + `cards_2`).
 
 ---
 

docs/design/index.md

@@ -18,6 +18,7 @@ implementation contract; it is not, by itself, a general support claim.
 ### Runtime and stage execution
 
 - [Disaggregated Inference](feature/disaggregated_inference.md)
+- [Host Weight Runtime](feature/host_weight_runtime.md)
 - [Async Chunk](feature/async_chunk.md)
 - [Async Diffusion Output](feature/async_diffusion_output.md)
 - [Async Omni Output Materialization](feature/omni_async_output_materialization.md)
@@ -88,14 +89,15 @@ The design contracts separate selection mechanics from backend algorithms:
 - [Model Integration](module/model_integration.md)
 - [Autoregressive Runtime](module/ar_runtime.md)
 - Diffusion
-  - [Overview](module/diffusion/index.md)
-  - [Runtime](module/diffusion/diffusion_runtime.md)
-  - [Model Integration](module/diffusion/diffusion_model_integration.md)
-  - [Continuous Batching](module/diffusion/continuous_batching.md)
-  - [Parallelism](module/diffusion/parallelism.md)
-  - [Offloader](module/diffusion/offloader.md)
+    - [Overview](module/diffusion/index.md)
+    - [Runtime](module/diffusion/diffusion_runtime.md)
+    - [Model Integration](module/diffusion/diffusion_model_integration.md)
+    - [Continuous Batching](module/diffusion/continuous_batching.md)
+    - [Parallelism](module/diffusion/parallelism.md)
+    - [Offloader](module/diffusion/offloader.md)
 - [Execution Platforms](module/execution_platforms.md)
 - [Cache Management](module/cache_management.md)
+- [Host Weight Runtime](module/host_weight_runtime.md)
 - [Quantization](module/quantization.md)
 - [Observability](module/observability.md)
 - [Profiling](module/profiling.md)