Dev/debug qwen tts (#903 )

Signed-off-by: Taichang Zhou <tzhouam@connect.ust.hk>
2026-01-22 23:13:47 +08:00
588 changed files with 18555 additions and 88095 deletions
@@ -3,38 +3,234 @@ steps:
    key: image-build
    commands:
      - "aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin public.ecr.aws/q9t5s3a7"
-      - "docker build --progress=plain --file docker/Dockerfile.ci -t vllm-omni-ci ."
+      - "docker build --file docker/Dockerfile.ci -t vllm-omni-ci ."
      - "docker tag vllm-omni-ci public.ecr.aws/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT"
      - "docker push public.ecr.aws/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT"
    agents:
      queue: "cpu_queue_premerge"

-  # L2 Test
-  - label: "Upload Ready Pipeline"
-    depends_on: image-build
-    key: upload-ready-pipeline
-    if: build.branch != "main"
+  # - label: "Test on NPU"
+  #   depends_on: ~
+  #   key: npu-test
+  #   commands:
+  #     - ".buildkite/scripts/hardware_ci/run_npu_test.sh"
+  #   agents:
+  #     queue: "ascend"
+
+  - label: "Simple Unit Test"
+    depends_on: ~
    commands:
-      - buildkite-agent pipeline upload .buildkite/test-ready.yml
+      - ".buildkite/scripts/simple_test.sh"
    agents:
      queue: "cpu_queue_premerge"

-  # L3 Test
-  - label: "Upload Merge Pipeline"
+  - label: "Diffusion Model Test"
+    timeout_in_minutes: 20
    depends_on: image-build
-    key: upload-merge-pipeline
-    if: build.branch == "main"
    commands:
-      - buildkite-agent pipeline upload .buildkite/test-merge.yml
+      - pytest -s -v tests/e2e/offline_inference/test_t2i_model.py
    agents:
-      queue: "cpu_queue_premerge"
+      queue: "gpu_1_queue" # g6.4xlarge instance on AWS, has 1 L4 GPU
+    plugins:
+      - docker#v5.2.0:
+          image: public.ecr.aws/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
+          always-pull: true
+          propagate-environment: true
+          environment:
+            - "HF_HOME=/fsx/hf_cache"
+          volumes:
+            - "/fsx/hf_cache:/fsx/hf_cache"

-  # L4 Test
-  - label: "Upload Nightly Pipeline"
+  - label: "Diffusion Model CPU offloading Test"
+    timeout_in_minutes: 20
    depends_on: image-build
-    key: upload-nightly-pipeline
-    if: build.env("NIGHTLY") == "1"
    commands:
-      - buildkite-agent pipeline upload .buildkite/test-nightly.yml
+      - pytest -s -v tests/e2e/offline_inference/test_diffusion_cpu_offload.py
    agents:
-      queue: "cpu_queue_premerge"
+      queue: "gpu_1_queue" # g6.4xlarge instance on AWS, has 1 L4 GPU
+    plugins:
+      - docker#v5.2.0:
+          image: public.ecr.aws/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
+          always-pull: true
+          propagate-environment: true
+          environment:
+            - "HF_HOME=/fsx/hf_cache"
+          volumes:
+            - "/fsx/hf_cache:/fsx/hf_cache"
+
+  - label: "Audio Generation Model Test"
+    timeout_in_minutes: 20
+    depends_on: image-build
+    commands:
+      - pytest -s -v tests/e2e/offline_inference/test_stable_audio_model.py
+    agents:
+      queue: "gpu_1_queue" # g6.4xlarge instance on AWS, has 1 L4 GPU
+    plugins:
+      - docker#v5.2.0:
+          image: public.ecr.aws/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
+          always-pull: true
+          propagate-environment: true
+          environment:
+            - "HF_HOME=/fsx/hf_cache"
+          volumes:
+            - "/fsx/hf_cache:/fsx/hf_cache"
+
+  - label: "Diffusion Cache Backend Test"
+    timeout_in_minutes: 15
+    depends_on: image-build
+    commands:
+      - pytest -s -v tests/e2e/offline_inference/test_cache_dit.py tests/e2e/offline_inference/test_teacache.py
+    agents:
+      queue: "gpu_1_queue" # g6.4xlarge instance on AWS, has 1 L4 GPU
+    plugins:
+      - docker#v5.2.0:
+          image: public.ecr.aws/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
+          always-pull: true
+          propagate-environment: true
+          environment:
+            - "HF_HOME=/fsx/hf_cache"
+          volumes:
+            - "/fsx/hf_cache:/fsx/hf_cache"
+
+  - label: "Diffusion Sequence Parallelism Test"
+    timeout_in_minutes: 20
+    depends_on: image-build
+    commands:
+      - pytest -s -v tests/e2e/offline_inference/test_sequence_parallel.py
+    agents:
+      queue: "gpu_4_queue" # g6.12xlarge instance on AWS, has 4 L4 GPU
+    plugins:
+      - docker#v5.2.0:
+          image: public.ecr.aws/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
+          always-pull: true
+          propagate-environment: true
+          shm-size: "8gb"
+          environment:
+            - "HF_HOME=/fsx/hf_cache"
+          volumes:
+            - "/fsx/hf_cache:/fsx/hf_cache"
+
+  - label: "Diffusion Tensor Parallelism Test"
+    timeout_in_minutes: 20
+    depends_on: image-build
+    commands:
+      - pytest -s -v tests/e2e/offline_inference/test_zimage_tensor_parallel.py
+    agents:
+      queue: "gpu_4_queue" # g6.12xlarge instance on AWS, has 4 L4 GPU
+    plugins:
+      - docker#v5.2.0:
+          image: public.ecr.aws/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
+          always-pull: true
+          propagate-environment: true
+          shm-size: "8gb"
+          environment:
+            - "HF_HOME=/fsx/hf_cache"
+          volumes:
+            - "/fsx/hf_cache:/fsx/hf_cache"
+
+  - label: "Diffusion GPU Worker Test"
+    timeout_in_minutes: 20
+    depends_on: image-build
+    commands:
+      - pytest -s -v tests/diffusion/test_gpu_diffusion_worker.py
+    agents:
+      queue: "gpu_4_queue" # g6.12xlarge instance on AWS, has 4 L4 GPU
+    plugins:
+      - docker#v5.2.0:
+          image: public.ecr.aws/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
+          always-pull: true
+          propagate-environment: true
+          shm-size: "8gb"
+          environment:
+            - "HF_HOME=/fsx/hf_cache"
+          volumes:
+            - "/fsx/hf_cache:/fsx/hf_cache"
+
+  - label: "Omni Model Test"
+    timeout_in_minutes: 15
+    depends_on: image-build
+    commands:
+      - export VLLM_LOGGING_LEVEL=DEBUG
+      - export VLLM_WORKER_MULTIPROC_METHOD=spawn
+      - pytest -s -v tests/e2e/offline_inference/test_qwen2_5_omni.py
+    agents:
+      queue: "gpu_4_queue" # g6.12xlarge instance on AWS, has 4 L4 GPU
+    plugins:
+      - docker#v5.2.0:
+          image: public.ecr.aws/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
+          always-pull: true
+          propagate-environment: true
+          environment:
+            - "HF_HOME=/fsx/hf_cache"
+          volumes:
+            - "/fsx/hf_cache:/fsx/hf_cache"
+
+  - label: "Omni Model Test with H100"
+    timeout_in_minutes: 30
+    depends_on: image-build
+    commands:
+      - export VLLM_WORKER_MULTIPROC_METHOD=spawn
+      - pytest -s -v tests/e2e/offline_inference/test_qwen3_omni.py tests/e2e/online_serving/test_qwen3_omni.py tests/e2e/online_serving/test_async_omni.py
+    agents:
+      queue: "mithril-h100-pool"
+    plugins:
+      - kubernetes:
+          podSpec:
+            containers:
+              - image: public.ecr.aws/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
+                resources:
+                  limits:
+                    nvidia.com/gpu: 2
+                volumeMounts:
+                  - name: devshm
+                    mountPath: /dev/shm
+                  - name: hf-cache
+                    mountPath: /root/.cache/huggingface
+                env:
+                  - name: HF_HOME
+                    value: /root/.cache/huggingface
+            nodeSelector:
+              node.kubernetes.io/instance-type: gpu-h100-sxm
+            volumes:
+              - name: devshm
+                emptyDir:
+                  medium: Memory
+              - name: hf-cache
+                hostPath:
+                  path: /mnt/hf-cache
+                  type: DirectoryOrCreate
+
+  - label: "Diffusion Image Edit Test with H100 (1 GPU)"
+    timeout_in_minutes: 20
+    depends_on: image-build
+    commands:
+      - export VLLM_WORKER_MULTIPROC_METHOD=spawn
+      - pytest -s -v tests/e2e/online_serving/test_i2i_multi_image_input.py
+    agents:
+      queue: "mithril-h100-pool"
+    plugins:
+      - kubernetes:
+          podSpec:
+            containers:
+              - image: public.ecr.aws/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
+                resources:
+                  limits:
+                    nvidia.com/gpu: 1
+                volumeMounts:
+                  - name: devshm
+                    mountPath: /dev/shm
+                  - name: hf-cache
+                    mountPath: /root/.cache/huggingface
+                env:
+                  - name: HF_HOME
+                    value: /root/.cache/huggingface
+            nodeSelector:
+              node.kubernetes.io/instance-type: gpu-h100-sxm
+            volumes:
+              - name: devshm
+                emptyDir:
+                  medium: Memory
+              - name: hf-cache
+                hostPath:
+                  path: /mnt/hf-cache
+                  type: DirectoryOrCreate
@@ -116,9 +116,6 @@ if [[ $commands == *"--shard-id="* ]]; then
        --shm-size=16gb \
        --group-add "$render_gid" \
        --rm \
-        -e MIOPEN_DEBUG_CONV_DIRECT=0 \
-        -e MIOPEN_DEBUG_CONV_GEMM=0 \
-        -e VLLM_ROCM_USE_AITER=1 \
        -e HIP_VISIBLE_DEVICES="${GPU}" \
        -e HF_TOKEN \
        -e AWS_ACCESS_KEY_ID \
@@ -151,9 +148,6 @@ else
          --shm-size=16gb \
          --group-add "$render_gid" \
          --rm \
-          -e MIOPEN_DEBUG_CONV_DIRECT=0 \
-          -e MIOPEN_DEBUG_CONV_GEMM=0 \
-          -e VLLM_ROCM_USE_AITER=1 \
          -e HF_TOKEN \
          -e AWS_ACCESS_KEY_ID \
          -e AWS_SECRET_ACCESS_KEY \
@@ -0,0 +1,57 @@
+#!/usr/bin/env bash
+
+set -euo pipefail
+
+SCRIPT_DIR="$(cd -- "$(dirname -- "${BASH_SOURCE[0]}")" && pwd)"
+REPO_ROOT="$(cd -- "${SCRIPT_DIR}/../.." && pwd)"
+UV_BIN="uv"
+PYTHON_BOOTSTRAP="${PYTHON:-python3}"
+VENV_DIR="${VENV_DIR:-${REPO_ROOT}/.venv-simple-test}"
+TARGET_PY_VERSION="3.12"
+
+if ! command -v "${PYTHON_BOOTSTRAP}" >/dev/null 2>&1; then
+  PYTHON_BOOTSTRAP="python"
+fi
+
+cd "${REPO_ROOT}"
+
+# Ensure pip-installed scripts (including uv) are on PATH
+PYTHON_SCRIPTS="$(${PYTHON_BOOTSTRAP} -c 'import sysconfig; print(sysconfig.get_path("scripts"))')"
+USER_BASE="$(${PYTHON_BOOTSTRAP} -m site --user-base 2>/dev/null || true)"
+PATH="${PYTHON_SCRIPTS}:${PATH}"
+if [[ -n "${USER_BASE}" ]]; then
+  PATH="${USER_BASE}/bin:${PATH}"
+fi
+
+if ! command -v "${UV_BIN}" >/dev/null 2>&1; then
+  "${PYTHON_BOOTSTRAP}" -m pip install --upgrade pip
+  "${PYTHON_BOOTSTRAP}" -m pip install uv
+  hash -r
+fi
+
+TARGET_PY_PATH="$(${UV_BIN} python find "${TARGET_PY_VERSION}" 2>/dev/null | head -n1 || true)"
+if [[ -z "${TARGET_PY_PATH}" ]]; then
+  "${UV_BIN}" python install "${TARGET_PY_VERSION}"
+  TARGET_PY_PATH="$(${UV_BIN} python find "${TARGET_PY_VERSION}" 2>/dev/null | head -n1 || true)"
+fi
+
+if [[ -z "${TARGET_PY_PATH}" ]]; then
+  echo "Failed to locate python ${TARGET_PY_VERSION}" >&2
+  exit 1
+fi
+
+if [[ ! -d "${VENV_DIR}" ]]; then
+  "${UV_BIN}" venv --python "${TARGET_PY_PATH}" "${VENV_DIR}"
+fi
+
+VENV_PYTHON="${VENV_DIR}/bin/python"
+[[ -x "${VENV_PYTHON}" ]] || { echo "Python not found in ${VENV_DIR}"; exit 1; }
+
+"${UV_BIN}" pip install --python "${VENV_PYTHON}" vllm==0.14.0
+"${UV_BIN}" pip install --python "${VENV_PYTHON}" -e ".[dev]"
+
+"${VENV_PYTHON}" -m pytest -v -s tests/entrypoints/
+"${VENV_PYTHON}" -m pytest -v -s tests/diffusion/cache/
+"${VENV_PYTHON}" -m pytest -v -s tests/model_executor/models/qwen2_5_omni/test_audio_length.py
+"${VENV_PYTHON}" -m pytest -v -s tests/worker/
+"${VENV_PYTHON}" -m pytest -v -s tests/distributed/omni_connectors/test_kv_flow.py
@@ -8,33 +8,14 @@ steps:
  grade: Blocking
  commands:
    - export GPU_ARCHS=gfx942
+    - export MIOPEN_DEBUG_CONV_DIRECT=0
+    - export MIOPEN_DEBUG_CONV_GEMM=0
+    - export VLLM_ROCM_USE_AITER=1
+    - export VLLM_ROCM_USE_AITER_MHA=1
+    - export VLLM_ROCM_USE_AITER_LINEAR=0
+    - export VLLM_ROCM_USE_AITER_RMSNORM=0
    - pytest -s -v tests/e2e/offline_inference/test_t2i_model.py

- label: "Diffusion Images API LoRA E2E"
-  timeout_in_minutes: 20
-  agent_pool: mi325_1
-  depends_on: amd-build
-  mirror_hardwares: [amdproduction]
-  grade: Blocking
-  commands:
-    - export GPU_ARCHS=gfx942
-    - export VLLM_LOGGING_LEVEL=DEBUG
-    - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-    - pytest -s -v tests/e2e/online_serving/test_images_generations_lora.py
-
- label: "Diffusion Model CPU offloading Test"
-  timeout_in_minutes: 20
-  agent_pool: mi325_1
-  depends_on: amd-build
-  mirror_hardwares: [amdproduction]
-  grade: Blocking
-  commands:
-    - export GPU_ARCHS=gfx942
-    - export VLLM_LOGGING_LEVEL=DEBUG
-    - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-    - pytest -s -v tests/e2e/offline_inference/test_diffusion_cpu_offload.py
-    - pytest -s -v tests/e2e/offline_inference/test_diffusion_layerwise_offload.py
-
 - label: "Diffusion Cache Backend Test"
  timeout_in_minutes: 15
  agent_pool: mi325_1
@@ -45,29 +26,24 @@ steps:
    - export GPU_ARCHS=gfx942
    - export VLLM_LOGGING_LEVEL=DEBUG
    - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-    - pytest -s -v -m 'core_model and cache and diffusion and not distributed_rocm and MI325'
+    - export MIOPEN_DEBUG_CONV_DIRECT=0
+    - export MIOPEN_DEBUG_CONV_GEMM=0
+    - export VLLM_ROCM_USE_AITER=1
+    - export VLLM_ROCM_USE_AITER_MHA=1
+    - export VLLM_ROCM_USE_AITER_LINEAR=0
+    - export VLLM_ROCM_USE_AITER_RMSNORM=0
+    - pytest -s -v tests/e2e/offline_inference/test_cache_dit.py tests/e2e/offline_inference/test_teacache.py

- label: "Diffusion Sequence Parallelism Test"
-  timeout_in_minutes: 20
+- label: "Diffusion Parallelism Test"
+  timeout_in_minutes: 15
  agent_pool: mi325_2
  depends_on: amd-build
  mirror_hardwares: [amdproduction]
  grade: Blocking
  commands:
-    - export GPU_ARCHS=gfx942
-    - export VLLM_LOGGING_LEVEL=DEBUG
-    - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-    - pytest -s -v tests/e2e/offline_inference/test_sequence_parallel.py -m core_model
-
- label: "Diffusion Tensor Parallelism Test"
-  timeout_in_minutes: 20
-  agent_pool: mi325_2
-  depends_on: amd-build
-  commands:
-    - export GPU_ARCHS=gfx942
-    - export VLLM_LOGGING_LEVEL=DEBUG
-    - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-    - pytest -s -v tests/e2e/offline_inference/test_zimage_parallelism.py
+    - export MIOPEN_DEBUG_CONV_DIRECT=0
+    - export MIOPEN_DEBUG_CONV_GEMM=0
+    - pytest -s -v tests/e2e/offline_inference/test_sequence_parallel.py

 - label: "Diffusion GPU Worker Test"
  timeout_in_minutes: 20
@@ -76,7 +52,9 @@ steps:
  mirror_hardwares: [amdproduction]
  grade: Blocking
  commands:
-    - pytest -s -v tests/diffusion/test_diffusion_worker.py
+    - export MIOPEN_DEBUG_CONV_DIRECT=0
+    - export MIOPEN_DEBUG_CONV_GEMM=0
+    - pytest -s -v tests/diffusion/test_gpu_diffusion_worker.py

 - label: "Omni Model Test Qwen2-5-Omni"
  timeout_in_minutes: 15
@@ -88,8 +66,13 @@ steps:
    - export GPU_ARCHS=gfx942
    - export VLLM_LOGGING_LEVEL=DEBUG
    - export VLLM_WORKER_MULTIPROC_METHOD=spawn
+    - export MIOPEN_DEBUG_CONV_DIRECT=0
+    - export MIOPEN_DEBUG_CONV_GEMM=0
+    - export VLLM_ROCM_USE_AITER=1
+    - export VLLM_ROCM_USE_AITER_MHA=1
+    - export VLLM_ROCM_USE_AITER_LINEAR=0
+    - export VLLM_ROCM_USE_AITER_RMSNORM=0
    - pytest -s -v tests/e2e/offline_inference/test_qwen2_5_omni.py
-    - pytest -s -v tests/engine/test_async_omni_engine_abort.py

 - label: "Omni Model Test Qwen3-Omni"
  timeout_in_minutes: 15
@@ -100,10 +83,9 @@ steps:
  commands:
    - export VLLM_LOGGING_LEVEL=DEBUG
    - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-    - export VLLM_TEST_CLEAN_GPU_MEMORY="1"
-    - pytest -s -v tests/e2e/offline_inference/test_qwen3_omni.py
-    - pytest -s -v tests/e2e/online_serving/test_qwen3_omni.py
-
+    - export MIOPEN_DEBUG_CONV_DIRECT=0
+    - export MIOPEN_DEBUG_CONV_GEMM=0
+    - pytest -s -v tests/e2e/offline_inference/test_qwen3_omni.py tests/e2e/online_serving/test_qwen3_omni.py

 - label: "Diffusion Image Edit Test"
  timeout_in_minutes: 15
@@ -115,4 +97,10 @@ steps:
    - export GPU_ARCHS=gfx942
    - export VLLM_LOGGING_LEVEL=DEBUG
    - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-    - pytest -s -v tests/e2e/online_serving/test_image_gen_edit.py
+    - export MIOPEN_DEBUG_CONV_DIRECT=0
+    - export MIOPEN_DEBUG_CONV_GEMM=0
+    - export VLLM_ROCM_USE_AITER=1
+    - export VLLM_ROCM_USE_AITER_MHA=1
+    - export VLLM_ROCM_USE_AITER_LINEAR=0
+    - export VLLM_ROCM_USE_AITER_RMSNORM=0
+    - pytest -s -v tests/e2e/online_serving/test_i2i_multi_image_input.py
@@ -1,308 +0,0 @@
-steps:
-  - label: "Simple Unit Test"
-    depends_on: upload-merge-pipeline
-    commands:
-      - "pytest -v -s -m 'core_model and cpu' --cov=vllm_omni --cov-branch --cov-report=term-missing --cov-report=html --cov-report=xml"
-    agents:
-      queue: "gpu_1_queue"
-    plugins:
-      - docker#v5.2.0:
-          image: public.ecr.aws/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
-          always-pull: true
-          propagate-environment: true
-          environment:
-            - "HF_HOME=/fsx/hf_cache"
-          volumes:
-            - "/fsx/hf_cache:/fsx/hf_cache"
-
-  - label: "Diffusion Model Test"
-    timeout_in_minutes: 20
-    depends_on: upload-merge-pipeline
-    commands:
-      - pytest -s -v tests/e2e/offline_inference/test_t2i_model.py -m "advanced_model and diffusion" --run-level "advanced_model"
-    agents:
-      queue: "gpu_1_queue" # g6.4xlarge instance on AWS, has 1 L4 GPU
-    plugins:
-      - docker#v5.2.0:
-          image: public.ecr.aws/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
-          always-pull: true
-          propagate-environment: true
-          environment:
-            - "HF_HOME=/fsx/hf_cache"
-          volumes:
-            - "/fsx/hf_cache:/fsx/hf_cache"
-
-  - label: "Diffusion Images API LoRA E2E"
-    timeout_in_minutes: 20
-    depends_on: upload-merge-pipeline
-    commands:
-      - pytest -s -v tests/e2e/online_serving/test_images_generations_lora.py
-    agents:
-      queue: "gpu_1_queue" # g6.4xlarge instance on AWS, has 1 L4 GPU
-    plugins:
-      - docker#v5.2.0:
-          image: public.ecr.aws/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
-          always-pull: true
-          propagate-environment: true
-          environment:
-            - "HF_HOME=/fsx/hf_cache"
-          volumes:
-            - "/fsx/hf_cache:/fsx/hf_cache"
-
-  - label: "Diffusion Model CPU offloading Test"
-    timeout_in_minutes: 20
-    depends_on: upload-merge-pipeline
-    commands:
-      - pytest -s -v tests/e2e/offline_inference/test_diffusion_cpu_offload.py
-      - pytest -s -v tests/e2e/offline_inference/test_diffusion_layerwise_offload.py
-    agents:
-      queue: "gpu_1_queue" # g6.4xlarge instance on AWS, has 1 L4 GPU
-    plugins:
-      - docker#v5.2.0:
-          image: public.ecr.aws/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
-          always-pull: true
-          propagate-environment: true
-          environment:
-            - "HF_HOME=/fsx/hf_cache"
-          volumes:
-            - "/fsx/hf_cache:/fsx/hf_cache"
-
-  - label: "Audio Generation Model Test"
-    timeout_in_minutes: 20
-    depends_on: upload-merge-pipeline
-    commands:
-      - pytest -s -v tests/e2e/offline_inference/test_stable_audio_model.py
-    agents:
-      queue: "gpu_1_queue" # g6.4xlarge instance on AWS, has 1 L4 GPU
-    plugins:
-      - docker#v5.2.0:
-          image: public.ecr.aws/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
-          always-pull: true
-          propagate-environment: true
-          environment:
-            - "HF_HOME=/fsx/hf_cache"
-          volumes:
-            - "/fsx/hf_cache:/fsx/hf_cache"
-
-  - label: "Diffusion Cache Backend Test"
-    timeout_in_minutes: 15
-    depends_on: upload-merge-pipeline
-    commands:
-      - pytest -s -v -m 'core_model and cache and diffusion and not distributed_cuda and L4'
-    agents:
-      queue: "gpu_1_queue" # g6.4xlarge instance on AWS, has 1 L4 GPU
-    plugins:
-      - docker#v5.2.0:
-          image: public.ecr.aws/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
-          always-pull: true
-          propagate-environment: true
-          environment:
-            - "HF_HOME=/fsx/hf_cache"
-          volumes:
-            - "/fsx/hf_cache:/fsx/hf_cache"
-
-  - label: "Diffusion Sequence Parallelism Test"
-    timeout_in_minutes: 20
-    depends_on: upload-merge-pipeline
-    commands:
-      - pytest -s -v tests/e2e/offline_inference/test_sequence_parallel.py
-    agents:
-      queue: "gpu_4_queue" # g6.12xlarge instance on AWS, has 4 L4 GPU
-    plugins:
-      - docker#v5.2.0:
-          image: public.ecr.aws/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
-          always-pull: true
-          propagate-environment: true
-          shm-size: "8gb"
-          environment:
-            - "HF_HOME=/fsx/hf_cache"
-          volumes:
-            - "/fsx/hf_cache:/fsx/hf_cache"
-
-  - label: "Diffusion Tensor Parallelism Test"
-    timeout_in_minutes: 20
-    depends_on: upload-merge-pipeline
-    commands:
-      - pytest -s -v tests/e2e/offline_inference/test_zimage_parallelism.py
-    agents:
-      queue: "gpu_4_queue" # g6.12xlarge instance on AWS, has 4 L4 GPU
-    plugins:
-      - docker#v5.2.0:
-          image: public.ecr.aws/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
-          always-pull: true
-          propagate-environment: true
-          shm-size: "8gb"
-          environment:
-            - "HF_HOME=/fsx/hf_cache"
-          volumes:
-            - "/fsx/hf_cache:/fsx/hf_cache"
-
-  - label: "Diffusion GPU Worker Test"
-    timeout_in_minutes: 20
-    depends_on: upload-merge-pipeline
-    commands:
-      - pytest -s -v tests/diffusion/test_diffusion_worker.py
-    agents:
-      queue: "gpu_4_queue" # g6.12xlarge instance on AWS, has 4 L4 GPU
-    plugins:
-      - docker#v5.2.0:
-          image: public.ecr.aws/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
-          always-pull: true
-          propagate-environment: true
-          shm-size: "8gb"
-          environment:
-            - "HF_HOME=/fsx/hf_cache"
-          volumes:
-            - "/fsx/hf_cache:/fsx/hf_cache"
-
-  - label: "Benchmark & Engine Test"
-    depends_on: upload-merge-pipeline
-    commands:
-      - |
-        timeout 15m bash -c '
-                export VLLM_WORKER_MULTIPROC_METHOD=spawn
-                set +e
-                pytest -s -v tests/benchmarks/test_serve_cli.py
-                EXIT1=$$?
-                pytest -s -v tests/engine/test_async_omni_engine_abort.py
-                EXIT2=$$?
-                exit $$((EXIT1 | EXIT2))
-        '
-    agents:
-      queue: "gpu_4_queue" # g6.12xlarge instance on AWS, has 4 L4 GPU
-    plugins:
-      - docker#v5.2.0:
-          image: public.ecr.aws/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
-          always-pull: true
-          propagate-environment: true
-          shm-size: "8gb"
-          environment:
-            - "HF_HOME=/fsx/hf_cache"
-          volumes:
-            - "/fsx/hf_cache:/fsx/hf_cache"
-
-  - label: "Omni Model Test"
-    timeout_in_minutes: 15
-    depends_on: upload-merge-pipeline
-    commands:
-      - export VLLM_LOGGING_LEVEL=DEBUG
-      - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-      - pytest -s -v tests/e2e/offline_inference/test_qwen2_5_omni.py
-    agents:
-      queue: "gpu_4_queue" # g6.12xlarge instance on AWS, has 4 L4 GPU
-    plugins:
-      - docker#v5.2.0:
-          image: public.ecr.aws/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
-          always-pull: true
-          propagate-environment: true
-          environment:
-            - "HF_HOME=/fsx/hf_cache"
-          volumes:
-            - "/fsx/hf_cache:/fsx/hf_cache"
-
-  - label: "Omni Model Test with H100"
-    timeout_in_minutes: 30
-    depends_on: upload-merge-pipeline
-    commands:
-      - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-      - export VLLM_TEST_CLEAN_GPU_MEMORY="1"
-      - pytest -s -v tests/e2e/offline_inference/test_qwen3_omni.py
-      - pytest -s -v tests/e2e/online_serving/test_qwen3_omni.py -m "advanced_model" --run-level "advanced_model"
-    agents:
-      queue: "mithril-h100-pool"
-    plugins:
-      - kubernetes:
-          podSpec:
-            containers:
-              - image: 936637512419.dkr.ecr.us-west-2.amazonaws.com/vllm-ci-pull-through-cache/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
-                resources:
-                  limits:
-                    nvidia.com/gpu: 2
-                volumeMounts:
-                  - name: devshm
-                    mountPath: /dev/shm
-                  - name: hf-cache
-                    mountPath: /root/.cache/huggingface
-                env:
-                  - name: HF_HOME
-                    value: /root/.cache/huggingface
-            nodeSelector:
-              node.kubernetes.io/instance-type: gpu-h100-sxm
-            volumes:
-              - name: devshm
-                emptyDir:
-                  medium: Memory
-              - name: hf-cache
-                hostPath:
-                  path: /mnt/hf-cache
-                  type: DirectoryOrCreate
-
-  - label: "Diffusion Image Edit Test with H100 (1 GPU)"
-    timeout_in_minutes: 20
-    depends_on: upload-merge-pipeline
-    commands:
-      - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-      - pytest -s -v tests/e2e/online_serving/test_image_gen_edit.py
-    agents:
-      queue: "mithril-h100-pool"
-    plugins:
-      - kubernetes:
-          podSpec:
-            containers:
-              - image: 936637512419.dkr.ecr.us-west-2.amazonaws.com/vllm-ci-pull-through-cache/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
-                resources:
-                  limits:
-                    nvidia.com/gpu: 1
-                volumeMounts:
-                  - name: devshm
-                    mountPath: /dev/shm
-                  - name: hf-cache
-                    mountPath: /root/.cache/huggingface
-                env:
-                  - name: HF_HOME
-                    value: /root/.cache/huggingface
-            nodeSelector:
-              node.kubernetes.io/instance-type: gpu-h100-sxm
-            volumes:
-              - name: devshm
-                emptyDir:
-                  medium: Memory
-              - name: hf-cache
-                hostPath:
-                  path: /mnt/hf-cache
-                  type: DirectoryOrCreate
-
-  # - label: "Bagel Text2Img Model Test with H100"
-  #   timeout_in_minutes: 30
-  #   depends_on: upload-merge-pipeline
-  #   commands:
-  #     - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-  #     - pytest -s -v tests/e2e/offline_inference/test_bagel_text2img.py
-  #   agents:
-  #     queue: "mithril-h100-pool"
-  #   plugins:
-  #     - kubernetes:
-  #         podSpec:
-  #           containers:
-  #             - image: 936637512419.dkr.ecr.us-west-2.amazonaws.com/vllm-ci-pull-through-cache/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
-  #               resources:
-  #                 limits:
-  #                   nvidia.com/gpu: 1
-  #               volumeMounts:
-  #                 - name: devshm
-  #                   mountPath: /dev/shm
-  #                 - name: hf-cache
-  #                   mountPath: /root/.cache/huggingface
-  #               env:
-  #                 - name: HF_HOME
-  #                   value: /root/.cache/huggingface
-  #           nodeSelector:
-  #             node.kubernetes.io/instance-type: gpu-h100-sxm
-  #           volumes:
-  #             - name: devshm
-  #               emptyDir:
-  #                 medium: Memory
-  #             - name: hf-cache
-  #               hostPath:
-  #                 path: /mnt/hf-cache
-  #                 type: DirectoryOrCreate
@@ -1,98 +0,0 @@
-steps:
-  - label: "Omni Model Test with H100"
-    timeout_in_minutes: 90
-    depends_on: upload-nightly-pipeline
-    if: build.env("NIGHTLY") == "1"
-    commands:
-      - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-      - |
-        set +e
-        pytest -s -v tests/e2e/online_serving/test_qwen3_omni_expansion.py -m "advanced_model" --run-level "advanced_model"
-        EXIT1=$$?
-        pytest -s -v tests/examples/online_serving/test_qwen3_omni.py -m "advanced_model" --run-level "advanced_model"
-        EXIT2=$$?
-        exit $$((EXIT1 | EXIT2))
-    agents:
-      queue: "mithril-h100-pool"
-    plugins:
-      - kubernetes:
-          podSpec:
-            containers:
-              - image: 936637512419.dkr.ecr.us-west-2.amazonaws.com/vllm-ci-pull-through-cache/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
-                resources:
-                  limits:
-                    nvidia.com/gpu: 2
-                volumeMounts:
-                  - name: devshm
-                    mountPath: /dev/shm
-                  - name: hf-cache
-                    mountPath: /root/.cache/huggingface
-                env:
-                  - name: HF_HOME
-                    value: /root/.cache/huggingface
-            nodeSelector:
-              node.kubernetes.io/instance-type: gpu-h100-sxm
-            volumes:
-              - name: devshm
-                emptyDir:
-                  medium: Memory
-              - name: hf-cache
-                hostPath:
-                  path: /mnt/hf-cache
-                  type: DirectoryOrCreate
-
-  - label: "Omni Model Test"
-    timeout_in_minutes: 60
-    depends_on: upload-nightly-pipeline
-    if: build.env("NIGHTLY") == "1"
-    commands:
-      - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-      - pytest -s -v tests/examples/online_serving/test_qwen2_5_omni.py -m "advanced_model" --run-level "advanced_model"
-    agents:
-      queue: "gpu_4_queue" # g6.12xlarge instance on AWS, has 4 L4 GPU
-    plugins:
-      - docker#v5.2.0:
-          image: public.ecr.aws/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
-          always-pull: true
-          shm-size: "8gb"
-          propagate-environment: true
-          environment:
-            - "HF_HOME=/fsx/hf_cache"
-          volumes:
-            - "/fsx/hf_cache:/fsx/hf_cache"
-
-  - label: "Omni Model Perf Test"
-    timeout_in_minutes: 120
-    depends_on: upload-nightly-pipeline
-    if: build.env("NIGHTLY") == "1"
-    commands:
-      - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-      - pytest -s -v tests/perf/scripts/run_benchmark.py
-    agents:
-      queue: "mithril-h100-pool"
-    plugins:
-      - kubernetes:
-          podSpec:
-            containers:
-              - image: 936637512419.dkr.ecr.us-west-2.amazonaws.com/vllm-ci-pull-through-cache/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
-                resources:
-                  limits:
-                    nvidia.com/gpu: 2
-                volumeMounts:
-                  - name: devshm
-                    mountPath: /dev/shm
-                  - name: hf-cache
-                    mountPath: /root/.cache/huggingface
-                env:
-                  - name: HF_HOME
-                    value: /root/.cache/huggingface
-            nodeSelector:
-              node.kubernetes.io/instance-type: gpu-h100-sxm
-            volumes:
-              - name: devshm
-                emptyDir:
-                  medium: Memory
-              - name: hf-cache
-                hostPath:
-                  path: /mnt/hf-cache
-                  type: DirectoryOrCreate
@@ -1,306 +0,0 @@
-steps:
-  - label: "Simple Unit Test"
-    depends_on: upload-ready-pipeline
-    commands:
-      - "timeout 20m pytest -v -s -m 'core_model and cpu' --cov=vllm_omni --cov-branch --cov-report=term-missing --cov-report=html --cov-report=xml"
-    agents:
-      queue: "gpu_1_queue"
-    plugins:
-      - docker#v5.2.0:
-          image: public.ecr.aws/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
-          always-pull: true
-          propagate-environment: true
-          environment:
-            - "HF_HOME=/fsx/hf_cache"
-          volumes:
-            - "/fsx/hf_cache:/fsx/hf_cache"
-
-  - label: "Diffusion Model Test"
-    depends_on: upload-ready-pipeline
-    commands:
-      - timeout 20m pytest -s -v tests/e2e/offline_inference/test_t2i_model.py -m "core_model and diffusion" --run-level "core_model"
-    agents:
-      queue: "gpu_1_queue"
-    plugins:
-      - docker#v5.2.0:
-          image: public.ecr.aws/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
-          always-pull: true
-          propagate-environment: true
-          environment:
-            - "HF_HOME=/fsx/hf_cache"
-          volumes:
-            - "/fsx/hf_cache:/fsx/hf_cache"
-
-  - label: "Diffusion Model CPU offloading Test"
-    depends_on: upload-ready-pipeline
-    commands:
-      - |
-        timeout 20m bash -c '
-          set +e
-          pytest -s -v tests/e2e/offline_inference/test_diffusion_cpu_offload.py
-          EXIT1=$$?
-          pytest -s -v tests/e2e/offline_inference/test_diffusion_layerwise_offload.py
-          EXIT2=$$?
-          exit $$((EXIT1 | EXIT2))
-        '
-    agents:
-      queue: "gpu_1_queue" # g6.4xlarge instance on AWS, has 1 L4 GPU
-    plugins:
-      - docker#v5.2.0:
-          image: public.ecr.aws/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
-          always-pull: true
-          propagate-environment: true
-          environment:
-            - "HF_HOME=/fsx/hf_cache"
-          volumes:
-            - "/fsx/hf_cache:/fsx/hf_cache"
-
-  - label: "Audio Generation Model Test"
-    depends_on: upload-ready-pipeline
-    commands:
-      - timeout 20m pytest -s -v tests/e2e/offline_inference/test_stable_audio_model.py
-    agents:
-      queue: "gpu_1_queue" # g6.4xlarge instance on AWS, has 1 L4 GPU
-    plugins:
-      - docker#v5.2.0:
-          image: public.ecr.aws/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
-          always-pull: true
-          propagate-environment: true
-          environment:
-            - "HF_HOME=/fsx/hf_cache"
-          volumes:
-            - "/fsx/hf_cache:/fsx/hf_cache"
-
-  - label: "Diffusion Cache Backend Test"
-    depends_on: upload-ready-pipeline
-    commands:
-      - timeout 15m pytest -s -v -m 'core_model and cache and diffusion and not distributed_cuda and L4'
-    agents:
-      queue: "gpu_1_queue" # g6.4xlarge instance on AWS, has 1 L4 GPU
-    plugins:
-      - docker#v5.2.0:
-          image: public.ecr.aws/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
-          always-pull: true
-          propagate-environment: true
-          environment:
-            - "HF_HOME=/fsx/hf_cache"
-          volumes:
-            - "/fsx/hf_cache:/fsx/hf_cache"
-
-  - label: "Diffusion Sequence Parallelism Test"
-    depends_on: upload-ready-pipeline
-    commands:
-      - timeout 20m pytest -s -v tests/e2e/offline_inference/test_sequence_parallel.py -m core_model
-    agents:
-      queue: "gpu_4_queue" # g6.12xlarge instance on AWS, has 4 L4 GPU
-    plugins:
-      - docker#v5.2.0:
-          image: public.ecr.aws/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
-          always-pull: true
-          propagate-environment: true
-          shm-size: "8gb"
-          environment:
-            - "HF_HOME=/fsx/hf_cache"
-          volumes:
-            - "/fsx/hf_cache:/fsx/hf_cache"
-
-  - label: "Diffusion GPU Worker Test"
-    depends_on: upload-ready-pipeline
-    commands:
-      - timeout 20m pytest -s -v tests/diffusion/test_diffusion_worker.py
-    agents:
-      queue: "gpu_4_queue" # g6.12xlarge instance on AWS, has 4 L4 GPU
-    plugins:
-      - docker#v5.2.0:
-          image: public.ecr.aws/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
-          always-pull: true
-          propagate-environment: true
-          shm-size: "8gb"
-          environment:
-            - "HF_HOME=/fsx/hf_cache"
-          volumes:
-            - "/fsx/hf_cache:/fsx/hf_cache"
-
-
-  - label: "Benchmark & Engine Test"
-    depends_on: upload-ready-pipeline
-    commands:
-      - |
-        timeout 15m bash -c '
-                export VLLM_WORKER_MULTIPROC_METHOD=spawn
-                set +e
-                pytest -s -v tests/benchmarks/test_serve_cli.py
-                EXIT1=$$?
-                pytest -s -v tests/engine/test_async_omni_engine_abort.py
-                EXIT2=$$?
-                exit $$((EXIT1 | EXIT2))
-        '
-    agents:
-      queue: "gpu_4_queue" # g6.12xlarge instance on AWS, has 4 L4 GPU
-    plugins:
-      - docker#v5.2.0:
-          image: public.ecr.aws/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
-          always-pull: true
-          propagate-environment: true
-          shm-size: "8gb"
-          environment:
-            - "HF_HOME=/fsx/hf_cache"
-          volumes:
-            - "/fsx/hf_cache:/fsx/hf_cache"
-
-
-  - label: "Omni Model Test"
-    depends_on: upload-ready-pipeline
-    commands:
-      - |
-        timeout 17m bash -c '
-          export VLLM_LOGGING_LEVEL=DEBUG
-          export VLLM_WORKER_MULTIPROC_METHOD=spawn
-          pytest -s -v tests/e2e/offline_inference/test_qwen2_5_omni.py
-        '
-    agents:
-      queue: "gpu_4_queue" # g6.12xlarge instance on AWS, has 4 L4 GPU
-    plugins:
-      - docker#v5.2.0:
-          image: public.ecr.aws/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
-          always-pull: true
-          propagate-environment: true
-          environment:
-            - "HF_HOME=/fsx/hf_cache"
-          volumes:
-            - "/fsx/hf_cache:/fsx/hf_cache"
-
-  # - label: "Omni Model Test with H100"
-  #   depends_on: upload-ready-pipeline
-  #   commands:
-  #     - |
-  #       timeout 20m bash -c '
-  #         export VLLM_WORKER_MULTIPROC_METHOD=spawn
-  #         export VLLM_TEST_CLEAN_GPU_MEMORY="1"
-  #         # - pytest -s -v tests/e2e/offline_inference/test_qwen3_omni.py
-  #         pytest -s -v tests/e2e/online_serving/test_qwen3_omni.py -m "core_model" --run-level "core_model"
-  #       '
-  #   agents:
-  #     queue: "mithril-h100-pool"
-  #   plugins:
-  #     - kubernetes:
-  #         podSpec:
-  #           containers:
-  #             - image: 936637512419.dkr.ecr.us-west-2.amazonaws.com/vllm-ci-pull-through-cache/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
-  #               resources:
-  #                 limits:
-  #                   nvidia.com/gpu: 2
-  #               volumeMounts:
-  #                 - name: devshm
-  #                   mountPath: /dev/shm
-  #                 - name: hf-cache
-  #                   mountPath: /root/.cache/huggingface
-  #               env:
-  #                 - name: HF_HOME
-  #                   value: /root/.cache/huggingface
-  #           nodeSelector:
-  #             node.kubernetes.io/instance-type: gpu-h100-sxm
-  #           volumes:
-  #             - name: devshm
-  #               emptyDir:
-  #                 medium: Memory
-  #             - name: hf-cache
-  #               hostPath:
-  #                 path: /mnt/hf-cache
-  #                 type: DirectoryOrCreate
-
-  - label: "Qwen3-TTS E2E Test"
-    depends_on: upload-ready-pipeline
-    commands:
-      - |
-        timeout 20m bash -c '
-          export VLLM_LOGGING_LEVEL=DEBUG
-          export VLLM_WORKER_MULTIPROC_METHOD=spawn
-          pytest -s -v tests/e2e/online_serving/test_qwen3_tts.py
-        '
-    agents:
-      queue: "gpu_4_queue"
-    plugins:
-      - docker#v5.2.0:
-          image: public.ecr.aws/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
-          always-pull: true
-          propagate-environment: true
-          shm-size: "8gb"
-          environment:
-            - "HF_HOME=/fsx/hf_cache"
-          volumes:
-            - "/fsx/hf_cache:/fsx/hf_cache"
-
-  # - label: "Diffusion Image Edit Test with H100 (1 GPU)"
-  #   depends_on: upload-ready-pipeline
-  #   commands:
-  #     - |
-  #       timeout 20m bash -c '
-  #         export VLLM_WORKER_MULTIPROC_METHOD=spawn
-  #         pytest -s -v tests/e2e/online_serving/test_image_gen_edit.py
-  #       '
-  #   agents:
-  #     queue: "mithril-h100-pool"
-  #   plugins:
-  #     - kubernetes:
-  #         podSpec:
-  #           containers:
-  #             - image: 936637512419.dkr.ecr.us-west-2.amazonaws.com/vllm-ci-pull-through-cache/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
-  #               resources:
-  #                 limits:
-  #                   nvidia.com/gpu: 1
-  #               volumeMounts:
-  #                 - name: devshm
-  #                   mountPath: /dev/shm
-  #                 - name: hf-cache
-  #                   mountPath: /root/.cache/huggingface
-  #               env:
-  #                 - name: HF_HOME
-  #                   value: /root/.cache/huggingface
-  #           nodeSelector:
-  #             node.kubernetes.io/instance-type: gpu-h100-sxm
-  #           volumes:
-  #             - name: devshm
-  #               emptyDir:
-  #                 medium: Memory
-  #             - name: hf-cache
-  #               hostPath:
-  #                 path: /mnt/hf-cache
-  #                 type: DirectoryOrCreate
-
-  # - label: "Bagel Text2Img Model Test with H100"
-  #   depends_on: upload-ready-pipeline
-  #   commands:
-  #     - |
-  #       timeout 30m bash -c '
-  #         export VLLM_WORKER_MULTIPROC_METHOD=spawn
-  #         pytest -s -v tests/e2e/offline_inference/test_bagel_text2img.py
-  #       '
-  #   agents:
-  #     queue: "mithril-h100-pool"
-  #   plugins:
-  #     - kubernetes:
-  #         podSpec:
-  #           containers:
-  #             - image: 936637512419.dkr.ecr.us-west-2.amazonaws.com/vllm-ci-pull-through-cache/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
-  #               resources:
-  #                 limits:
-  #                   nvidia.com/gpu: 1
-  #               volumeMounts:
-  #                 - name: devshm
-  #                   mountPath: /dev/shm
-  #                 - name: hf-cache
-  #                   mountPath: /root/.cache/huggingface
-  #               env:
-  #                 - name: HF_HOME
-  #                   value: /root/.cache/huggingface
-  #           nodeSelector:
-  #             node.kubernetes.io/instance-type: gpu-h100-sxm
-  #           volumes:
-  #             - name: devshm
-  #               emptyDir:
-  #                 medium: Memory
-  #             - name: hf-cache
-  #               hostPath:
-  #                 path: /mnt/hf-cache
-  #                 type: DirectoryOrCreate
@@ -14,7 +14,7 @@
        soft_fail: false
        commands:
          - "source .buildkite/scripts/docker_login_ecr_public.sh && safe_docker_login_ecr_public"
-          - "docker build -f docker/Dockerfile.rocm -t {{ docker_image_amd }} --target test --build-arg ARG_PYTORCH_ROCM_ARCH=gfx942 --progress plain ."
+          - "docker build -f docker/Dockerfile.rocm -t {{ docker_image_amd }} --target final --progress plain ."
          - "docker push {{ docker_image_amd }}"
        key: "amd-build"
        env:
@@ -12,10 +12,10 @@ PLEASE FILL IN THE PR DESCRIPTION HERE ENSURING ALL CHECKLIST ITEMS (AT THE BOTT
 <summary> Essential Elements of an Effective PR Description Checklist </summary>

 - [ ] The purpose of the PR, such as "Fix some issue (link existing issues this PR will resolve)".
- [ ] The test plan. Please provide the test scripts & test commands. Please state the reasons if your codes don't require additional test scripts. For test file guidelines, please check the [test style doc](https://docs.vllm.ai/projects/vllm-omni/en/latest/contributing/ci/tests_style/)
- [ ] The test results. Please paste the results comparison before and after, or the e2e results.
- [ ] (Optional) The necessary documentation update, such as updating `supported_models.md` and `examples` for a new model. **Please run `mkdocs serve` to sync the documentation editions to `./docs`.**
- [ ] (Optional) Release notes update. If your change is user-facing, please update the release notes draft.
+- [ ] The test plan, such as providing test command.
+- [ ] The test results, such as pasting the results comparison before and after, or e2e results
+- [ ] (Optional) The necessary documentation update, such as updating `supported_models.md` and `examples` for a new model.
+- [ ] (Optional) Release notes update. If your change is user facing, please update the release notes draft.
 </details>

 **BEFORE SUBMITTING, PLEASE READ <https://github.com/vllm-project/vllm-omni/blob/main/CONTRIBUTING.md>** (anything written below this line will be removed by GitHub Actions)
@@ -1,629 +0,0 @@
-#!/usr/bin/env python3
-"""
-PR Reviewer using GLM API for vllm-omni project.
-"""
-
-import json
-import logging
-import os
-import sys
-import time
-from dataclasses import dataclass
-from typing import Any, TypedDict
-
-import requests
-
-
-# Type definitions for API responses
-class PRDetails(TypedDict):
-    """Type definition for GitHub PR details response."""
-
-    title: str
-    body: str
-    number: int
-    state: str
-    user: dict[str, Any]
-
-
-class GLMMessage(TypedDict):
-    """Type definition for GLM API message."""
-
-    role: str
-    content: str
-
-
-class GLMChoice(TypedDict):
-    """Type definition for GLM API choice."""
-
-    message: GLMMessage
-    finish_reason: str
-
-
-class GLMResponse(TypedDict):
-    """Type definition for GLM API response."""
-
-    choices: list[GLMChoice]
-    usage: dict[str, int] | None
-
-
-class GitHubComment(TypedDict):
-    """Type definition for GitHub comment."""
-
-    id: int
-    body: str
-    created_at: str
-    user: dict[str, Any]
-
-
-# Configuration
-TRIGGER_PHRASE: str = "@vllm-omni-reviewer"
-DEFAULT_GLM_API_URL: str = "https://open.bigmodel.cn/api/paas/v4/chat/completions"  # noqa: E501
-DEFAULT_GLM_MODEL: str = "glm-5"
-DEFAULT_COOLDOWN_MINUTES: int = 5
-DEFAULT_MAX_RETRIES: int = 3
-DEFAULT_RETRY_DELAY: float = 1.0
-MAX_DIFF_SIZE: int = 100_000  # Maximum diff size in characters
-
-
-@dataclass
-class Config:
-    """Configuration for the PR reviewer."""
-
-    glm_api_url: str
-    glm_model: str
-    cooldown_minutes: int
-    max_retries: int
-    retry_delay: float
-    max_diff_size: int
-
-
-# Setup logging
-logging.basicConfig(
-    level=logging.INFO,
-    format="[PR Reviewer] %(asctime)s - %(levelname)s - %(message)s",
-    datefmt="%Y-%m-%d %H:%M:%S",
-)
-logger: logging.Logger = logging.getLogger(__name__)
-
-
-def get_config() -> Config:
-    """Load configuration from environment variables with defaults."""
-    return Config(
-        glm_api_url=os.getenv("GLM_API_URL", DEFAULT_GLM_API_URL),
-        glm_model=os.getenv("GLM_MODEL", DEFAULT_GLM_MODEL),
-        cooldown_minutes=int(
-            os.getenv(
-                "PR_REVIEWER_COOLDOWN_MINUTES",
-                str(DEFAULT_COOLDOWN_MINUTES),
-            )
-        ),
-        max_retries=int(
-            os.getenv(
-                "PR_REVIEWER_MAX_RETRIES",
-                str(DEFAULT_MAX_RETRIES),
-            )
-        ),
-        retry_delay=float(os.getenv("PR_REVIEWER_RETRY_DELAY", str(DEFAULT_RETRY_DELAY))),
-        max_diff_size=int(os.getenv("PR_REVIEWER_MAX_DIFF_SIZE", str(MAX_DIFF_SIZE))),  # noqa: E501
-    )
-
-
-def get_env_var(name: str) -> str:
-    """
-    Get an environment variable or raise an error.
-
-    Args:
-        name: Name of the environment variable.
-
-    Returns:
-        The value of the environment variable.
-
-    Raises:
-        SystemExit: If the environment variable is not set.
-    """
-    value = os.environ.get(name)
-    if not value:
-        logger.error(f"Environment variable {name} is not set")
-        sys.exit(1)
-    return value
-
-
-def check_trigger(comment_body: str) -> bool:
-    """
-    Check if the comment contains the trigger phrase.
-
-    Args:
-        comment_body: The body of the comment to check.
-
-    Returns:
-        True if the trigger phrase is found, False otherwise.
-    """
-    return TRIGGER_PHRASE in comment_body
-
-
-def fetch_pr_diff(
-    repo_name: str,
-    pr_number: int,
-    token: str,
-    max_size: int = MAX_DIFF_SIZE,
-) -> str | None:
-    """
-    Fetch the diff for a pull request.
-
-    Args:
-        repo_name: The repository name in format "owner/repo".
-        pr_number: The pull request number.
-        token: GitHub authentication token.
-        max_size: Maximum diff size in characters.
-
-    Returns:
-        The diff content as a string, or None if fetching failed.
-        Returns empty string if diff is larger than max_size.
-    """
-    url: str = f"https://api.github.com/repos/{repo_name}/pulls/{pr_number}"
-    headers: dict[str, str] = {
-        "Authorization": f"Bearer {token}",
-        "Accept": "application/vnd.github.v3.diff",
-    }
-
-    logger.info(f"Fetching PR diff from {url}")
-    response = requests.get(url, headers=headers, timeout=30)
-
-    if response.status_code == 200:
-        diff: str = response.text
-        if len(diff) > max_size:
-            logger.warning(
-                f"Diff size ({len(diff)} bytes) exceeds maximum "
-                f"({max_size} bytes), truncating to first "
-                f"{max_size} bytes"
-            )
-            return diff[:max_size] + "\n\n... [Diff truncated due to size] ..."
-        logger.info(f"Successfully fetched diff ({len(diff)} bytes)")
-        return diff
-    else:
-        logger.error(f"Failed to fetch PR diff: {response.status_code}")
-        logger.error(f"Response: {response.text}")
-        return None
-
-
-def fetch_pr_details(
-    repo_name: str,
-    pr_number: int,
-    token: str,
-) -> PRDetails | None:
-    """
-    Fetch PR details including title and description.
-
-    Args:
-        repo_name: The repository name in format "owner/repo".
-        pr_number: The pull request number.
-        token: GitHub authentication token.
-
-    Returns:
-        A dictionary containing PR details, or None if fetching failed.
-    """
-    url: str = f"https://api.github.com/repos/{repo_name}/pulls/{pr_number}"
-    headers: dict[str, str] = {
-        "Authorization": f"Bearer {token}",
-        "Accept": "application/vnd.github.v3+json",
-    }
-
-    logger.info(f"Fetching PR details from {url}")
-    response = requests.get(url, headers=headers, timeout=30)
-
-    if response.status_code == 200:
-        return response.json()
-    else:
-        logger.error(f"Failed to fetch PR details: {response.status_code}")
-        return None
-
-
-def build_review_prompt(pr_title: str, pr_description: str, diff: str) -> str:
-    """
-    Build the prompt for the GLM-4.7 API.
-
-    Args:
-        pr_title: The title of the pull request.
-        pr_description: The description/body of the pull request.
-        diff: The diff content of the pull request.
-
-    Returns:
-        The formatted prompt string for the API.
-    """
-    return f"""You are an expert code reviewer for the VLLM-Omni project. \
-Please review the following pull request:
-
-## Pull Request Details
-**Title:** {pr_title}
-
-**Description:**
-{pr_description if pr_description else "No description provided."}
-
-## Code Changes (Diff)
-{diff}
-
-## Review Guidelines
-
-Please provide a comprehensive code review with the following sections:
-
-### 1. Overview
- Brief summary of the changes
- Overall assessment (positive, neutral, or concerns)
-
-### 2. Code Quality
- Code style and consistency
- Potential bugs or edge cases
- Performance considerations
- Error handling
-
-### 3. Architecture & Design
- Integration with existing codebase
- Design patterns and best practices
- Potential improvements
-
-### 4. Security & Safety
- Security concerns (if any)
- Resource management
- Input validation
-
-### 5. Testing & Documentation
- Test coverage considerations
- Documentation completeness
- Examples and usage clarity
-
-### 6. Specific Suggestions
- Line-by-line specific feedback (use `file:line` format)
- Concrete actionable suggestions
- Code examples for improvements (if applicable)
-
-### 7. Approval Status
- **LGTM** (Looks Good To Me) if the PR is ready to merge
- **LGTM with suggestions** if the PR is good but has minor suggestions
- **Changes requested** if significant changes are needed
-
-## Important Notes
- Be constructive and helpful
- Focus on objective technical feedback
- Acknowledge good practices when you see them
- Prioritize critical issues over nitpicks
- If the diff is empty or minimal, acknowledge this and provide
-  any relevant context-specific guidance
-
-Please format your response in Markdown with clear section headers.
-"""
-
-
-def validate_glm_response(data: dict[str, Any]) -> str | None:
-    """
-    Validate and extract content from GLM API response.
-
-    Args:
-        data: The response data from GLM API.
-
-    Returns:
-        The review content string if valid, None otherwise.
-    """
-    # Check if choices exists and is a non-empty list
-    if "choices" not in data:
-        logger.error("GLM API response missing 'choices' field")
-        logger.error(f"Response structure: {json.dumps(data, indent=2)}")
-        return None
-
-    choices = data["choices"]
-    if not isinstance(choices, list):
-        logger.error(f"GLM API 'choices' is not a list: {type(choices)}")
-        return None
-
-    if len(choices) == 0:
-        logger.error("GLM API 'choices' is an empty list")
-        return None
-
-    # Check if first choice has message
-    try:
-        first_choice = choices[0]
-        if not isinstance(first_choice, dict):
-            logger.error(f"GLM API choice is not a dict: {type(first_choice)}")
-            return None
-
-        if "message" not in first_choice:
-            logger.error("GLM API choice missing 'message' field")
-            logger.error(f"Choice structure: {json.dumps(first_choice, indent=2)}")  # noqa: E501
-            return None
-
-        message = first_choice["message"]
-        if not isinstance(message, dict):
-            logger.error(f"GLM API message is not a dict: {type(message)}")
-            return None
-
-        if "content" not in message:
-            logger.error("GLM API message missing 'content' field")
-            logger.error(f"Message structure: {json.dumps(message, indent=2)}")
-            return None
-
-        content = message["content"]
-        if not isinstance(content, str):
-            logger.error(f"GLM API content is not a string: {type(content)}")
-            return None
-
-        return content
-
-    except (KeyError, IndexError, TypeError) as e:
-        logger.error(f"Failed to parse GLM API response: {e}")
-        logger.error(f"Response: {json.dumps(data, indent=2)}")
-        return None
-
-
-def call_glm_api(prompt: str, api_key: str, config: Config) -> str | None:
-    """
-    Call the GLM-4.7 API to get code review with retry logic.
-
-    Args:
-        prompt: The prompt to send to the API.
-        api_key: The GLM API key.
-        config: Configuration object.
-
-    Returns:
-        The review content as a string, or None if all retries failed.
-    """
-    headers: dict[str, str] = {
-        "Authorization": f"Bearer {api_key}",
-        "Content-Type": "application/json",
-    }
-
-    payload: dict[str, Any] = {
-        "model": config.glm_model,
-        "messages": [{"role": "user", "content": prompt}],
-        "temperature": 0.3,
-        "max_tokens": 32000,
-        "top_p": 0.9,
-    }
-
-    last_error: str | None = None
-
-    for attempt in range(config.max_retries):
-        try:
-            logger.info(f"Calling GLM API ({config.glm_model}) - Attempt {attempt + 1}/{config.max_retries}")
-            response = requests.post(
-                config.glm_api_url,
-                headers=headers,
-                json=payload,
-                timeout=120,
-            )
-
-            if response.status_code == 200:
-                data = response.json()
-                review = validate_glm_response(data)
-                if review:
-                    logger.info(f"Successfully received review ({len(review)} chars)")  # noqa: E501
-                    return review
-                else:
-                    last_error = "Failed to validate API response structure"
-                    logger.error(last_error)
-            else:
-                last_error = f"GLM API request failed: {response.status_code} - {response.text}"
-                logger.error(last_error)
-
-        except requests.exceptions.Timeout:
-            last_error = f"GLM API request timed out (attempt {attempt + 1})"
-            logger.error(last_error)
-        except requests.exceptions.RequestException as e:
-            last_error = f"GLM API request exception: {e}"
-            logger.error(last_error)
-        except json.JSONDecodeError as e:
-            last_error = f"Failed to decode GLM API response as JSON: {e}"
-            logger.error(last_error)
-
-        # Exponential backoff before retry
-        if attempt < config.max_retries - 1:
-            wait_time: float = config.retry_delay * (2**attempt)
-            logger.info(f"Waiting {wait_time}s before retry...")  # noqa: E501
-            time.sleep(wait_time)
-
-    logger.error(
-        f"All {config.max_retries} attempts failed. Last error: {last_error}"  # noqa: E501
-    )
-    return None
-
-
-def check_cooldown(  # noqa: E501
-    repo_name: str,
-    pr_number: int,
-    token: str,
-    cooldown_minutes: int,
-) -> bool:
-    """
-    Check if the PR is within the cooldown period.
-
-    Args:
-        repo_name: The repository name in format "owner/repo".
-        pr_number: The pull request number.
-        token: GitHub authentication token.
-        cooldown_minutes: Cooldown period in minutes.
-
-    Returns:
-        True if within cooldown period (should skip), False otherwise.
-    """
-    from datetime import datetime, timedelta
-
-    url: str = (
-        f"https://api.github.com/repos/{repo_name}/issues/"
-        f"{pr_number}/comments"  # noqa: E501
-    )
-    headers: dict[str, str] = {
-        "Authorization": f"Bearer {token}",
-        "Accept": "application/vnd.github.v3+json",
-    }
-
-    logger.info(f"Checking cooldown period ({cooldown_minutes} minutes)")
-    response = requests.get(url, headers=headers, timeout=30)
-
-    if response.status_code != 200:
-        logger.warning(f"Failed to check cooldown: {response.status_code}, proceeding with review")
-        return False
-
-    comments: list[dict[str, Any]] = response.json()
-    cutoff_time: datetime = datetime.utcnow() - timedelta(minutes=cooldown_minutes)  # noqa: E501
-
-    for comment in reversed(comments):
-        # Check if this is a bot comment
-        body: str = comment.get("body", "")
-        if "VLLM-Omni PR Review" in body or "PR Reviewer Bot" in body:
-            created_at_str: str = comment.get("created_at", "")
-            try:
-                # Parse GitHub timestamp format
-                created_at = datetime.fromisoformat(created_at_str.replace("Z", "+00:00"))
-                created_at = created_at.replace(tzinfo=None)
-                if created_at > cutoff_time:
-                    logger.info(f"PR is within cooldown period (last review: {created_at_str})")
-                    return True
-            except ValueError:
-                logger.warning(f"Failed to parse comment timestamp: {created_at_str}")  # noqa: E501
-                continue
-
-    logger.info("PR is outside cooldown period, proceeding with review")
-    return False
-
-
-def post_review_comment(  # noqa: E501
-    repo_name: str,
-    pr_number: int,
-    token: str,
-    review: str,
-) -> bool:
-    """
-    Post the review as a comment on the PR.
-
-    Args:
-        repo_name: The repository name in format "owner/repo".
-        pr_number: The pull request number.
-        token: GitHub authentication token.
-        review: The review content to post.
-
-    Returns:
-        True if posting succeeded, False otherwise.
-    """
-    url: str = (
-        f"https://api.github.com/repos/{repo_name}/issues/"
-        f"{pr_number}/comments"  # noqa: E501
-    )
-    headers: dict[str, str] = {
-        "Authorization": f"Bearer {token}",
-        "Accept": "application/vnd.github.v3+json",
-    }
-
-    # Format the review comment
-    comment_body: str = f"""## 🤖 VLLM-Omni PR Review
-
-{review}
-
---
-*This review was generated automatically by the VLLM-Omni PR Reviewer Bot
-using {os.getenv("GLM_MODEL", DEFAULT_GLM_MODEL)}.*
-"""
-
-    payload: dict[str, str] = {"body": comment_body}
-
-    logger.info(f"Posting review comment to PR #{pr_number}")
-    response = requests.post(url, headers=headers, json=payload, timeout=30)
-
-    if response.status_code == 201:
-        logger.info("Successfully posted review comment")
-        return True
-    else:
-        logger.error(f"Failed to post comment: {response.status_code}")
-        logger.error(f"Response: {response.text}")
-        return False
-
-
-def main() -> int:
-    """
-    Main entry point for the PR reviewer bot.
-
-    Returns:
-        0 on success, 1 on error.
-    """
-    logger.info("VLLM-Omni PR Reviewer Bot starting...")
-
-    # Load configuration
-    config: Config = get_config()
-    logger.info(
-        f"Configuration: model={config.glm_model}, "
-        f"cooldown={config.cooldown_minutes}min, "
-        f"max_retries={config.max_retries}"
-    )
-
-    # Get environment variables
-    token: str = get_env_var("GITHUB_TOKEN")
-    api_key: str = get_env_var("GLM_API_KEY")
-    repo_name: str = get_env_var("REPO_NAME")
-    pr_number_str: str = get_env_var("PR_NUMBER")
-    comment_body: str = get_env_var("COMMENT_BODY")
-
-    try:
-        pr_number: int = int(pr_number_str)
-    except ValueError:
-        logger.error(f"Invalid PR number: {pr_number_str}")
-        return 1
-
-    logger.info(f"Repository: {repo_name}")
-    logger.info(f"PR Number: {pr_number}")
-
-    # Check if the comment contains the trigger phrase
-    if not check_trigger(comment_body):
-        logger.info(
-            f"Comment does not contain trigger phrase '{TRIGGER_PHRASE}', exiting"  # noqa: E501
-        )
-        return 0
-
-    logger.info("Trigger phrase detected! Starting review process...")
-
-    # Check cooldown period
-    if check_cooldown(repo_name, pr_number, token, config.cooldown_minutes):
-        logger.info("Skipping review due to cooldown period")
-        return 0
-
-    # Fetch PR details
-    logger.info("Step 1/4: Fetching PR details...")
-    pr_details: PRDetails | None = fetch_pr_details(repo_name, pr_number, token)  # noqa: E501
-    if not pr_details:
-        logger.error("Failed to fetch PR details")
-        return 1
-
-    pr_title: str = pr_details.get("title", "Unknown")
-    pr_description: str = pr_details.get("body", "")
-
-    logger.info(f"PR Title: {pr_title}")
-
-    # Fetch PR diff
-    logger.info("Step 2/4: Fetching PR diff...")
-    diff: str | None = fetch_pr_diff(repo_name, pr_number, token, config.max_diff_size)
-    if diff is None:
-        logger.error("Failed to fetch PR diff")
-        return 1
-
-    if not diff:
-        logger.warning("Warning: Empty diff - this might be a draft PR or no code changes")
-
-    # Build prompt
-    logger.info("Step 3/4: Building review prompt...")
-    prompt: str = build_review_prompt(pr_title, pr_description, diff)
-
-    # Call GLM API
-    logger.info("Step 4/4: Calling GLM API...")
-    review: str | None = call_glm_api(prompt, api_key, config)
-    if not review:
-        logger.error("Failed to get review from GLM API")
-        return 1
-
-    # Post review comment
-    logger.info("Posting review comment...")
-    if not post_review_comment(repo_name, pr_number, token, review):
-        logger.error("Failed to post review comment")
-        return 1
-
-    logger.info("PR review completed successfully!")
-    return 0
-
-
-if __name__ == "__main__":
-    sys.exit(main())
@@ -7,7 +7,6 @@ on:
    paths-ignore:
      - 'docs/**'
      - 'examples/**'
-      - 'apps/**'
      - '.github/**'
      - '.buildkite/**'
      - '**.md'
@@ -21,7 +20,6 @@ on:
    paths-ignore:
      - 'docs/**'
      - 'examples/**'
-      - 'apps/**'
      - '.github/**'
      - '.buildkite/**'
      - '*.yml'
@@ -1,62 +0,0 @@
-name: VLLM-Omni PR Reviewer
-
-on:
-  issue_comment:
-    types: [created]
-
-permissions:
-  contents: read
-  pull-requests: write
-  issues: write
-
-jobs:
-  pr-reviewer:
-    name: Review Pull Request
-    runs-on: ubuntu-latest
-    timeout-minutes: 10
-    # Only run when the comment is from a collaborator/owner/member
-    if: |
-      github.event_name == 'issue_comment' &&
-      github.event.issue.pull_request != null &&
-      contains(github.event.comment.body, '@vllm-omni-reviewer') &&
-      (github.event.comment.author_association == 'MEMBER' ||
-       github.event.comment.author_association == 'COLLABORATOR' ||
-       github.event.comment.author_association == 'OWNER')
-    steps:
-      - name: Checkout repository
-        uses: actions/checkout@v4.2.2
-        with:
-          fetch-depth: 0
-
-      - name: Set up Python
-        uses: actions/setup-python@v5.3.0
-        with:
-          python-version: '3.11'
-          cache: 'pip'
-
-      - name: Install dependencies
-        run: |
-          python -m pip install --upgrade pip==24.0
-          pip install requests==2.31.0 pyyaml==6.0.1
-
-      - name: Run PR Reviewer
-        id: reviewer
-        env:
-          GLM_API_KEY: ${{ secrets.GLM_API_KEY }}
-          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
-          PR_NUMBER: ${{ github.event.issue.number || github.event.pull_request.number }}
-          COMMENT_BODY: ${{ github.event.comment.body }}
-          REPO_NAME: ${{ github.repository }}
-          PR_REVIEWER_COOLDOWN_MINUTES: 5
-          PR_REVIEWER_MAX_RETRIES: 3
-        run: |
-          python .github/scripts/pr_reviewer.py 2>&1 | tee "/tmp/pr_review_${PR_NUMBER}.log"
-
-      - name: Upload review logs
-        if: always()
-        uses: actions/upload-artifact@v4.4.3
-        with:
-          name: pr-review-logs-${{ github.event.issue.number || github.event.pull_request.number }}
-          path: /tmp/pr_review_*.log
-          retention-days: 7
-          if-no-files-found: ignore
@@ -153,14 +153,6 @@ dmypy.json
 # Cython debug symbols
 cython_debug/

-# Claude
-CLAUDE.md
-.claude/
-
-# Codex
-AGENTS.md
-.codex/
-
 # cursor
 .cursor/

@@ -222,7 +214,6 @@ data/
 datasets/
 *.csv
 *.json
-!apps/ComfyUI-vLLM-Omni/example_workflows/*.json
 *.jsonl
 *.parquet

@@ -242,8 +233,3 @@ configs/development.yaml
 Dockerfile.dev
 discussion
 tmp_test
-
-# Auto-generated version file (created by setuptools_scm during build)
-vllm_omni/_version.py
-# output files
-*.wav
@@ -29,10 +29,7 @@ repos:
        # only for staged files

  - repo: https://github.com/rhysd/actionlint
-    # v1.7.8+ sets `go 1.24.0` in go.mod, which older Go toolchains (and most
-    # current CI images) cannot parse. Pin to v1.7.7 until actionlint fixes the
-    # go.mod directive.
-    rev: v1.7.7
+    rev: v1.7.9
    hooks:
      - id: actionlint
        files: ^\.github/workflows/.*\.ya?ml$
@@ -9,16 +9,13 @@ Easy, fast, and cheap omni-modality model serving for everyone
 </h3>

 <p align="center">
-| <a href="https://vllm-omni.readthedocs.io/en/latest/"><b>Documentation</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/1qv4qMW1rKAqDREMXiUDLIgqqHQe7TDPj/edit?usp=sharing&ouid=110473603432222024453&rtpof=true&sd=true"><b>Slides</b></a> |
+| <a href="https://vllm-omni.readthedocs.io/en/latest/"><b>Documentation</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> |
 </p>

-
 ---

 *Latest News* 🔥
-
- [2026/02] We released [0.14.0](https://github.com/vllm-project/vllm-omni/releases/tag/v0.14.0) - This is the first **stable release** of vLLM-Omni that expands Omni’s diffusion / image-video generation and audio / TTS stack, improves distributed execution and memory efficiency, and broadens platform/backend coverage (GPU/ROCm/NPU/XPU). It also brings meaningful upgrades to serving APIs, profiling & benchmarking, and overall stability. Please check our latest [paper](https://arxiv.org/abs/2602.02204) for architecture design and performance results.
- [2026/01] We released [0.12.0rc1](https://github.com/vllm-project/vllm-omni/releases/tag/v0.12.0rc1) - a major RC milestone focused on maturing the diffusion stack, strengthening OpenAI-compatible serving, expanding omni-model coverage, and improving stability across platforms (GPU/NPU/ROCm).
+- [2026/01] We released [0.12.0rc1](https://github.com/vllm-project/vllm-omni/releases/tag/v0.12.0rc1) - a major RC milestone focused on maturing the diffusion stack, strengthening OpenAI-compatible serving, expanding omni-model coverage, and improving stability across platforms (GPU/NPU/ROCm), please check our latest [design](https://docs.google.com/presentation/d/1qv4qMW1rKAqDREMXiUDLIgqqHQe7TDPj/edit?usp=sharing&ouid=110473603432222024453&rtpof=true&sd=true).
 - [2025/11] vLLM community officially released [vllm-project/vllm-omni](https://github.com/vllm-project/vllm-omni) in order to support omni-modality models serving.

 ---
@@ -69,19 +66,6 @@ Visit our [documentation](https://vllm-omni.readthedocs.io/en/latest/) to learn
 We welcome and value any contributions and collaborations.
 Please check out [Contributing to vLLM-Omni](https://vllm-omni.readthedocs.io/en/latest/contributing/) for how to get involved.

-## Citation
-
-If you use vLLM-Omni for your research, please cite our [paper](https://arxiv.org/abs/2602.02204):
-
-```bibtex
-@article{yin2026vllmomni,
-  title={vLLM-Omni: Fully Disaggregated Serving for Any-to-Any Multimodal Models},
-  author={Peiqi Yin, Jiangyun Zhu, Han Gao, Chenguang Zheng, Yongxiang Huang, Taichang Zhou, Ruirui Yang, Weizhi Liu, Weiqing Chen, Canlin Guo, Didan Deng, Zifeng Mo, Cong Wang, James Cheng, Roger Wang, Hongsheng Liu},
-  journal={arXiv preprint arXiv:2602.02204},
-  year={2026}
-}
-```
-
 ## 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).

@@ -1,115 +0,0 @@
-# Byte-compiled / optimized / DLL files
-__pycache__/
-*.py[cod]
-*$py.class
-
-# OSX useful to ignore
-*.DS_Store
-.AppleDouble
-.LSOverride
-
-# Thumbnails
-._*
-
-# Files that might appear in the root of a volume
-.DocumentRevisions-V100
-.fseventsd
-.Spotlight-V100
-.TemporaryItems
-.Trashes
-.VolumeIcon.icns
-.com.apple.timemachine.donotpresent
-
-# Directories potentially created on remote AFP share
-.AppleDB
-.AppleDesktop
-Network Trash Folder
-Temporary Items
-.apdisk
-
-# C extensions
-*.so
-
-# Distribution / packaging
-.Python
-env/
-venv/
-build/
-develop-eggs/
-dist/
-downloads/
-eggs/
-.eggs/
-lib/
-lib64/
-parts/
-sdist/
-var/
-*.egg-info/
-.installed.cfg
-*.egg
-
-# PyInstaller
-#  Usually these files are written by a python script from a template
-#  before PyInstaller builds the exe, so as to inject date/other infos into it.
-*.manifest
-*.spec
-
-# Installer logs
-pip-log.txt
-pip-delete-this-directory.txt
-
-# Unit test / coverage reports
-htmlcov/
-.tox/
-.coverage
-.coverage.*
-.cache
-nosetests.xml
-coverage.xml
-*,cover
-.hypothesis/
-.pytest_cache/
-
-# Translations
-*.mo
-*.pot
-
-# Django stuff:
-*.log
-
-# Sphinx documentation
-docs/_build/
-
-# IntelliJ Idea
-.idea
-*.iml
-*.ipr
-*.iws
-
-# PyBuilder
-target/
-
-# Cookiecutter
-output/
-python_boilerplate/
-cookiecutter-pypackage-env/
-
-# vscode settings
-.history/
-*.code-workspace
-
-# Frontend extension
-node_modules/
-.env
-.env.local
-.env.development.local
-.env.test.local
-.env.production.local
-npm-debug.log*
-yarn-debug.log*
-yarn-error.log*
-node.zip
-.vscode/
-.claude/
-.codemate/
@@ -1,15 +0,0 @@
-Apache Software License 2.0
-
-Copyright (c) 2026, Zeyu Huang
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
@@ -1,169 +0,0 @@
-# vLLM-Omni
-
-vLLM-Omni offers a ComfyUI integration on top of its online serving API.
-It can send model inference requests to either a locally running vLLM-Omni service or a remote one.
-
-## Requirement
-
- Python 3.12 or above
- [ComfyUI installed](https://docs.comfy.org/installation/system_requirements)
- [vLLM-Omni installed](https://docs.vllm.ai/projects/vllm-omni/en/latest/getting_started/installation/) on either the same device or another device discoverable via the internet.
- No need to install additional packages apart from those already required by ComfyUI.
-
-> [!TIP]
-> If you run both ComfyUI and vLLM-Omni on the same device, you can create separate virtual environments and use different Python versions for them.
-
-
-## Installation
-
-Copy this folder to the `custom_nodes` subfolder of your ComfyUI installation. Your directory should look like `ComfyUI/custom_nodes/ComfyUI-vLLM-Omni`.
-
-If you are running ComfyUI during copying, you should restart ComfyUI to load this extension.
-
-> [!TIP]
-> You can use utility websites such as https://download-directory.github.io/ to download a subdirectory of a repo. Also checkout community discussions (e.g., https://stackoverflow.com/questions/7106012/download-a-single-folder-or-directory-from-a-github-repository) for more info.
-
-On the device and virtual environment you run ComfyUI, launch ComfyUI with
-```bash
-cd ComfyUI
-
-# The regular way
-python main.py
-
-# If you are mainly using this node, launch it faster with
-python main.py --cpu
-```
-
-On the device and virtual environment you run vLLM-Omni, start a model service with
-```bash
-vllm serve The_Model_ID_to_Serve --omni --port 8000
-```
-
-Check **ComfyUI's sidebar -> Node Library**. There should be a new folder named **vLLM-Omni**.
-If no, check your shell running the ComfyUI process. There may be some error messages before the line `Import times for custom nodes:` and the line `To see the GUI go to: http://127.0.0.1:8188`.
-
-## Quickstart
-
-This extension offers the following nodes based on the output modalities (at **ComfyUI sidebar -> Node Library**):
-
- **Generate Image** for text-to-image and image-to-image tasks
- **Multimodality Understanding** for multimodality-to-text and multimodality-to-audio tasks
- **TTS** and **TTS Voice Clone** for TTS tasks
-
-This extension also offers example workflows (at **ComfyUI sidebar -> Templates -> vLLM-Omni**)
-
-> [!NOTE]
-> The node UI and feature designs are intended to match vLLM-Omni online serving interfaces. It cannot offer more than what the interfaces support.
-
-To build a simple workflow yourself,
-
- Drag a generation node onto the canvas.
- Depending on your need, grab built-in multimedia file loader nodes, such as **image->Load Image**, **image->video->Load Video**, **audio->Load Audio**
- Depending on your need, grab built-in multimedia file preview nodes, such as **image->Preview Image**, **image->video->Save Video**, **audio->Preview Audio**. For text output, you can install [ComfyUI-Custom-Scripts plugin](https://github.com/pythongosssss/ComfyUI-Custom-Scripts/) and grab its **utils->Show Text 🐍** node.
- If you want to tune sampling parameters, grab corresponding nodes from **vLLM-Omni-> Sampling Params**.
-    - For multi-stage models, you can connect multiple **AR Sampling Params** and **Diffusion Sampling Params** nodes to a **Multi-Stage Sampling Params List** node, and connect this node to the generation node.
-    - For some multi-stage models like BAGEL, [only one stage's sampling parameters are exposed and tunable via vLLM-Omni's online serving API](https://docs.vllm.ai/projects/vllm-omni/en/latest/user_guide/examples/online_serving/bagel/). Thus, these models are treated as single-stage ones. Please check the vLLM-Omni documentation on how to correctly set each model's sampling parameters.
-    - For multi-stage models where all stages are either autoregression or diffusion, you can also connect only a single Sampling Params node, indicating that this set of sampling parameters will be used for all stages.
-
-## Screenshots and Examples
-
-### Multimodal understanding (e.g., Qwen Omni series, BAGEL)
-
-(Also available at **ComfyUI sidebar->Template->vLLM-Omni->vLLM-Omni Multimodal Understanding**)
-
-<p align="center">
-  <picture>
-    <source media="(prefers-color-scheme: dark)" src="https://raw.githubusercontent.com/vllm-project/vllm-omni/refs/heads/main/apps/ComfyUI-vLLM-Omni/docs/images/comfyui-understanding.jpg">
-    <img alt="vLLM-Omni multimodal understanding" src="https://raw.githubusercontent.com/vllm-project/vllm-omni/refs/heads/main/apps/ComfyUI-vLLM-Omni/docs/images/comfyui-understanding.jpg" width=55%>
-  </picture>
-</p>
-
-> [!TIP]
-> Although this node enables all-modality input, you should check whether the specific model you host and request for supports the modalities you connect to the node.
-
-You can configure per-stage sampling parameters for multi-stage models.
-
-<p align="center">
-  <picture>
-    <source media="(prefers-color-scheme: dark)" src="https://raw.githubusercontent.com/vllm-project/vllm-omni/refs/heads/main/apps/ComfyUI-vLLM-Omni/docs/images/comfyui-multi-stage.jpg">
-    <img alt="vLLM-Omni multiple stages" src="https://raw.githubusercontent.com/vllm-project/vllm-omni/refs/heads/main/apps/ComfyUI-vLLM-Omni/docs/images/comfyui-multi-stage.jpg" width=55%>
-  </picture>
-</p>
-
-### Text-to-image and image-to-image generation (e.g., Z-Image-Turbo, Qwen-Image-Edit, BAGEL)
-
-(Also available at **ComfyUI sidebar->Template->vLLM-Omni->vLLM-Omni Image Generation**)
-
-<p align="center">
-  <picture>
-    <source media="(prefers-color-scheme: dark)" src="https://raw.githubusercontent.com/vllm-project/vllm-omni/refs/heads/main/apps/ComfyUI-vLLM-Omni/docs/images/comfyui-image-generation.jpg">
-    <img alt="vLLM-Omni image generation" src="https://raw.githubusercontent.com/vllm-project/vllm-omni/refs/heads/main/apps/ComfyUI-vLLM-Omni/docs/images/comfyui-image-generation.jpg" width=55%>
-  </picture>
-</p>
-
-> [!TIP]
-> The node automatically choose text-to-image or image-to-image API endpoints depending on whether you connect an image input or not.
-
-### TTS (e.g., Qwen TTS series)
-
-(Also available at **ComfyUI sidebar->Template->vLLM-Omni->vLLM-Omni TTS**)
-
-<p align="center">
-  <picture>
-    <source media="(prefers-color-scheme: dark)" src="https://raw.githubusercontent.com/vllm-project/vllm-omni/refs/heads/main/apps/ComfyUI-vLLM-Omni/docs/images/comfyui-tts.jpg">
-    <img alt="vLLM-Omni TTS" src="https://raw.githubusercontent.com/vllm-project/vllm-omni/refs/heads/main/apps/ComfyUI-vLLM-Omni/docs/images/comfyui-tts.jpg" width=55%>
-  </picture>
-</p>
-
-> [!TIP]
-> There is a dedicated node for VoiceClone tasks with reference audio input. Other simple text-to-speech tasks should use the regular TTS node.
-
-### Chaining multiple model services
-
-(Also available at **ComfyUI sidebar->Template->vLLM-Omni->vLLM-Omni Chaining Services**)
-
-<p align="center">
-  <picture>
-    <source media="(prefers-color-scheme: dark)" src="https://raw.githubusercontent.com/vllm-project/vllm-omni/refs/heads/main/apps/ComfyUI-vLLM-Omni/docs/images/comfyui-chaining-services.jpg">
-    <img alt="vLLM-Omni TTS" src="https://raw.githubusercontent.com/vllm-project/vllm-omni/refs/heads/main/apps/ComfyUI-vLLM-Omni/docs/images/comfyui-chaining-services.jpg" width=55%>
-  </picture>
-</p>
-
-## Develop
-
-Follow the [development convention and rules of vLLM-Omni](https://docs.vllm.ai/projects/vllm-omni/en/latest/contributing/).
-
-## Limitation and Non-Goals
-
- Single server mode only. No automatic load balancing or failover.
- Features set is bounded to vLLM-Omni's online service capability, including
-    - The types of models supported in online mode,
-    - The types of sampling parameters supported in the online mode,
-    - The ways to send files (primarily through full-length base64 in JSON payload),
-    - Figuring out errors in the payload (such as unsupported fields by a specific model) if the endpoint does not explicitly return an error,
-    - (The lack of) Authentication
-    - (The lack of) Progress indicator
-
-## Support
-
-If you are new to ComfyUI, please check out [its documentation](https://docs.comfy.org/) for usage instructions.
-
-If you are new to vLLM-Omni, please also check out [its documentation](https://docs.vllm.ai/projects/vllm-omni/en/latest/) for usage instructions.
-
-Whenever you find an issue or problem, please
-
- First find out if this is an upstream limitation of vLLM-Omni's online serving mode, by [checking their documentation](https://docs.vllm.ai/projects/vllm-omni/en/latest/examples/).
- [Open an issue](https://github.com/vllm-project/vllm-omni/issues) that clearly describes this ComfyUI or online service problem.
-
-## Acknowledgements
-
-Features
-
- https://github.com/dougbtv/comfyui-vllm-omni/ The official reference implementation for ComfyUI integration with vLLM-Omni's DALL-E compatible image generation API.
- https://github.com/Comfy-Org/ComfyUI/tree/master/comfy_extras ComfyUI's built-in node implementations.
-
-UI/UX design references
-
- https://github.com/sgl-project/sglang/pull/15271 SGLang Diffusion's official ComfyUI integration for image and video generation.
- https://github.com/SXQBW/ComfyUI-Qwen-Omni A third party ComfyUI integration for Qwen Omni series.
- https://github.com/flybirdxx/ComfyUI-Qwen-TTS https://github.com/DarioFT/ComfyUI-Qwen3-TTS Third  party ComfyUI integrations for Qwen TTS series.
@@ -1,52 +0,0 @@
-"""Top-level package for comfyui_vllm_omni."""  # noqa: N999  # This is not a python library intended to be imported
-
-__all__ = [
-    "NODE_CLASS_MAPPINGS",
-    "NODE_DISPLAY_NAME_MAPPINGS",
-    "WEB_DIRECTORY",
-]
-
-__author__ = """vLLM-Omni Team"""
-__email__ = "vllm-omni@vllm.ai"
-__version__ = "0.0.1"
-
-from .comfyui_vllm_omni.nodes import (
-    VLLMOmniARSampling,
-    VLLMOmniDiffusionSampling,
-    VLLMOmniGenerateImage,
-    VLLMOmniQwenTTSParams,
-    VLLMOmniSamplingParamsList,
-    VLLMOmniTTS,
-    VLLMOmniUnderstanding,
-    VLLMOmniVoiceClone,
-)
-
-# A dictionary that contains all nodes you want to export with their names
-NODE_CLASS_MAPPINGS = {
-    # === Generation ===
-    "VLLMOmniGenerateImage": VLLMOmniGenerateImage,
-    "VLLMOmniUnderstanding": VLLMOmniUnderstanding,
-    "VLLMOmniTTS": VLLMOmniTTS,
-    "VLLMOmniVoiceClone": VLLMOmniVoiceClone,
-    # === Params ===
-    "VLLMOmniARSampling": VLLMOmniARSampling,
-    "VLLMOmniDiffusionSampling": VLLMOmniDiffusionSampling,
-    "VLLMOmniSamplingParamsList": VLLMOmniSamplingParamsList,
-    "VLLMOmniQwenTTSParams": VLLMOmniQwenTTSParams,
-}
-
-# A dictionary that contains the friendly/humanly readable titles for the nodes
-NODE_DISPLAY_NAME_MAPPINGS = {
-    # === Generation ===
-    "VLLMOmniGenerateImage": "Generate Image",
-    "VLLMOmniUnderstanding": "Multimodality Understanding",
-    "VLLMOmniTTS": "TTS (Text to Speech)",
-    "VLLMOmniVoiceClone": "TTS Voice Cloning",
-    # === Params ===
-    "VLLMOmniARSampling": "AR Sampling Params",
-    "VLLMOmniDiffusionSampling": "Diffusion Sampling Params",
-    "VLLMOmniSamplingParamsList": "Multi-Stage Sampling Params List",
-    "VLLMOmniQwenTTSParams": "Qwen TTS Params",
-}
-
-WEB_DIRECTORY = "./web"
@@ -1 +0,0 @@
-# noqa: N999   # This is not a python library intended to be imported
@@ -1,574 +0,0 @@
-from typing import Literal
-
-import torch
-from comfy_api.input import AudioInput, VideoInput
-
-from .utils.api_client import VLLMOmniClient
-from .utils.logger import get_logger
-from .utils.models import lookup_model_spec
-from .utils.types import (
-    AudioFormat,
-    AutoregressionSamplingParams,
-    DiffusionSamplingParams,
-    QwenTTSModelSpecificParams,
-)
-from .utils.validators import (
-    add_sampling_parameters_to_stage,
-    validate_model_and_sampling_params_types,
-)
-
-logger = get_logger(__name__)
-
-
-class _VLLMOmniGenerateBase:
-    """Base class for vLLM-Omni generation nodes with shared functionality."""
-
-    CATEGORY = "vLLM-Omni"
-
-    @classmethod
-    def VALIDATE_INPUTS(cls, url, model) -> str | Literal[True]:
-        """
-        Can only validate this model's own input. Cannot check inputs from other nodes.
-        See: https://docs.comfy.org/custom-nodes/backend/server_overview#validate_inputs
-        """
-        if not url:
-            return "URL must not be empty"
-        if not model:
-            return "Model must not be empty"
-        return True
-
-
-class VLLMOmniGenerateImage(_VLLMOmniGenerateBase):
-    @classmethod
-    def INPUT_TYPES(cls):
-        return {
-            "required": {
-                "url": ("STRING", {"default": "http://localhost:8000/v1"}),
-                "model": ("STRING", {"default": "Tongyi-MAI/Z-Image-Turbo"}),
-                "prompt": ("STRING", {"multiline": True}),
-                "negative_prompt": ("STRING", {"multiline": True, "default": ""}),
-                "width": ("INT", {"default": 512, "min": 64, "max": 2048}),
-                "height": ("INT", {"default": 512, "min": 64, "max": 2048}),
-            },
-            "optional": {
-                "image": ("IMAGE",),
-                "mask": ("MASK",),
-                # "video": ("VIDEO",),
-                # "audio": ("AUDIO",),
-                "sampling_params": ("SAMPLING_PARAMS",),
-            },
-        }
-
-    RETURN_TYPES = ("IMAGE",)
-    RETURN_NAMES = ("image",)
-    FUNCTION = "generate"
-
-    async def generate(
-        self,
-        url: str,
-        model: str,
-        prompt: str,
-        width: int,
-        height: int,
-        negative_prompt: str | None = None,
-        image: torch.Tensor | None = None,
-        mask: torch.Tensor | None = None,
-        audio: AudioInput | None = None,  # Hidden & unused
-        video: VideoInput | None = None,  # Hidden & unused
-        sampling_params: dict | list[dict] | None = None,
-        **kwargs,
-    ):
-        logger.info("Uncaught kwargs: %s", kwargs)
-        logger.debug("Got sampling params: %s", sampling_params)
-        validate_model_and_sampling_params_types(model, sampling_params)
-        if image is None and mask is not None:
-            raise ValueError("Mask input provided without an image input.")
-
-        client = VLLMOmniClient(url)
-
-        spec, pattern = lookup_model_spec(model)
-        is_bagel = pattern is not None and "bagel" in pattern.lower()
-
-        # Prefer DALL-E compatible API for simple (one-stage) diffusion models
-        if (spec is None or spec["stages"] == ["diffusion"]) and not is_bagel:
-            # The number of sampling parameter groups should have been validated.
-            # Now, simply convert single-item list to dict.
-            if isinstance(sampling_params, list):
-                sampling_params = sampling_params[0]
-            if audio is None and image is None and video is None:
-                # No multimodal input --- use DALL-E image generation
-                logger.info("Using DALL-E image generation endpoint")
-                output = await client.generate_image(
-                    model=model,
-                    prompt=prompt,
-                    width=width,
-                    height=height,
-                    negative_prompt=negative_prompt,
-                    sampling_params=sampling_params,
-                )
-                return (output,)
-            elif image is not None and audio is None and video is None:
-                # Image and text input --- use DALL-E image edit
-                logger.info("Using DALL-E image edit endpoint")
-                output = await client.edit_image(
-                    model=model,
-                    prompt=prompt,
-                    image=image,
-                    width=width,
-                    height=height,
-                    negative_prompt=negative_prompt,
-                    mask=mask,
-                    sampling_params=sampling_params,
-                )
-                return (output,)
-
-        logger.info("Using chat completion endpoint")
-        sampling_params = add_sampling_parameters_to_stage(
-            model, sampling_params, "diffusion", width=width, height=height
-        )
-        logger.debug("Edited sampling params: %s", sampling_params)
-
-        output = await client.generate_image_chat_completion(
-            model=model,
-            prompt=prompt,
-            negative_prompt=negative_prompt,
-            image=image,
-            audio=audio,
-            video=video,
-            sampling_params=sampling_params,
-        )
-
-        return (output,)
-
-
-class VLLMOmniUnderstanding(_VLLMOmniGenerateBase):
-    @classmethod
-    def INPUT_TYPES(cls):
-        return {
-            "required": {
-                "url": ("STRING", {"default": "http://localhost:8000/v1"}),
-                "model": ("STRING", {"default": "Qwen/Qwen2.5-Omni-7B"}),
-                "prompt": ("STRING", {"multiline": True}),
-                "output_text": ("BOOLEAN", {"default": True}),
-                "output_audio": ("BOOLEAN", {"default": True}),
-                "use_audio_in_video": ("BOOLEAN", {"default": True}),
-            },
-            "optional": {
-                "image": ("IMAGE",),
-                "video": ("VIDEO",),
-                "audio": ("AUDIO",),
-                "sampling_params": ("SAMPLING_PARAMS",),
-            },
-        }
-
-    RETURN_TYPES = ("STRING", "AUDIO")
-    RETURN_NAMES = ("text_response", "audio_response")
-    FUNCTION = "generate"
-
-    @classmethod
-    def VALIDATE_INPUTS(cls, url, model, output_text, output_audio) -> str | Literal[True]:  # type: ignore[reportIncompatibleMethodOverride]
-        super_validation = super().VALIDATE_INPUTS(url, model)
-        if isinstance(super_validation, str):
-            return super_validation
-        if not output_text and not output_audio:
-            return "At least one of output_text or output_audio must be True."
-        return True
-
-    async def generate(
-        self,
-        url: str,
-        model: str,
-        prompt: str,
-        image: torch.Tensor | None = None,
-        audio: AudioInput | None = None,
-        video: VideoInput | None = None,
-        sampling_params: dict | list[dict] | None = None,
-        output_text: bool = True,
-        output_audio: bool = True,
-        use_audio_in_video: bool = True,
-        **kwargs,
-    ) -> tuple[str, AudioInput]:
-        logger.info("Uncaught kwargs: %s", kwargs)
-        logger.debug("Got sampling params: %s", sampling_params)
-        validate_model_and_sampling_params_types(model, sampling_params)
-
-        client = VLLMOmniClient(url)
-        spec, pattern = lookup_model_spec(model)
-        is_bagel = pattern is not None and "bagel" in pattern.lower()
-
-        if is_bagel:
-            # A lot of special handlings here...
-            if output_audio:
-                raise ValueError("BAGEL models do not support audio output.")
-            if audio is not None or video is not None:
-                raise ValueError("BAGEL models do not support audio or video input.")
-            (
-                text_response,
-                _,
-            ) = await client.generate_understanding_chat_completion(
-                model=model,
-                prompt=prompt,
-                image=image,
-                audio=None,
-                video=None,
-                sampling_params=sampling_params,
-                modalities=["text"],
-            )
-        else:
-            modalities = []
-            if output_text:
-                modalities.append("text")
-            if output_audio:
-                modalities.append("audio")
-
-            if use_audio_in_video and video is not None:
-                use_audio_in_video = True
-            else:
-                use_audio_in_video = False
-
-            (
-                text_response,
-                audio,
-            ) = await client.generate_understanding_chat_completion(
-                model=model,
-                prompt=prompt,
-                image=image,
-                audio=audio,
-                video=video,
-                sampling_params=sampling_params,
-                modalities=modalities,
-                # == extra kwargs ==
-                mm_processor_kwargs={"use_audio_in_video": use_audio_in_video},
-            )
-
-        if text_response is None:
-            text_response = ""
-        if audio is None:
-            channels = 1
-            duration = 1
-            sample_rate = 44100
-            num_samples = int(round(duration * sample_rate))
-            waveform = torch.zeros((1, channels, num_samples), dtype=torch.float32)
-            audio = {"waveform": waveform, "sample_rate": sample_rate}
-
-        return (text_response, audio)
-
-
-class VLLMOmniTTS(_VLLMOmniGenerateBase):
-    @classmethod
-    def INPUT_TYPES(cls):
-        return {
-            "required": {
-                "url": ("STRING", {"default": "http://localhost:8000/v1"}),
-                "model": (
-                    "STRING",
-                    {"default": "Qwen/Qwen3-TTS-12Hz-1.7B-CustomVoice"},
-                ),
-                "input": ("STRING", {"multiline": True}),
-                "voice": ("STRING", {"default": "Vivian"}),
-                "response_format": (["mp3", "opus", "aac", "flac", "wav", "pcm"],),
-                "speed": (
-                    "FLOAT",
-                    {"default": 1.0, "min": 0.25, "max": 4.0, "step": 0.01},
-                ),
-            },
-            "optional": {
-                "model_specific_params": ("TTS_PARAMS",),
-            },
-        }
-
-    RETURN_TYPES = ("AUDIO",)
-    RETURN_NAMES = ("audio",)
-    FUNCTION = "generate"
-
-    async def generate(
-        self,
-        url: str,
-        model: str,
-        input: str,
-        voice: str,
-        response_format: AudioFormat,
-        speed: float,
-        model_specific_params: dict | None,
-        **kwargs,
-    ) -> tuple[AudioInput]:
-        logger.info("Got extra kwargs in TTS: %s", kwargs)
-
-        is_qwen_tts = "qwen3-tts" in model.lower()
-        if not is_qwen_tts and isinstance(model_specific_params, QwenTTSModelSpecificParams):
-            raise ValueError(
-                "You have provided Qwen-specific TTS params."
-                "However, the model appears to not be a Qwen TTS model (no 'Qwen3-TTS' in model name)."
-            )
-
-        combined_params = {**kwargs, **(model_specific_params or {})}
-
-        client = VLLMOmniClient(url)
-
-        audio = await client.generate_speech(
-            model=model,
-            input=input,
-            voice=voice,
-            response_format=response_format,
-            speed=speed,
-            **combined_params,
-        )
-        return (audio,)
-
-
-class VLLMOmniVoiceClone(_VLLMOmniGenerateBase):
-    @classmethod
-    def INPUT_TYPES(cls):
-        return {
-            "required": {
-                "url": ("STRING", {"default": "http://localhost:8000/v1"}),
-                "model": ("STRING", {"default": "Qwen/Qwen3-TTS-12Hz-1.7B-Base"}),
-                "input": ("STRING", {"multiline": True}),
-                "voice": ("STRING", {"default": "Vivian"}),
-                "response_format": (["mp3", "opus", "aac", "flac", "wav", "pcm"],),
-                "speed": (
-                    "FLOAT",
-                    {"default": 1.0, "min": 0.25, "max": 4.0, "step": 0.01},
-                ),
-                "ref_audio": ("AUDIO",),
-                "ref_text": ("STRING", {"multiline": True}),
-                "x_vector_only_mode": ("BOOLEAN", {"default": False}),
-            },
-            "optional": {
-                "model_specific_params": ("TTS_PARAMS",),
-            },
-        }
-
-    RETURN_TYPES = ("AUDIO",)
-    RETURN_NAMES = ("audio",)
-    FUNCTION = "generate"
-
-    async def generate(
-        self,
-        url: str,
-        model: str,
-        input: str,
-        voice: str,
-        response_format: AudioFormat,
-        speed: float,
-        ref_audio: AudioInput,
-        ref_text: str,
-        x_vector_only_mode: bool,
-        model_specific_params: dict | None,
-        **kwargs,
-    ):
-        is_qwen_tts = "qwen3-tts" in model.lower()
-        if not is_qwen_tts and isinstance(model_specific_params, QwenTTSModelSpecificParams):
-            raise ValueError(
-                "You have provided Qwen-specific TTS params."
-                "However, the model appears to not be a Qwen TTS model (no 'Qwen3-TTS' in model name)."
-            )
-
-        combined_params = {
-            "ref_audio": ref_audio,
-            "ref_text": ref_text,
-            "x_vector_only_mode": x_vector_only_mode,
-            **kwargs,
-            **(model_specific_params or {}),
-        }
-
-        client = VLLMOmniClient(url)
-
-        audio = await client.generate_speech(
-            model=model,
-            input=input,
-            voice=voice,
-            response_format=response_format,
-            speed=speed,
-            **combined_params,
-        )
-        return (audio,)
-
-
-class VLLMOmniARSampling:
-    @classmethod
-    def INPUT_TYPES(cls):
-        return {
-            "required": {
-                "max_tokens": ("INT", {"default": 100, "min": 1, "max": 10000}),
-                "temperature": (
-                    "FLOAT",
-                    {"default": 1.0, "min": 0.0, "max": 2.0, "step": 0.01},
-                ),
-                "top_p": (
-                    "FLOAT",
-                    {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01},
-                ),
-                "repetition_penalty": (
-                    "FLOAT",
-                    {"default": 1.0, "min": 0.0, "max": 5.0, "step": 0.01},
-                ),
-                # === Put seed at last. ===
-                # Whenever a field named "seed" is present, ComfyUI adds another field called "control after generate"
-                "seed": (
-                    "INT",
-                    {
-                        "default": -1,
-                        "min": -1,
-                        "step": 1,
-                        "tooltip": "-1 means to not provide a seed.",
-                    },
-                ),
-            }
-        }
-
-    RETURN_TYPES = ("SAMPLING_PARAMS",)
-    RETURN_NAMES = ("AR sampling params",)
-    FUNCTION = "get_params"
-    CATEGORY = "vLLM-Omni/Sampling Params"
-
-    def get_params(self, seed, **kwargs):
-        params = AutoregressionSamplingParams(kwargs)
-        if seed >= 0:
-            params["seed"] = seed
-        return (params,)
-
-
-class VLLMOmniDiffusionSampling:
-    @classmethod
-    def INPUT_TYPES(cls):
-        return {
-            "required": {
-                "n": (
-                    "INT",
-                    {
-                        "default": 1,
-                        "min": 0,
-                        "max": 10,
-                        "step": 1,
-                        "tooltip": "Number of images to generate",
-                    },
-                ),
-                "num_inference_steps": (
-                    "INT",
-                    {
-                        "default": 50,
-                        "min": 1,
-                        "max": 1000,
-                        "tooltip": "Number of denoising steps (higher = better quality, slower).",
-                    },
-                ),
-                "guidance_scale": (
-                    "FLOAT",
-                    {
-                        "default": 7.5,
-                        "min": 0.0,
-                        "max": 20.0,
-                        "step": 0.1,
-                        "tooltip": "Classifier-free guidance scale (higher = more prompt adherence).",
-                    },
-                ),
-                "true_cfg_scale": (
-                    "FLOAT",
-                    {
-                        "default": 1.0,
-                        "min": 0.0,
-                        "max": 20.0,
-                        "step": 0.5,
-                        "tooltip": "True CFG scale for advanced control (model-specific).",
-                    },
-                ),
-                "vae_use_slicing": (
-                    "BOOLEAN",
-                    {
-                        "default": False,
-                        "tooltip": "Enable VAE slicing for reduced memory usage (slight quality trade-off)",
-                    },
-                ),
-                "vae_use_tiling": (
-                    "BOOLEAN",
-                    {
-                        "default": False,
-                        "tooltip": "Enable VAE tiling for reduced memory usage (slight quality trade-off)",
-                    },
-                ),
-                # === Put seed at last. ===
-                # Whenever a field named "seed" is present, ComfyUI adds another field called "control after generate"
-                "seed": (
-                    "INT",
-                    {
-                        "default": -1,
-                        "min": -1,
-                        "step": 1,
-                        "tooltip": "-1 means to not provide a seed.",
-                    },
-                ),
-            }
-        }
-
-    RETURN_TYPES = ("SAMPLING_PARAMS",)
-    RETURN_NAMES = ("diffusion sampling params",)
-    FUNCTION = "get_params"
-    CATEGORY = "vLLM-Omni/Sampling Params"
-
-    def get_params(self, seed, **kwargs):
-        params = DiffusionSamplingParams(kwargs)
-        if seed >= 0:
-            params["seed"] = seed
-        return (params,)
-
-
-class VLLMOmniSamplingParamsList:
-    @classmethod
-    def INPUT_TYPES(cls):
-        return {
-            "required": {
-                "param1": ("SAMPLING_PARAMS",),
-            },
-            "optional": {
-                "param2": ("SAMPLING_PARAMS",),
-                "param3": ("SAMPLING_PARAMS",),
-            },
-        }
-
-    RETURN_TYPES = ("SAMPLING_PARAMS",)
-    RETURN_NAMES = ("param list",)
-    FUNCTION = "aggregate"
-    CATEGORY = "vLLM-Omni/Sampling Params"
-
-    def aggregate(self, param1: dict, param2: dict | None = None, param3: dict | None = None):
-        for i, p in enumerate((param1, param2, param3)):
-            if isinstance(p, list):
-                raise ValueError(
-                    f"Input {i} is a Multi-Stage Sampling Params List. "
-                    f"Expected a single sampling parameters node (either AR or Diffusion)."
-                )
-
-        params = [param1]
-        if param2 is not None:
-            params.append(param2)
-        if param3 is not None:
-            params.append(param3)
-        return (params,)
-
-
-class VLLMOmniQwenTTSParams:
-    @classmethod
-    def INPUT_TYPES(cls):
-        return {
-            "required": {
-                "task_type": (
-                    ["CustomVoice", "VoiceDesign", "Base"],
-                    {"default": "CustomVoice"},
-                ),
-                "language": (
-                    ["Auto", "Chinese", "English", "Japanese", "Korean"],
-                    {"default": "Auto"},
-                ),
-                "instructions": ("STRING", {"multiline": True}),
-                "max_new_tokens": ("INT", {"default": 2048, "min": 1}),
-            }
-        }
-
-    RETURN_TYPES = ("TTS_PARAMS",)
-    RETURN_NAMES = ("Qwen TTS params",)
-    FUNCTION = "get_params"
-    CATEGORY = "vLLM-Omni/TTS Params"
-
-    def get_params(self, **kwargs):
-        return (QwenTTSModelSpecificParams(kwargs),)
@@ -1,459 +0,0 @@
-"""
-An high-level API client adapter that forwards ComfyUI inputs to vLLM-Omni's REST API,
-and transforms the API responses back to ComfyUI formats.
-
-The image generation part is derived from dougbtv/comfyui-vllm-omni by Doug (@dougbtv).
-Original source at https://github.com/dougbtv/comfyui-vllm-omni, distributed under the MIT License.
-"""
-
-from typing import Any
-
-import aiohttp
-import av.error
-import torch
-from comfy_api.input import AudioInput, VideoInput
-
-from .format import (
-    audio_to_base64,
-    base64_to_audio,
-    base64_to_image_tensor,
-    bytes_to_audio,
-    image_tensor_to_base64,
-    image_tensor_to_png_bytes,
-    video_to_base64,
-)
-from .logger import get_logger, pretty_printer
-from .models import lookup_model_spec
-from .types import AudioFormat
-
-logger = get_logger(__name__)
-
-
-class VLLMOmniClient:
-    def __init__(self, base_url: str, timeout: float = 300.0):
-        self.base_url = base_url
-        self.timeout = aiohttp.ClientTimeout(total=timeout)
-
-    async def generate_image(
-        self,
-        *,
-        model: str,
-        prompt: str,
-        width: int,
-        height: int,
-        negative_prompt: str | None = None,
-        sampling_params: dict | None = None,
-    ) -> torch.Tensor:
-        """Run text-to-image generation via DALLE API"""
-        await self._check_model_exist(model)
-
-        size = f"{width}x{height}"
-        payload = {
-            "model": model,
-            "prompt": prompt,
-            "size": size,
-            "response_format": "b64_json",
-        }
-        if negative_prompt:
-            payload["negative_prompt"] = negative_prompt
-        if sampling_params is not None:
-            payload.update(sampling_params)
-        logger.debug("img gen payload: %s", payload)
-
-        url = self.base_url + "/images/generations"
-        async with aiohttp.ClientSession(timeout=self.timeout) as session:
-            try:
-                async with session.post(
-                    url,
-                    json=payload,
-                    headers={"Content-Type": "application/json"},
-                ) as response:
-                    if not response.ok:
-                        error_text = await response.text()
-                        raise (ValueError if response.status < 500 else RuntimeError)(
-                            f"vLLM-Omni API returned status {response.status}: {error_text}"
-                        )
-
-                    try:
-                        data = await response.json()
-                    except aiohttp.ContentTypeError as e:
-                        raise RuntimeError(f"Invalid JSON response from vLLM-Omni: {e}")
-                    if "data" not in data:
-                        raise RuntimeError("API response missing 'data' field - expected OpenAI DALL-E format")
-                    if not data["data"]:
-                        raise RuntimeError("API returned empty data array")
-
-                    image_tensors = []
-                    for idx, img in enumerate(data["data"]):
-                        if "b64_json" not in img:
-                            raise RuntimeError(f"API returned image #{idx} without 'b64_json' field")
-                        base64_str = img["b64_json"]
-                        tensor = base64_to_image_tensor(base64_str)
-                        image_tensors.append(tensor)
-                        logger.debug("Image #%d has shape %s", idx, tensor.shape)
-
-                    batch_tensor = torch.stack(image_tensors, dim=0)
-                    logger.debug("batch_tensor output has shape: %s", batch_tensor.shape)
-                    return batch_tensor
-
-            except aiohttp.ClientError as e:
-                raise RuntimeError(f"Network error connecting to vLLM-Omni at {url}: {e}")
-
-    async def edit_image(
-        self,
-        *,
-        model: str,
-        prompt: str,
-        image: torch.Tensor,
-        width: int,
-        height: int,
-        negative_prompt: str | None = None,
-        mask: torch.Tensor | None = None,
-        sampling_params: dict | None = None,
-    ) -> torch.Tensor:
-        """Run image editing via DALLE API"""
-        await self._check_model_exist(model)
-
-        size = f"{width}x{height}"
-        image_filename = "image.png"  # Required for multipart form
-        form = aiohttp.FormData()
-        form.add_field("model", model)
-        form.add_field(
-            "image",
-            image_tensor_to_png_bytes(image, image_filename),
-            filename=image_filename,
-            content_type="image/png",
-        )
-        form.add_field("prompt", prompt)
-        form.add_field("size", size)
-        if negative_prompt:
-            form.add_field("negative_prompt", negative_prompt)
-        if sampling_params is not None:
-            for k, v in sampling_params.items():
-                form.add_field(k, str(v))
-        if mask is not None:
-            mask_filename = "mask.png"
-            form.add_field(
-                "mask",
-                image_tensor_to_png_bytes(mask, mask_filename),
-                filename=mask_filename,
-                content_type="image/png",
-            )
-
-        url = self.base_url + "/images/edits"
-        async with aiohttp.ClientSession(timeout=self.timeout) as session:
-            try:
-                async with session.post(url, data=form) as response:
-                    if not response.ok:
-                        error_text = await response.text()
-                        raise (ValueError if response.status < 500 else RuntimeError)(
-                            f"vLLM-Omni API returned status {response.status}: {error_text}"
-                        )
-
-                    try:
-                        data = await response.json()
-                    except aiohttp.ContentTypeError as e:
-                        raise RuntimeError(f"Invalid JSON response from vLLM-Omni: {e}")
-
-                    if "data" not in data:
-                        raise RuntimeError("API response missing 'data' field - expected OpenAI DALL-E format")
-                    if not data["data"]:
-                        raise RuntimeError("API returned empty data array")
-
-                    image_tensors = []
-                    for idx, img in enumerate(data["data"]):
-                        if "b64_json" not in img:
-                            raise RuntimeError(f"API returned image #{idx} without 'b64_json' field")
-                        base64_str = img["b64_json"]
-                        tensor = base64_to_image_tensor(base64_str)
-                        image_tensors.append(tensor)
-
-                    return torch.stack(image_tensors, dim=0)
-
-            except aiohttp.ClientError as e:
-                raise RuntimeError(f"Network error connecting to vLLM-Omni at {url}: {e}")
-
-    async def generate_image_chat_completion(
-        self,
-        *,
-        model: str,
-        prompt: str,
-        negative_prompt: str | None = None,
-        image: torch.Tensor | None = None,
-        audio: AudioInput | None = None,
-        video: VideoInput | None = None,
-        sampling_params: dict | list[dict] | None = None,
-    ) -> torch.Tensor:
-        payload = VLLMOmniClient._prepare_chat_completion_messages(
-            model=model,
-            prompt=prompt,
-            negative_prompt=negative_prompt,
-            image=image,
-            audio=audio,
-            video=video,
-            sampling_params=sampling_params,
-            modalities=["image"],
-        )
-        choices = await self._generate_base_chat_completion(model, payload)
-
-        image_tensors = []
-        for idx, img_content in enumerate(choices[0]["message"]["content"]):
-            base64_str = img_content.get("image_url", {}).get("url", "")
-            if not base64_str:
-                raise RuntimeError(f"API returned image #{idx} without image url")
-            tensor = base64_to_image_tensor(base64_str)
-            image_tensors.append(tensor)
-
-        return torch.stack(image_tensors, dim=0)
-
-    async def generate_understanding_chat_completion(
-        self,
-        *,
-        model: str,
-        prompt: str,
-        image: torch.Tensor | None = None,
-        audio: AudioInput | None = None,
-        video: VideoInput | None = None,
-        sampling_params: dict | list[dict] | None = None,
-        modalities: list[str] = ["text", "audio"],
-        **extra_body,
-    ) -> tuple[str | None, AudioInput | None]:
-        # Response may contain two choices: one with text, one with audio
-        payload = VLLMOmniClient._prepare_chat_completion_messages(
-            model=model,
-            prompt=prompt,
-            negative_prompt=None,
-            image=image,
-            audio=audio,
-            video=video,
-            sampling_params=sampling_params,
-            modalities=modalities,
-            **extra_body,
-        )
-
-        choices = await self._generate_base_chat_completion(model, payload)
-        text_response = None
-        audio_base64 = None
-        for choice in choices:
-            try:
-                text_response = choice["message"]["content"]
-            except (KeyError, TypeError):
-                # Either this case (text response) or the audio response case will be hit. Checking None's later.
-                pass
-            try:
-                audio_base64 = choice["message"]["audio"]["data"]
-            except (KeyError, TypeError):
-                # Either this case (text response) or the audio response case will be hit. Checking None's later.
-                pass
-        if audio_base64 is None and text_response is None:
-            raise RuntimeError(
-                "API response missing both '.message.audio' and 'message.content' fields."
-                f"The choices object is {choices}"
-            )
-        if audio_base64 is not None:
-            audio = base64_to_audio(audio_base64)
-            logger.debug(
-                "audio sample rate %d, audio shape %s, duration in second %f",
-                audio["sample_rate"],
-                audio["waveform"].shape,
-                audio["waveform"].shape[2] / audio["sample_rate"],
-            )
-        else:
-            audio = None
-        return text_response, audio
-
-    async def generate_speech(
-        self,
-        *,
-        model: str,
-        input: str,
-        voice: str,
-        response_format: AudioFormat,
-        speed: float,
-        **extra_params,
-    ) -> AudioInput:
-        await self._check_model_exist(model)
-
-        ref_audio: AudioInput | None = extra_params.pop("ref_audio", None)
-
-        payload = {
-            "model": model,
-            "input": input,
-            "voice": voice,
-            "response_format": response_format,
-            "speed": speed,
-            **extra_params,
-        }
-
-        if ref_audio is not None:
-            audio_base64 = audio_to_base64(ref_audio)
-            payload["ref_audio"] = audio_base64
-
-        logger.debug("Omni TTS payload: %s", pretty_printer.pformat(payload))
-
-        url = self.base_url + "/audio/speech"
-        async with aiohttp.ClientSession(timeout=self.timeout) as session:
-            try:
-                async with session.post(
-                    url,
-                    json=payload,
-                    headers={"Content-Type": "application/json"},
-                ) as response:
-                    if not response.ok:
-                        error_text = await response.text()
-                        raise (ValueError if response.status < 500 else RuntimeError)(
-                            f"vLLM-Omni API returned status {response.status}: {error_text}"
-                        )
-
-                    try:
-                        audio_bytes = await response.read()
-                    except aiohttp.ContentTypeError as e:
-                        raise RuntimeError(f"Invalid JSON response from vLLM-Omni: {e}")
-
-                    try:
-                        audio = bytes_to_audio(audio_bytes)
-                    except av.error.InvalidDataError as e:
-                        raise ValueError(
-                            f"Invalid audio data received from vLLM-Omni: {e}"
-                            "Check if you have input unsupported arguments (such as 'voice')"
-                        )
-                    return audio
-
-            except aiohttp.ClientError as e:
-                raise RuntimeError(f"Network error connecting to vLLM-Omni at {url}: {e}")
-
-    async def _generate_base_chat_completion(self, model: str, payload: dict[str, Any]) -> list[dict[str, Any]]:
-        logger.debug("Omni payload: %s", pretty_printer.pformat(payload))
-        await self._check_model_exist(model)
-
-        url = self.base_url + "/chat/completions"
-        async with aiohttp.ClientSession(timeout=self.timeout) as session:
-            try:
-                async with session.post(
-                    url,
-                    json=payload,
-                    headers={"Content-Type": "application/json"},
-                ) as response:
-                    if not response.ok:
-                        error_text = await response.text()
-                        raise (ValueError if response.status < 500 else RuntimeError)(
-                            f"vLLM-Omni API returned status {response.status}: {error_text}"
-                        )
-
-                    try:
-                        data = await response.json()
-                    except aiohttp.ContentTypeError as e:
-                        raise RuntimeError(f"Invalid JSON response from vLLM-Omni: {e}")
-
-                    logger.debug(
-                        "chat completion response: %s",
-                        pretty_printer.pformat(data),
-                    )
-
-                    try:
-                        return data["choices"]
-                    except (KeyError, TypeError):
-                        raise RuntimeError("Invalid JSON response from vLLM-Omni: missing 'choices' field")
-
-            except aiohttp.ClientError as e:
-                raise RuntimeError(f"Network error connecting to vLLM-Omni at {self.base_url}: {e}")
-
-    async def _check_model_exist(self, model: str):
-        url = self.base_url + "/models"
-        async with aiohttp.ClientSession(timeout=self.timeout) as session:
-            try:
-                async with session.get(
-                    url,
-                    headers={"Content-Type": "application/json"},
-                ) as response:
-                    if not response.ok:
-                        error_text = await response.text()
-                        raise (ValueError if response.status < 500 else RuntimeError)(
-                            f"vLLM-Omni API returned status {response.status} "
-                            f"when getting hosted model list: {error_text}"
-                        )
-
-                    try:
-                        data = await response.json()
-                    except aiohttp.ContentTypeError as e:
-                        raise RuntimeError(f"Invalid JSON response when getting hosted model list from vLLM-Omni: {e}")
-
-            except aiohttp.ClientError as e:
-                raise RuntimeError(f"Network error connecting to vLLM-Omni at {self.base_url}: {e}")
-        try:
-            model_list = data["data"]
-            model_found = next((True for m in model_list if m["id"] == model), False)
-        except (KeyError, TypeError):
-            raise RuntimeError(f"Invalid JSON response of the hosted model list: {data}")
-
-        if not model_found:
-            raise ValueError(f"Model {model} not served at {self.base_url}.")
-
-    @staticmethod
-    def _prepare_chat_completion_messages(
-        *,
-        model: str,
-        prompt: str,
-        negative_prompt: str | None,
-        image: torch.Tensor | None = None,
-        audio: AudioInput | None = None,
-        video: VideoInput | None = None,
-        sampling_params: dict | list[dict] | None = None,
-        modalities: list[str] | None = None,  # diffusion don't have this field
-        **extra_body,
-    ):
-        message_content: list[dict] = [{"type": "text", "text": prompt}]
-        if image is not None:
-            message_content.append(
-                {
-                    "type": "image_url",
-                    "image_url": {"url": image_tensor_to_base64(image)},
-                }
-            )
-        if audio is not None:
-            message_content.append({"type": "audio_url", "audio_url": {"url": audio_to_base64(audio)}})
-        if video is not None:
-            message_content.append({"type": "video_url", "video_url": {"url": video_to_base64(video)}})
-        messages = [{"role": "user", "content": message_content}]
-
-        payload: dict[str, Any] = {"messages": messages, "model": model}
-        if modalities:
-            payload["modalities"] = modalities
-
-        combined_extra_body: dict[str, Any] = {}
-        if sampling_params is not None:
-            spec, _ = lookup_model_spec(model)
-            is_single_sampling_param = isinstance(sampling_params, dict) or len(sampling_params) == 1
-
-            if (spec is None and is_single_sampling_param) or (spec is not None and spec["stages"] == ["diffusion"]):
-                # Diffusion format: extra_body directly contains sampling params.
-                # Validation has already taken care of matching sampling params' types and length. Safe to take [0].
-                # * Use this mode if the model is a simple one-stage diffusion model.
-                # * Fallback to this mode if model is not registered and a single sampling param is provided.
-                sampling_params = sampling_params if isinstance(sampling_params, dict) else sampling_params[0]
-                combined_extra_body: dict[str, Any] = sampling_params.copy()
-                if "n" in combined_extra_body:
-                    combined_extra_body["num_outputs_per_prompt"] = combined_extra_body.pop("n")
-            else:
-                # AR format: the payload has a sampling_params_list field, containing a list.
-                sampling_params_list = sampling_params if isinstance(sampling_params, list) else [sampling_params]
-                payload["sampling_params_list"] = sampling_params_list
-
-        if negative_prompt:
-            combined_extra_body["negative_prompt"] = negative_prompt
-
-        if extra_body:
-            combined_extra_body.update(extra_body)
-
-        # Add extra_body only if it has any content.
-        if combined_extra_body:
-            payload["extra_body"] = combined_extra_body
-
-        # Place to inject any model-specific payload adjustment
-        spec, _ = lookup_model_spec(model)
-        if spec:
-            preprocessor = spec.get("payload_preprocessor", None)
-            if preprocessor is not None:
-                payload = preprocessor(payload)
-
-        return payload
@@ -1,230 +0,0 @@
-"""Image/tensor format helpers.
-
-The image generation part is derived from dougbtv/comfyui-vllm-omni by Doug (@dougbtv).
-Original source at https://github.com/dougbtv/comfyui-vllm-omni, distributed under the MIT License.
-"""
-
-import base64
-import mimetypes
-from io import BytesIO
-
-import av
-import numpy as np
-import torch
-from comfy_api.input import AudioInput, VideoInput
-from comfy_extras import nodes_audio
-from PIL import Image
-
-from .logger import get_logger
-
-logger = get_logger(__name__)
-
-
-def base64_to_image_tensor(base64_str: str, mode: str = "RGB") -> torch.Tensor:
-    """
-    Convert base64-encoded image to ComfyUI image tensor.
-
-    Args:
-        base64_str: Base64-encoded image string
-        mode: PIL image mode (default RGB for transparency support)
-
-    Returns:
-        torch.Tensor with shape (1, H, W, C) in float32 [0, 1] range
-
-    Raises:
-        ValueError: If base64 string is invalid or image cannot be decoded
-    """
-    if base64_str.startswith("data:image"):
-        _, base64_str = base64_str.split(",", 1)
-
-    try:
-        # Decode base64 to bytes
-        image_bytes = base64.b64decode(base64_str)
-    except Exception as e:
-        raise ValueError(f"Invalid base64 string: {e}")
-
-    # Create BytesIO object for PIL
-    image_bytesio = BytesIO(image_bytes)
-
-    # Open with PIL and convert to desired mode
-    try:
-        pil_image = Image.open(image_bytesio)
-        pil_image = pil_image.convert(mode)
-    except Exception as e:
-        raise RuntimeError(f"Failed to open image: {e}")
-
-    image_array = np.asarray(pil_image).astype(np.float32) / 255.0
-    image_tensor = torch.from_numpy(image_array)
-    return image_tensor
-
-
-def image_tensor_to_png_bytes(tensor: torch.Tensor, filename: str = "image.png") -> BytesIO:
-    """
-    Convert ComfyUI image tensor to PNG BytesIO for multipart upload.
-
-    This function converts a ComfyUI IMAGE tensor to a PNG-encoded BytesIO object
-    suitable for multipart/form-data upload. The BytesIO object has its .name
-    attribute set, which is required by aiohttp for file uploads.
-
-    Args:
-        tensor: ComfyUI IMAGE tensor with shape (B, H, W, C), dtype float32, range [0, 1]
-        filename: Name attribute to set on BytesIO (default: "image.png")
-
-    Returns:
-        BytesIO object containing PNG-encoded image with .name attribute set
-
-    Raises:
-        ValueError: If tensor format is invalid (not 4D, wrong dtype, etc.)
-    """
-    if tensor.ndim != 4:
-        raise ValueError(f"Expected 4D tensor with shape (B, H, W, C), got {tensor.ndim}D tensor")
-
-    image_tensor = tensor[0]  # Shape: (H, W, C)
-    image_np = (image_tensor.cpu().numpy() * 255.0).astype(np.uint8)
-    pil_image = Image.fromarray(image_np)
-
-    # Save to BytesIO as image file
-    img_bytes = BytesIO()
-    # Set name attribute (required for multipart upload and mimetype detection)
-    img_bytes.name = filename
-    try:
-        pil_image.save(img_bytes)
-    except Exception as e:
-        raise RuntimeError(f"Failed to save image as file: {e}")
-
-    # Reset position to beginning
-    img_bytes.seek(0)
-
-    return img_bytes
-
-
-def image_tensor_to_base64(tensor: torch.Tensor, filename: str = "image.png") -> str:
-    """
-    Convert ComfyUI image tensor to base64-encoded image string.
-
-    Args:
-        tensor: ComfyUI IMAGE tensor with shape (B, H, W, C), dtype float32, range [0, 1]
-        filename: Name attribute to set on BytesIO (default: "image.png")
-        format: File format of the output image file buffer (default: "PNG")
-
-    Returns:
-        Base64-encoded image string
-
-    Raises:
-        ValueError: If tensor format is invalid (not 4D, wrong dtype, etc.)
-    """
-    img_bytes = image_tensor_to_png_bytes(tensor, filename)
-    img_bytes.seek(0)
-    byte_data = img_bytes.read()
-    base64_str = base64.b64encode(byte_data).decode("utf-8")
-    mime_type = mimetypes.guess_type(filename)[0] or "application/octet-stream"
-    return f"data:{mime_type};base64,{base64_str}"
-
-
-def video_to_bytes(video: VideoInput, filename: str = "video.mp4") -> BytesIO:
-    output_buffer = BytesIO()
-    output_buffer.name = filename
-    video.save_to(output_buffer)
-    output_buffer.seek(0)
-    return output_buffer
-
-
-def video_to_base64(video: VideoInput, filename: str = "video.mp4") -> str:
-    video_buffer = video_to_bytes(video, filename)
-    video_buffer.seek(0)
-    byte_data = video_buffer.read()
-    base64_str = base64.b64encode(byte_data).decode("utf-8")
-    mime_type = mimetypes.guess_type(filename)[0] or "application/octet-stream"
-    return f"data:{mime_type};base64,{base64_str}"
-
-
-def audio_to_bytes(audio: AudioInput, filename: str = "audio.mp3", quality: str = "128k") -> BytesIO:
-    waveform = audio["waveform"][0]  # Shape: (C, T)
-    sample_rate = audio["sample_rate"]
-    format = filename.rsplit(".", maxsplit=1)[1]
-    layout = "mono" if waveform.shape[0] == 1 else "stereo"
-
-    output_buffer = BytesIO()
-    output_buffer.name = filename
-    output_container = av.open(output_buffer, mode="w", format=format)
-    if format == "opus":
-        out_stream = output_container.add_stream("libopus", rate=sample_rate, layout=layout)
-        if quality == "64k":
-            out_stream.bit_rate = 64000  # type: ignore # copy from ComfyUI comfy_api/latest/_ui.py
-        elif quality == "96k":
-            out_stream.bit_rate = 96000  # type: ignore # copy from ComfyUI comfy_api/latest/_ui.py
-        elif quality == "128k":
-            out_stream.bit_rate = 128000  # type: ignore # copy from ComfyUI comfy_api/latest/_ui.py
-        elif quality == "192k":
-            out_stream.bit_rate = 192000  # type: ignore # copy from ComfyUI comfy_api/latest/_ui.py
-        elif quality == "320k":
-            out_stream.bit_rate = 320000  # type: ignore # copy from ComfyUI comfy_api/latest/_ui.py
-    elif format == "mp3":
-        out_stream = output_container.add_stream("libmp3lame", rate=sample_rate, layout=layout)
-        if quality == "V0":
-            out_stream.codec_context.qscale = 1  # type: ignore # copy from ComfyUI comfy_api/latest/_ui.py
-        elif quality == "128k":
-            out_stream.bit_rate = 128000  # type: ignore # copy from ComfyUI comfy_api/latest/_ui.py
-        elif quality == "320k":
-            out_stream.bit_rate = 320000  # type: ignore # copy from ComfyUI comfy_api/latest/_ui.py
-    else:  # format == "flac":
-        out_stream = output_container.add_stream("flac", rate=sample_rate, layout=layout)
-
-    frame = av.AudioFrame.from_ndarray(
-        waveform.movedim(0, 1).reshape(1, -1).float().numpy(),
-        format="flt",
-        layout=layout,
-    )
-    frame.sample_rate = sample_rate
-    frame.pts = 0
-    output_container.mux(out_stream.encode(frame))  # type: ignore # copy from ComfyUI comfy_api/latest/_ui.py
-    # Flush encoder
-    output_container.mux(out_stream.encode(None))  # type: ignore # copy from ComfyUI comfy_api/latest/_ui.py
-    output_container.close()
-    output_buffer.seek(0)
-
-    return output_buffer
-
-
-def audio_to_base64(audio: AudioInput, filename: str = "audio.mp3", quality: str = "128k") -> str:
-    audio_buffer = audio_to_bytes(audio, filename, quality)
-    audio_buffer.seek(0)
-    byte_data = audio_buffer.read()
-    base64_str = base64.b64encode(byte_data).decode("utf-8")
-    mime_type = mimetypes.guess_type(filename)[0] or "application/octet-stream"
-    return f"data:{mime_type};base64,{base64_str}"
-
-
-def bytes_to_audio(audio_bytes: bytes) -> AudioInput:
-    """
-    Convert audio bytes to ComfyUI audio tensor.
-
-    Args:
-        audio_bytes: Audio file bytes
-    Returns:
-        torch.Tensor with shape (B, C, T) in float32 range [-1, 1]
-    """
-    audio_buffer = BytesIO(audio_bytes)
-    waveform, sample_rate = nodes_audio.load(audio_buffer)  # type: ignore # Although expect string argument, it calls av.open underneath, which supports BytesIO (file-like)
-    return {"waveform": waveform.unsqueeze(0), "sample_rate": sample_rate}
-
-
-def base64_to_audio(base64_str: str) -> AudioInput:
-    """
-    Convert base64-encoded audio to ComfyUI audio tensor.
-
-    Args:
-        base64_str: Base64-encoded audio string
-    Returns:
-        torch.Tensor with shape (B, C, T) in float32 range [-1, 1]
-    """
-    if base64_str.startswith("data:audio"):
-        _, base64_str = base64_str.split(",", 1)
-
-    try:
-        # Decode base64 to bytes
-        audio_bytes = base64.b64decode(base64_str)
-    except Exception as e:
-        raise ValueError(f"Invalid base64 string: {e}")
-
-    return bytes_to_audio(audio_bytes)
@@ -1,125 +0,0 @@
-"""Centralized logger configuration for vLLM-Omni ComfyUI."""
-
-import logging
-import pprint
-import sys
-from typing import Any
-
-
-def get_logger(name: str) -> logging.Logger:
-    """
-    Get or create a logger with proper formatting.
-
-    Args:
-        name: Logger name (typically __name__ of the calling module)
-
-    Returns:
-        Configured logger instance
-    """
-    logger = logging.getLogger(name)
-
-    # Only configure if not already configured
-    if not logger.handlers:
-        logger.setLevel(logging.DEBUG)
-
-        # Create console handler
-        handler = logging.StreamHandler(sys.stdout)
-        handler.setLevel(logging.INFO)
-
-        # Create formatter
-        formatter = logging.Formatter(
-            fmt="(ComfyUI-vLLM-Omni) [%(levelname)s] %(asctime)s [%(filename)s:%(lineno)s] %(message)s",
-            datefmt="%Y-%m-%d %H:%M:%S",
-        )
-        handler.setFormatter(formatter)
-
-        # Add handler to logger
-        logger.addHandler(handler)
-
-        # Prevent propagation to root logger
-        logger.propagate = False
-
-    return logger
-
-
-class OmitBase64PrettyPrinter(pprint.PrettyPrinter):
-    """
-    A PrettyPrinter that redacts specific field names with '...'
-    wherever they appear in nested structures.
-    """
-
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-
-    def _format(self, obj: Any, stream, indent: int, allowance: int, context, level: int) -> None:
-        # Check if this is a dict with redacted keys
-        if isinstance(obj, dict):
-            # Create a copy with redacted values
-            display_obj = {}
-            for key, value in obj.items():
-                if key == "data" or key == "url":
-                    if value.startswith("data:"):
-                        base64_header = value.split(",", 1)[0]
-                        display_obj[key] = f"{base64_header},***"
-                    elif value.startswith("http://") or value.startswith("https://"):
-                        display_obj[key] = value
-                    elif len(value) > 10:
-                        display_obj[key] = f"{value[:10]}***"
-                    else:
-                        display_obj[key] = value
-                else:
-                    display_obj[key] = value
-            obj = display_obj
-
-        # Handle list/tuple/set containing dicts that might have redacted keys
-        # (pprint will recursively call _format on nested items, so this
-        # handles arbitrary nesting automatically)
-
-        super()._format(obj, stream, indent, allowance, context, level)
-
-
-pretty_printer = OmitBase64PrettyPrinter()
-
-
-# ========== PPrint EXAMPLES ==========
-
-if __name__ == "__main__":
-    data = {
-        "messages": [
-            {
-                "role": "system",
-                "content": (
-                    "You are Qwen, a virtual human developed by the Qwen Team, Alibaba Group,"
-                    "capable of perceiving auditory and visual inputs, as well as generating text and speech."
-                ),
-            },
-            {
-                "role": "user",
-                "content": [
-                    {
-                        "type": "text",
-                        "text": "What sound is it, and what is the drawing about?",
-                    },
-                    {
-                        "type": "text",
-                        "text": "What sound is it, and what is the drawing about?",
-                    },
-                    {
-                        "type": "image_url",
-                        "image_url": {"url": "data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAwAAAA"},
-                    },
-                    {
-                        "type": "audio_url",
-                        "audio_url": {"url": "data:audio/mpeg;base64,SUQzBAAAAAAAIlRTU0UAAAAOAAADT="},
-                    },
-                ],
-            },
-        ],
-        "extra_body": {"mm_processor_kwargs": {"use_audio_in_video": False}},
-        "modalities": ["text"],
-    }
-
-    # Create printer that redacts 'password' and 'token' fields
-
-    print("\nRedactingPrettyPrinter (hides secrets):")
-    pretty_printer.pprint(data)
@@ -1,118 +0,0 @@
-import re
-
-from .types import Modality, ModelMode, Spec
-
-
-def _bagel_payload_preprocessor(payload: dict) -> dict:
-    try:
-        for message in payload["messages"]:
-            for content in message["content"]:
-                if content["type"] == "text":
-                    content["text"] = "<|im_start|>" + content["text"] + "<|im_end|>"
-    except (KeyError, TypeError):
-        raise RuntimeError("Internal Error: malformatted BAGEL payload")
-    return payload
-
-
-def _qwen25_payload_preprocessor(payload: dict) -> dict:
-    if payload["messages"][0]["role"] != "system":
-        payload["messages"] = [
-            {
-                "role": "system",
-                "content": (
-                    "You are Qwen, a virtual human developed by the Qwen Team, Alibaba Group,"
-                    "capable of perceiving auditory and visual inputs, as well as generating text and speech."
-                ),
-            },
-            *payload["messages"],
-        ]
-    return payload
-
-
-_MODEL_PIPELINE_SPECS: dict[str, Spec] = {
-    r"BAGEL-7B-MoT": {
-        "stages": [
-            "diffusion"  # The vLLM-Omni interface treats it as a single-stage diffusion model
-        ],
-        "modes": [
-            {
-                "mode": ModelMode.UNDERSTANDING,
-                "input_modalities": [Modality.TEXT, Modality.IMAGE],
-            }
-        ],
-        "payload_preprocessor": _bagel_payload_preprocessor,
-    },
-    r"Qwen2.5-Omni*": {
-        "stages": ["autoregression", "autoregression", "autoregression"],
-        "payload_preprocessor": _qwen25_payload_preprocessor,
-        "modes": [
-            {
-                "mode": ModelMode.UNDERSTANDING,
-                "input_modalities": [
-                    Modality.TEXT,
-                    Modality.IMAGE,
-                    Modality.VIDEO,
-                    Modality.AUDIO,
-                ],
-            }
-        ],
-    },
-    r"Qwen3-Omni*": {
-        "stages": ["autoregression", "autoregression", "autoregression"],
-        "modes": [
-            {
-                "mode": ModelMode.UNDERSTANDING,
-                "input_modalities": [
-                    Modality.TEXT,
-                    Modality.IMAGE,
-                    Modality.VIDEO,
-                    Modality.AUDIO,
-                ],
-            }
-        ],
-    },
-}
-# Convert dict keys to regex patterns
-MODEL_PIPELINE_SPECS: dict[re.Pattern, Spec] = {}
-for k, v in _MODEL_PIPELINE_SPECS.items():
-    MODEL_PIPELINE_SPECS[re.compile(k)] = v
-del _MODEL_PIPELINE_SPECS
-
-
-def lookup_model_spec(model: str) -> tuple[Spec | None, str | None]:
-    try:
-        last_component = model.rstrip("/").rsplit("/", 1)[-1]
-    except IndexError:
-        last_component = model
-    for pattern, spec in MODEL_PIPELINE_SPECS.items():
-        if pattern.search(last_component):
-            return spec, pattern.pattern
-    return None, None
-
-
-# ============== DEMONSTRATION ==============
-
-if __name__ == "__main__":
-    test_paths = [
-        "Qwen/Qwen2.5-Omni-7B",
-        "MyModels/Qwen2.5-Omni-3B",
-        "/root/home/Qwen2.5-Omni-7B",
-        "Qwen/Qwen3-Omni",
-        "Qwen/Qwen3-Omni-30B-A3B-Instruct",
-        "Custom/Path/UnknownModel-Instruct",
-        "Not/Matching/Anything",
-    ]
-
-    test_payload = {"messages": [{"role": "user", "content": "prompt"}]}
-
-    print("Testing registry lookups:\n")
-    for path in test_paths:
-        spec, _ = lookup_model_spec(path)
-        if spec:
-            if preprocessor := spec.get("payload_preprocessor"):
-                result = preprocessor(test_payload)
-                print(f"✓ {path:<40} → {result}")
-            else:
-                print(f"✓ {path:<40} → No preprocessor")
-        else:
-            print(f"✗ {path:<40} → No match")
@@ -1,51 +0,0 @@
-from collections.abc import Callable
-from enum import Enum, auto
-from typing import (
-    Any,
-    Literal,
-    NotRequired,
-    TypeAlias,
-    TypedDict,
-)
-
-AudioFormat: TypeAlias = Literal["mp3", "opus", "aac", "flac", "wav", "pcm"]
-
-
-class AutoregressionSamplingParams(dict):
-    pass
-
-
-class DiffusionSamplingParams(dict):
-    pass
-
-
-class QwenTTSModelSpecificParams(dict):
-    pass
-
-
-class ModelMode(Enum):
-    IMAGE_GENERATION = auto()
-    VIDEO_GENERATION = auto()
-    AUDIO_GENERATION = auto()
-    UNDERSTANDING = auto()
-
-
-class Modality(Enum):
-    TEXT = auto()  # maybe not useful. Prompt is always required
-    IMAGE = auto()
-    VIDEO = auto()
-    AUDIO = auto()
-
-
-class ModelModeSpec(TypedDict):
-    mode: ModelMode
-    input_modalities: list[Modality]
-
-
-PayloadPreprocessor: TypeAlias = Callable[[dict[str, Any]], dict[str, Any]]
-
-
-class Spec(TypedDict):
-    stages: list[Literal["diffusion", "autoregression"]]
-    modes: list[ModelModeSpec]
-    payload_preprocessor: NotRequired[PayloadPreprocessor]
@@ -1,89 +0,0 @@
-from .logger import get_logger
-from .models import lookup_model_spec
-from .types import AutoregressionSamplingParams, DiffusionSamplingParams
-
-logger = get_logger(__name__)
-
-
-def validate_model_and_sampling_params_types(
-    model_name: str,
-    sampling_param_list: dict | list[dict] | None = None,
-):
-    # Check if model name exists
-    if not model_name:
-        raise ValueError("Model name must not be empty.")
-
-    # Skip if no spec or no sampling params
-    pipeline_spec, _ = lookup_model_spec(model_name)
-    if pipeline_spec is None:
-        logger.info("skipping sampling params check because spec is not found")
-        return
-    if sampling_param_list is None:
-        return
-
-    # Check the number of stages and their data types
-    stages = pipeline_spec["stages"]
-    if isinstance(sampling_param_list, list):
-        # Check that the lengths match
-        if len(stages) != len(sampling_param_list):
-            raise ValueError(
-                f"Sampling parameter list length {len(sampling_param_list)} does not match "
-                f"number of stages {len(stages)} for model {model_name}."
-            )
-        # Check that each stage's type match
-        for i, sp in enumerate(sampling_param_list):
-            if not _check_sampling_param_matches_stage(sp, stages[i]):
-                raise ValueError(
-                    f"Sampling parameter type ({sp.__class__.__name__}) does not match "
-                    f"stage type ({stages[i]}) at index {i} for model {model_name}."
-                )
-    elif isinstance(sampling_param_list, dict):
-        # Check that the provided single sampling param matches all stages
-        for i, stage in enumerate(stages):
-            if not _check_sampling_param_matches_stage(sampling_param_list, stage):
-                raise ValueError(
-                    f"Provided single sampling parameter type ({sampling_param_list.__class__.__name__}) must match "
-                    f"the types of all stages of the model. "
-                    f"However, stage {i} of model {model_name} is of type {stage}."
-                )
-
-
-def add_sampling_parameters_to_stage(
-    model_name: str,
-    sampling_param_list: dict | list[dict] | None,
-    stage_type: str,
-    /,
-    **params_to_add,
-) -> dict | list[dict]:
-    """
-    Given a model's name and the sampling parameter list to query this model,
-    add arbitrary additional parameters to the sampling parameters of all stages of the given type.
-    """
-    pipeline_spec, _ = lookup_model_spec(model_name)
-    if not pipeline_spec:
-        logger.warning(
-            f"Since the model {model_name} is not in our list, we cannot ensure if "
-            f"the fields ({tuple(params_to_add.keys())}) are added to the correct stage's sampling params. "
-            f"We will do it heuristically."
-        )
-        pipeline_spec = {"stages": ["diffusion"]}
-
-    stages = pipeline_spec["stages"]
-    if isinstance(sampling_param_list, dict):
-        sampling_param_list.update(params_to_add)
-    elif sampling_param_list is None:
-        sampling_param_list = params_to_add.copy()
-    else:
-        for i, stage in enumerate(stages):
-            if stage == stage_type:
-                sampling_param_list[i].update(params_to_add)
-
-    return sampling_param_list
-
-
-def _check_sampling_param_matches_stage(sampling_param: dict, stage_type: str) -> bool:
-    if stage_type == "autoregression":
-        return isinstance(sampling_param, AutoregressionSamplingParams)
-    if stage_type == "diffusion":
-        return isinstance(sampling_param, DiffusionSamplingParams)
-    raise RuntimeError(f"Internal error: unknown stage type {stage_type}.")
@@ -1,552 +0,0 @@
-{
-    "id": "6643e5fd-fa2a-4f25-935a-483173a8097c",
-    "revision": 0,
-    "last_node_id": 8,
-    "last_link_id": 6,
-    "nodes": [
-        {
-            "id": 1,
-            "type": "PreviewImage",
-            "pos": [
-                1446.2005205859512,
-                -316.7359686049902
-            ],
-            "size": [
-                305.6628685610174,
-                307.0336884172169
-            ],
-            "flags": {},
-            "order": 5,
-            "mode": 0,
-            "inputs": [
-                {
-                    "localized_name": "images",
-                    "name": "images",
-                    "type": "IMAGE",
-                    "link": 1
-                }
-            ],
-            "outputs": [],
-            "properties": {
-                "Node name for S&R": "PreviewImage"
-            },
-            "widgets_values": []
-        },
-        {
-            "id": 8,
-            "type": "PreviewImage",
-            "pos": [
-                1010.1848219273943,
-                -311.54216706114363
-            ],
-            "size": [
-                305.6628685610174,
-                307.0336884172169
-            ],
-            "flags": {},
-            "order": 4,
-            "mode": 0,
-            "inputs": [
-                {
-                    "localized_name": "images",
-                    "name": "images",
-                    "type": "IMAGE",
-                    "link": 6
-                }
-            ],
-            "outputs": [],
-            "properties": {
-                "Node name for S&R": "PreviewImage"
-            },
-            "widgets_values": []
-        },
-        {
-            "id": 7,
-            "type": "VLLMOmniDiffusionSampling",
-            "pos": [
-                234.61958293819296,
-                9.405681270043619
-            ],
-            "size": [
-                284.205078125,
-                226
-            ],
-            "flags": {},
-            "order": 0,
-            "mode": 0,
-            "inputs": [
-                {
-                    "localized_name": "n",
-                    "name": "n",
-                    "type": "INT",
-                    "widget": {
-                        "name": "n"
-                    },
-                    "link": null
-                },
-                {
-                    "localized_name": "num_inference_steps",
-                    "name": "num_inference_steps",
-                    "type": "INT",
-                    "widget": {
-                        "name": "num_inference_steps"
-                    },
-                    "link": null
-                },
-                {
-                    "localized_name": "guidance_scale",
-                    "name": "guidance_scale",
-                    "type": "FLOAT",
-                    "widget": {
-                        "name": "guidance_scale"
-                    },
-                    "link": null
-                },
-                {
-                    "localized_name": "true_cfg_scale",
-                    "name": "true_cfg_scale",
-                    "type": "FLOAT",
-                    "widget": {
-                        "name": "true_cfg_scale"
-                    },
-                    "link": null
-                },
-                {
-                    "localized_name": "vae_use_slicing",
-                    "name": "vae_use_slicing",
-                    "type": "BOOLEAN",
-                    "widget": {
-                        "name": "vae_use_slicing"
-                    },
-                    "link": null
-                },
-                {
-                    "localized_name": "vae_use_tiling",
-                    "name": "vae_use_tiling",
-                    "type": "BOOLEAN",
-                    "widget": {
-                        "name": "vae_use_tiling"
-                    },
-                    "link": null
-                },
-                {
-                    "localized_name": "seed",
-                    "name": "seed",
-                    "type": "INT",
-                    "widget": {
-                        "name": "seed"
-                    },
-                    "link": null
-                }
-            ],
-            "outputs": [
-                {
-                    "localized_name": "diffusion sampling params",
-                    "name": "diffusion sampling params",
-                    "type": "SAMPLING_PARAMS",
-                    "links": [
-                        5
-                    ]
-                }
-            ],
-            "properties": {
-                "Node name for S&R": "VLLMOmniDiffusionSampling"
-            },
-            "widgets_values": [
-                1,
-                50,
-                1,
-                1,
-                false,
-                false,
-                1525,
-                "randomize"
-            ]
-        },
-        {
-            "id": 4,
-            "type": "VLLMOmniDiffusionSampling",
-            "pos": [
-                666.8380026154548,
-                268.86271068330126
-            ],
-            "size": [
-                284.205078125,
-                226
-            ],
-            "flags": {},
-            "order": 1,
-            "mode": 0,
-            "inputs": [
-                {
-                    "localized_name": "n",
-                    "name": "n",
-                    "type": "INT",
-                    "widget": {
-                        "name": "n"
-                    },
-                    "link": null
-                },
-                {
-                    "localized_name": "num_inference_steps",
-                    "name": "num_inference_steps",
-                    "type": "INT",
-                    "widget": {
-                        "name": "num_inference_steps"
-                    },
-                    "link": null
-                },
-                {
-                    "localized_name": "guidance_scale",
-                    "name": "guidance_scale",
-                    "type": "FLOAT",
-                    "widget": {
-                        "name": "guidance_scale"
-                    },
-                    "link": null
-                },
-                {
-                    "localized_name": "true_cfg_scale",
-                    "name": "true_cfg_scale",
-                    "type": "FLOAT",
-                    "widget": {
-                        "name": "true_cfg_scale"
-                    },
-                    "link": null
-                },
-                {
-                    "localized_name": "vae_use_slicing",
-                    "name": "vae_use_slicing",
-                    "type": "BOOLEAN",
-                    "widget": {
-                        "name": "vae_use_slicing"
-                    },
-                    "link": null
-                },
-                {
-                    "localized_name": "vae_use_tiling",
-                    "name": "vae_use_tiling",
-                    "type": "BOOLEAN",
-                    "widget": {
-                        "name": "vae_use_tiling"
-                    },
-                    "link": null
-                },
-                {
-                    "localized_name": "seed",
-                    "name": "seed",
-                    "type": "INT",
-                    "widget": {
-                        "name": "seed"
-                    },
-                    "link": null
-                }
-            ],
-            "outputs": [
-                {
-                    "localized_name": "diffusion sampling params",
-                    "name": "diffusion sampling params",
-                    "type": "SAMPLING_PARAMS",
-                    "links": [
-                        2
-                    ]
-                }
-            ],
-            "properties": {
-                "Node name for S&R": "VLLMOmniDiffusionSampling"
-            },
-            "widgets_values": [
-                4,
-                50,
-                7,
-                1,
-                false,
-                false,
-                42,
-                "fixed"
-            ]
-        },
-        {
-            "id": 5,
-            "type": "VLLMOmniGenerateImage",
-            "pos": [
-                984.723613585788,
-                63.376900027553276
-            ],
-            "size": [
-                416.56628685610167,
-                372.1662621294205
-            ],
-            "flags": {},
-            "order": 3,
-            "mode": 0,
-            "inputs": [
-                {
-                    "localized_name": "image",
-                    "name": "image",
-                    "shape": 7,
-                    "type": "IMAGE",
-                    "link": 4
-                },
-                {
-                    "localized_name": "mask",
-                    "name": "mask",
-                    "shape": 7,
-                    "type": "MASK",
-                    "link": null
-                },
-                {
-                    "localized_name": "sampling_params",
-                    "name": "sampling_params",
-                    "shape": 7,
-                    "type": "SAMPLING_PARAMS",
-                    "link": 2
-                },
-                {
-                    "localized_name": "url",
-                    "name": "url",
-                    "type": "STRING",
-                    "widget": {
-                        "name": "url"
-                    },
-                    "link": null
-                },
-                {
-                    "localized_name": "model",
-                    "name": "model",
-                    "type": "STRING",
-                    "widget": {
-                        "name": "model"
-                    },
-                    "link": null
-                },
-                {
-                    "localized_name": "prompt",
-                    "name": "prompt",
-                    "type": "STRING",
-                    "widget": {
-                        "name": "prompt"
-                    },
-                    "link": null
-                },
-                {
-                    "localized_name": "negative_prompt",
-                    "name": "negative_prompt",
-                    "type": "STRING",
-                    "widget": {
-                        "name": "negative_prompt"
-                    },
-                    "link": null
-                },
-                {
-                    "localized_name": "width",
-                    "name": "width",
-                    "type": "INT",
-                    "widget": {
-                        "name": "width"
-                    },
-                    "link": null
-                },
-                {
-                    "localized_name": "height",
-                    "name": "height",
-                    "type": "INT",
-                    "widget": {
-                        "name": "height"
-                    },
-                    "link": null
-                }
-            ],
-            "outputs": [
-                {
-                    "localized_name": "image",
-                    "name": "image",
-                    "type": "IMAGE",
-                    "links": [
-                        1
-                    ]
-                }
-            ],
-            "properties": {
-                "Node name for S&R": "VLLMOmniGenerateImage"
-            },
-            "widgets_values": [
-                "http://localhost:8001/v1",
-                "/home/models/Qwen/Qwen-Image-Edit",
-                "A high-quality, high contrast, stylized portrait of the object in the uploaded reference image. Pop art and doodle style, with abundant scribbling patterns, such as a teal crown, orange lightning bolts, colorful handwritten scripts.",
-                "Realistic",
-                800,
-                800
-            ]
-        },
-        {
-            "id": 6,
-            "type": "VLLMOmniGenerateImage",
-            "pos": [
-                541.2702717429818,
-                -160.70392744708548
-            ],
-            "size": [
-                416.56628685610167,
-                372.1662621294205
-            ],
-            "flags": {},
-            "order": 2,
-            "mode": 0,
-            "inputs": [
-                {
-                    "localized_name": "image",
-                    "name": "image",
-                    "shape": 7,
-                    "type": "IMAGE",
-                    "link": null
-                },
-                {
-                    "localized_name": "mask",
-                    "name": "mask",
-                    "shape": 7,
-                    "type": "MASK",
-                    "link": null
-                },
-                {
-                    "localized_name": "sampling_params",
-                    "name": "sampling_params",
-                    "shape": 7,
-                    "type": "SAMPLING_PARAMS",
-                    "link": 5
-                },
-                {
-                    "localized_name": "url",
-                    "name": "url",
-                    "type": "STRING",
-                    "widget": {
-                        "name": "url"
-                    },
-                    "link": null
-                },
-                {
-                    "localized_name": "model",
-                    "name": "model",
-                    "type": "STRING",
-                    "widget": {
-                        "name": "model"
-                    },
-                    "link": null
-                },
-                {
-                    "localized_name": "prompt",
-                    "name": "prompt",
-                    "type": "STRING",
-                    "widget": {
-                        "name": "prompt"
-                    },
-                    "link": null
-                },
-                {
-                    "localized_name": "negative_prompt",
-                    "name": "negative_prompt",
-                    "type": "STRING",
-                    "widget": {
-                        "name": "negative_prompt"
-                    },
-                    "link": null
-                },
-                {
-                    "localized_name": "width",
-                    "name": "width",
-                    "type": "INT",
-                    "widget": {
-                        "name": "width"
-                    },
-                    "link": null
-                },
-                {
-                    "localized_name": "height",
-                    "name": "height",
-                    "type": "INT",
-                    "widget": {
-                        "name": "height"
-                    },
-                    "link": null
-                }
-            ],
-            "outputs": [
-                {
-                    "localized_name": "image",
-                    "name": "image",
-                    "type": "IMAGE",
-                    "links": [
-                        4,
-                        6
-                    ]
-                }
-            ],
-            "properties": {
-                "Node name for S&R": "VLLMOmniGenerateImage"
-            },
-            "widgets_values": [
-                "http://localhost:8000/v1",
-                "/home/models/Tongyi-MAI/Z-Image-Turbo",
-                "A headshot of a cute Siamese kitty. Blurred background due to wide aperture. Close-up look. Realistic.",
-                "Cartoonish.",
-                800,
-                800
-            ]
-        }
-    ],
-    "links": [
-        [
-            1,
-            5,
-            0,
-            1,
-            0,
-            "IMAGE"
-        ],
-        [
-            2,
-            4,
-            0,
-            5,
-            2,
-            "SAMPLING_PARAMS"
-        ],
-        [
-            4,
-            6,
-            0,
-            5,
-            0,
-            "IMAGE"
-        ],
-        [
-            5,
-            7,
-            0,
-            6,
-            2,
-            "SAMPLING_PARAMS"
-        ],
-        [
-            6,
-            6,
-            0,
-            8,
-            0,
-            "IMAGE"
-        ]
-    ],
-    "groups": [],
-    "config": {},
-    "extra": {
-        "workflowRendererVersion": "LG",
-        "ds": {
-            "scale": 1.0426432563169903,
-            "offset": [
-                -90.4071374799346,
-                575.2948920069742
-            ]
-        }
-    },
-    "version": 0.4
-}
@@ -1 +0,0 @@
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-            "title": "Note: Sampling Parameters",
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-            "widgets_values": [
-                "## Sampling Parameter Types\n\nThere are two types of sampling parameters: one for autoregression and one for diffusion.\nYou should ensure that you have chosen the correct type of sampling parameters for the model you request.\n\n## Stages & Shorthand\n\nFor multi-stage models such as Qwen Omni, you can either\n- connect one sampling parameter node, which is applied to all stages.\n- connect exactly the same number of sampling parameter nodes to a \"Multi-Stage Sampling Parameter List\" node, then connect this node to the primary request node.\n\nNote that this shorthand is intended to stay consistent with the [online serving API](https://docs.vllm.ai/projects/vllm-omni/en/latest/user_guide/examples/online_serving/qwen2_5_omni/)"
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-                307.1974296197726
-            ]
-        }
-    },
-    "version": 0.4
-}
@@ -1 +0,0 @@
-{"id":"c0a11241-f0e6-4f5c-8488-b3d344a9df3d","revision":0,"last_node_id":7,"last_link_id":3,"nodes":[{"id":4,"type":"PreviewAudio","pos":[690.1759063763212,226.76444957981545],"size":[270,88],"flags":{},"order":5,"mode":0,"inputs":[{"localized_name":"audio","name":"audio","type":"AUDIO","link":2},{"localized_name":"audioUI","name":"audioUI","type":"AUDIO_UI","widget":{"name":"audioUI"},"link":null}],"outputs":[],"properties":{"Node name for S&R":"PreviewAudio"},"widgets_values":[]},{"id":5,"type":"LoadAudio","pos":[-83.61093919682321,103.80510898465587],"size":[282.798828125,136],"flags":{},"order":0,"mode":0,"inputs":[{"localized_name":"audio","name":"audio","type":"COMBO","widget":{"name":"audio"},"link":null},{"localized_name":"audioUI","name":"audioUI","type":"AUDIO_UI","widget":{"name":"audioUI"},"link":null},{"localized_name":"choose file to upload","name":"upload","type":"AUDIOUPLOAD","widget":{"name":"upload"},"link":null}],"outputs":[{"localized_name":"AUDIO","name":"AUDIO","type":"AUDIO","links":[3]}],"properties":{"Node name for S&R":"LoadAudio"},"widgets_values":["Megan-Fox.mp3",null,null]},{"id":3,"type":"VLLMOmniVoiceClone","pos":[248.63466280064262,168.13112893706375],"size":[400,306],"flags":{},"order":4,"mode":0,"inputs":[{"localized_name":"ref_audio","name":"ref_audio","type":"AUDIO","link":3},{"localized_name":"model_specific_params","name":"model_specific_params","shape":7,"type":"TTS_PARAMS","link":1},{"localized_name":"url","name":"url","type":"STRING","widget":{"name":"url"},"link":null},{"localized_name":"model","name":"model","type":"STRING","widget":{"name":"model"},"link":null},{"localized_name":"input","name":"input","type":"STRING","widget":{"name":"input"},"link":null},{"localized_name":"voice","name":"voice","type":"STRING","widget":{"name":"voice"},"link":null},{"localized_name":"response_format","name":"response_format","type":"COMBO","widget":{"name":"response_format"},"link":null},{"localized_name":"speed","name":"speed","type":"FLOAT","widget":{"name":"speed"},"link":null},{"localized_name":"ref_text","name":"ref_text","type":"STRING","widget":{"name":"ref_text"},"link":null},{"localized_name":"x_vector_only_mode","name":"x_vector_only_mode","type":"BOOLEAN","widget":{"name":"x_vector_only_mode"},"link":null}],"outputs":[{"localized_name":"audio","name":"audio","type":"AUDIO","links":[2]}],"properties":{"Node name for S&R":"VLLMOmniVoiceClone"},"widgets_values":["http://localhost:8000/v1","Qwen/Qwen3-TTS-12Hz-1.7B-Base","Someone just spilled a cup of coffee on my jacket this morning in the subway. Now I have to wear this stained jacket for a whole day!","","mp3",1,"",true]},{"id":6,"type":"MarkdownNote","pos":[255.52667408769662,27.26842775454861],"size":[381.58204108637847,88],"flags":{},"order":2,"mode":0,"inputs":[],"outputs":[],"title":"Note: TTS Nodes","properties":{},"widgets_values":["Apart from the Voice Cloning node, there is also another TTS node for simple text-to-speech tasks (without extra inputs for reference audio)."],"color":"#432","bgcolor":"#000"},{"id":7,"type":"MarkdownNote","pos":[-196.99453003423977,598.3693693727904],"size":[382.4539509209102,88],"flags":{},"order":3,"mode":0,"inputs":[],"outputs":[],"title":"Note: Model-Specific Parameters","properties":{},"widgets_values":["TTS models often require some tailor-made parameters. If you need to customize these parameters, grab one that matches the model you are requesting from \"TTS Params\" subfolder."],"color":"#432","bgcolor":"#000"},{"id":2,"type":"VLLMOmniQwenTTSParams","pos":[-207.8925310560497,342.7942867650348],"size":[400,200],"flags":{},"order":1,"mode":0,"inputs":[{"localized_name":"task_type","name":"task_type","type":"COMBO","widget":{"name":"task_type"},"link":null},{"localized_name":"language","name":"language","type":"COMBO","widget":{"name":"language"},"link":null},{"localized_name":"instructions","name":"instructions","type":"STRING","widget":{"name":"instructions"},"link":null},{"localized_name":"max_new_tokens","name":"max_new_tokens","type":"INT","widget":{"name":"max_new_tokens"},"link":null}],"outputs":[{"localized_name":"Qwen TTS params","name":"Qwen TTS params","type":"TTS_PARAMS","links":[1]}],"properties":{"Node name for S&R":"VLLMOmniQwenTTSParams"},"widgets_values":["Base","Auto","Super angry",2048]}],"links":[[1,2,0,3,1,"TTS_PARAMS"],[2,3,0,4,0,"AUDIO"],[3,5,0,3,0,"AUDIO"]],"groups":[{"id":1,"title":"Model-Specific Parameters","bounding":[-228.38328407738007,264.42790274716276,443.9887293164586,439.6853171248897],"color":"#3f789e","font_size":24,"flags":{}}],"config":{},"extra":{"workflowRendererVersion":"LG","ds":{"scale":1.1469075819486911,"offset":[898.8819468322333,258.7179979718389]}},"version":0.4}
@@ -1,21 +0,0 @@
-/**
- * @file This file is intended to add dynamic fields to vLLM-Omni nodes
- * based on widget (in-node form fields) and input (connection link) values and changes.
- * However, this functionality is currently disabled/commented out.
- * Because it introduces too much complexity,
- * and it may even conflict with the current backend (Python) validation for unknown reasons (pending ComfyUI upstream fixes).
- */
-
-import { app } from "../../scripts/app.js";
-app.registerExtension({
-    name: "vllm.vllm_omni",
-    async beforeRegisterNodeDef(nodeType, nodeData, app) {
-        if (!nodeData.name.startsWith("VLLMOmni")) {
-            return
-        }
-        // Stub frontend plugin for now
-    },
-    async setup() {
-        console.info("vLLM-Omni Setup complete!")
-    },
-})
@@ -56,7 +56,7 @@ python extract_tts_prompts.py -i meta.lst -o top100.txt -n 100
 **Options:**
 - `-i, --input`: Input metadata file (default: `meta.lst`)
 - `-o, --output`: Output prompts file (default: `prompts.txt`)
- `-n, --num-lines`: Number of prompts to extract (required)
+- `-n, --num_lines`: Number of prompts to extract (required)

 ### 7. Clean Up (Optional)

@@ -56,7 +56,7 @@ def main():
        "-o", "--output", type=str, default="prompts.txt", help="Output txt file path (default: prompts.txt)"
    )
    parser.add_argument(
-        "-n", "--num-lines", type=int, required=True, help="Number of lines to extract from the beginning"
+        "-n", "--num_lines", type=int, required=True, help="Number of lines to extract from the beginning"
    )

    args = parser.parse_args()
@@ -1,198 +0,0 @@
-import base64
-import mimetypes
-import os
-import time
-import uuid
-from dataclasses import dataclass, field
-from typing import Any
-
-import aiohttp
-from tqdm import tqdm
-
-
-@dataclass
-class RequestFuncInput:
-    prompt: str
-    api_url: str
-    model: str
-    width: int | None = None
-    height: int | None = None
-    num_frames: int | None = None
-    num_inference_steps: int | None = None
-    seed: int | None = None
-    fps: int | None = None
-    timestamp: float | None = None
-    slo_ms: float | None = None
-    extra_body: dict[str, Any] = field(default_factory=dict)
-    image_paths: list[str] | None = None
-    request_id: str = field(default_factory=lambda: str(uuid.uuid4()))
-
-
-@dataclass
-class RequestFuncOutput:
-    success: bool = False
-    latency: float = 0.0
-    error: str = ""
-    start_time: float = 0.0
-    response_body: dict[str, Any] = field(default_factory=dict)
-    peak_memory_mb: float = 0.0
-    slo_achieved: bool | None = None
-
-
-def _guess_mime_type(path: str) -> str:
-    mime, _ = mimetypes.guess_type(path)
-    return mime or "application/octet-stream"
-
-
-def _encode_image_as_data_url(path: str) -> str:
-    with open(path, "rb") as f:
-        encoded = base64.b64encode(f.read()).decode("utf-8")
-    mime = _guess_mime_type(path)
-    return f"data:{mime};base64,{encoded}"
-
-
-async def async_request_chat_completions(
-    input: RequestFuncInput,
-    session: aiohttp.ClientSession,
-    pbar: tqdm | None = None,
-) -> RequestFuncOutput:
-    output = RequestFuncOutput()
-    output.start_time = time.perf_counter()
-
-    extra_body = dict(input.extra_body)
-    if input.width and input.height:
-        extra_body.setdefault("height", input.height)
-        extra_body.setdefault("width", input.width)
-    if input.num_frames:
-        extra_body.setdefault("num_frames", input.num_frames)
-    if input.num_inference_steps:
-        extra_body.setdefault("num_inference_steps", input.num_inference_steps)
-    if input.seed is not None:
-        extra_body.setdefault("seed", input.seed)
-    if input.fps:
-        extra_body.setdefault("fps", input.fps)
-
-    if input.image_paths and len(input.image_paths) > 0:
-        content = []
-        if input.prompt:
-            content.append({"type": "text", "text": input.prompt})
-        for img_path in input.image_paths:
-            if not os.path.exists(img_path):
-                output.error = f"Image file not found: {img_path}"
-                output.success = False
-                if pbar:
-                    pbar.update(1)
-                return output
-            content.append(
-                {
-                    "type": "image_url",
-                    "image_url": {"url": _encode_image_as_data_url(img_path)},
-                }
-            )
-        messages = [{"role": "user", "content": content}]
-    else:
-        messages = [{"role": "user", "content": input.prompt}]
-
-    payload = {
-        "model": input.model,
-        "messages": messages,
-    }
-    if extra_body:
-        payload["extra_body"] = extra_body
-
-    try:
-        async with session.post(input.api_url, json=payload) as response:
-            if response.status == 200:
-                resp_json = await response.json()
-                output.response_body = resp_json
-                output.success = True
-                if "peak_memory_mb" in resp_json:
-                    output.peak_memory_mb = resp_json["peak_memory_mb"]
-            else:
-                output.error = f"HTTP {response.status}: {await response.text()}"
-                output.success = False
-    except Exception as e:
-        output.error = str(e)
-        output.success = False
-
-    output.latency = time.perf_counter() - output.start_time
-
-    if output.success and input.slo_ms is not None:
-        output.slo_achieved = (output.latency * 1000.0) <= float(input.slo_ms)
-
-    if pbar:
-        pbar.update(1)
-    return output
-
-
-async def async_request_openai_images(
-    input: RequestFuncInput,
-    session: aiohttp.ClientSession,
-    pbar: tqdm | None = None,
-) -> RequestFuncOutput:
-    """
-    Send request to OpenAI's /v1/images/generations endpoint.
-    """
-    output = RequestFuncOutput()
-    output.start_time = time.perf_counter()
-
-    # Build size string from width/height
-    width = input.width or 1024
-    height = input.height or 1024
-    size = f"{width}x{height}"
-
-    payload: dict[str, Any] = {
-        "model": input.model,
-        "prompt": input.prompt,
-        "n": 1,
-        "size": size,
-        "response_format": "b64_json",
-    }
-
-    # Add optional parameters
-    if input.seed is not None:
-        payload["seed"] = input.seed
-    if input.num_inference_steps is not None:
-        payload["num_inference_steps"] = input.num_inference_steps
-
-    # Add any extra body parameters
-    if input.extra_body:
-        for key, value in input.extra_body.items():
-            if key not in payload:
-                payload[key] = value
-
-    headers = {
-        "Content-Type": "application/json",
-        "Authorization": "Bearer EMPTY",
-    }
-
-    try:
-        async with session.post(input.api_url, json=payload, headers=headers) as response:
-            if response.status == 200:
-                resp_json = await response.json()
-                output.response_body = resp_json
-                output.success = True
-                # Check for usage/memory info if available
-                if "usage" in resp_json and "peak_memory_mb" in resp_json.get("usage", {}):
-                    output.peak_memory_mb = resp_json["usage"]["peak_memory_mb"]
-            else:
-                output.error = f"HTTP {response.status}: {await response.text()}"
-                output.success = False
-    except Exception as e:
-        output.error = str(e)
-        output.success = False
-
-    output.latency = time.perf_counter() - output.start_time
-
-    if output.success and input.slo_ms is not None:
-        output.slo_achieved = (output.latency * 1000.0) <= float(input.slo_ms)
-
-    if pbar:
-        pbar.update(1)
-    return output
-
-
-backends_function_mapping = {
-    "vllm-omni": (async_request_chat_completions, "/v1/chat/completions"),
-    "openai": (async_request_openai_images, "/v1/images/generations"),
-}
@@ -6,60 +6,80 @@
 Benchmark online serving for diffusion models (Image/Video Generation).
 If you want to use i2v, i2i dataset, you should `uv pip install gdown` first

-Supports multiple backends:
-    - vllm-omni: Uses /v1/chat/completions endpoint (default)
-    - openai: Uses /v1/images/generations endpoint
-
 Usage:
-    # Video (vllm-omni backend)
+    # Video
    t2v:
    python3 benchmarks/diffusion/diffusion_benchmark_serving.py \
-        --backend vllm-omni --dataset vbench --task t2v --num-prompts 10 \
+        --dataset vbench --task t2v --num-prompts 10 \
        --height 480 --width 640 --fps 16 --num-frames 80

    i2v:
    python3 benchmarks/diffusion/diffusion_benchmark_serving.py \
-        --backend vllm-omni --dataset vbench --task i2v --num-prompts 10
+        --dataset vbench --task i2v --num-prompts 10


-    # Image (vllm-omni backend)
+    # Image
    t2i:
    python3 benchmarks/diffusion/diffusion_benchmark_serving.py \
-        --backend vllm-omni --dataset vbench --task t2i --num-prompts 10 \
+        --dataset vbench --task t2i --num-prompts 10 \
        --height 1024 --width 1024

    i2i:
    python3 benchmarks/diffusion/diffusion_benchmark_serving.py \
-        --backend vllm-omni --dataset vbench --task i2i --num-prompts 10
-
-    # Image (openai backend)
-    t2i:
-    python3 benchmarks/diffusion/diffusion_benchmark_serving.py \
-        --backend openai --dataset vbench --task t2i --num-prompts 10 \
-        --height 1024 --width 1024 --port 3000
+        --dataset vbench --task i2i --num-prompts 10

 """

 import argparse
 import ast
 import asyncio
+import base64
 import glob
 import json
+import mimetypes
 import os
 import time
 import uuid
 from abc import ABC, abstractmethod
 from collections.abc import AsyncGenerator
-from dataclasses import replace
+from dataclasses import dataclass, field, replace
 from typing import Any

 import aiohttp
 import numpy as np
 import requests
-from backends import RequestFuncInput, RequestFuncOutput, backends_function_mapping
 from tqdm.asyncio import tqdm


+@dataclass
+class RequestFuncInput:
+    prompt: str
+    api_url: str
+    model: str
+    width: int | None = None
+    height: int | None = None
+    num_frames: int | None = None
+    num_inference_steps: int | None = None
+    seed: int | None = None
+    fps: int | None = None
+    timestamp: float | None = None
+    slo_ms: float | None = None
+    extra_body: dict[str, Any] = field(default_factory=dict)
+    image_paths: list[str] | None = None
+    request_id: str = field(default_factory=lambda: str(uuid.uuid4()))
+
+
+@dataclass
+class RequestFuncOutput:
+    success: bool = False
+    latency: float = 0.0
+    error: str = ""
+    start_time: float = 0.0
+    response_body: dict[str, Any] = field(default_factory=dict)
+    peak_memory_mb: float = 0.0
+    slo_achieved: bool | None = None
+
+
 class BaseDataset(ABC):
    def __init__(self, args, api_url: str, model: str):
        self.args = args
@@ -664,6 +684,92 @@ async def iter_requests(
        yield req


+def _guess_mime_type(path: str) -> str:
+    mime, _ = mimetypes.guess_type(path)
+    return mime or "application/octet-stream"
+
+
+def _encode_image_as_data_url(path: str) -> str:
+    with open(path, "rb") as f:
+        encoded = base64.b64encode(f.read()).decode("utf-8")
+    mime = _guess_mime_type(path)
+    return f"data:{mime};base64,{encoded}"
+
+
+async def async_request_chat_completions(
+    input: RequestFuncInput,
+    session: aiohttp.ClientSession,
+    pbar: tqdm | None = None,
+) -> RequestFuncOutput:
+    output = RequestFuncOutput()
+    output.start_time = time.perf_counter()
+
+    extra_body = dict(input.extra_body)
+    if input.width and input.height:
+        extra_body.setdefault("height", input.height)
+        extra_body.setdefault("width", input.width)
+    if input.num_frames:
+        extra_body.setdefault("num_frames", input.num_frames)
+    if input.num_inference_steps:
+        extra_body.setdefault("num_inference_steps", input.num_inference_steps)
+    if input.seed is not None:
+        extra_body.setdefault("seed", input.seed)
+    if input.fps:
+        extra_body.setdefault("fps", input.fps)
+
+    if input.image_paths and len(input.image_paths) > 0:
+        content = []
+        if input.prompt:
+            content.append({"type": "text", "text": input.prompt})
+        for img_path in input.image_paths:
+            if not os.path.exists(img_path):
+                output.error = f"Image file not found: {img_path}"
+                output.success = False
+                if pbar:
+                    pbar.update(1)
+                return output
+            content.append(
+                {
+                    "type": "image_url",
+                    "image_url": {"url": _encode_image_as_data_url(img_path)},
+                }
+            )
+        messages = [{"role": "user", "content": content}]
+    else:
+        messages = [{"role": "user", "content": input.prompt}]
+
+    payload = {
+        "model": input.model,
+        "messages": messages,
+    }
+    if extra_body:
+        payload["extra_body"] = extra_body
+
+    try:
+        async with session.post(input.api_url, json=payload) as response:
+            if response.status == 200:
+                resp_json = await response.json()
+                output.response_body = resp_json
+                output.success = True
+                if "peak_memory_mb" in resp_json:
+                    output.peak_memory_mb = resp_json["peak_memory_mb"]
+            else:
+                output.error = f"HTTP {response.status}: {await response.text()}"
+                output.success = False
+    except Exception as e:
+        output.error = str(e)
+        output.success = False
+
+    output.latency = time.perf_counter() - output.start_time
+
+    if output.success and input.slo_ms is not None:
+        output.slo_achieved = (output.latency * 1000.0) <= float(input.slo_ms)
+
+    if pbar:
+        pbar.update(1)
+    return output
+
+
 def calculate_metrics(
    outputs: list[RequestFuncOutput],
    total_duration: float,
@@ -742,9 +848,9 @@ async def benchmark(args):
    if args.base_url is None:
        args.base_url = f"http://{args.host}:{args.port}"

-    # Setup API URL and request function based on backend
-    request_func, api_url = backends_function_mapping[args.backend]
-    api_url = f"{args.base_url}{api_url}"
+    # Setup dataset (vLLM-Omni supports diffusion via /v1/chat/completions)
+    api_url = f"{args.base_url}/v1/chat/completions"
+    request_func = async_request_chat_completions

    if args.dataset == "vbench":
        dataset = VBenchDataset(args, api_url, args.model)
@@ -814,16 +920,9 @@ async def benchmark(args):
    # Calculate metrics
    metrics = calculate_metrics(outputs, total_duration, requests_list, args, args.slo)

-    # Add configuration info to metrics for JSON output
-    metrics["backend"] = args.backend
-    metrics["model"] = args.model
-    metrics["dataset"] = args.dataset
-    metrics["task"] = args.task
-
    print("\n{s:{c}^{n}}".format(s=" Serving Benchmark Result ", n=60, c="="))

    # Section 1: Configuration
-    print("{:<40} {:<15}".format("Backend:", args.backend))
    print("{:<40} {:<15}".format("Model:", args.model))
    print("{:<40} {:<15}".format("Dataset:", args.dataset))
    print("{:<40} {:<15}".format("Task:", args.task))
@@ -879,13 +978,6 @@ if __name__ == "__main__":
    parser.add_argument("--host", type=str, default="localhost", help="Server host.")
    parser.add_argument("--port", type=int, default=8091, help="Server port.")
    parser.add_argument("--model", type=str, default="default", help="Model name.")
-    parser.add_argument(
-        "--backend",
-        type=str,
-        default="vllm-omni",
-        choices=["vllm-omni", "openai"],
-        help="Backend to target the benchmark to.",
-    )
    parser.add_argument(
        "--dataset",
        type=str,
@@ -1,397 +0,0 @@
-# RDMA Test Configuration Guide
-
-This document explains how to configure the RDMA environment and run tests for `MooncakeTransferEngineConnector`.
-
-## Table of Contents
-
- [Docker Container Permissions](#docker-container-permissions)
- [Single-Node Testing](#single-node-testing)
- [Multi-Node Testing](#multi-node-testing)
- [Running Tests](#running-tests)
- [Cross-Node Testing](#cross-node-testing)
- [Troubleshooting](#troubleshooting)
-
---
-
-## Docker Container Permissions
-
-RDMA tests require access to InfiniBand/RoCE devices and system topology. Add the following permissions when running `docker run`.
-
-### Option 1: Minimal Permissions (Recommended)
-
-```bash
-docker run -it \
-    --cap-add=SYS_PTRACE \
-    --cap-add=IPC_LOCK \
-    --security-opt seccomp=unconfined \
-    --network=host \
-    --device=/dev/infiniband \
-    -v /sys/class/infiniband:/sys/class/infiniband:ro \
-    your-image:tag
-```
-
-Parameter explanation:
- `--cap-add=SYS_PTRACE`: Allow reading system topology information
- `--cap-add=IPC_LOCK`: Allow memory locking (required for RDMA memory registration)
- `--security-opt seccomp=unconfined`: Disable seccomp restrictions
- `--network=host`: Use host network (required for RDMA)
- `--device=/dev/infiniband`: Mount InfiniBand devices
- `-v /sys/class/infiniband`: Mount IB device info (read-only)
-
-### Option 2: Full Permissions (Quick but not recommended for production)
-
-```bash
-docker run -it \
-    --privileged \
-    --network=host \
-    your-image:tag
-```
-
-`--privileged` grants full host permissions. Suitable for quick testing but not recommended for production.
-
---
-
-## Single-Node Testing
-
-When running single-node tests (producer and consumer on the same machine), ensure they use the **same RDMA device**.
-
-### Problem Background
-
-InfiniBand devices use LID (Local Identifier) for routing. Different devices have different LIDs and cannot communicate directly. If no device is specified, Mooncake may assign different devices to connectors, causing handshake failures.
-
-Common error:
-```
-[Handshake] Failed to modify QP to RTR, check mtu, gid, peer lid, peer qp num: Invalid argument [22]
-```
-
-### Solution
-
-**Method 1: Set Environment Variable (Recommended)**
-
-```bash
-# List available RDMA devices
-ibstat
-
-# Select a device (e.g., mlx5_0)
-export RDMA_DEVICE_NAME='mlx5_0'
-
-# Run tests
-pytest test_mooncake_transfer_engine_rdma.py -v -s
-```
-
-**Method 2: Use RoCE Devices**
-
-If the system has RoCE devices (using IPv4 routing), the test code will automatically detect and prefer them. RoCE device GIDs start with `00:00:00:00:00:00:00:00:00:00:ff:ff` (IPv4-mapped).
-
-**Method 3: Ensure MTU Consistency**
-
-Make sure both endpoints use the same MTU:
-
-```bash
-# Check device MTU
-ibstatus mlx5_0
-```
-
---
-
-## Multi-Node Testing
-
-For multi-node tests, producer and consumer run on different machines connected via InfiniBand switch.
-
-### Prerequisites
-
-1. Both machines have Mooncake and RDMA drivers installed
-2. Both machines are in the same InfiniBand subnet
-3. Switch is properly configured
-
-### Configuration
-
-**Machine A (Producer):**
-
-```bash
-# Set RDMA host IP (InfiniBand interface IP)
-export RDMA_TEST_HOST='10.0.0.1'
-
-# Optional: Specify device
-export RDMA_DEVICE_NAME='mlx5_0'
-```
-
-**Machine B (Consumer):**
-
-```bash
-# Set RDMA host IP
-export RDMA_TEST_HOST='10.0.0.2'
-
-# Optional: Specify device
-export RDMA_DEVICE_NAME='mlx5_0'
-```
-
-### Verify Connectivity
-
-```bash
-# Ping IB interface
-ping 10.0.0.2
-
-# Test RDMA connectivity with ibping
-# On Machine B (server)
-ibping -S
-
-# On Machine A (client)
-ibping -G <Machine_B_GID>
-```
-
---
-
-## Running Tests
-
-### Run All RDMA Tests (Single-Node, fast suite)
-
-Slow tests (large payloads, stress, concurrency integrity) are marked `@pytest.mark.slow`. Use `-m "not slow"` to skip them in quick CI or local fast iteration.
-
-```bash
-cd tests/distributed/omni_connectors
-
-# Fast suite only (excludes slow/stress tests)
-pytest test_mooncake_transfer_engine_rdma.py test_mooncake_transfer_engine_buffer.py -v -s -m "not slow"
-```
-
-### Run Including Slow Tests
-
-```bash
-# Run ALL tests including slow/stress tests
-pytest test_mooncake_transfer_engine_rdma.py test_mooncake_transfer_engine_buffer.py -v -s
-
-# Run ONLY the slow/stress tests
-pytest test_mooncake_transfer_engine_rdma.py test_mooncake_transfer_engine_buffer.py -v -s -m slow
-```
-
-### Run Buffer Management Tests
-
-```bash
-# Fast only
-pytest test_mooncake_transfer_engine_buffer.py -v -s -m "not slow"
-
-# Including allocator invariant tests (double-free, overlap, merge)
-pytest test_mooncake_transfer_engine_buffer.py -v -s
-```
-
-### Run Specific Test Classes
-
-```bash
-# Basic connector tests
-pytest test_mooncake_transfer_engine_rdma.py::TestBasicConnector -v -s
-
-# End-to-end RDMA transfer tests
-pytest test_mooncake_transfer_engine_rdma.py::TestEndToEnd -v -s
-
-# Lifecycle & resource management tests
-pytest test_mooncake_transfer_engine_rdma.py::TestLifecycle -v -s
-
-# GPU memory pool tests (requires CUDA)
-pytest test_mooncake_transfer_engine_rdma.py::TestGPUPool -v -s
-
-# Stress / correctness tests (slow)
-pytest test_mooncake_transfer_engine_rdma.py::TestStressCorrectness -v -s
-```
-
-### RDMA Environment Diagnostics
-
-For quick diagnostics (device status, Mooncake availability, env vars, etc.),
-see the [Troubleshooting section](../../../docs/design/feature/omni_connectors/mooncake_transfer_engine_connector.md#troubleshooting)
-in the connector documentation.
-
---
-
-## Cross-Node Testing
-
-The `cross_node_mooncake_transfer_engine.py` script enables testing RDMA transfers between two separate physical machines. This script is **not** auto-discovered by `pytest` (it does not start with `test_`) — it must be run manually on each node.
-
-### Prerequisites
-
-1. Both machines have Mooncake installed
-2. Both machines are connected via InfiniBand/RoCE switch
-3. Firewall allows ZMQ ports (default: 15500, 15501)
-4. Same RDMA device name on both nodes (if multiple devices exist)
-
-### Running Cross-Node Tests
-
-**On Machine A (Producer) — start first:**
-
-```bash
-cd benchmarks/distributed/omni_connectors/
-
-# Optional: specify device if multiple exist
-export RDMA_DEVICE_NAME='mlx5_0'
-
-python cross_node_mooncake_transfer_engine.py \
-    --role producer \
-    --local-host <PRODUCER_IP> \
-    --remote-host <CONSUMER_IP> \
-    --tensor-size-mb 100 \
-    --num-transfers 3
-```
-
-**On Machine B (Consumer) — start after producer:**
-
-```bash
-cd benchmarks/distributed/omni_connectors/
-
-export RDMA_DEVICE_NAME='mlx5_0'
-
-python cross_node_mooncake_transfer_engine.py \
-    --role consumer \
-    --local-host <CONSUMER_IP> \
-    --remote-host <PRODUCER_IP> \
-    --tensor-size-mb 100 \
-    --num-transfers 3
-```
-
-### Transfer Modes
-
-| Mode | Description | Example |
-|------|-------------|---------|
-| `copy` | Normal path — tensor copied to RDMA pool (default) | `--mode copy` |
-| `zerocopy` | Zero-copy path — data created directly in RDMA pool | `--mode zerocopy` |
-| `gpu` | GPU transfer — RDMA pool on GPU, uses GPUDirect | `--mode gpu --gpu-id 0` |
-
-### Benchmark Mode
-
-Skip MD5 verification and measure pure RDMA throughput:
-
-```bash
-# Producer
-python cross_node_mooncake_transfer_engine.py \
-    --role producer \
-    --local-host <PRODUCER_IP> \
-    --remote-host <CONSUMER_IP> \
-    --tensor-size-mb 1024 \
-    --num-transfers 20 \
-    --benchmark
-
-# Consumer
-python cross_node_mooncake_transfer_engine.py \
-    --role consumer \
-    --local-host <CONSUMER_IP> \
-    --remote-host <PRODUCER_IP> \
-    --tensor-size-mb 1024 \
-    --num-transfers 20 \
-    --benchmark
-```
-
-### Cross-Node Test Options
-
-| Option | Description | Default |
-|--------|-------------|---------|
-| `--role` | `producer` or `consumer` | Required |
-| `--local-host` | Local RDMA IP address | Required |
-| `--remote-host` | Remote RDMA IP address | Required |
-| `--local-port` | Local ZMQ port for RDMA data | 15500 |
-| `--remote-port` | Remote ZMQ port for RDMA data | 15500 |
-| `--ctrl-port` | Control channel port | 15501 |
-| `--tensor-size-mb` | Tensor size in MB | 100 |
-| `--num-transfers` | Number of transfers | 3 |
-| `--mode` | `copy`, `zerocopy`, or `gpu` | `copy` |
-| `--gpu-id` | GPU ID for GPU mode | 0 |
-| `--benchmark` | Skip MD5, pure performance test | off |
-
---
-
-## Troubleshooting
-
-### 1. "Failed to modify QP to RTR" Error
-
-**Cause**: QP handshake failed, usually due to device configuration mismatch.
-
-**Solution**:
-```bash
-# Force using the same device
-export RDMA_DEVICE_NAME='mlx5_0'
-```
-
-### 2. "Mooncake TransferEngine is not available"
-
-**Cause**: Mooncake not installed or import failed.
-
-**Solution**:
-```bash
-# Check Mooncake installation
-python -c "from mooncake.engine import TransferEngine; print('OK')"
-
-# Reinstall if needed
-pip install mooncake-transfer-engine
-# Or using uv
-uv pip install mooncake-transfer-engine
-
-```
-
-### 3. "Permission denied" accessing /dev/infiniband
-
-**Cause**: Container lacks IB device access permissions.
-
-**Solution**:
-```bash
-docker run --device=/dev/infiniband --cap-add=IPC_LOCK ...
-```
-
-### 4. Test Timeout
-
-**Cause**: RDMA connection establishment failed or network latency.
-
-**Solution**:
-```bash
-# Check network status
-ibstat
-ibstatus
-```
-
-### 5. GPU Test Failed "CUDA is not available"
-
-**Cause**: CUDA environment not configured or GPU unavailable.
-
-**Solution**:
-```bash
-# Check CUDA
-python -c "import torch; print(torch.cuda.is_available())"
-
-# Docker needs NVIDIA runtime
-docker run --gpus all ...
-```
-
---
-
-## Environment Variables Reference
-
-| Variable | Description | Example |
-|----------|-------------|---------|
-| `RDMA_DEVICE_NAME` | Specify RDMA device name | `mlx5_0` |
-| `RDMA_TEST_HOST` | Specify test host IP | `10.0.0.1` |
-| `MC_TE_METRIC` | Enable Mooncake metrics | `1` |
-| `MC_IB_PCI_RELAXED_ORDERING` | Enable PCIe relaxed ordering | `1` |
-
---
-
-## Test Files Overview
-
-| File | Description | Auto-discovered by pytest |
-|------|-------------|--------------------------|
-| `test_mooncake_transfer_engine_rdma.py` | Integration tests for MooncakeTransferEngineConnector (basic, E2E, lifecycle, GPU) | Yes |
-| `test_mooncake_transfer_engine_buffer.py` | Memory pool and buffer management unit tests | Yes |
-| `cross_node_mooncake_transfer_engine.py` | Cross-node (multi-machine) testing script — run manually | No (filename does not start with `test_`) |
-
-### test_mooncake_transfer_engine_rdma.py — Test Classes
-
-| Test Class | Memory Pool | Marker | Description |
-|------------|-------------|--------|-------------|
-| `TestBasicConnector` | CPU | — | Initialization, put tensor/bytes/object, cleanup, pool exhaustion |
-| `TestEndToEnd` | CPU | — | E2E RDMA transfer: tensor, bytes, object, zero-copy, large payload (100MB), mixed types, concurrency |
-| `TestLifecycle` | CPU | — | Close, context manager, double-close safety |
-| `TestGPUPool` | GPU | — | GPU pool init, put CPU/GPU tensor, GPU E2E transfer |
-| `TestStressCorrectness` | CPU | `slow` | Concurrent put+get with MD5 integrity, bidirectional concurrency, edge cases (1-element tensor, empty bytes), 500MB payload, rapid alloc/free cycles |
-
-### test_mooncake_transfer_engine_buffer.py — Test Classes
-
-| Test Class | Marker | Description |
-|------------|--------|-------------|
-| `TestBufferAllocator` | — | Basic alloc/free, alignment, exhaustion/recovery, thread safety |
-| `TestAllocatorInvariants` | `slow` | Double-free safety, overlap corruption detection, adjacent-block merging, fragmentation/defrag |
-| `TestManagedBuffer` | — | Tensor views, context manager |
@@ -1,644 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-
-"""
-Cross-Node RDMA Test Script (Automated Version)
-
-This script enables testing RDMA transfers between two separate machines.
-Supports three transfer modes:
-  - copy:      Normal path - tensor copied to RDMA pool (default)
-  - zerocopy:  Zero-copy path - data created directly in RDMA pool
-  - gpu:       GPU transfer - RDMA pool on GPU, uses GPUDirect
-
-Usage:
-    # On Machine A (Producer) - start first:
-    python cross_node_mooncake_transfer_engine.py --role producer --local-host hostname_A --remote-host hostname_B
-
-    # On Machine B (Consumer) - start after producer:
-    python cross_node_mooncake_transfer_engine.py --role consumer --local-host hostname_B --remote-host hostname_A
-
-    # Zero-copy mode:
-    python cross_node_mooncake_transfer_engine.py --role producer ... --mode zerocopy
-
-    # GPU mode (requires GPUDirect RDMA support):
-    python cross_node_mooncake_transfer_engine.py --role producer ... --mode gpu --gpu-id 0
-
-    # Benchmark mode (skip random data generation and MD5 verification,
-    # measures pure RDMA throughput):
-    python cross_node_mooncake_transfer_engine.py --role producer ... --benchmark
-
-Environment Variables:
-    RDMA_DEVICE_NAME:              Specify RDMA device (e.g., mlx5_0)
-    MC_IB_PCI_RELAXED_ORDERING:    Set to 1 to enable PCIe relaxed ordering
-                                   for higher RDMA throughput
-"""
-
-import argparse
-import hashlib
-import os
-import sys
-import time
-from abc import ABC, abstractmethod
-from dataclasses import dataclass
-from typing import Any
-
-import msgspec
-import torch
-import zmq
-
-# Add parent path for imports
-sys.path.insert(
-    0, os.path.dirname(os.path.dirname(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))))
-)
-
-from vllm_omni.distributed.omni_connectors.connectors.mooncake_transfer_engine_connector import (
-    ManagedBuffer,
-    MooncakeTransferEngineConnector,
-    TransferEngine,
-)
-
-
-def compute_md5(tensor: torch.Tensor) -> str:
-    """Compute MD5 checksum of a tensor."""
-    if tensor.is_cuda:
-        tensor = tensor.cpu()
-    data = tensor.contiguous().view(torch.uint8).numpy().tobytes()
-    return hashlib.md5(data).hexdigest()
-
-
-# Control channel message types
-class CtrlMsg(msgspec.Struct):
-    """Control channel message."""
-
-    msg_type: str  # "READY", "TRANSFER", "ACK", "DONE", "ERROR"
-    request_id: str = ""
-    md5: str = ""
-    data_size: int = 0
-    error: str = ""
-
-
-@dataclass
-class TransferConfig:
-    """Configuration for cross-node transfer test."""
-
-    local_host: str
-    remote_host: str
-    local_port: int
-    remote_port: int
-    ctrl_port: int
-    num_transfers: int
-    tensor_size_mb: int
-    mode: str  # "copy", "zerocopy", "gpu"
-    gpu_id: int = 0
-    benchmark: bool = False  # Skip MD5 verification for pure performance test
-
-
-@dataclass
-class TransferStats:
-    """Statistics for transfer operations."""
-
-    success_count: int = 0
-    fail_count: int = 0
-    total_bytes: int = 0
-    elapsed_time: float = 0.0
-
-    @property
-    def throughput_mbps(self) -> float:
-        if self.elapsed_time > 0:
-            return (self.total_bytes / (1024 * 1024)) / self.elapsed_time
-        return 0.0
-
-    def print_summary(self, role: str):
-        print(f"\n{'=' * 60}")
-        print(f" {role.upper()} SUMMARY")
-        print(f"  Successful: {self.success_count}/{self.success_count + self.fail_count}")
-        print(f"  Failed:     {self.fail_count}/{self.success_count + self.fail_count}")
-        print(f"  Total:      {self.total_bytes / (1024 * 1024):.2f} MB")
-        print(f"  Time:       {self.elapsed_time:.2f} s")
-        print(f"  Throughput: {self.throughput_mbps:.2f} MB/s")
-        print(f"{'=' * 60}")
-
-
-class CrossNodeTester(ABC):
-    """Abstract base class for cross-node RDMA testing."""
-
-    def __init__(self, config: TransferConfig):
-        self.config = config
-        self.connector: MooncakeTransferEngineConnector | None = None
-        self.zmq_ctx: zmq.Context | None = None
-        self.ctrl_socket: zmq.Socket | None = None
-        self.stats = TransferStats()
-
-    def get_connector_config(self) -> dict:
-        """Get connector configuration based on mode."""
-        pool_size = int(self.config.tensor_size_mb * 1.5) * 1024 * 1024
-        pool_size = max(pool_size, 128 * 1024 * 1024)
-
-        conn_config = {
-            "host": self.config.local_host,
-            "zmq_port": self.config.local_port,
-            "protocol": "rdma",
-            "memory_pool_size": pool_size,
-        }
-
-        # Set device based on mode
-        if self.config.mode == "gpu":
-            conn_config["memory_pool_device"] = f"cuda:{self.config.gpu_id}"
-        else:
-            conn_config["memory_pool_device"] = "cpu"
-
-        # RDMA device name from environment
-        device_name = os.environ.get("RDMA_DEVICE_NAME")
-        if device_name:
-            conn_config["device_name"] = device_name
-            print(f"[CONFIG] Using RDMA device: {device_name}")
-
-        return conn_config
-
-    def initialize(self):
-        """Initialize connector and ZMQ context."""
-        print(f"[{self.role}] Initializing connector...")
-        conn_config = self.get_connector_config()
-        self.connector = MooncakeTransferEngineConnector(conn_config)
-        self.zmq_ctx = zmq.Context()
-        print(f"[{self.role}] Ready at {self.config.local_host}:{self.config.local_port}")
-
-    def cleanup(self):
-        """Cleanup resources."""
-        if self.ctrl_socket:
-            self.ctrl_socket.close()
-        if self.zmq_ctx:
-            self.zmq_ctx.term()
-        if self.connector:
-            self.connector.close()
-        print(f"[{self.role}] Closed.")
-
-    @property
-    @abstractmethod
-    def role(self) -> str:
-        pass
-
-    @abstractmethod
-    def run(self):
-        pass
-
-
-class Producer(CrossNodeTester):
-    """Producer node - sends data to consumer."""
-
-    @property
-    def role(self) -> str:
-        return "PRODUCER"
-
-    def print_header(self):
-        print(f"\n{'=' * 60}")
-        print(f" PRODUCER MODE ({self.config.mode.upper()})")
-        print(f" Local:  {self.config.local_host}:{self.config.local_port}")
-        print(f" Remote: {self.config.remote_host}:{self.config.remote_port}")
-        print(f" Control Port: {self.config.ctrl_port}")
-        print(f" Transfer Mode: {self.config.mode}")
-        if self.config.mode == "gpu":
-            print(f" GPU ID: {self.config.gpu_id}")
-        print(f"{'=' * 60}\n")
-
-    def setup_control_channel(self):
-        """Setup ZMQ control channel as server (REP socket)."""
-        self.ctrl_socket = self.zmq_ctx.socket(zmq.REP)
-        self.ctrl_socket.bind(f"tcp://*:{self.config.ctrl_port}")
-        print(f"[PRODUCER] Control channel listening on port {self.config.ctrl_port}")
-        print("[PRODUCER] Waiting for consumer to connect...")
-
-    def wait_for_consumer(self) -> bool:
-        """Wait for consumer READY signal."""
-        msg_data = self.ctrl_socket.recv()
-        msg = msgspec.msgpack.decode(msg_data, type=CtrlMsg)
-        if msg.msg_type != "READY":
-            print(f"[PRODUCER] Unexpected message: {msg.msg_type}")
-            return False
-        print("[PRODUCER] Consumer connected!")
-        self.ctrl_socket.send(msgspec.msgpack.encode(CtrlMsg(msg_type="ACK")))
-        return True
-
-    def create_test_data(self, transfer_idx: int) -> tuple[Any, str, int]:
-        """
-        Create test data based on transfer mode.
-
-        Returns:
-            (data, md5, size) tuple
-        """
-        num_elements = (self.config.tensor_size_mb * 1024 * 1024) // 4
-        data_size = num_elements * 4
-
-        if self.config.mode == "zerocopy":
-            # Zero-Copy Path: Allocate directly from connector's pool
-            offset = self.connector.allocator.alloc(data_size)
-            managed_buf = ManagedBuffer(self.connector.allocator, offset, data_size, self.connector.pool)
-
-            if self.config.benchmark:
-                # In benchmark mode, skip random data generation (use uninitialized memory)
-                return managed_buf, "", data_size
-            else:
-                # Fill buffer with random data using tensor view
-                tensor_view = managed_buf.as_tensor(dtype=torch.float32, shape=(num_elements,))
-                random_data = torch.randn(num_elements, dtype=torch.float32)
-                if tensor_view.is_cuda:
-                    tensor_view.copy_(random_data.to(tensor_view.device))
-                else:
-                    tensor_view.copy_(random_data)
-                md5 = compute_md5(tensor_view)
-                return managed_buf, md5, data_size
-
-        elif self.config.mode == "gpu":
-            # GPU Path: Create tensor on GPU
-            device = f"cuda:{self.config.gpu_id}"
-            if self.config.benchmark:
-                # In benchmark mode, use empty tensor (no random generation)
-                gpu_tensor = torch.empty(num_elements, dtype=torch.float32, device=device)
-                return gpu_tensor, "", data_size
-            else:
-                cpu_tensor = torch.randn(num_elements, dtype=torch.float32)
-                md5 = compute_md5(cpu_tensor)
-                gpu_tensor = cpu_tensor.to(device)
-                return gpu_tensor, md5, data_size
-
-        else:
-            # Copy Path (default): Create regular CPU tensor
-            if self.config.benchmark:
-                # In benchmark mode, use empty tensor (no random generation)
-                tensor = torch.empty(num_elements, dtype=torch.float32)
-                return tensor, "", data_size
-            else:
-                tensor = torch.randn(num_elements, dtype=torch.float32)
-                md5 = compute_md5(tensor)
-                return tensor, md5, data_size
-
-    def do_transfer(self, transfer_idx: int) -> bool:
-        """Perform a single transfer."""
-        req_id = f"cross_node_transfer_{transfer_idx}"
-
-        if not self.config.benchmark:
-            print(f"\n[PRODUCER] Transfer {transfer_idx + 1}/{self.config.num_transfers}")
-
-        # Create test data
-        t0 = time.time()
-        data, md5, data_size = self.create_test_data(transfer_idx)
-        t_create = time.time() - t0
-
-        if not self.config.benchmark:
-            print(f"  Mode: {self.config.mode}")
-            print(f"  Size: {self.config.tensor_size_mb} MB")
-            if md5:
-                print(f"  MD5:  {md5[:16]}...")
-            print(f"  Create time: {t_create * 1000:.1f} ms")
-
-        # Put data
-        t1 = time.time()
-        success, size, metadata = self.connector.put("producer", "consumer", req_id, data)
-        t_put = time.time() - t1
-
-        if not success:
-            print("  [FAIL] Put failed")
-            return False
-
-        if not self.config.benchmark:
-            print(f"  [OK] Put successful, {size} bytes ({t_put * 1000:.1f} ms)")
-
-        # Wait for consumer to request transfer info
-        msg_data = self.ctrl_socket.recv()
-        msg = msgspec.msgpack.decode(msg_data, type=CtrlMsg)
-
-        if msg.msg_type != "READY":
-            print(f"  [ERROR] Unexpected message: {msg.msg_type}")
-            return False
-
-        # Send transfer metadata to consumer
-        transfer_msg = CtrlMsg(
-            msg_type="TRANSFER",
-            request_id=req_id,
-            md5=md5,
-            data_size=data_size,
-        )
-        self.ctrl_socket.send(msgspec.msgpack.encode(transfer_msg))
-
-        # Wait for consumer ACK (this includes RDMA transfer time)
-        t2 = time.time()
-        msg_data = self.ctrl_socket.recv()
-        t_rdma = time.time() - t2
-        msg = msgspec.msgpack.decode(msg_data, type=CtrlMsg)
-
-        success = msg.msg_type == "ACK"
-        if success:
-            if not self.config.benchmark:
-                print(f"  [OK] RDMA transfer complete ({t_rdma * 1000:.1f} ms)")
-            self.stats.success_count += 1
-            self.stats.total_bytes += size
-        else:
-            print(f"  [WARN] Consumer reported error: {msg.error}")
-            self.stats.fail_count += 1
-
-        # Send ACK to allow consumer to continue
-        self.ctrl_socket.send(msgspec.msgpack.encode(CtrlMsg(msg_type="ACK")))
-
-        # Cleanup buffer
-        self.connector.cleanup(req_id)
-
-        return success
-
-    def run(self):
-        """Run the producer."""
-        self.print_header()
-        self.initialize()
-        self.setup_control_channel()
-
-        try:
-            if not self.wait_for_consumer():
-                return
-
-            if self.config.benchmark:
-                print(
-                    f"[BENCHMARK] Running {self.config.num_transfers} "
-                    f"transfers of {self.config.tensor_size_mb} MB each..."
-                )
-
-            start_time = time.time()
-
-            for i in range(self.config.num_transfers):
-                self.do_transfer(i)
-                if self.config.benchmark and (i + 1) % 10 == 0:
-                    elapsed = time.time() - start_time
-                    current_throughput = (self.stats.total_bytes / (1024 * 1024)) / elapsed
-                    print(f"  Progress: {i + 1}/{self.config.num_transfers}, Throughput: {current_throughput:.2f} MB/s")
-
-            self.stats.elapsed_time = time.time() - start_time
-            self.stats.print_summary("PRODUCER")
-
-            # Wait for final consumer message and send DONE
-            self.ctrl_socket.recv()
-            self.ctrl_socket.send(msgspec.msgpack.encode(CtrlMsg(msg_type="DONE")))
-
-        finally:
-            self.cleanup()
-
-
-class Consumer(CrossNodeTester):
-    """Consumer node - receives data from producer."""
-
-    @property
-    def role(self) -> str:
-        return "CONSUMER"
-
-    def print_header(self):
-        print(f"\n{'=' * 60}")
-        print(f" CONSUMER MODE ({self.config.mode.upper()})")
-        print(f" Local:  {self.config.local_host}:{self.config.local_port}")
-        print(f" Remote: {self.config.remote_host}:{self.config.remote_port}")
-        print(f" Control Port: {self.config.ctrl_port}")
-        print(f" Transfer Mode: {self.config.mode}")
-        if self.config.mode == "gpu":
-            print(f" GPU ID: {self.config.gpu_id}")
-        print(f"{'=' * 60}\n")
-
-    def setup_control_channel(self):
-        """Setup ZMQ control channel as client (REQ socket)."""
-        self.ctrl_socket = self.zmq_ctx.socket(zmq.REQ)
-        ctrl_addr = f"tcp://{self.config.remote_host}:{self.config.ctrl_port}"
-        print(f"[CONSUMER] Connecting to producer control channel at {ctrl_addr}...")
-        self.ctrl_socket.connect(ctrl_addr)
-
-    def connect_to_producer(self) -> bool:
-        """Connect to producer and send READY signal."""
-        self.ctrl_socket.send(msgspec.msgpack.encode(CtrlMsg(msg_type="READY")))
-        msg_data = self.ctrl_socket.recv()
-        msg = msgspec.msgpack.decode(msg_data, type=CtrlMsg)
-        if msg.msg_type != "ACK":
-            print(f"[CONSUMER] Unexpected response: {msg.msg_type}")
-            return False
-        print("[CONSUMER] Connected to producer! Starting transfers...")
-        return True
-
-    def do_transfer(self, transfer_idx: int) -> bool:
-        """Perform a single transfer."""
-        if not self.config.benchmark:
-            print(f"\n[CONSUMER] Transfer {transfer_idx + 1}/{self.config.num_transfers}")
-
-        # Request next transfer info
-        self.ctrl_socket.send(msgspec.msgpack.encode(CtrlMsg(msg_type="READY")))
-        msg_data = self.ctrl_socket.recv()
-        msg = msgspec.msgpack.decode(msg_data, type=CtrlMsg)
-
-        if msg.msg_type == "DONE":
-            print("[CONSUMER] Producer signaled completion")
-            return False
-
-        if msg.msg_type != "TRANSFER":
-            print(f"[CONSUMER] Unexpected message: {msg.msg_type}")
-            return False
-
-        req_id = msg.request_id
-        expected_md5 = msg.md5
-        data_size = msg.data_size
-        num_elements = data_size // 4
-
-        if not self.config.benchmark:
-            print(f"  Request ID: {req_id}")
-            if expected_md5:
-                print(f"  Expected MD5: {expected_md5[:16]}...")
-            print(f"  Data Size: {data_size / (1024 * 1024):.2f} MB")
-
-        # Build metadata for get
-        metadata = {
-            "request_id": req_id,
-            "source_host": self.config.remote_host,
-            "source_port": self.config.remote_port,
-            "data_size": data_size,
-            "dtype": "float32",
-            "shape": [num_elements],
-            "is_fast_path": True,
-        }
-
-        if not self.config.benchmark:
-            print(f"  [INFO] Requesting from {self.config.remote_host}:{self.config.remote_port}")
-
-        # Get data with timing
-        t0 = time.time()
-        result = self.connector.get("producer", "consumer", req_id, metadata)
-        t_get = time.time() - t0
-
-        response_msg = CtrlMsg(msg_type="ERROR", error="Get failed")
-
-        if result is not None:
-            recv_buffer, recv_size = result
-            if not self.config.benchmark:
-                print(f"  [OK] Get successful, {recv_size} bytes ({t_get * 1000:.1f} ms)")
-
-            if isinstance(recv_buffer, ManagedBuffer):
-                # In benchmark mode, skip MD5 verification
-                if self.config.benchmark or not expected_md5:
-                    response_msg = CtrlMsg(msg_type="ACK")
-                    self.stats.success_count += 1
-                    self.stats.total_bytes += recv_size
-                else:
-                    # Verify data
-                    t1 = time.time()
-                    reconstructed = recv_buffer.as_tensor(dtype=torch.float32, shape=(num_elements,))
-                    recv_md5 = compute_md5(reconstructed)
-                    t_md5 = time.time() - t1
-                    print(f"  MD5: {recv_md5[:16]}... ({t_md5 * 1000:.1f} ms)")
-
-                    if recv_md5 == expected_md5:
-                        print("  [PASS] MD5 checksum verified!")
-                        response_msg = CtrlMsg(msg_type="ACK")
-                        self.stats.success_count += 1
-                        self.stats.total_bytes += recv_size
-                    else:
-                        print("  [FAIL] MD5 mismatch!")
-                        response_msg = CtrlMsg(msg_type="ERROR", error="MD5 mismatch")
-                        self.stats.fail_count += 1
-
-                recv_buffer.release()
-            else:
-                response_msg = CtrlMsg(msg_type="ACK")
-                self.stats.success_count += 1
-                self.stats.total_bytes += recv_size
-        else:
-            print("  [FAIL] Get failed")
-            self.stats.fail_count += 1
-
-        # Send response to producer
-        self.ctrl_socket.send(msgspec.msgpack.encode(response_msg))
-        # Wait for ACK
-        self.ctrl_socket.recv()
-
-        return response_msg.msg_type == "ACK"
-
-    def run(self):
-        """Run the consumer."""
-        self.print_header()
-        self.initialize()
-        self.setup_control_channel()
-
-        try:
-            if not self.connect_to_producer():
-                return
-
-            if self.config.benchmark:
-                print(
-                    f"[BENCHMARK] Running {self.config.num_transfers} "
-                    f"transfers of {self.config.tensor_size_mb} MB each..."
-                )
-
-            start_time = time.time()
-
-            for i in range(self.config.num_transfers):
-                if not self.do_transfer(i):
-                    break
-                if self.config.benchmark and (i + 1) % 10 == 0:
-                    elapsed = time.time() - start_time
-                    current_throughput = (self.stats.total_bytes / (1024 * 1024)) / elapsed
-                    print(f"  Progress: {i + 1}/{self.config.num_transfers}, Throughput: {current_throughput:.2f} MB/s")
-
-            self.stats.elapsed_time = time.time() - start_time
-            self.stats.print_summary("CONSUMER")
-
-            # Send final READY and wait for DONE
-            self.ctrl_socket.send(msgspec.msgpack.encode(CtrlMsg(msg_type="READY")))
-            self.ctrl_socket.recv()
-
-        finally:
-            self.cleanup()
-
-
-def main():
-    parser = argparse.ArgumentParser(
-        description="Cross-Node RDMA Test (Automated)",
-        formatter_class=argparse.RawDescriptionHelpFormatter,
-        epilog="""
-Transfer Modes:
-  copy      - Normal path: tensor copied to RDMA pool (default)
-  zerocopy  - Zero-copy path: data created directly in RDMA pool
-  gpu       - GPU transfer: RDMA pool on GPU, uses GPUDirect
-
-Examples:
-  # Copy mode (default):
-  python cross_node_mooncake_transfer_engine.py --role producer \
-  --local-host hostA --remote-host hostB
-
-  # Zero-copy mode:
-  python cross_node_mooncake_transfer_engine.py --role producer \
-  --local-host hostA --remote-host hostB --mode zerocopy
-
-  # GPU mode:
-  python cross_node_mooncake_transfer_engine.py --role producer \
-  --local-host hostA --remote-host hostB --mode gpu --gpu-id 0
-
-  # Benchmark mode (skip MD5, measure pure RDMA performance):
-  python cross_node_mooncake_transfer_engine.py --role producer \
-  --local-host hostA --remote-host hostB --benchmark
-
-  # With specific RDMA device:
-  RDMA_DEVICE_NAME=mlx5_0 python cross_node_mooncake_transfer_engine.py --role producer ...
-        """,
-    )
-
-    parser.add_argument(
-        "--role", required=True, choices=["producer", "consumer"], help="Role: producer (sends) or consumer (receives)"
-    )
-    parser.add_argument("--local-host", required=True, help="Local hostname or IP address")
-    parser.add_argument("--remote-host", required=True, help="Remote hostname or IP address")
-    parser.add_argument("--local-port", type=int, default=15500, help="Local ZMQ port for RDMA data (default: 15500)")
-    parser.add_argument("--remote-port", type=int, default=15500, help="Remote ZMQ port for RDMA data (default: 15500)")
-    parser.add_argument("--ctrl-port", type=int, default=15501, help="Control channel port (default: 15501)")
-    parser.add_argument("--num-transfers", type=int, default=20, help="Number of transfers to perform (default: 3)")
-    parser.add_argument("--tensor-size-mb", type=int, default=100, help="Tensor size in MB (default: 100)")
-    parser.add_argument(
-        "--mode",
-        choices=["copy", "zerocopy", "gpu"],
-        default="copy",
-        help="Transfer mode: copy, zerocopy, or gpu (default: copy)",
-    )
-    parser.add_argument("--gpu-id", type=int, default=0, help="GPU ID for GPU mode (default: 0)")
-    parser.add_argument(
-        "--benchmark", action="store_true", help="Benchmark mode: skip MD5 verification for pure performance test"
-    )
-
-    args = parser.parse_args()
-
-    # Check Mooncake
-    if TransferEngine is None:
-        print("[ERROR] Mooncake TransferEngine is not available.")
-        print("Install with: pip install mooncake")
-        sys.exit(1)
-
-    # Check CUDA for GPU mode
-    if args.mode == "gpu":
-        if not torch.cuda.is_available():
-            print("[ERROR] CUDA is not available but GPU mode was requested.")
-            sys.exit(1)
-        if args.gpu_id >= torch.cuda.device_count():
-            print(f"[ERROR] GPU {args.gpu_id} not available. Found {torch.cuda.device_count()} GPUs.")
-            sys.exit(1)
-        print(f"[INFO] Using GPU {args.gpu_id}: {torch.cuda.get_device_name(args.gpu_id)}")
-
-    config = TransferConfig(
-        local_host=args.local_host,
-        remote_host=args.remote_host,
-        local_port=args.local_port,
-        remote_port=args.remote_port,
-        ctrl_port=args.ctrl_port,
-        num_transfers=args.num_transfers,
-        tensor_size_mb=args.tensor_size_mb,
-        mode=args.mode,
-        gpu_id=args.gpu_id,
-        benchmark=args.benchmark,
-    )
-
-    if args.role == "producer":
-        tester = Producer(config)
-    else:
-        tester = Consumer(config)
-
-    tester.run()
-
-
-if __name__ == "__main__":
-    main()
@@ -36,7 +36,7 @@ bash benchmarks/qwen3-omni/transformers/eval_qwen3_moe_omni_transformers.sh
 ```

 What it does:
- Runs `qwen3_omni_moe_transformers.py` over `top100.txt` with `--num-prompts 100`.
+- Runs `qwen3_omni_moe_transformers.py` over `top100.txt` with `--num_prompts 100`.
 - Outputs to `benchmarks/qwen3-omni/transformers/benchmark_results/`:
  - `perf_stats.json` — aggregated & per-prompt TPS/latency (thinker/talker/code2wav/overall).
  - `results.json` — per-prompt outputs and audio paths.
@@ -53,7 +53,7 @@ bash benchmarks/qwen3-omni/vllm_omni/eval_qwen3_moe_omni.sh
 ```

 What it does:
- Runs `examples/offline_inference/qwen3_omni/end2end.py` with `--log-stats`.
+- Runs `examples/offline_inference/qwen3_omni/end2end.py` with `--enable-stats`.
 - Uses `benchmarks/build_dataset/top100.txt` and writes to:
  - Logs: `benchmarks/qwen3-omni/vllm_omni/logs/`
    - `omni_llm_pipeline_text.orchestrator.stats.jsonl` — per-stage latency stats.
@@ -16,7 +16,7 @@ if [[ ! -f "benchmarks/qwen3-omni/transformers/qwen3_omni_moe_transformers.py" ]
 else
    cd benchmarks/qwen3-omni/transformers

-    python qwen3_omni_moe_transformers.py --prompts-file ../../build_dataset/top100.txt --num-prompts 100
+    python qwen3_omni_moe_transformers.py --prompts_file ../../build_dataset/top100.txt --num_prompts 100

    echo "Logs and outputs are saved to $(pwd)/benchmark_results:"
    echo "  - perf_stats.json    Aggregated/per-prompt TPS and latency (thinker/talker/code2wav/overall)"
@@ -227,17 +227,17 @@ def print_stats(stats: dict):
 def main():
    parser = argparse.ArgumentParser(description="Qwen3-Omni Benchmark Script")
    parser.add_argument(
-        "--prompts-file",
+        "--prompts_file",
        type=str,
        default="benchmark/build_dataset/top100.txt",
        help="Path to the prompts file (one prompt per line)",
    )
    parser.add_argument(
-        "--output-dir", type=str, default="benchmark_results", help="Directory to save benchmark results"
+        "--output_dir", type=str, default="benchmark_results", help="Directory to save benchmark results"
    )
-    parser.add_argument("--model-path", type=str, default=MODEL_PATH, help="Path to the model")
+    parser.add_argument("--model_path", type=str, default=MODEL_PATH, help="Path to the model")
    parser.add_argument("--speaker", type=str, default="Ethan", help="Speaker voice for audio output")
-    parser.add_argument("--num-prompts", type=int, default=None, help="Number of prompts to process (default: all)")
+    parser.add_argument("--num_prompts", type=int, default=None, help="Number of prompts to process (default: all)")
    args = parser.parse_args()

    # Load model and processor
@@ -23,7 +23,7 @@ else
    python $end2end_script_path --output-wav $outputs_dir \
                      --query-type text \
                      --txt-prompts $build_dataset_path \
-                      --log-stats \
+                      --enable-stats \
                      --log-dir $log_dir
    echo "Logs and outputs are saved in ${log_dir} and ${outputs_dir} respectively:"
    echo "  - omni_llm_pipeline_text                       run dir/base name"
@@ -1,5 +1,5 @@
 ARG VLLM_BASE_IMAGE=vllm/vllm-openai
-ARG VLLM_BASE_TAG=v0.16.0
+ARG VLLM_BASE_TAG=v0.14.0
 FROM ${VLLM_BASE_IMAGE}:${VLLM_BASE_TAG}
 ARG APP_DIR=/workspace/vllm-omni
 WORKDIR ${APP_DIR}
@@ -8,13 +8,12 @@ COPY . .

 # Install system dependencies
 RUN apt-get update && \
-    apt-get install -y ffmpeg git sox libsox-fmt-all jq && \
+    apt-get install -y ffmpeg && \
    apt-get clean && \
    rm -rf /var/lib/apt/lists/*

 # Install vllm-omni into the same uv-managed Python environment used by the base image.
-# Use bash -c so that $(python3 -c ...) is expanded inside the container.
-RUN uv pip install --system --no-cache-dir ".[dev]"
+RUN uv pip install --python "$(python3 -c 'import sys; print(sys.executable)')" --no-cache-dir ".[dev]"

 RUN ln -sf /usr/bin/python3 /usr/bin/python

@@ -0,0 +1,15 @@
+ARG VLLM_ASCEND_IMAGE=quay.nju.edu.cn/ascend/vllm-ascend
+ARG VLLM_ASCEND_TAG=v0.11.0rc2
+FROM ${VLLM_ASCEND_IMAGE}:${VLLM_ASCEND_TAG}
+
+ARG APP_DIR=/vllm-workspace/vllm-omni
+WORKDIR ${APP_DIR}
+
+COPY . .
+
+# Install vllm-omni with dev dependencies
+RUN pip install --no-cache-dir -e ".[dev]"
+
+ENV VLLM_WORKER_MULTIPROC_METHOD=spawn
+
+ENTRYPOINT []
@@ -1,18 +0,0 @@
-ARG VLLM_ASCEND_IMAGE=quay.io/ascend/vllm-ascend
-ARG VLLM_ASCEND_TAG=v0.14.0rc1
-FROM ${VLLM_ASCEND_IMAGE}:${VLLM_ASCEND_TAG}
-
-WORKDIR /vllm-workspace/vllm-ascend
-RUN git checkout e2175d9c7e62b437391dfee996b1375674ba7c18
-RUN pip install -v -e .
-
-ARG APP_DIR=/vllm-workspace/vllm-omni
-WORKDIR ${APP_DIR}
-
-COPY . .
-
-RUN pip install -v -e .
-
-ENV VLLM_WORKER_MULTIPROC_METHOD=spawn
-
-ENTRYPOINT []
@@ -1,18 +0,0 @@
-ARG VLLM_ASCEND_IMAGE=quay.io/ascend/vllm-ascend
-ARG VLLM_ASCEND_TAG=v0.14.0rc1-a3
-FROM ${VLLM_ASCEND_IMAGE}:${VLLM_ASCEND_TAG}
-
-WORKDIR /vllm-workspace/vllm-ascend
-RUN git checkout e2175d9c7e62b437391dfee996b1375674ba7c18
-RUN pip install -v -e .
-
-ARG APP_DIR=/vllm-workspace/vllm-omni
-WORKDIR ${APP_DIR}
-
-COPY . .
-
-RUN pip install -v -e .
-
-ENV VLLM_WORKER_MULTIPROC_METHOD=spawn
-
-ENTRYPOINT []
@@ -1,53 +1,28 @@
-ARG BASE_IMAGE=vllm/vllm-openai-rocm:v0.16.0
-FROM ${BASE_IMAGE} AS base
-
-# Declare a variable to know if we want to use the nightly build or the stable build.
-# NOTE: REMINDER to vLLM-Omni rebase maintainer
-# Remember to set `USE_NIGHTLY_BUILD` to 0, when switching back to stable vLLM docker image
-# 1. If vLLM-omni maintainer is forced to use custom commits
-#    during rebasing, they can change the two variables
-# 2. Whenever vLLM upstream has released a stable version,
-#    we should swap over to use stable release ASAP.
-#    We should avoid relying on custom commits.
-ARG USE_NIGHTLY_BUILD=0
-ARG VLLM_VERSION_OR_COMMIT_HASH=2d5be1dd5ce2e44dfea53ea03ff61143da5137eb
-ARG ARG_PYTORCH_ROCM_ARCH
-ENV PYTORCH_ROCM_ARCH=${ARG_PYTORCH_ROCM_ARCH:-${PYTORCH_ROCM_ARCH}}
+ARG BASE_IMAGE=vllm/vllm-openai-rocm:v0.14.0
+FROM ${BASE_IMAGE} AS final

 ARG COMMON_WORKDIR=/app
+
 WORKDIR ${COMMON_WORKDIR}

 # Step 1: Setup - Install system dependencies
 RUN apt-get update && \
-    apt-get install -y ffmpeg git sox libsox-fmt-all jq && \
+    apt-get install -y ffmpeg && \
    apt-get clean && \
    rm -rf /var/lib/apt/lists/*

-# Step 2: Conditionally reinstall vllm from source for nightly builds
-RUN if [ "${USE_NIGHTLY_BUILD}" = "1" ]; then \
-        python3 -m pip uninstall -y vllm && rm -rf vllm && \
-        git clone https://github.com/vllm-project/vllm.git && \
-        cd vllm && \
-        git checkout ${VLLM_VERSION_OR_COMMIT_HASH} && \
-        python3 -m pip install -r requirements/rocm.txt && \
-        python3 setup.py clean --all && \
-        PYTORCH_ROCM_ARCH=${PYTORCH_ROCM_ARCH} python3 setup.py develop && \
-        cd ../ && \
-        rm -rf vllm/.git; \
-    fi
-
-# Step 3: Copy vllm-omni code and install without uv
 RUN mkdir -p ${COMMON_WORKDIR}/vllm-omni
+
+# Step 2: Copy vllm-omni code and install without uv
 COPY . ${COMMON_WORKDIR}/vllm-omni
-RUN cd ${COMMON_WORKDIR}/vllm-omni && uv pip install --python "$(python3 -c 'import sys; print(sys.executable)')" --no-cache-dir ".[dev]" --no-build-isolation
+RUN cd ${COMMON_WORKDIR}/vllm-omni && uv pip install --python "$(python3 -c 'import sys; print(sys.executable)')" --no-cache-dir ".[dev]"

 RUN ln -sf /usr/bin/python3 /usr/bin/python

-FROM base AS test
-
 CMD ["/bin/bash"]
+
 ENTRYPOINT []

-# Set entrypoint for vllm-openai official images
-FROM base AS vllm-openai
-ENTRYPOINT ["vllm", "serve", "--omni"]
+#Set entrypoint for vllm-openai official images
+FROM final AS vllm-openai
+ENTRYPOINT ["vllm", "serve"]
@@ -1,139 +0,0 @@
-# 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 && \
-    add-apt-repository -y ppa:kobuk-team/intel-graphics
-
-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
-
-RUN apt update && apt upgrade -y && \
-    apt install -y libze1 libze-dev libze-intel-gpu1 intel-opencl-icd libze-intel-gpu-raytracing intel-ocloc && \
-    apt install -y intel-oneapi-compiler-dpcpp-cpp-2025.3
-
-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:$PATH"
-
-# This oneccl contains the BMG support which is not the case for default version of oneapi 2025.2.
-ARG ONECCL_INSTALLER="intel-oneccl-2021.15.7.8_offline.sh"
-RUN wget "https://github.com/uxlfoundation/oneCCL/releases/download/2021.15.7/${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=v0.16.0
-RUN git clone -b ${VLLM_VERSION} https://github.com/vllm-project/vllm
-WORKDIR /workspace/vllm
-
-RUN --mount=type=cache,target=/root/.cache/uv \
-    uv pip install --upgrade pip && \
-    uv pip install -r requirements/xpu.txt
-
- # used for suffix method speculative decoding
- # build deps for proto + nanobind-based extensions to set up the build environment
-RUN --mount=type=cache,target=/root/.cache/uv \
-    uv pip install grpcio-tools protobuf nanobind
- # arctic-inference is built from source which needs torch-xpu properly installed first
-RUN --mount=type=cache,target=/root/.cache/uv \
-    source /opt/intel/oneapi/setvars.sh --force && \
-    source /opt/intel/oneapi/ccl/2021.15/env/vars.sh --force && \
-    export CMAKE_PREFIX_PATH="$(python -c 'import site; print(site.getsitepackages()[0])'):${CMAKE_PREFIX_PATH}" && \
-    uv pip install --no-build-isolation arctic-inference==0.1.1
-
-ENV LD_LIBRARY_PATH="$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 install --no-build-isolation .
-
-CMD ["/bin/bash"]
-
-FROM vllm-base AS vllm-openai
-
-# 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
-
-# install nixl from source code
-ENV NIXL_VERSION=0.7.0
-RUN python /workspace/vllm/tools/install_nixl_from_source_ubuntu.py
-
-# ensure vllm is properly installed
-RUN python -c "import vllm, inspect; print(vllm.__file__)"
-RUN uv pip show vllm
-
-CMD ["/bin/bash"]
-
-ENTRYPOINT []
-
-FROM ${VLLM_BASE} AS vllm-omni
-
-WORKDIR /workspace/vllm-omni
-COPY . .
-
-ENV VLLM_OMNI_TARGET_DEVICE=xpu
-RUN uv pip install --no-cache-dir ".[dev]" --no-build-isolation
-
-# FIX triton
-RUN --mount=type=cache,target=/root/.cache/uv \
-    uv pip uninstall triton triton-xpu && \
-    uv pip install triton-xpu==3.6.0
-
-# remove torch bundled oneccl to avoid conflicts
-RUN --mount=type=cache,target=/root/.cache/uv \
-    uv pip uninstall oneccl oneccl-devel
-
-FROM vllm-omni AS vllm-omni-openai
-
-RUN ln -sf /usr/bin/python3 /usr/bin/python
-
-ENV VLLM_WORKER_MULTIPROC_METHOD=spawn
-
-ENTRYPOINT ["vllm", "serve", "--omni"]
@@ -7,33 +7,21 @@ nav:
  - Serving:
    - OpenAI-Compatible API:
      - Image Generation: serving/image_generation_api.md
-      - Image Edit: serving/image_edit_api.md
-      - Text to Speech: serving/speech_api.md
  - Examples:
    - examples/README.md
    - Offline Inference:
-      - BAGEL-7B-MoT: user_guide/examples/offline_inference/bagel.md
-      - GLM-Image Multistage End-to-End Inference: user_guide/examples/offline_inference/glm_image.md
      - Image-To-Image: user_guide/examples/offline_inference/image_to_image.md
      - Image-To-Video: user_guide/examples/offline_inference/image_to_video.md
-      - MiMo-Audio Offline Inference: user_guide/examples/offline_inference/mimo_audio.md
      - Qwen2.5-Omni: user_guide/examples/offline_inference/qwen2_5_omni.md
      - Qwen3-Omni: user_guide/examples/offline_inference/qwen3_omni.md
-      - Qwen3-TTS: user_guide/examples/offline_inference/qwen3_tts.md
-      - Text-To-Audio: user_guide/examples/offline_inference/text_to_audio.md
+      - Qwen3-TTS Offline Inference: user_guide/examples/offline_inference/qwen3_tts.md
      - Text-To-Image: user_guide/examples/offline_inference/text_to_image.md
      - Text-To-Video: user_guide/examples/offline_inference/text_to_video.md
    - Online Serving:
-      - BAGEL-7B-MoT: user_guide/examples/online_serving/bagel.md
-      - GLM-Image Online Serving: user_guide/examples/online_serving/glm_image.md
      - Image-To-Image: user_guide/examples/online_serving/image_to_image.md
-      - Image-To-Video: user_guide/examples/online_serving/image_to_video.md
-      - Online serving Example of vLLM-Omni for MiMo-Audio: user_guide/examples/online_serving/mimo_audio.md
      - Qwen2.5-Omni: user_guide/examples/online_serving/qwen2_5_omni.md
      - Qwen3-Omni: user_guide/examples/online_serving/qwen3_omni.md
-      - Qwen3-TTS: user_guide/examples/online_serving/qwen3_tts.md
      - Text-To-Image: user_guide/examples/online_serving/text_to_image.md
-      - Text-To-Video: user_guide/examples/online_serving/text_to_video.md
  - General:
    - usage/*
  - Configuration:
@@ -44,23 +32,14 @@ nav:
  - Features:
    - Sleep Mode: features/sleep_mode.md
    - Diffusion Features:
-      - Acceleration Overview: user_guide/diffusion_acceleration.md
+      - Overview: user_guide/diffusion_acceleration.md
      - TeaCache: user_guide/diffusion/teacache.md
      - Cache-DiT: user_guide/diffusion/cache_dit_acceleration.md
-      - Quantization:
-        - Overview: user_guide/diffusion/quantization/overview.md
-        - FP8: user_guide/diffusion/quantization/fp8.md
-        - GGUF: user_guide/diffusion/quantization/gguf.md
      - Parallelism Acceleration: user_guide/diffusion/parallelism_acceleration.md
      - CPU Offloading: user_guide/diffusion/cpu_offload_diffusion.md
-      - LoRA: user_guide/diffusion/lora.md
-      - Hybrid Sharded Data Parallel: design/feature/hsdp.md
-      - Custom Pipeline: features/custom_pipeline.md
-    - ComfyUI: features/comfyui.md
 - Developer Guide:
  - General:
    - contributing/README.md
-    - pr_reviewer.md
    - glob: contributing/*
      flatten_single_child_sections: true
  - Model Implementation:
@@ -74,13 +53,6 @@ nav:
    - Feature Design:
      - design/feature/disaggregated_inference.md
      - design/feature/ray_based_execution.md
-      - design/feature/omni_connectors/
-      - design/feature/cfg_parallel.md
-      - design/feature/sequence_parallel.md
-      - design/feature/tensor_parallel.md
-      - design/feature/cache_dit.md
-      - design/feature/teacache.md
-      - design/feature/async_chunk_design.md
    - Module Design:
      - design/module/ar_module.md
      - design/module/dit_module.md
@@ -7,28 +7,29 @@ Main entry points for vLLM-Omni inference and serving.
 - [vllm_omni.entrypoints.async_omni.AsyncOmni][]
 - [vllm_omni.entrypoints.async_omni_diffusion.AsyncOmniDiffusion][]
 - [vllm_omni.entrypoints.async_omni_llm.AsyncOmniLLM][]
- [vllm_omni.entrypoints.cli.benchmark.base.OmniBenchmarkSubcommandBase][]
- [vllm_omni.entrypoints.cli.benchmark.main.OmniBenchmarkSubcommand][]
- [vllm_omni.entrypoints.cli.benchmark.serve.OmniBenchmarkServingSubcommand][]
+- [vllm_omni.entrypoints.chat_utils.OmniAsyncMultiModalContentParser][]
+- [vllm_omni.entrypoints.chat_utils.OmniAsyncMultiModalItemTracker][]
+- [vllm_omni.entrypoints.chat_utils.parse_chat_messages_futures][]
 - [vllm_omni.entrypoints.cli.serve.OmniServeCommand][]
 - [vllm_omni.entrypoints.client_request_state.ClientRequestState][]
+- [vllm_omni.entrypoints.log_utils.OrchestratorMetrics][]
+- [vllm_omni.entrypoints.log_utils.StageRequestMetrics][]
+- [vllm_omni.entrypoints.log_utils.StageStats][]
 - [vllm_omni.entrypoints.omni.Omni][]
 - [vllm_omni.entrypoints.omni.OmniBase][]
 - [vllm_omni.entrypoints.omni_diffusion.OmniDiffusion][]
 - [vllm_omni.entrypoints.omni_llm.OmniLLM][]
 - [vllm_omni.entrypoints.omni_stage.OmniStage][]
 - [vllm_omni.entrypoints.stage_utils.OmniStageTaskType][]
- [vllm_omni.entrypoints.zmq_utils.ZmqQueue][]

 ## Inputs

 Input data structures for multi-modal inputs.

- [vllm_omni.inputs.data.OmniDiffusionSamplingParams][]
 - [vllm_omni.inputs.data.OmniEmbedsPrompt][]
- [vllm_omni.inputs.data.OmniTextPrompt][]
 - [vllm_omni.inputs.data.OmniTokenInputs][]
 - [vllm_omni.inputs.data.OmniTokensPrompt][]
+- [vllm_omni.inputs.parse.parse_singleton_prompt_omni][]
 - [vllm_omni.inputs.preprocess.OmniInputPreprocessor][]

 ## Engine
@@ -36,12 +37,6 @@ Input data structures for multi-modal inputs.
 Engine classes for offline and online inference.

 - [vllm_omni.diffusion.diffusion_engine.DiffusionEngine][]
- [vllm_omni.distributed.omni_connectors.connectors.mooncake_transfer_engine_connector.BufferAllocator][]
- [vllm_omni.distributed.omni_connectors.connectors.mooncake_transfer_engine_connector.ManagedBuffer][]
- [vllm_omni.distributed.omni_connectors.connectors.mooncake_transfer_engine_connector.MooncakeAgentMetadata][]
- [vllm_omni.distributed.omni_connectors.connectors.mooncake_transfer_engine_connector.MooncakeTransferEngineConnector][]
- [vllm_omni.distributed.omni_connectors.connectors.mooncake_transfer_engine_connector.QueryRequest][]
- [vllm_omni.distributed.omni_connectors.connectors.mooncake_transfer_engine_connector.QueryResponse][]
 - [vllm_omni.engine.AdditionalInformationEntry][]
 - [vllm_omni.engine.AdditionalInformationPayload][]
 - [vllm_omni.engine.OmniEngineCoreOutput][]
@@ -58,12 +53,9 @@ Engine classes for offline and online inference.

 Core scheduling and caching components.

- [vllm_omni.core.sched.omni_ar_scheduler.KVCacheTransferData][]
 - [vllm_omni.core.sched.omni_ar_scheduler.OmniARScheduler][]
 - [vllm_omni.core.sched.omni_generation_scheduler.OmniGenerationScheduler][]
- [vllm_omni.core.sched.output.OmniCachedRequestData][]
 - [vllm_omni.core.sched.output.OmniNewRequestData][]
- [vllm_omni.core.sched.output.OmniSchedulerOutput][]
 - [vllm_omni.model_executor.models.qwen3_tts.tokenizer_25hz.vq.core_vq.DistributedGroupResidualVectorQuantization][]
 - [vllm_omni.model_executor.models.qwen3_tts.tokenizer_25hz.vq.core_vq.DistributedResidualVectorQuantization][]
 - [vllm_omni.model_executor.models.qwen3_tts.tokenizer_25hz.vq.core_vq.EuclideanCodebook][]
@@ -78,8 +70,6 @@ Configuration classes.
 - [vllm_omni.diffusion.cache.teacache.config.TeaCacheConfig][]
 - [vllm_omni.distributed.omni_connectors.utils.config.ConnectorSpec][]
 - [vllm_omni.distributed.omni_connectors.utils.config.OmniTransferConfig][]
- [vllm_omni.model_executor.models.mimo_audio.config_mimo_audio.MiMoAudioConfig][]
- [vllm_omni.model_executor.models.mimo_audio.config_mimo_audio.MiMoAudioTokenizerConfig][]
 - [vllm_omni.model_executor.models.qwen3_tts.configuration_qwen3_tts.Qwen3TTSConfig][]
 - [vllm_omni.model_executor.models.qwen3_tts.configuration_qwen3_tts.Qwen3TTSSpeakerEncoderConfig][]
 - [vllm_omni.model_executor.models.qwen3_tts.configuration_qwen3_tts.Qwen3TTSTalkerCodePredictorConfig][]
@@ -96,27 +86,20 @@ Configuration classes.

 Worker classes and model runners for distributed inference.

- [vllm_omni.diffusion.worker.diffusion_model_runner.DiffusionModelRunner][]
- [vllm_omni.diffusion.worker.diffusion_worker.CustomPipelineWorkerExtension][]
- [vllm_omni.diffusion.worker.diffusion_worker.DiffusionWorker][]
- [vllm_omni.diffusion.worker.diffusion_worker.WorkerProc][]
- [vllm_omni.diffusion.worker.diffusion_worker.WorkerWrapperBase][]
- [vllm_omni.platforms.npu.worker.npu_ar_model_runner.ExecuteModelState][]
- [vllm_omni.platforms.npu.worker.npu_ar_model_runner.NPUARModelRunner][]
- [vllm_omni.platforms.npu.worker.npu_ar_worker.NPUARWorker][]
- [vllm_omni.platforms.npu.worker.npu_generation_model_runner.NPUGenerationModelRunner][]
- [vllm_omni.platforms.npu.worker.npu_generation_worker.NPUGenerationWorker][]
- [vllm_omni.platforms.npu.worker.npu_model_runner.OmniNPUModelRunner][]
- [vllm_omni.platforms.xpu.worker.xpu_ar_model_runner.XPUARModelRunner][]
- [vllm_omni.platforms.xpu.worker.xpu_ar_worker.XPUARWorker][]
- [vllm_omni.platforms.xpu.worker.xpu_generation_model_runner.XPUGenerationModelRunner][]
- [vllm_omni.platforms.xpu.worker.xpu_generation_worker.XPUGenerationWorker][]
- [vllm_omni.worker.base.OmniGPUWorkerBase][]
+- [vllm_omni.diffusion.worker.gpu_diffusion_model_runner.GPUDiffusionModelRunner][]
+- [vllm_omni.diffusion.worker.gpu_diffusion_worker.GPUDiffusionWorker][]
+- [vllm_omni.diffusion.worker.gpu_diffusion_worker.WorkerProc][]
+- [vllm_omni.diffusion.worker.npu.npu_worker.NPUWorker][]
+- [vllm_omni.diffusion.worker.npu.npu_worker.NPUWorkerProc][]
 - [vllm_omni.worker.gpu_ar_model_runner.ExecuteModelState][]
 - [vllm_omni.worker.gpu_ar_model_runner.GPUARModelRunner][]
 - [vllm_omni.worker.gpu_ar_worker.GPUARWorker][]
 - [vllm_omni.worker.gpu_generation_model_runner.GPUGenerationModelRunner][]
 - [vllm_omni.worker.gpu_generation_worker.GPUGenerationWorker][]
- [vllm_omni.worker.gpu_memory_utils.parse_cuda_visible_devices][]
 - [vllm_omni.worker.gpu_model_runner.OmniGPUModelRunner][]
- [vllm_omni.worker.mixins.OmniWorkerMixin][]
+- [vllm_omni.worker.npu.npu_ar_model_runner.ExecuteModelState][]
+- [vllm_omni.worker.npu.npu_ar_model_runner.NPUARModelRunner][]
+- [vllm_omni.worker.npu.npu_ar_worker.NPUARWorker][]
+- [vllm_omni.worker.npu.npu_generation_model_runner.NPUGenerationModelRunner][]
+- [vllm_omni.worker.npu.npu_generation_worker.NPUGenerationWorker][]
+- [vllm_omni.worker.npu.npu_model_runner.OmniNPUModelRunner][]
@@ -22,21 +22,3 @@ If you have custom stage configs file, launch the server with command below
 ```bash
 vllm serve Qwen/Qwen2.5-Omni-7B --omni --stage-configs-path /path/to/stage_configs_file
 ```
-
-
-## bench
-
-Run benchmark tests for online serving throughput.
-Available Commands:
-
-```bash
-vllm bench serve --omni \
-    --model Qwen/Qwen2.5-Omni-7B \
-    --host server-host \
-    --port server-port \
-    --random-input-len 32 \
-    --random-output-len 4  \
-    --num-prompts  5
-```
-
-See [vllm bench serve](./bench/serve.md) for the full reference of all available arguments.
@@ -1,359 +0,0 @@
-# vLLM-Omni Benchmark CLI Guide
-The vllm bench command launches the vLLM-Omni benchmark to evaluate the performance of multimodal models.
-
-## Notes
-We currently only support using the "openai-chat-omni" backend.
-
-## 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:
- `--omni`  
-  Enable Omni (multimodal) mode, supporting multimodal inputs and outputs such as images, videos, and audio.
-
- `--backend`  
-  Specify the backend adapter as openai-chat-omni, using OpenAI Chat compatible API behavior as the protocol. Currently only openai-chat-omni is supported.
-
- `--model`  
-  The model identifier to load, filled according to the models supported by vLLM-Omni.
-
- `--endpoint`  
-  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).
-
- `--num-prompts`  
-  The total number of requests to send, an integer.
-
- `--max-concurrency`  
-  "Maximum number of concurrent requests. This can be used "
-        "to help simulate an environment where a higher level component "
-        "is enforcing a maximum number of concurrent requests. While the "
-        "--request-rate argument controls the rate at which requests are "
-        "initiated, this argument will control how many are actually allowed "
-        "to execute at a time. This means that when used in combination, the "
-        "actual request rate may be lower than specified with --request-rate, "
-        "if the server is not processing requests fast enough to keep up."
-
- `--request-rate`  
-  "Number of requests per second. If this is inf, "
-        "then all the requests are sent at time 0. "
-        "Otherwise, we use Poisson process or gamma distribution "
-        "to synthesize the request arrival times."
-
- `--ignore-eos`  
-  "Set ignore_eos flag when sending the benchmark request."
-
- `--metric-percentiles`  
-  Comma-separated list of percentiles for selected metrics. "
-        "To report 25-th, 50-th, and 75-th percentiles, use \"25,50,75\". "
-        "Default value is \"99\"."
-        "Use \"--percentile-metrics\" to select metrics.
-
- `--percentile-metrics`  
-        "Comma-separated list of selected metrics to report percentiles."
-                    "This argument specifies the metrics to report percentiles."
-                    'Allowed metric names are "ttft", "tpot", "itl", "e2el", "audio_ttfp", "audio_rtf", "audio_duration". '
-
- `--save-result`  
-Specify to save benchmark results to a json file
-
- `--save-detailed`  
-"When saving the results, whether to include per request "
-        "information such as response, error, ttfs, tpots, etc."
-
- `--result-dir`  
- "Specify directory to save benchmark json results."
-        "If not specified, results are saved in the current directory."
-
- `--result-filename`  
-"Specify the filename to save benchmark json results."
-        "If not specified, results will be saved in "
-        "{label}-{args.request_rate}qps-{base_model_id}-{current_dt}.json"
-
- `--random-prefix-len`  
-  Number of fixed prefix tokens before the random context in a request.
-  The total input length is the sum of random-prefix-len and a random
-  context length sampled from [input_len * (1 - range_ratio),
-  input_len * (1 + range_ratio)].Only the random and random-mm modes
-  support this parameter.
-
- `--random-input-len`  
-  Number of input tokens per request.Only the random and random-mm modes support this parameter.
-
- `--random-output-len`  
-  Number of output tokens per request.Only the random and random-mm modes support this parameter.
-
- `--random-range-ratio`  
-  Range ratio for sampling input/output length,
-  used only for random sampling. Must be in the range [0, 1) to define
-  a symmetric sampling range
-  [length * (1 - range_ratio), length * (1 + range_ratio)].
-  Only the random and random-mm modes support this parameter.
-
- `--random-mm-base-items-per-request`  
-  Base number of multimodal items per request for random-mm.
-  Actual per-request count is sampled around this base using
-  --random-mm-num-mm-items-range-ratio.
-  Only the random-mm mode supports this parameter.
-
- `--random-mm-limit-mm-per-prompt`  
-  Per-modality hard caps for items attached per request, e.g.
-  '{"image": 3, "video": 1, "audio": 1}'. The sampled per-request item
-  count is clamped to the sum of these limits. When a modality
-  reaches its cap, its buckets are excluded and probabilities are
-  renormalized.
-  Only the random-mm mode supports this parameter.
-
- `--random-mm-num-mm-items-range-ratio`  
-  Range ratio r in [0, 1] for sampling items per request.
-  We sample uniformly from the closed integer range
-  [floor(n*(1-r)), ceil(n*(1+r))]
-  where n is the base items per request.
-  r=0 keeps it fixed; r=1 allows 0 items. The maximum is clamped
-  to the sum of per-modality limits from
-  --random-mm-limit-mm-per-prompt.
-  An error is raised if the computed min exceeds the max.
-  Only the random-mm mode supports this parameter.
-
- `--random-mm-bucket-config`  
-  The bucket config is a dictionary mapping a multimodal item
-  sampling configuration to a probability.
-  Currently allows for 3 modalities: audio, images and videos.
-  A bucket key is a tuple of (height, width, num_frames)
-  The value is the probability of sampling that specific item.
-  Example:
-  --random-mm-bucket-config
-  "{(256, 256, 1): 0.5, (720, 1280, 16): 0.4, (0, 1, 5): 0.10}"
-  First item: images with resolution 256x256 w.p. 0.5
-  Second item: videos with resolution 720x1280 and 16 frames
-  Third item: audios with 1s duration and 5 channels w.p. 0.1
-  OBS.: If the probabilities do not sum to 1, they are normalized.
-  Only the random-mm mode supports this parameter
-
-## Usage Examples
-
-### Online Benchmark
-<details class="admonition abstract" markdown="1">
-<summary>Show more</summary>
-
-First start serving your model:
-
-```bash
-vllm serve Qwen/Qwen2.5-Omni-7B --omni
-```
-
-Then run the benchmarking for sharegpt:
-
-```bash
-# download dataset
-# wget https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/resolve/main/ShareGPT_V3_unfiltered_cleaned_split.json
-vllm bench serve \
-  --omni \
-  --port 43845 \
-  --model /home/models/Qwen/Qwen3-Omni-30B-A3B-Instruct \
-  --endpoint /v1/chat/completions \
-  --backend openai-chat-omni \
-  --num-prompts 2 \
-  --dataset-name sharegpt \
-  --dataset-path ShareGPT_V3_unfiltered_cleaned_split.json \
-  --percentile-metrics ttft,tpot,itl,e2el
-```
-If successful, you will see the following output:
-```text
-============ Serving Benchmark Result ============
-Successful requests:                     2
-Failed requests:                         0
-Benchmark duration (s):                  81.63
-Request throughput (req/s):              0.02
-Peak concurrent requests:                2.00
----------------End-to-end Latency----------------
-Mean E2EL (ms):                          56966.13
-Median E2EL (ms):                        56966.13
-P99 E2EL (ms):                           81016.80
-================== Text Result ===================
-Total input tokens:                      36
-Total generated tokens:                  5926
-Output token throughput (tok/s):         72.60
-Peak output token throughput (tok/s):    103.00
-Peak concurrent requests:                2.00
-Total Token throughput (tok/s):          73.04
---------------Time to First Token----------------
-Mean TTFT (ms):                          124.76
-Median TTFT (ms):                        124.76
-P99 TTFT (ms):                           156.10
-----Time per Output Token (excl. 1st token)------
-Mean TPOT (ms):                          481.30
-Median TPOT (ms):                        481.30
-P99 TPOT (ms):                           947.55
---------------Inter-token Latency----------------
-Mean ITL (ms):                           25.11
-Median ITL (ms):                         0.33
-P99 ITL (ms):                            25.17
-================== Audio Result ==================
-Total audio duration generated(s):       3.95
-Total audio frames generated:            94890
-Audio throughput(audio duration/s):      0.05
-==================================================
-```
-
-Or run the benchmarking for random:
-
-```bash
-vllm bench serve \
-  --omni \
-  --port 43845 \
-  --endpoint /v1/chat/completions \
-  --backend openai-chat-omni \
-  --model /home/models/Qwen/Qwen3-Omni-30B-A3B-Instruct \
-  --dataset-name random \
-  --num-prompts 2 \
-  --random-prefix-len 5 \
-  --random-input-len 10 \
-  --random-output-len 100 \
-  --percentile-metrics ttft,tpot,itl,e2el,audio_ttfp,audio_rtf \
-  --ignore-eos
-```
-
-If successful, you will see the following output:
-
-```text
-============ Serving Benchmark Result ============
-Successful requests:                     2
-Failed requests:                         0
-Benchmark duration (s):                  3.89
-Request throughput (req/s):              0.51
-Peak concurrent requests:                2.00
----------------End-to-end Latency----------------
-Mean E2EL (ms):                          3824.76
-Median E2EL (ms):                        3824.76
-P99 E2EL (ms):                           3888.54
-================== Text Result ===================
-Total input tokens:                      30
-Total generated tokens:                  10101
-Output token throughput (tok/s):         2595.57
-Peak output token throughput (tok/s):    111.00
-Peak concurrent requests:                2.00
-Total Token throughput (tok/s):          2603.28
---------------Time to First Token----------------
-Mean TTFT (ms):                          117.15
-Median TTFT (ms):                        117.15
-P99 TTFT (ms):                           142.69
-----Time per Output Token (excl. 1st token)------
-Mean TPOT (ms):                          0.73
-Median TPOT (ms):                        0.73
-P99 TPOT (ms):                           0.74
---------------Inter-token Latency----------------
-Mean ITL (ms):                           16.47
-Median ITL (ms):                         16.19
-P99 ITL (ms):                            52.55
-================== Audio Result ==================
-Total audio duration generated(s):       15.79
-Total audio frames generated:            379050
-Audio throughput(audio duration/s):      4.06
---------------Time to First Packet---------------
-Mean AUDIO_TTFP (ms):                    3701.37
-Median AUDIO_TTFP (ms):                  3701.37
-P99 AUDIO_TTFP (ms):                     3762.25
-----------------Real Time Factor-----------------
-Mean AUDIO_RTF:                          0.47
-Median AUDIO_RTF:                        0.47
-P99 AUDIO_RTF:                           0.48
-==================================================
-```
-Notes:
-We use audio generation time / audio duration to calculate RTF.
-
-</details>
-
-### Multi-Modal Benchmark
-
-<details class="admonition abstract" markdown="1">
-<summary>Show more</summary>
-
-Benchmark the performance of multi-modal requests in vLLM-Omni.
-
-Generate synthetic image、video、audio inputs alongside random text prompts to stress-test vision models without external datasets.
-
-Notes:
-
- Works only with online benchmark via the OpenAI backend (`--backend openai-chat-omni`) and endpoint `/v1/chat/completions`.
-
-Start the server (example):
-
-```bash
-vllm serve Qwen/Qwen2.5-Omni-7B --omni
-```
-
-It is recommended to use the flag `--ignore-eos` to simulate real responses. You can set the size of the output via the arg `random-output-len`.
-
-Then run the benchmarking script:
-```bash
-vllm bench serve \
-    --omni \
-  --dataset-name random-mm \
-  --port 40849 \
-  --model /home/models/Qwen/Qwen3-Omni-30B-A3B-Instruct \
-  --endpoint /v1/chat/completions \
-  --backend openai-chat-omni \
-  --request-rate 1 \
-  --num-prompts 1 \
-  --random-input-len 10 \
-  --random-range-ratio 0.0 \
-  --random-mm-base-items-per-request 2 \
-  --random-mm-num-mm-items-range-ratio 0 \
-  --random-mm-limit-mm-per-prompt '{"image":1,"video":1, "audio": 1}' \
-  --random-mm-bucket-config '{"(32, 32, 1)": 0.5, "(0, 1, 1)": 0.1, "(32, 32, 2)":0.4}' \
-  --ignore-eos \
-  --percentile-metrics ttft,tpot,itl \
-  --random-output-len 2 \
-  --extra_body '{"modalities": ["text"]}'
-```
-
-If successful, you will see the following output:
-
-```text
-============ Serving Benchmark Result ============
-Successful requests:                     1
-Failed requests:                         0
-Request rate configured (RPS):           1.00
-Benchmark duration (s):                  1.21
-Request throughput (req/s):              0.83
-Peak concurrent requests:                1.00
-================== Text Result ===================
-Total input tokens:                      10
-Total generated tokens:                  3
-Output token throughput (tok/s):         2.49
-Peak output token throughput (tok/s):    3.00
-Peak concurrent requests:                1.00
-Total Token throughput (tok/s):          10.77
---------------Time to First Token----------------
-Mean TTFT (ms):                          179.74
-Median TTFT (ms):                        179.74
-P99 TTFT (ms):                           179.74
-----Time per Output Token (excl. 1st token)------
-Mean TPOT (ms):                          12.76
-Median TPOT (ms):                        12.76
-P99 TPOT (ms):                           12.76
---------------Inter-token Latency----------------
-Mean ITL (ms):                           12.76
-Median ITL (ms):                         12.76
-P99 ITL (ms):                            25.24
-================== Audio Result ==================
-Total audio duration generated(s):       0.00
-Total audio frames generated:            0
-Audio throughput(audio duration/s):      0.00
-==================================================
-```
-
-Behavioral notes:
-
- If the requested base item count cannot be satisfied under the provided per-prompt limits, the tool raises an error rather than silently clamping.
-
-How sampling works:
-
- Determine per-request item count k by sampling uniformly from the integer range defined by `--random-mm-base-items-per-request` and `--random-mm-num-mm-items-range-ratio`, then clamp k to at most the sum of per-modality limits.
- For each of the k items, sample a bucket (H, W, T) according to the normalized probabilities in `--random-mm-bucket-config`, while tracking how many items of each modality have been added.
- If a modality (e.g., image) reaches its limit from `--random-mm-limit-mm-per-prompt`, all buckets of that modality are excluded and the remaining bucket probabilities are renormalized before continuing.
-This should be seen as an edge case, and if this behavior can be avoided by setting `--random-mm-limit-mm-per-prompt` to a large number. Note that this might result in errors due to engine config `--limit-mm-per-prompt`.
- The resulting request contains synthetic image data in `multi_modal_data` (OpenAI Chat format). When `random-mm` is used with the OpenAI Chat backend, prompts remain text and MM content is attached via `multi_modal_data`.
-</details>
@@ -1,9 +0,0 @@
-# vllm-omni serve
-
-## JSON CLI Arguments
-
--8<-- "docs/cli/json_tip.inc.md"
-
-## Arguments
-
--8<-- "docs/generated/argparse_omni/omni_serve.inc.md"
@@ -40,7 +40,7 @@ stage_args:
    engine_args: # Engine arguments for a certain engine
      model_stage: thinker
      model_arch: Qwen2_5OmniForConditionalGeneration # The model implementation registered in model_executor/models/registry.py
-      worker_type: ar # The specific worker used
+      worker_cls: vllm_omni.worker.gpu_ar_worker.GPUARWorker # 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
@@ -66,7 +66,7 @@ stage_args:
    engine_args:
      model_stage: talker
      model_arch: Qwen2_5OmniForConditionalGeneration
-      worker_type: ar
+      worker_cls: vllm_omni.worker.gpu_ar_worker.GPUARWorker
      scheduler_cls: vllm_omni.core.sched.omni_ar_scheduler.OmniARScheduler
      gpu_memory_utilization: 0.8
      enforce_eager: true
@@ -92,7 +92,7 @@ stage_args:
    engine_args:
      model_stage: code2wav
      model_arch: Qwen2_5OmniForConditionalGeneration
-      worker_type: generation
+      worker_cls: vllm_omni.worker.gpu_generation_worker.GPUGenerationWorker
      scheduler_cls: vllm_omni.core.sched.omni_generation_scheduler.OmniGenerationScheduler
      gpu_memory_utilization: 0.15
      enforce_eager: true
@@ -273,15 +273,3 @@ Default: `True`
 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.
@@ -8,7 +8,7 @@ stage_args:
    engine_args:
      model_stage: thinker
      model_arch: Qwen2_5OmniForConditionalGeneration
-      worker_type: ar
+      worker_cls: vllm_omni.worker.gpu_ar_worker.GPUARWorker
      scheduler_cls: vllm_omni.core.sched.omni_ar_scheduler.OmniARScheduler
      gpu_memory_utilization: 0.8
      enforce_eager: true  # Now we only support eager mode
@@ -34,7 +34,7 @@ stage_args:
    engine_args:
      model_stage: talker
      model_arch: Qwen2_5OmniForConditionalGeneration
-      worker_type: ar
+      worker_cls: vllm_omni.worker.gpu_ar_worker.GPUARWorker
      scheduler_cls: vllm_omni.core.sched.omni_ar_scheduler.OmniARScheduler
      gpu_memory_utilization: 0.8
      enforce_eager: true
@@ -60,7 +60,7 @@ stage_args:
    engine_args:
      model_stage: code2wav
      model_arch: Qwen2_5OmniForConditionalGeneration
-      worker_type: generation
+      worker_cls: vllm_omni.worker.gpu_generation_worker.GPUGenerationWorker
      scheduler_cls: vllm_omni.core.sched.omni_generation_scheduler.OmniGenerationScheduler
      gpu_memory_utilization: 0.15
      enforce_eager: true
@@ -1,678 +0,0 @@
-# Multi-Level Automated Testing System Documentation
-
-## Document Overview
-
-This testing system aims to build a complete, efficient, and well-structured quality assurance framework for the development, integration, and release of model services. It draws on the concept of the test pyramid from modern software engineering, progressively expanding testing activities from basic code logic verification to complex end-to-end (E2E) functionality, performance, accuracy, and even long-term stability validation.
-
-Through five levels (L1-L5) and common (Common) specifications, the system clarifies the testing objectives, scope, execution frequency, and required resources for different development stages (e.g., each commit, PR merge, daily build, pre-release). This ensures that models meet high standards for functionality, performance, and reliability across various deployment scenarios (online serving and offline inference).
-
-<table>
-  <thead>
-    <tr>
-      <th>Level</th>
-      <th>Scope & Focus</th>
-      <th>Time Cost</th>
-      <th>Test Dir</th>
-      <th>Doc</th>
-      <th>Frequency</th>
-      <th>Hardware</th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <td rowspan="2"><strong>Common</strong></td>
-      <td>Contribution Guideline & PR checklist</td>
-      <td>/</td>
-      <td>/</td>
-      <td>.github/PULL_REQUEST_TEMPLATE.md <a href="../tests_style/"> Test Style (PR Checklist)</a></td>
-      <td>/</td>
-      <td>/</td>
-    </tr>
-    <tr>
-      <td>CI Failure Description</td>
-      <td>/</td>
-      <td>/</td>
-      <td><a href="../failures/"> CI Failures</a></td>
-      <td>/</td>
-      <td>/</td>
-    </tr>
-    <tr>
-      <td><strong>L1</strong><br>(Unit & Logic)</td>
-      <td>Unit tests for components like entrypoints, models</td>
-      <td rowspan="2">&lt;15min</td>
-      <td>/tests/{component_name}/test_xxx</td>
-      <td>
-        <a href="#chapter-1-l1-l2-level-testing-unit-testing-and-basic-end-to-end-verification">Chapter 1</a><br>
-        Section 1 L1&amp;L2: Purpose, Test Content, Directory Location, Example
-      </td>
-      <td>PR with ready label (also can run locally)</td>
-      <td>CPU</td>
-    </tr>
-    <tr>
-      <td><strong>L2</strong><br>(E2E across models & GPU-required UT)</td>
-      <td>Online & Offline (basic deployment scenarios):<br>dummy, normal inference function (output format, stream), some instance startup UT</td>
-      <td>
-        /tests/e2e/online_serving/test_{model_name}.py<br>
-        /tests/e2e/offline_inference/test_{model_name}.py
-      </td>
-      <td>
-        <a href="#chapter-1-l1-l2-level-testing-unit-testing-and-basic-end-to-end-verification">Chapter 1</a><br>
-        L1&amp;L2: Purpose, Test Content, Directory Location, Example
-      </td>
-      <td>PR with ready label</td>
-      <td>GPU</td>
-    </tr>
-    <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>&lt;30min</td>
-      <td>
-        /tests/e2e/online_serving/test_{model_name}_expansion.py<br>
-        /tests/e2e/offline_inference/test_{model_name}_expansion.py
-      </td>
-      <td>
-        <a href="#chapter-2-l3-level-testing-core-integration-performance-and-accuracy-verification">Chapter 2</a><br>
-        L3: Purpose, Test Content, Directory Location, Example
-      </td>
-      <td>PR Merged (Also run L1&L2 Tests)</td>
-      <td>GPU</td>
-    </tr>
-    <tr>
-      <td><strong>L4</strong><br>(Perf & Integration & Accuracy)</td>
-      <td>Online & Offline: full functional scenarios + performance test + doc test</td>
-      <td>&lt;3 hour</td>
-      <td>
-        <strong>Full Function:</strong><br>
-        /tests/e2e/online_serving/test_{model_name}_expansion.py<br>
-        /tests/e2e/offline_inference/test_{model_name}_expansion.py<br>
-        <strong>Performance:</strong><br>
-        /tests/e2e/perf/nightly.json<br>
-        <strong>Doc Test:</strong><br>
-        tests/example/online_serving/test_{model_name}.py<br>
-        tests/example/offline_inference/test_{model_name}.py
-      </td>
-      <td>
-        <a href="#chapter-3-l4-level-testing-full-functionality-performance-and-documentation-testing">Chapter 3</a><br>
-        L4: Purpose, Test Content, Directory Location, Example
-      </td>
-      <td>Nightly</td>
-      <td>GPU</td>
-    </tr>
-    <tr>
-      <td><strong>L5</strong><br>(Stability & Reliability)</td>
-      <td>Online & Offline: long-term stability test + reliability test</td>
-      <td> Depends on reality </td>
-      <td>
-        <strong>Stability:</strong><br>
-        tests/e2e/stability/weekly.json<br>
-        <strong>Reliability:</strong><br>
-        tests/e2e/reliability/test_{model_name}.py
-      </td>
-      <td>
-        <a href="#chapter-4-l5-level-testing-stability-and-reliability-testing">Chapter 4</a><br>
-        L5: Purpose, Test Content, Directory Location, Example
-      </td>
-      <td>Weekly / Days before Release</td>
-      <td>GPU</td>
-    </tr>
-  </tbody>
-</table>
-
-
---
-<details>
-<summary> The folder structure for tests file based on the 5 levels design</summary>
-Legend: `✅` = test exists, `⬜` = suggested to add.
-```
-vllm_omni/                                    tests/
-├── config/                             →     ├── config/
-│   ├── model.py                              │   └── test_model.py                    ⬜
-│   └── lora.py                               │   └── test_lora.py                      ⬜
-│
-├── core/                               →     ├── core/
-│   └── sched/                                 │   └── sched/
-│       ├── omni_ar_scheduler.py               │       ├── test_omni_ar_scheduler.py    ⬜
-│       ├── omni_generation_scheduler.py       │       ├── test_omni_generation_scheduler.py  ⬜
-│       └── output.py                          │       └── test_output.py               ✅ currently in entrypoints/test_omni_new_request_data.py (tests output.OmniNewRequestData)
-│
-├── diffusion/                          →     ├── diffusion/
-│   ├── diffusion_engine.py                    │   ├── test_diffusion_engine.py          ⬜
-│   ├── attention/                             │   ├── attention/
-│   │   ├── layer.py                            │   │   ├── test_attention_sp.py         ✅
-│   │   └── backends/                           │   │   └── test_flash_attn.py           ✅
-│   ├── distributed/                           │   ├── distributed/
-│   │   └── ...                                 │   │   ├── test_comm.py                 ✅
-│   │                                            │   │   ├── test_cfg_parallel.py        ✅
-│   │                                            │   │   └── test_sp_plan_hooks.py       ✅
-│   ├── lora/                                   │   ├── lora/
-│   │   └── ...                                 │   │   ├── test_base_linear.py          ✅
-│   │                                            │   │   └── test_lora_manager.py        ✅
-│   ├── models/                                 │   ├── models/
-│   │   ├── qwen_image/                         │   │   ├── qwen_image/                 (e2e coverage)
-│   │   ├── z_image/                            │   │   └── z_image/
-│   │   └── ...                                 │   │       └── test_zimage_tp_constraints.py  ✅
-│   └── worker/                                 │   └── worker/
-│       ├── diffusion_worker.py                 │       └── test_diffusion_worker.py   ✅ file at diffusion/test_diffusion_worker.py
-│       └── diffusion_model_runner.py            │
-│
-├── distributed/                         →     ├── distributed/
-│   └── omni_connectors/                         │   └── omni_connectors/
-│       ├── adapter.py                           │       ├── test_adapter_and_flow.py   ✅
-│       ├── kv_transfer_manager.py               │       ├── test_basic_connectors.py   ✅
-│       ├── connectors/                           │       ├── test_kv_flow.py             ✅
-│       └── utils/                               │       └── test_omni_connector_configs.py  ✅
-│
-├── engine/                             →     ├── engine/
-│   ├── input_processor.py                      │   ├── test_input_processor.py         ⬜  (no processor.py in source)
-│   ├── output_processor.py                     │   └── test_output_processor.py         ⬜
-│   └── arg_utils.py                            │   └── test_arg_utils.py               ⬜
-│
-├── entrypoints/                        →     ├── entrypoints/
-│   ├── omni.py                                 │   ├── test_omni.py                    ⬜  (E2E covered by e2e/offline, e2e/online)
-│   ├── omni_llm.py                             │   ├── test_omni_llm.py                ✅
-│   ├── omni_stage.py                            │   ├── test_omni_stage.py              ⬜  (partial in test_omni_stage_diffusion_config.py)
-│   ├── omni_diffusion.py                       │   ├── test_omni_diffusion.py          ✅
-│   ├── async_omni.py                            │   ├── test_async_omni.py              ✅ actually in e2e/online_serving/test_async_omni.py
-│   ├── async_omni_diffusion.py                 │   ├── test_async_omni_diffusion_config.py  ✅
-│   ├── stage_utils.py                          │   ├── test_stage_utils.py            ✅
-│   ├── cli/                                     │   ├── cli/                           (benchmarks/test_serve_cli.py covers CLI serve)
-│   │   └── ...                                  │   │   └── test_*.py                  ⬜
-│   └── openai/                                  │   └── openai_api/                    # maps to entrypoints/openai/
-│       ├── api_server.py                        │       ├── test_api_server.py         ⬜  (e2e indirect coverage)
-│       ├── serving_chat.py                       │       ├── test_serving_chat_sampling_params.py  ✅
-│       ├── serving_speech.py                     │       ├── test_serving_speech.py     ✅
-│       └── image_api_utils.py                   │       └── test_image_server.py      ✅
-│
-├── inputs/                             →     ├── inputs/
-│   ├── data.py                                 │   ├── test_data.py                   ⬜
-│   ├── parse.py                                │   ├── test_parse.py                 ⬜
-│   └── preprocess.py                            │   └── test_preprocess.py            ✅ currently in entrypoints/test_omni_input_preprocessor.py
-│
-├── model_executor/                     →     ├── model_executor/
-│   ├── layers/                                  │   ├── layers/
-│   │   └── mrope.py                             │   │   └── test_mrope.py              ⬜
-│   ├── model_loader/                            │   ├── model_loader/
-│   │   └── weight_utils.py                      │   │   └── test_weight_utils.py      ⬜
-│   ├── models/                                  │   ├── models/
-│   │   ├── qwen2_5_omni/                         │   │   ├── qwen2_5_omni/
-│   │   │   ├── qwen2_5_omni_thinker.py           │   │   │   ├── test_audio_length.py  ✅
-│   │   │   ├── qwen2_5_omni_talker.py            │   │   │   ├── test_qwen2_5_omni_thinker.py  ⬜
-│   │   │   └── qwen2_5_omni_token2wav.py         │   │   │   ├── test_qwen2_5_omni_talker.py  ⬜
-│   │   └── qwen3_omni/                          │   │   │   └── test_qwen2_5_omni_token2wav.py  ⬜
-│   │       └── ...                               │   │   └── qwen3_omni/
-│   ├── stage_configs/                           │   │       └── test_*.py              ⬜
-│   │   └── *.yaml                               │   └── stage_configs/                 (used by e2e, test_*.py can be added)  ⬜
-│   └── stage_input_processors/                  │   └── stage_input_processors/
-│       └── ...                                  │       └── test_*.py                 ⬜
-│
-├── sample/                             →     ├── sample/
-│   └── __init__.py                             │   └── test_*.py                      ⬜
-│
-├── utils/                              →     ├── utils/
-│   └── __init__.py                             │   └── test_*.py                       ⬜  (no platform_utils.py currently)
-│
-├── worker/                             →     ├── worker/
-│   ├── gpu_ar_model_runner.py                  │   ├── test_gpu_ar_model_runner.py    ⬜
-│   ├── gpu_ar_worker.py                        │   ├── test_gpu_ar_worker.py           ⬜
-│   ├── gpu_generation_model_runner.py          │   ├── test_gpu_generation_model_runner.py  ✅
-│   ├── gpu_generation_worker.py                │   ├── test_gpu_generation_worker.py  ⬜
-│   ├── gpu_model_runner.py                     │   ├── test_omni_gpu_model_runner.py   ✅
-│   └── mixins.py                               │   └── (npu under platforms/npu/worker/)  # not worker/npu/
-│
-├── platforms/                          →     (no tests/platforms/, e2e and stage_configs provide indirect coverage)
-│   ├── cuda/
-│   ├── npu/worker/                             # NPU worker here, not vllm_omni/worker/npu/
-│   ├── rocm/
-│   └── xpu/worker/
-│
-├── outputs.py                          →     test_outputs.py                         ✅ (at tests root)
-├── (logger, patch, request, version)    →     (no corresponding unit test)
-│
-└── e2e (tests side only)               →     ├── e2e/
-                                               ├── online_serving/                     ✅ non-empty
-                                               │   ├── test_async_omni.py
-                                               │   ├── test_qwen3_omni.py
-                                               │   ├── test_qwen3_omni_expansion.py
-                                               │   ├── test_image_gen_edit.py
-                                               │   ├── test_images_generations_lora.py
-                                               │   └── stage_configs/
-                                               └── offline_inference/                  ✅
-                                                   ├── test_qwen2_5_omni.py
-                                                   ├── test_qwen3_omni.py
-                                                   ├── test_bagel_text2img.py
-                                                   ├── test_t2i_model.py
-                                                   ├── test_t2v_model.py
-                                                   ├── test_ovis_image.py
-                                                   ├── test_zimage_tensor_parallel.py
-                                                   ├── test_cache_dit.py
-                                                   ├── test_teacache.py
-                                                   ├── test_stable_audio_model.py
-                                                   ├── test_diffusion_cpu_offload.py
-                                                   ├── test_diffusion_layerwise_offload.py
-                                                   ├── test_diffusion_lora.py
-                                                   ├── test_sequence_parallel.py
-                                                   └── stage_configs/
-                                                       ├── qwen2_5_omni_ci.yaml
-                                                       ├── qwen3_omni_ci.yaml
-                                                       ├── bagel_*.yaml
-                                                       └── npu/, rocm/, etc.
-```
-
-
-</details>
-
-
-## Common Specifications
-
-Before entering specific testing levels, the project establishes two common specifications aimed at standardizing the development process and quickly locating issues.
-
-1.  ***PR Checklist ([Tests Style](../ci/tests_style.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](../ci/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.
-
-## Chapter 1: L1 & L2 Level Testing - Unit Testing and Basic End-to-End Verification
-
-### 1.1 Testing Purpose
-
-L1 and L2 level testing form the foundation of the quality assurance system. L1 level testing focuses on verifying the internal logic correctness of code units (e.g., functions, classes), ensuring each independent component behaves as designed.
-
-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` models 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, etc. Also includes some unit tests that require launching independent service instances.
-    -   ***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.
-
-   ***L1 Test Directory***: `/tests/{component_name}/test_xxx.py`
-   -   Here, `{component_name}` corresponds to modules in the source code, such as `distributed`, `entrypoints`, etc., and `test_xxx.py` is the specific test file.
-   ***L2 Test Directory***:
-   -   Online Serving: `/tests/e2e/online_serving/test_{model_name}.py`
-    -   Offline Inference: `/tests/e2e/offline_inference/test_{model_name}.py`
-
-### 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.
-   ***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
-
-import pytest
-
-pytestmark = [pytest.mark.core_model, pytest.mark.cpu]
-
-def test_resolve_max_mel_frames_default():
-    from vllm_omni.model_executor.models.qwen2_5_omni.audio_length import resolve_max_mel_frames
-
-    assert resolve_max_mel_frames(None, default=30000) == 30000
-    assert resolve_max_mel_frames(None, default=6000) == 6000
-
-
-def test_resolve_max_mel_frames_explicit():
-    from vllm_omni.model_executor.models.qwen2_5_omni.audio_length import resolve_max_mel_frames
-
-    # Explicit argument always wins over default
-    assert resolve_max_mel_frames(123, default=30000) == 123
-    assert resolve_max_mel_frames(6000, default=30000) == 6000
-    assert resolve_max_mel_frames(0, default=30000) == 0
-
-
-@pytest.mark.parametrize("repeats", [2, 4])
-@pytest.mark.parametrize("code_len", [0, 1, 32768])
-@pytest.mark.parametrize("max_mel_frames", [None, -1, 0, 1, 6000, 30000])
-def test_cap_and_align_mel_length_no_mismatch(repeats, code_len, max_mel_frames):
-    """Guard that any max_mel_frames yields a mel length aligned to repeats, and
-    consistent with the truncated code length (prevents concat mismatch).
-    """
-    from vllm_omni.model_executor.models.qwen2_5_omni.audio_length import cap_and_align_mel_length
-
-    target_code_len, target_mel_len = cap_and_align_mel_length(
-        code_len=code_len,
-        repeats=repeats,
-        max_mel_frames=max_mel_frames,
-    )
-
-    assert isinstance(target_code_len, int)
-    assert isinstance(target_mel_len, int)
-
-    if code_len == 0:
-        assert target_code_len == 0
-        assert target_mel_len == 0
-        return
-
-    assert target_code_len >= 1
-    assert target_mel_len >= repeats
-    assert target_mel_len % repeats == 0
-    assert target_mel_len == target_code_len * repeats
-    assert target_code_len <= code_len
-
-    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>
-<summary> L2 Test Examples</summary>
-You can refer to Test Examples in Chapter 2 to see example test cases that incorporate both L2 and L3 testing logic.
-</details>
-
-   -   ***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`
-
-## Chapter 2: L3 Level Testing - Core Integration, Performance, and Accuracy Verification
-
-### 2.1 Testing Purpose
-
-L3 level testing executes after code is merged into the main branch. Its core purpose is to verify the integration effect, key performance indicators, and output accuracy of ***real models*** in ***multiple deployment scenarios***
-
-. 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.
-
-### 2.3 Test Directory and Execution Files
-
-   ***Functional Testing***:
-   -   Online Serving: `/tests/e2e/online_serving/test_{model_name}_expansion.py`
-    -   Offline Inference: `/tests/e2e/offline_inference/test_{model_name}_expansion.py`
-    -   (Note: `_expansion.py` likely means it contains more comprehensive scenario cases compared to L2 tests).
-
-### 2.4 Execution Method and Example
-
-   ***Trigger Timing***: **`PR Merged`**. Automatically triggered after code review is approved and merged into the main branch.
-   ***Execution Environment***: ***GPU*** servers.
-   ***Script Example***:
-
-???+ example "Test Examples"
-
-    **2.4.1 Mark Declaration Section**
-
-    ```python
-    @pytest.mark.advanced_model
-    @pytest.mark.core_model
-    @pytest.mark.parametrize("omni_server", test_params, indirect=True)
-    ```
-
-    **Explanation**:
-
-    @pytest.mark.advanced_model: Marks the test as L3 or L4 level, indicating that this test case performs deep validation, using real models for performance, integration, and accuracy testing. This forms a "basic-advanced" correspondence with the core_model mark at the L2 level.
-
-    @pytest.mark.core_model: Marks the test as L1 or L2 level, indicating that this test case validates the basic functionality of the core model. It uses mock weights and only checks if the relevant interface functions correctly.
-
-    @pytest.mark.parametrize: A parameterization decorator that allows abstracting test data into parameters, enabling reuse of the same test logic across different data configurations. indirect=True indicates that parameters will be passed to the fixture for processing.
-
-    **Notes**: If you believe the test case only needs to execute basic run logic at the PR-level CI, you can mark it only with @pytest.mark.core_model. If you believe it only needs to execute deep validation run logic at the merge or nightly level, you can mark it only with @pytest.mark.advanced_model. If you believe the test case needs to accommodate both basic run and deep validation test logic, you should mark it with both @pytest.mark.core_model and @pytest.mark.advanced_model.
-
-    **2.4.2 Test Function Definition and Documentation**
-
-    ```python
-    def test_mix_to_text_audio_001(omni_server, openai_client) -> None:
-        """
-        Test multi-modal input processing and text/audio output generation via OpenAI API.
-        Deploy Setting: default yaml
-        Input Modal: text + audio + video + image
-        Output Modal: text + audio
-        Input Setting: stream=True
-        Datasets: single request
-        """
-    ```
-
-    **Explanation**:
-
-    **Function Naming Convention**: Uses the test_ prefix, describes the test scenario mix_to_text_audio, and the number 001 indicates the first test case for this scenario.
-
-    **Parameter Explanation**:
-
-    omni_server: Omni server instance obtained via fixture, containing model information and configuration.
-
-    openai_client: Unified OpenAI client processing instance, encapsulating request sending and response validation logic.
-
-    Docstring: Describes the test purpose, deployment settings, input/output modalities, streaming settings, and dataset type in detail, providing clear context for test maintenance.
-
-    **2.4.3 Multimodal Data Preparation**
-
-    ```python
-    video_data_url = f"data:video/mp4;base64,{generate_synthetic_video(224, 224, 300)['base64']}"
-    image_data_url = f"data:image/jpeg;base64,{generate_synthetic_image(224, 224)['base64']}"
-    audio_data_url = f"data:audio/wav;base64,{generate_synthetic_audio(5, 1)['base64']}"
-    ```
-
-    **Explanation**:
-
-    **Data Generation Functions**: Use the generate_synthetic_* series of functions to generate synthetic test data, avoiding reliance on external resources and ensuring test reproducibility and stability.
-
-    **Parameter Explanation**:
-
-    Video: width, height, duration_frames
-
-    Image: width, height
-
-    Audio: duration_seconds, channels
-
-    **2.4.4 Request Configuration and Keyword Validation**
-
-    ```python
-    request_config = {
-        "model": omni_server.model,
-        "messages": messages,
-        "stream": True,
-        "key_words": {
-            "audio": ["water", "cricket"],
-            "video": ["sphere", "globe", "circle", "round"],
-            "image": ["square", "quadrate"],
-            "text": ["beijing"]
-        },
-    }
-    ```
-
-    **Explanation**:
-
-    **Model Specification**: Uses omni_server.model to ensure the test aligns with the model configured on the server.
-
-    **Keyword Validation Mechanism**: This is an innovative design of the template to address the specific needs of multimodal testing:
-
-    Audio Keywords: Validate whether the generated text's description of audio content contains expected elements (e.g., "water" for water sounds, "cricket" for cricket sounds). If you provide multiple keywords, the validation is considered successful if at least one keyword is present.
-
-    **Video Keywords**: Validate whether the generated text's description of video content contains expected elements. If you provide multiple keywords, the validation is considered successful if at least one keyword is present.
-
-    Image Keywords: Validate whether the generated text's description of image content contains expected elements. If you provide multiple keywords, the validation is considered successful if at least one keyword is present.
-
-    Text Keywords: Validate whether the generated text contains expected elements. If you provide multiple keywords, the validation is considered successful if at least one keyword is present.
-
-    **2.4.5 Request Execution**
-
-    ```python
-    openai_client.send_request(request_config, request_num=1)
-    ```
-
-    **Explanation**:
-
-    **Unified Client**: Uses the OpenAIClientHandler instance to send requests. This client encapsulates error handling, retry mechanisms, and response validation logic.
-
-    **Single Request**: The comment clearly states this is a single-request completion test. For concurrent testing, it can be extended to multiple requests using request_num = n.
-
-    **Implicit Validation**: The send_request method internally includes validation logic dynamically selected based on the --run-level parameter: core_model performs basic validation, while advanced_model performs deep validation.
-
-   ***Run Command***: `pytest -s -v /tests/e2e/online_serving/test_{model_name}.py -m advanced_model --run-level=advanced_model`
-
-## Chapter 3: L4 Level Testing - Full Functionality, Performance, and Documentation Testing
-
-### 3.1 Testing Purpose
-
-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 the `/tests/e2e/perf/nightly.json` configuration file to drive performance testing tools for stress, load, and endurance tests, collecting metrics like throughput, response time, and resource utilization.
-   ***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/e2e/perf/nightly.json`
-   ***Documentation Example Tests***:
-   -   `tests/example/online_serving/test_{model_name}.py`
-    -   `tests/example/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***:
-<details>
-<summary> Test Examples</summary>
-When you want to add L4-level performance test cases, you can refer to the following format for case addition in tests/perf/tests/test.json:
-
-```JSON
-{
-    "test_name": "test_qwen3_omni",
-    "server_params": {
-        "model": "Qwen/Qwen3-Omni-30B-A3B-Instruct",
-        "stage_config_name": "qwen3_omni.yaml"
-    },
-    "benchmark_params": [
-        {
-            "dataset_name": "random",
-            "num_prompts": [10, 20],
-            "request_rate": [0.5, 1],
-            "random_input_len": 2500,
-            "random_output_len": 900,
-            "ignore_eos": true,
-            "percentile-metrics": "ttft,tpot,itl,e2el,audio_rtf,audio_ttfp,audio_duration",
-            "baseline": {
-                "mean_ttft_ms": 100000,
-                "mean_audio_ttfp_ms": 100000,
-                "mean_audio_rtf": 100000
-            }
-        }
-    ]
-}
-```
-
-#### Parameter Explanation
-
-***Overview***
-
-| Field            | Required | Description                                                     |
-| ---------------- | -------- | --------------------------------------------------------------- |
-| test_name        | Yes      | Unique identifier for the test case                             |
-| server_params    | Yes      | Server-side configuration parameters                            |
-| benchmark_params | Yes      | Benchmark running parameters (supports multiple configurations) |
-
-#### server_params Configuration
-
-##### 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 |
-
-##### 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***:
-```
-"update": {
-    "async_chunk": true,  // Enable asynchronous chunk processing
-    "stage_args": {
-        "0": {
-            "engine_args.custom_process_next_stage_input_func": "vllm_omni.model_executor.stage_input_processors.qwen3_omni.thinker2talker_async_chunk"
-        }
-    }
-},
-"delete": {
-    "stage_args": {
-        "2": ["custom_process_input_func"]  // Delete this configuration for stage 2
-    }
-}
-```
-#### 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.  Add the baseline parameter to specify the required validation values, ensuring the validation metric names match those in the result.json generated by the benchmark.
-4.  The qps and concurrency modes are mutually exclusive. For detailed explanations, see the table below:
-
-| Parameter       | Type        | Required | Example/Values  | Description                                                                                                                                                                                                                                                          |
-| --------------- | ----------- | -------- | --------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| 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    | int / array | No       | 1, [1, 2, 3]    | 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]    | 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.                          |
-</details>
-
-   -   ***Run Command***: (Specific commands would depend on the performance testing tool and configuration defined in `nightly.json`).
-
-## Chapter 4: L5 Level Testing - Stability and Reliability Testing
-
-### 4.1 Testing Purpose
-
-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 the `tests/e2e/stability/weekly.json` configuration 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 `tests/e2e/reliability/test_{model_name}.py` to actively simulate various fault and abnormal scenarios, such as: dependent service interruption, abnormal input data, network flicker, hardware resource preemption, etc., to verify the system's fault tolerance, self-healing, and graceful degradation capabilities.
-
-### 4.3 Test Directory and Execution Files
-
-   ***Stability Test Configuration***: `tests/e2e/stability/weekly.json`
-   ***Reliability Test Suite***: `tests/e2e/reliability/test_{model_name}.py`
-
-### 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***:
-<details>
-<summary> Test Examples</summary>
-```python
-# WIP
-```
-</details>
-
-   -   ***Stability***: (Execution would be driven by the configuration in `weekly.json`).
-    -   ***Reliability***: `pytest -s -v tests/e2e/reliability/test_{model_name}.py`
-
-## Summary
-
-This multi-level testing system achieves continuous, progressive validation of model service quality by tightly integrating testing activities with the development workflow (commit, review, merge, release). From rapid unit testing to comprehensive end-to-end testing, and further to in-depth performance, stability, and reliability verification, each level has clear objectives, collectively building a robust quality protection net. By following this system, teams can deliver high-quality, highly reliable model services more efficiently.
@@ -1,10 +1,4 @@
 # CI Failures

-What should I do when a CI job fails on my PR, but I don't think my PR caused the failure?
-
-
-## Common Case 1:
-
-🚨 Error: The command was interrupted by a signal: signal: terminated
-
-Reason: The test is terminated due to exceed the time limits. Sometimes rebuild the test will pass.
+What should I do when a CI job fails on my PR, but I don't think my PR caused
+the failure?
@@ -5,34 +5,33 @@ By adding markers before test functions, tests can later be executed uniformly b
 ## Current Markers
 Defined in `pyproject.toml`:

-| Marker             | Description                                               |
-| ------------------ | --------------------------------------------------------- |
-| `core_model`       | L1&L2 tests (run in each PR)                              |
-| `advanced_model`   | L3&L4 level tests (run in each merge or nightly)          |
-| `diffusion`        | Diffusion model tests                                     |
-| `omni`             | Omni 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 *                              |
-| `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)                         |
-| `benchmark`        | Benchmark tests                                           |
+| Marker             | Description                                             |
+| ------------------ | ------------------------------------------------------- |
+| `core_model`       | Core model tests (run in each PR)                       |
+| `diffusion`        | Diffusion model tests                                   |
+| `omni`             | Omni model tests                                        |
+| `cache`            | Cache backend tests                                     |
+| `parallel`         | Parallelism/distributed tests                           |
+| `cpu`              | Tests that run on CPU                                   |
+| `gpu`              | Tests that run on GPU (auto-added)                      |
+| `cuda`             | Tests that run on CUDA (auto-added)                     |
+| `rocm`             | Tests that run on AMD/ROCm (auto-added)                 |
+| `npu`              | Tests that run on NPU/Ascend (auto-added)               |
+| `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)                       |
+| `benchmark`        | Benchmark tests                                         |

-\* Means those markers are auto-added, and they will be added by the `@hardware_test` decorator.
+For those markers shown as auto-added, they will be added by the `@hardware_test` decorator.

 ### Example usage for markers

@@ -72,7 +71,10 @@ This decorator is intended to make hardware-aware, cross-platform test authoring
   Support for `skipif_rocm` and `skipif_npu` will be implemented later.


-5. **Works with pytest filtering**  
+5. **Runs each test in a new process**  
+   Automatically wraps the distributed test with a decorator (`@create_new_process_for_each_test`) to ensure isolation and compatibility with multi-process hardware backends.
+
+6. **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"`).

 #### Example usage for decorator
@@ -92,6 +94,7 @@ This decorator is intended to make hardware-aware, cross-platform test authoring
    ```
 - `res` must be a dict; supported resources: CUDA (L4/H100), ROCm (MI325), NPU (A2/A3)
 - `num_cards` can be int (all platforms) or dict (per platform); defaults to 1 when missing
+- `hardware_test` automatically applies `@create_new_process_for_each_test` for distributed tests.
 - 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"`
@@ -2,18 +2,8 @@

 To ensure project maintainability and sustainable development, we encourage contributors to submit test code (unit tests, system tests, or end-to-end tests) alongside their code changes. This document outlines the guidelines for organizing and naming test files.

-## Checklist before submitting your test files
-
-1. The file is saved in an appropriate place and the file name is clear.
-2. The coding style follows the requirements outlined below.
-3. All test functions have appropriate pytest markers.
-4. For tests that need run in CI, please ensure it labeled as ``@pytest.mark.core_model` the test is configured under the `./buildkite/` folder.
-
-
 ## Test Types

-For more details about our [Five Levels Tests design](../ci/CI_5levels.md).
-
 ### Unit Tests and System Tests
 For unit tests and system tests, we strongly recommend placing test files in the same directory structure as the source code being tested, using the naming convention `test_*.py`.

@@ -28,141 +18,95 @@ End-to-end tests verify the complete functionality of a system or component. For

 ## Test Directory Structure

-The ideal directory structure mirrors the source code organization. Legend: `✅` = test exists, `⬜` = suggested to add.
+The ideal directory structure mirrors the source code organization:

 ```
-vllm_omni/                                    tests/
-├── config/                             →     ├── config/
-│   ├── model.py                              │   └── test_model.py                    ⬜
-│   └── lora.py                               │   └── test_lora.py                      ⬜
+vllm_omni/                          tests/
+├── config/                    →    ├── config/
+│   └── model.py                    │   └── test_model.py
 │
-├── core/                               →     ├── core/
-│   └── sched/                                 │   └── sched/
-│       ├── omni_ar_scheduler.py               │       ├── test_omni_ar_scheduler.py    ⬜
-│       ├── omni_generation_scheduler.py       │       ├── test_omni_generation_scheduler.py  ⬜
-│       └── output.py                          │       └── test_output.py               ✅ currently in entrypoints/test_omni_new_request_data.py (tests output.OmniNewRequestData)
+├── core/                      →    ├── core/
+│   └── sched/                      │   └── sched/                    # Maps to core/sched/
+│       ├── omni_ar_scheduler.py    │       ├── test_omni_ar_scheduler.py
+│       ├── omni_generation_scheduler.py │  ├── test_omni_generation_scheduler.py
+│       └── output.py               │       └── test_output.py
 │
-├── diffusion/                          →     ├── diffusion/
-│   ├── diffusion_engine.py                    │   ├── test_diffusion_engine.py          ⬜
-│   ├── attention/                             │   ├── attention/
-│   │   ├── layer.py                            │   │   ├── test_attention_sp.py         ✅
-│   │   └── backends/                           │   │   └── test_flash_attn.py           ✅
-│   ├── distributed/                           │   ├── distributed/
-│   │   └── ...                                 │   │   ├── test_comm.py                 ✅
-│   │                                            │   │   ├── test_cfg_parallel.py        ✅
-│   │                                            │   │   └── test_sp_plan_hooks.py       ✅
-│   ├── lora/                                   │   ├── lora/
-│   │   └── ...                                 │   │   ├── test_base_linear.py          ✅
-│   │                                            │   │   └── test_lora_manager.py        ✅
-│   ├── models/                                 │   ├── models/
-│   │   ├── qwen_image/                         │   │   ├── qwen_image/                 (e2e coverage)
-│   │   ├── z_image/                            │   │   └── z_image/
-│   │   └── ...                                 │   │       └── test_zimage_tp_constraints.py  ✅
-│   └── worker/                                 │   └── worker/
-│       ├── diffusion_worker.py                 │       └── test_diffusion_worker.py   ✅ file at diffusion/test_diffusion_worker.py
-│       └── diffusion_model_runner.py            │
+├── diffusion/                 →    ├── diffusion/
+│   ├── diffusion_engine.py         │   ├── test_diffusion_engine.py
+│   ├── omni_diffusion.py           │   ├── test_omni_diffusion.py
+│   ├── attention/                  │   ├── attention/                # Maps to diffusion/attention/
+│   │   └── backends/               │   │   └── test_*.py
+│   ├── models/                     │   ├── models/                   # Maps to diffusion/models/
+│   │   ├── qwen_image/             │   │   ├── qwen_image/
+│   │   │   └── ...                 │   │   │   └── test_*.py
+│   │   └── z_image/                │   │   └── z_image/
+│   │       └── ...                 │   │       └── test_*.py
+│   └── worker/                     │   └── worker/                   # Maps to diffusion/worker/
+│       └── ...                     │       └── test_*.py
 │
-├── distributed/                         →     ├── distributed/
-│   └── omni_connectors/                         │   └── omni_connectors/
-│       ├── adapter.py                           │       ├── test_adapter_and_flow.py   ✅
-│       ├── kv_transfer_manager.py               │       ├── test_basic_connectors.py   ✅
-│       ├── connectors/                           │       ├── test_kv_flow.py             ✅
-│       └── utils/                               │       └── test_omni_connector_configs.py  ✅
+├── distributed/               →    ├── distributed/
+│   └── ...                         │   └── test_*.py
 │
-├── engine/                             →     ├── engine/
-│   ├── input_processor.py                      │   ├── test_input_processor.py         ⬜  (no processor.py in source)
-│   ├── output_processor.py                     │   └── test_output_processor.py         ⬜
-│   └── arg_utils.py                            │   └── test_arg_utils.py               ⬜
+├── engine/                    →    ├── engine/
+│   ├── processor.py                │   ├── test_processor.py
+│   └── output_processor.py         │   └── test_output_processor.py
 │
-├── entrypoints/                        →     ├── entrypoints/
-│   ├── omni.py                                 │   ├── test_omni.py                    ⬜  (E2E covered by e2e/offline, e2e/online)
-│   ├── omni_llm.py                             │   ├── test_omni_llm.py                ✅
-│   ├── omni_stage.py                            │   ├── test_omni_stage.py              ⬜  (partial in test_omni_stage_diffusion_config.py)
-│   ├── omni_diffusion.py                       │   ├── test_omni_diffusion.py          ✅
-│   ├── async_omni.py                            │   ├── test_async_omni.py              ✅ actually in e2e/online_serving/test_async_omni.py
-│   ├── async_omni_diffusion.py                 │   ├── test_async_omni_diffusion_config.py  ✅
-│   ├── stage_utils.py                          │   ├── test_stage_utils.py            ✅
-│   ├── cli/                                     │   ├── cli/                           (benchmarks/test_serve_cli.py covers CLI serve)
-│   │   └── ...                                  │   │   └── test_*.py                  ⬜
-│   └── openai/                                  │   └── openai_api/                    # maps to entrypoints/openai/
-│       ├── api_server.py                        │       ├── test_api_server.py         ⬜  (e2e indirect coverage)
-│       ├── serving_chat.py                       │       ├── test_serving_chat_sampling_params.py  ✅
-│       ├── serving_speech.py                     │       ├── test_serving_speech.py     ✅
-│       └── image_api_utils.py                   │       └── test_image_server.py      ✅
+├── entrypoints/               →    ├── entrypoints/
+│   ├── omni_llm.py                 │   ├── test_omni_llm.py          # UT: OmniLLM core logic (mocked)
+│   ├── omni_stage.py               │   ├── test_omni_stage.py         # UT: OmniStage logic
+│   ├── omni.py                     │   ├── test_omni.py               # E2E: Omni class (offline inference)
+│   ├── async_omni.py               │   ├── test_async_omni.py         # E2E: AsyncOmni class
+│   ├── cli/                        │   ├── cli/                       # Maps to entrypoints/cli/
+│   │   └── ...                     │   │   └── test_*.py
+│   └── openai/                     │   └── openai/                     # Maps to entrypoints/openai/
+│       ├── api_server.py           │       ├── test_api_server.py     # E2E: API server (online serving)
+│       └── serving_chat.py         │       └── test_serving_chat.py
 │
-├── inputs/                             →     ├── inputs/
-│   ├── data.py                                 │   ├── test_data.py                   ⬜
-│   ├── parse.py                                │   ├── test_parse.py                 ⬜
-│   └── preprocess.py                            │   └── test_preprocess.py            ✅ currently in entrypoints/test_omni_input_preprocessor.py
+├── inputs/                    →    ├── inputs/
+│   ├── data.py                     │   ├── test_data.py
+│   ├── parse.py                    │   ├── test_parse.py
+│   └── preprocess.py               │   └── test_preprocess.py
 │
-├── model_executor/                     →     ├── model_executor/
-│   ├── layers/                                  │   ├── layers/
-│   │   └── mrope.py                             │   │   └── test_mrope.py              ⬜
-│   ├── model_loader/                            │   ├── model_loader/
-│   │   └── weight_utils.py                      │   │   └── test_weight_utils.py      ⬜
-│   ├── models/                                  │   ├── models/
-│   │   ├── qwen2_5_omni/                         │   │   ├── qwen2_5_omni/
-│   │   │   ├── qwen2_5_omni_thinker.py           │   │   │   ├── test_audio_length.py  ✅
-│   │   │   ├── qwen2_5_omni_talker.py            │   │   │   ├── test_qwen2_5_omni_thinker.py  ⬜
-│   │   │   └── qwen2_5_omni_token2wav.py         │   │   │   ├── test_qwen2_5_omni_talker.py  ⬜
-│   │   └── qwen3_omni/                          │   │   │   └── test_qwen2_5_omni_token2wav.py  ⬜
-│   │       └── ...                               │   │   └── qwen3_omni/
-│   ├── stage_configs/                           │   │       └── test_*.py              ⬜
-│   │   └── *.yaml                               │   └── stage_configs/                 (used by e2e, test_*.py can be added)  ⬜
-│   └── stage_input_processors/                  │   └── stage_input_processors/
-│       └── ...                                  │       └── test_*.py                 ⬜
+├── model_executor/            →    ├── model_executor/
+│   ├── layers/                     │   ├── layers/
+│   │   └── mrope.py                │   │   └── test_mrope.py
+│   ├── model_loader/               │   ├── model_loader/
+│   │   └── weight_utils.py         │   │   └── test_weight_utils.py
+│   ├── models/                     │   ├── models/
+│   │   ├── qwen2_5_omni/           │   │   ├── qwen2_5_omni/
+│   │   │   ├── qwen2_5_omni_thinker.py │ │   │   ├── test_qwen2_5_omni_thinker.py  # UT
+│   │   │   ├── qwen2_5_omni_talker.py │ │   │   ├── test_qwen2_5_omni_talker.py  # UT
+│   │   │   └── qwen2_5_omni_token2wav.py │ │   │   └── test_qwen2_5_omni_token2wav.py  # UT
+│   │   └── qwen3_omni/             │   │   └── qwen3_omni/
+│   │       └── ...                 │   │       └── test_*.py
+│   ├── stage_configs/              │   └── stage_configs/             # Configuration tests (if needed)
+│   │   └── ...                     │       └── test_*.py
+│   └── stage_input_processors/     │   └── stage_input_processors/
+│       └── ...                     │       └── test_*.py
 │
-├── sample/                             →     ├── sample/
-│   └── __init__.py                             │   └── test_*.py                      ⬜
+├── sample/                    →    ├── sample/
+│   └── ...                         │   └── test_*.py
 │
-├── utils/                              →     ├── utils/
-│   └── __init__.py                             │   └── test_*.py                       ⬜  (no platform_utils.py currently)
+├── utils/                     →    ├── utils/
+│   └── platform_utils.py           │   └── test_platform_utils.py
 │
-├── worker/                             →     ├── worker/
-│   ├── gpu_ar_model_runner.py                  │   ├── test_gpu_ar_model_runner.py    ⬜
-│   ├── gpu_ar_worker.py                        │   ├── test_gpu_ar_worker.py           ⬜
-│   ├── gpu_generation_model_runner.py          │   ├── test_gpu_generation_model_runner.py  ✅
-│   ├── gpu_generation_worker.py                │   ├── test_gpu_generation_worker.py  ⬜
-│   ├── gpu_model_runner.py                     │   ├── test_omni_gpu_model_runner.py   ✅
-│   └── mixins.py                               │   └── (npu under platforms/npu/worker/)  # not worker/npu/
+├── worker/                    →    ├── worker/
+    ├── gpu_ar_worker.py            │   ├── test_gpu_ar_worker.py
+    ├── gpu_generation_worker.py    │   ├── test_gpu_generation_worker.py
+    ├── gpu_model_runner.py         │   ├── test_gpu_model_runner.py
+    └── npu/                        │   └── npu/                       # Maps to worker/npu/
+        └── ...                     │       └── test_*.py
 │
-├── platforms/                          →     (no tests/platforms/, e2e and stage_configs provide indirect coverage)
-│   ├── cuda/
-│   ├── npu/worker/                             # NPU worker here, not vllm_omni/worker/npu/
-│   ├── rocm/
-│   └── xpu/worker/
-│
-├── outputs.py                          →     test_outputs.py                         ✅ (at tests root)
-├── (logger, patch, request, version)    →     (no corresponding unit test)
-│
-└── e2e (tests side only)               →     ├── e2e/
-                                               ├── online_serving/                     ✅ non-empty
-                                               │   ├── test_async_omni.py
-                                               │   ├── test_qwen3_omni.py
-                                               │   ├── test_qwen3_omni_expansion.py
-                                               │   ├── test_image_gen_edit.py
-                                               │   ├── test_images_generations_lora.py
-                                               │   └── stage_configs/
-                                               └── offline_inference/                  ✅
-                                                   ├── test_qwen2_5_omni.py
-                                                   ├── test_qwen3_omni.py
-                                                   ├── test_bagel_text2img.py
-                                                   ├── test_t2i_model.py
-                                                   ├── test_t2v_model.py
-                                                   ├── test_ovis_image.py
-                                                   ├── test_zimage_tensor_parallel.py
-                                                   ├── test_cache_dit.py
-                                                   ├── test_teacache.py
-                                                   ├── test_stable_audio_model.py
-                                                   ├── test_diffusion_cpu_offload.py
-                                                   ├── test_diffusion_layerwise_offload.py
-                                                   ├── test_diffusion_lora.py
-                                                   ├── test_sequence_parallel.py
-                                                   └── stage_configs/
-                                                       ├── qwen2_5_omni_ci.yaml
-                                                       ├── qwen3_omni_ci.yaml
-                                                       ├── bagel_*.yaml
-                                                       └── npu/, rocm/, etc.
+└── e2e/                       →    ├── e2e/                # End-to-end scenarios (no 1:1 source mirror)
+                                    ├── online_serving/       # Full-stack online serving flows
+                                    │   └── (empty for now)
+                                    └── offline_inference/    # Full offline inference flows
+                                        ├── test_qwen2_5_omni.py     # Moved from multi_stages/
+                                        ├── test_qwen3_omni.py       # Moved from multi_stages_h100/
+                                        ├── test_t2i_model.py  # Moved from single_stage/
+                                        └── stage_configs/           # Shared stage configs
+                                            ├── qwen2_5_omni_ci.yaml
+                                            └── qwen3_omni_ci.yaml
 ```


@@ -173,8 +117,6 @@ vllm_omni/                                    tests/

 - **E2E Tests**: Place in `tests/e2e/offline_inference/` or `tests/e2e/online_serving/` with descriptive names. Example: `tests/e2e/offline_inference/test_qwen3_omni.py`, `tests/e2e/offline_inference/test_diffusion_model.py`

- **Expansion Tests**
-
 ### Best Practices

 1. **Mirror Source Structure**: Test directories should mirror the source code structure
@@ -202,217 +144,81 @@ vllm_omni/                                    tests/
 ### Template
 #### E2E - Online serving

-E2E Online tests for Qwen3-Omni model with mix input and audio+text output. Based on `tests/e2e/online_serving/test_qwen3_omni.py`.
-
 ```python
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 """
-E2E Online tests for Qwen3-Omni model with mix input and audio+text output.
+Online E2E smoke test for an omni model (video,text,audio → audio).
 """
-
-import os
-
-os.environ["VLLM_WORKER_MULTIPROC_METHOD"] = "spawn"
-os.environ["VLLM_TEST_CLEAN_GPU_MEMORY"] = "0"
-
-import threading
 from pathlib import Path

-import openai
 import pytest
+import openai

-from tests.conftest import (
-    OmniServer,
-    convert_audio_to_text,
-    cosine_similarity_text,
-    dummy_messages_from_mix_data,
-    generate_synthetic_video,
-    merge_base64_and_convert_to_text,
-)
-from vllm_omni.platforms import current_omni_platform
+from tests.utils import hardware_test

-# Edit: model name and stage config path
-models = ["Qwen/Qwen3-Omni-30B-A3B-Instruct"]
-
-#If you use the default configuration file, you can directly use the following address.
-def get_default_config():
-    return str(Path(__file__).parent.parent / "stage_configs" / "qwen3_omni_ci.yaml")
-
-#If you need to modify the configuration file, you can use modify_stage_config.
-def get_chunk_config():
-    path = modify_stage_config(
-        get_default_config(),
-        updates={
-            "async_chunk": True,
-            "stage_args": {
-                0: {
-                    "engine_args.custom_process_next_stage_input_func": "vllm_omni.model_executor.stage_input_processors.qwen3_omni.thinker2talker_async_chunk"
-                },
-                1: {
-                    "engine_args.custom_process_next_stage_input_func": "vllm_omni.model_executor.stage_input_processors.qwen3_omni.talker2code2wav_async_chunk"
-                },
-            },
-        },
-        deletes={"stage_args": {2: ["custom_process_input_func"]}},
-    )
-    return path
-
-stage_configs = [get_default_config(), CHUNK_CONFIG_PATH]
+# Optional: set process start method for workers
+os.environ["VLLM_WORKER_MULTIPROC_METHOD"] = "spawn"

+models = ["{your model name}"] #Edit here to load your model
+stage_configs = [str(Path(__file__).parent / "stage_configs" / {your model yaml})] #Edit here to load your model yaml
 test_params = [(model, stage_config) for model in models for stage_config in stage_configs]

-
-#Please use this method to launch the online instance.
-_omni_server_lock = threading.Lock()
-
-@pytest.fixture(scope="module")
-def omni_server(request):
-    """Start vLLM-Omni server as a subprocess. Use module scope for multi-stage init (10-20+ min)."""
-    with _omni_server_lock:
-        model, stage_config_path = request.param
-        with OmniServer(
-            model,
-            ["--stage-configs-path", stage_config_path, "--stage-init-timeout", "120"],
-        ) as server:
-            yield server
+#OmniServer，Used to start the vllm-omni server
+class OmniServer:
+    xxx


@pytest.fixture
-def client(omni_server):
-    """OpenAI client for the running vLLM-Omni server."""
-    return openai.OpenAI(
-        base_url=f"http://{omni_server.host}:{omni_server.port}/v1",
-        api_key="EMPTY",
-    )
+def omni_server(request):
+    model, stage_config_path = request.param
+    with OmniServer(model, ["--stage-configs-path", stage_config_path]) as server:
+        yield server

-#Please use function definitions above the test function to define the prompts and other parameters you need.
-def get_system_prompt():
-    return {
-        "role": "system",
-        "content": [
-            {
-                "type": "text",
-                "text": (
-                    "You are Qwen, a virtual human developed by the Qwen Team, "
-                    "Alibaba Group, capable of perceiving auditory and visual inputs, "
-                    "as well as generating text and speech."
-                ),
-            }
-        ],
-    }

-...
+#handle request message
+@pytest.fixture(scope="session")
+def base64_encoded_video() -> str:
+    xxx
+
+@pytest.fixture(scope="session")
+def dummy_messages_from_video_data(video_data_url: str, content_text: str) -> str:
+    xxx

-#Please define test case tags according to the instructions in the marker documentation.
@pytest.mark.core_model
@pytest.mark.omni
+@hardware_test(
+    res={"cuda": "L4", "rocm": "MI325", "npu": "A2"},
+    num_cards={"cuda": 2, "rocm": 2, "npu": 4},
+)
@pytest.mark.parametrize("omni_server", test_params, indirect=True)
-def test_mix_to_text_audio_001(client: openai.OpenAI, omni_server, request) -> None:
-    # PLEASE FOLLOW THESE TEMPLATE INSTRUCTIONS:
-    # ============================================================================
-    # TEMPLATE USAGE GUIDE:
-    # 1. Copy this entire function as a starting point for multi-modal tests
-    # 2. Update the test name to reflect your specific test scenario
-    # 3. Modify input/output modalities as needed (see OPTIONS section below)
-    # 4. Adjust assertions based on your expected outcomes
-    # 5. Add custom validation logic for your specific use case
-    # ============================================================================
+def test_video_to_audio(
+    client: openai.OpenAI,
+    omni_server,
+    base64_encoded_video: str,
+) -> None:
+    #set message
+    video_data_url = f"data:video/mp4;base64, {base64_encoded_video}"
+    messages = dummy_messages_from_video_data(video_data_url)

-    #Please list the relevant test points.
-    """
-    Test multi-modal input processing and text/audio output generation via OpenAI API.
-    Deploy Setting: default yaml
-    Input Modal: text + audio + video + image
-    Output Modal: text + audio
-    Input Setting: stream=True
-    Datasets: single request
-    """
-    # SECTION 1: TEST SETUP AND INITIALIZATION
-    # =========================================
-    # INSTRUCTIONS: Initialize test variables and prepare test environment
-    # MODIFY: Add any additional test setup required for your scenario
-    e2e_list = list()
-    # SECTION 2: TEST DATA GENERATION
-    # ================================
-    # INSTRUCTIONS: Generate or load test data for each input modality
-    # MODIFY: Replace synthetic generators with your actual data sources
-    # VIDEO DATA - Generate synthetic video for testing
-    # FORMAT: data:video/mp4;base64,{base64_encoded_video}
-    # PARAMETERS: width, height, duration_frames
-    video_data_url = f"data:video/mp4;base64,{generate_synthetic_video(224, 224, 300)['base64']}"
-    # IMAGE DATA - Generate synthetic image for testing
-    # FORMAT: data:image/jpeg;base64,{base64_encoded_image}
-    # PARAMETERS: width, height
-    image_data_url = f"data:image/jpeg;base64,{generate_synthetic_image(224, 224)['base64']}"
-    # AUDIO DATA - Generate synthetic audio for testing
-    # FORMAT: data:audio/wav;base64,{base64_encoded_audio}
-    # PARAMETERS: duration_seconds, channels
-    audio_data_url = f"data:audio/wav;base64,{generate_synthetic_audio(5, 1)['base64']}"
-
-    # SECTION 3: MESSAGE CONSTRUCTION
-    # ================================
-    # INSTRUCTIONS: Assemble the complete message payload for API request
-    # MODIFY: Add/remove modalities or change prompt structure as needed
-
-    # USAGE: Construct a message containing all input modalities
-    # IMPORTANT: Ensure the message structure matches OpenAI API expectations
-    # CUSTOMIZATION POINTS:
-    #   - system_prompt: Controls the assistant's behavior
-    #   - content_text: The user's text prompt/question
-    #   - *_data_url: URLs for media content (video/image/audio)
-    messages = dummy_messages_from_mix_data(
-        system_prompt=get_system_prompt(),
-        video_data_url=video_data_url,
-        image_data_url=image_data_url,
-        audio_data_url=audio_data_url,
-        content_text=get_prompt("mix"),
+    #send request
+    chat_completion = client.chat.completions.create(
+        model=omni_server.model,
+        messages=messages,
    )

-    # SECTION 4: API REQUEST EXECUTION
-    # =================================
-    # INSTRUCTIONS: Make the API call and measure performance
-    # MODIFY: Add timeout, retry logic, or additional parameters
-    start_time = time.perf_counter()
-    chat_completion = client.chat.completions.create(model=omni_server.model, messages=messages, stream=True)
+    #verify text output
+    text_choice = chat_completion.choices[0]
+    assert text_choice.finish_reason == "length"

-    #Call using your preferred method and obtain the final audio and text outputs.
-    ...
-
-    # SECTION 5: OUTPUT VALIDATION
-    # =============================
-    # INSTRUCTIONS: Verify that outputs meet expected criteria
-    # MODIFY: Adjust validation logic for your specific requirements
-
-    # ASSERTION 1: E2E Validation
-    # PURPOSE: Verify that the E2E latency is less than the baseline.
-    current_e2e = time.perf_counter() - start_time
-    print(f"the request e2e is: {current_e2e}")
-    e2e_list.append(current_e2e)
-
-    print(f"the avg e2e is: {sum(e2e_list) / len(e2e_list)}")
-
-
-
-    # ASSERTION 2: Text Output Validation
-    # PURPOSE: Verify that text output was generated with keyword content
-    assert text_content is not None and len(text_content) >= 2, "No text output is generated"
-    assert any(
-        keyword in text_content.lower() for keyword in ["square", "quadrate", "sphere", "globe", "circle", "round"]
-    ), "The output does not contain any of the keywords."
-
-
-    # ASSERTION 3: Cross-Modal Consistency
-    # PURPOSE: Verify text and audio outputs convey the same information
-    # CUSTOMIZATION: Adjust similarity threshold (0.9) based on accuracy requirements
-    assert audio_data is not None, "No audio output is generated"
-    audio_content = merge_base64_and_convert_to_text(audio_data)
-    print(f"text content is: {text_content}")
-    print(f"audio content is: {audio_content}")
-    similarity = cosine_similarity_text(audio_content.lower(), text_content.lower())
-    print(f"similarity is: {similarity}")
-    assert similarity > 0.9, "The audio content is not same as the text"
+    #verify audio output
+    audio_choice = chat_completion.choices[1]
+    audio_message = audio_choice.message
+    if hasattr(audio_message, "audio") and audio_message.audio:
+        assert audio_message.audio.data is not None
+        assert len(audio_message.audio.data) > 0
 ```

-
 #### E2E - Offline inference
 ```python
 # SPDX-License-Identifier: Apache-2.0
@@ -465,3 +271,10 @@ def test_video_to_audio(omni_runner: type[OmniRunner], model: str) -> None:
        has_audio = any(o.final_output_type == "audio" for o in outputs)
        assert has_audio
 ```
+
+## Checklist before submitting your test files
+
+1. The file is saved in an appropriate place and the file name is clear.
+2. The coding style follows the requirements outlined above.
+3. **All test functions have appropriate pytest markers**
+4. For tests that need run in CI, please ensure the test is configured under the `./buildkite/` folder.
@@ -1,173 +0,0 @@
-
-# Metrics
-
-You can use these metrics in production to monitor the health and performance of the vLLM-omni system. Typical scenarios include:
-
- **Performance Monitoring**: Track throughput (e.g., `e2e_avg_tokens_per_s`), latency (e.g., `e2e_total_ms`), and resource utilization to verify that the system meets expected standards.
-
- **Debugging and Troubleshooting**: Use detailed per-request metrics to diagnose issues, such as high transfer times or unexpected token counts.
-
-## How to Enable and View Metrics
-
-### Start the Service with Metrics Logging
-
-```bash
-vllm serve /workspace/models/Qwen3-Omni-30B-A3B-Instruct --omni --port 8014 --log-stats
-```
-
-### Send a Request
-
-```bash
-python openai_chat_completion_client_for_multimodal_generation.py --query-type use_image
-```
-
-### What You Will See
-
-With `--log-stats` enabled, the server will output detailed metrics logs after each request. Example output:
-
-
-#### Overall Summary
-
-| Field                       | Value        |
-|-----------------------------|--------------|
-| e2e_requests                | 1            |
-| e2e_wall_time_ms            | 41,299.190   |
-| e2e_total_tokens            | 5,202        |
-| e2e_avg_time_per_request_ms | 41,299.190   |
-| e2e_avg_tokens_per_s        | 125.959      |
-| e2e_stage_0_wall_time_ms    | 10,192.289   |
-| e2e_stage_1_wall_time_ms    | 30,541.409   |
-| e2e_stage_2_wall_time_ms    |    207.496   |
-
-#### RequestE2EStats
-
-| Field                   | Value      |
-|-------------------------|------------|
-| e2e_total_ms            | 41,299.133 |
-| e2e_total_tokens        | 5,202      |
-| transfers_total_time_ms | 245.895    |
-| transfers_total_kbytes  | 138,089.939|
-
-#### StageRequestStats
-
-| Field                  | 0      | 1      | 2      |
-|------------------------|--------|--------|--------|
-| audio_generated_frames | 0      | 0      | 525,525|
-| batch_id               | 38     | 274    | 0      |
-| batch_size             | 1      | 1      | 1      |
-| num_tokens_in          | 4,860  | 4,826  | 4,384  |
-| num_tokens_out         | 67     | 275    | 0      |
-| postprocess_time_ms    | 256.158| 0.491  | 0.000  |
-| stage_gen_time_ms      | 9,910.007|30,379.198|160.745|
-
-#### TransferEdgeStats
-
-| Field               | 0->1        | 1->2       |
-|---------------------|-------------|------------|
-| size_kbytes         | 109,277.349 | 28,812.591 |
-| tx_time_ms          | 78.701      | 18.790     |
-| rx_decode_time_ms   | 111.865     | 31.706     |
-| in_flight_time_ms   | 2.015       | 2.819      |
-
-
-These logs include:
-
- **Overall summary**: total requests, wall time, average tokens/sec, etc.
-
- **E2E table**: per-request latency and token counts.
-
- **Stage table**: per-stage batch and timing details.
-
- **Transfer table**: data transfer and timing for each edge.
-
-You can use these logs to monitor system health, debug performance, and analyze request-level metrics as described above.
-
-
-## Metrics Scope: Offline vs Online Inference
-
-For **offline inference** (batch mode), the summary includes both system-level metrics (aggregated across all requests) and per-request metrics. In this case, `e2e_requests` can be greater than 1, reflecting multiple completed requests in a batch.
-
-For **online inference** (serving mode), the summary is always per-request. `e2e_requests` is always 1, and only request-level metrics are reported for each completion.
-
---
-
-## Parameter Details
-
-### Summary Metrics
-
-| Field                     | Meaning                                                                                       |
-|---------------------------|----------------------------------------------------------------------------------------------|
-| `e2e_requests`            | Number of completed requests.                                                                |
-| `e2e_wall_time_ms`        | Wall-clock time span from run start to last completion, in ms.                               |
-| `e2e_total_tokens`        | Total tokens counted across all completed requests (stage0 input + all stage outputs).       |
-| `e2e_avg_time_per_request_ms` | Average wall time per request: `e2e_wall_time_ms / e2e_requests`.                        |
-| `e2e_avg_tokens_per_s`    | Average token throughput over wall time: `e2e_total_tokens * 1000 / e2e_wall_time_ms`.      |
-| `e2e_stage_{i}_wall_time_ms` | Wall-clock time span for stage i, in ms. Each stage's wall time is reported as a separate field, e.g., `e2e_stage_0_wall_time_ms`, `e2e_stage_1_wall_time_ms`, etc. |
-
---
-
-### E2E Table (per request)
-
-| Field                     | Meaning                                                               |
-|---------------------------|-----------------------------------------------------------------------|
-| `e2e_total_ms`            | End-to-end latency in ms.                                             |
-| `e2e_total_tokens`        | Total tokens for the request (stage0 input + all stage outputs).      |
-| `transfers_total_time_ms` | Sum of transfer edge `total_time_ms` for this request.                |
-| `transfers_total_kbytes`  | Sum of transfer kbytes for this request.                              |
-
-
---
-
-### Stage Table (per stage event / request)
-
-| Field                     | Meaning                                                                                         |
-|---------------------------|-------------------------------------------------------------------------------------------------|
-| `batch_id`                | Batch index.                                                                                    |
-| `batch_size`              | Batch size.                                                                                     |
-| `num_tokens_in`           | Input tokens to the stage.                                                                      |
-| `num_tokens_out`          | Output tokens from the stage.                                                                   |
-| `stage_gen_time_ms`       | Stage compute time in ms, excluding postprocessing time (reported separately as `postprocess_time_ms`). |
-| `image_num`               | Number of images generated (for diffusion/image stages).                                        |
-| `resolution`              | Image resolution (for diffusion/image stages).                                                                  |
-| `postprocess_time_ms` | Diffusion/image: post-processing time in ms.                                                    |
-
---
-
-### Transfer Table (per edge / request)
-
-| Field                | Meaning                                                                   |
-|----------------------|---------------------------------------------------------------------------|
-| `size_kbytes`        | Total kbytes transferred.                                                 |
-| `tx_time_ms`         | Sender transfer time in ms.                                               |
-| `rx_decode_time_ms`  | Receiver decode time in ms.                                               |
-| `in_flight_time_ms`  | In-flight time in ms.                                                     |
-
-
-### Expectation of the Numbers (Verification)
-
-**Formulas:**
-
- `e2e_total_tokens = Stage0's num_tokens_in + sum(all stages' num_tokens_out)`
-
- `transfers_total_time_ms = sum(tx_time_ms + rx_decode_time_ms + in_flight_time_ms)` for every edge
-
-**Using the example above:**
-
-**e2e_total_tokens**
-
- Stage0's `num_tokens_in`: **4,860**
- Stage0's `num_tokens_out`: **67**
- Stage1's `num_tokens_out`: **275**
- Stage2's `num_tokens_out`: **0**
-
-so `e2e_total_tokens = 4,860 + 67 + 275 + 0 = 5,202`, which matches the table value `e2e_total_tokens`.
-
-**transfers_total_time_ms**
-
-For each edge:
-
- 0->1: tx_time_ms (**78.701**) + rx_decode_time_ms (**111.865**) + in_flight_time_ms (**2.015**) = **192.581**
-
- 1->2: tx_time_ms (**18.790**) + rx_decode_time_ms (**31.706**) + in_flight_time_ms (**2.819**) = **53.315**
-
-192.581 + 53.315 = **245.896** = transfers_total_time_ms, which matches the calculation (difference is due to rounding)
@@ -2,7 +2,7 @@

 > **Warning:** Profiling incurs significant overhead. Use only for development and debugging, never in production.

-vLLM-Omni uses the PyTorch Profiler to analyze performance across both **multi-stage omni-modality models** and **diffusion models**.
+vLLM-Omni uses the PyTorch Profiler to analyze performance across both **Multi-Stage LLMs** and **Diffusion Models**.

 ### 1. Set the Output Directory
 Before running any script, set this environment variable. The system detects this and automatically saves traces here.
@@ -11,7 +11,7 @@ Before running any script, set this environment variable. The system detects thi
 export VLLM_TORCH_PROFILER_DIR=./profiles
 ```

-### 2. Profiling Omni-Modality Models
+### 2. Start Profiling

 It is best to limit profiling to one iteration to keep trace files manageable.

@@ -77,12 +77,12 @@ omni_llm.close()

 **Examples**:

-1. **Qwen2.5-Omni**:  [https://github.com/vllm-project/vllm-omni/blob/main/examples/offline_inference/qwen2_5_omni/end2end.py](https://github.com/vllm-project/vllm-omni/blob/main/examples/offline_inference/qwen2_5_omni/end2end.py)
+1. **Qwen-omni 2.5**:  [https://github.com/vllm-project/vllm-omni/blob/main/examples/offline_inference/qwen2_5_omni/end2end.py](https://github.com/vllm-project/vllm-omni/blob/main/examples/offline_inference/qwen2_5_omni/end2end.py)

-2. **Qwen3-Omni**:   [https://github.com/vllm-project/vllm-omni/blob/main/examples/offline_inference/qwen3_omni/end2end.py](https://github.com/vllm-project/vllm-omni/blob/main/examples/offline_inference/qwen3_omni/end2end.py)
+2. **Qwen-omni 3.0**:   [https://github.com/vllm-project/vllm-omni/blob/main/examples/offline_inference/qwen3_omni/end2end.py](https://github.com/vllm-project/vllm-omni/blob/main/examples/offline_inference/qwen3_omni/end2end.py)


-### 3. Profiling diffusion models
+**For Diffusion Models as a single stage**

 Diffusion profiling is End-to-End, capturing encoding, denoising loops, and decoding.

@@ -106,7 +106,7 @@ python image_to_video.py \
    #    Reducing frames to the absolute minimum (2) keeps the
    #    tensor size small, ensuring the trace file doesn't become
    #    multi-gigabytes in size.
-    --num-frames 2 \
+    --num_frames 2 \
    \
    # Minimize Iteration Loop (Steps):
    #    This is the most critical setting for profiling.
@@ -114,12 +114,12 @@ python image_to_video.py \
    #    Profiling 2 steps gives you the exact same performance
    #    data as 50 steps, but saves minutes of runtime and
    #    prevents the trace viewer from freezing.
-    --num-inference-steps 2 \
+    --num_inference_steps 2 \
    \
-    --guidance-scale 5.0 \
-    --guidance-scale-high 6.0 \
-    --boundary-ratio 0.875 \
-    --flow-shift 12.0 \
+    --guidance_scale 5.0 \
+    --guidance_scale_high 6.0 \
+    --boundary_ratio 0.875 \
+    --flow_shift 12.0 \
    --fps 16 \
    --output i2v_output.mp4

@@ -131,7 +131,27 @@ python image_to_video.py \

 2. **Wan-AI/Wan2.2-I2V-A14B-Diffusers**:   [https://github.com/vllm-project/vllm-omni/tree/main/examples/offline_inference/image_to_video](https://github.com/vllm-project/vllm-omni/tree/main/examples/offline_inference/image_to_video)

-### 4. Analyzing Omni Traces
+
+**Online Inference(Async)**
+
+For online serving using AsyncOmni, the methods are asynchronous. This allows you to toggle profiling dynamically without restarting the server.
+
+```python
+from vllm_omni import AsyncOmni
+
+# Inside an async function:
+async_omni = AsyncOmni.from_engine_args(engine_args)
+
+await async_omni.start_profile()
+
+async for output in async_omni.generate(prompt, sampling_params, request_id):
+    # Process outputs...
+    pass
+
+await async_omni.stop_profile()
+```
+
+### 3. Analyzing Omni Traces

 Output files are saved to your configured ```VLLM_TORCH_PROFILER_DIR```.

@@ -143,4 +163,4 @@ Output files are saved to your configured ```VLLM_TORCH_PROFILER_DIR```.
 - [Perfetto](https://ui.perfetto.dev/)(recommended)
 - ```chrome://tracing```(Chrome only)

-**Note**: vLLM-Omni reuses the PyTorch Profiler infrastructure from vLLM. See the official vLLM profiler documentation:  [vLLM Profiling Guide](https://docs.vllm.ai/en/stable/contributing/profiling/)
+**Note**: vLLM-Omni reuses the PyTorch Profiler infrastructure from vLLM. See the official vLLM profiler documentation:  [vLLM Profiling Guide](https://docs.vllm.ai/en/latest/dev/profiling.html)
@@ -1,209 +0,0 @@
-# Async Chunk Design
-
-## Table of Contents
-
-1. [Overview](#overview)
-2. [Performance](#performance)
-3. [Architecture](#architecture)
-4. [Configuration](#configuration)
-5. [Related Files](#related-files)
-
-## Overview
-
-The `async_chunk` feature enables asynchronous, chunked processing of data across multiple stages in a multi-stage pipeline (e.g., Qwen3-Omni with Thinker → Talker → Code2Wav stages). Instead of waiting for a complete stage output before forwarding to the next stage, this feature allows stages to process and forward data in chunks as it becomes available, significantly reducing latency and improving throughput.
-
-**Chunk Size Definition**
-
- **Prefill Phase**: `chunk_size = num_scheduled_tokens` for chunked prefill processing
- **Decode Phase**: `chunk_size = num_scheduled_tokens = 1 ` for per-token streaming
-
-For qwen3-omni:
- **Thinker → Talker**: Per decode step (typically chunk_size=1)
- **Talker → Code2Wav**: Accumulated to code2wav chunk_size(default=25, currently only support default, will support chunk_size soon) before sending
- **Code2Wav**: Streaming decode with code2wav chunk_size
-
-With `async_chunk`:
- Stages can start processing as soon as chunks are available
- Overlapping execution across stages
- Reduced latency and improved throughput
- Better resource utilization
- Async scheduling: Chunk IO (get/put) overlaps with compute via background threads so the scheduler is not blocked waiting for chunks
-
-## Performance
-1. **Reduced Latency**: Next stage can start processing immediately
-2. **Streaming Support**: Enables streaming for audio generation
-3. **IO-Compute Overlap**: Chunk retrieval happens asynchronously while other requests compute
-4. **Non-blocking Scheduler**: Requests waiting for chunks don't block the entire scheduler
-5. **Code2Wav Batch Inference**: Supports batched processing in code2wav stage
-
-| Input     | Output           | Async_chunk enabled | Code2Wav batch size | Max_Concurrency | Prompts | Mean E2E   | Mean TTFT  | Mean TPOT | Mean TTFP   | Mean RTF | Mean ITL |
-|-----------|------------------|---------------------|---------------------|----------------|---------|------------|------------|-----------|-------------|----------|----------|
-| text 100  | text 100+audio   | False               | 1                   | 1              | 50      | 6581.80    | 43.22      | 8.31      | 6459.34     | 0.24     | 8.22     |
-| text 100  | text 100+audio   | False               | 1                   | 4              | 50      | 7398.63    | 67.57      | 9.14      | 7285.35     | 0.27     | 9.05     |
-| text 100  | text 100+audio   | False               | 1                   | 10             | 50      | 13522.99   | 131.82     | 12.72     | 13410.44    | 0.49     | 12.60    |
-| text 100  | text 100+audio   | False               | 64                  | 1              | 50      | 6505.13    | 43.14      | 8.52      | 6395.40     | 0.24     | 8.44     |
-| text 100  | text 100+audio   | False               | 64                  | 4              | 50      | 7668.15    | 51.15      | 9.36      | 7562.37     | 0.28     | 9.27     |
-| text 100  | text 100+audio   | False               | 64                  | 10             | 50      | 9516.18    | 138.06     | 14.75     | 9409.26     | 0.34     | 14.60    |
-| text 100  | text 100+audio   | True                | 1                   | 1              | 50      | 6179.79    | 44.58      | 8.69      | 522.99      | 0.22     | 8.60     |
-| text 100  | text 100+audio   | True                | 1                   | 4              | 50      | 7692.69    | 103.96     | 10.22     | 785.85      | 0.29     | 10.12    |
-| text 100  | text 100+audio   | True                | 1                   | 10             | 50      | 11152.71   | 685.60     | 17.64     | 1628.88     | 0.41     | 17.62    |
-
-
-Performance data collected on H800 GPUs through comprehensive benchmarking with cudagraph enabled. text input uses random dataset.
-
-Enabling **async_chunk** (False→True) sharply reduces time-to-first-audio (TTFP)—e.g. ~92% at concurrency 1 (6.5s→0.52s)—and improves E2E latency (e.g. ~6% at conc 1, ~17% at conc 10). RTF (Real Time Factor) also improves with async_chunk on (e.g. ~8% at conc 1: 0.24→0.22, ~16% at conc 10: 0.49→0.41). Enabling **Code2Wav batch size 64** (vs 1) improves E2E and TTFP at higher concurrency when async_chunk is off (e.g. ~30% at conc 10: 13.5s→9.5s E2E, 13.4s→9.4s TTFP).
-
-<p align="center">
-  <picture>
-    <source media="(prefers-color-scheme: dark)" src="https://raw.githubusercontent.com/vllm-project/vllm-omni/refs/heads/main/docs/source/performance/qwen3-omni_ttfp_performance.png">
-    <img alt="TTFP Performance Data Comparison" src="https://raw.githubusercontent.com/vllm-project/vllm-omni/refs/heads/main/docs/source/performance/qwen3-omni_ttfp_performance.png" width=100%>
-  </picture>
-</p>
-
-<p align="center">
-  <picture>
-    <source media="(prefers-color-scheme: dark)" src="https://raw.githubusercontent.com/vllm-project/vllm-omni/refs/heads/main/docs/source/performance/qwen3-omni_rtf_performance.png">
-    <img alt="RTF Performance Data Comparison" src="https://raw.githubusercontent.com/vllm-project/vllm-omni/refs/heads/main/docs/source/performance/qwen3-omni_rtf_performance.png" width=100%>
-  </picture>
-</p>
-
-## Architecture
-### Data Flow
-
-#### Sequential Flow
-<p align="center">
-  <picture>
-    <source media="(prefers-color-scheme: dark)" src="https://raw.githubusercontent.com/vllm-project/vllm-omni/refs/heads/main/docs/source/architecture/qwen3-omni-non-async-chunk.png">
-    <img alt="Data Flow between stages" src="https://raw.githubusercontent.com/vllm-project/vllm-omni/refs/heads/main/docs/source/architecture/qwen3-omni-non-async-chunk.png" width=100%>
-  </picture>
-</p>
-
-#### Async Chunk Flow
-
-<p align="center">
-  <picture>
-    <source media="(prefers-color-scheme: dark)" src="https://raw.githubusercontent.com/vllm-project/vllm-omni/refs/heads/main/docs/source/architecture/qwen3-omni-async-chunk.png">
-    <img alt="Data Flow between stages" src="https://raw.githubusercontent.com/vllm-project/vllm-omni/refs/heads/main/docs/source/architecture/qwen3-omni-async-chunk.png" width=100%>
-  </picture>
-</p>
-
-### Async Chunk architecture
-<p align="center">
-  <picture>
-    <source media="(prefers-color-scheme: dark)" src="https://raw.githubusercontent.com/vllm-project/vllm-omni/refs/heads/main/docs/source/architecture/async-chunk-architecture.png">
-    <img alt="Async Chunk Architecture" src="https://raw.githubusercontent.com/vllm-project/vllm-omni/refs/heads/main/docs/source/architecture/async-chunk-architecture.png" width=100%>
-  </picture>
-</p>
-
-
-### Key Components
-
-1. **OmniConnector**: Inter-stage data transport only
-   - Shared memory or other IPC mechanisms
-   - **Transport-only API**: `put(from_stage, to_stage, put_key, data)` and `get(from_stage, to_stage, get_key)` (optionally with timeout)
-   - **No request-specific state**: Connector does not track put_requests, get_requests, request_payload, finished_requests, or other request-bound metadata; it only performs put/get operations
-   - Chunk keys and request/chunk lifecycle are managed by **OmniChunkTransferAdapter**
-
-2. **Transfer Adapter Layer**: Extensible abstraction for managing data transfer via connectors
-   - **OmniTransferAdapterBase**: Base class with background **recv_loop** and **save_loop** threads;
-   - **OmniChunkTransferAdapter**: Chunk-specific implementation that owns the full chunk lifecycle when async_chunk is enabled
-     - **Chunk ID and key construction**: Builds keys like `{req_id}_{stage_id}_{chunk_id}` for put/get
-     - **Async get**: `load_async(request)` enqueues the request; background **recv_loop** polls the connector (non-blocking); when data is available, updates the request and marks it in `_finished_load_reqs`; scheduler calls `get_finished_requests()` to learn which requests have chunks ready
-     - **Async put**: `save_async(pooling_output, request)` invokes `custom_process_next_stage_input_func` in the main thread to build the payload, then enqueues a save task; background **save_loop** performs `connector.put()`; payload processing and chunk accumulation (e.g. code2wav chunk_size) remain in the main thread
-
-3. **Stage Input Processors**: Custom functions that process stage outputs into chunks for different models
-   - Receive **transfer_manager** (OmniChunkTransferAdapter)
-   - Qwen3-omni reference: `thinker2talker_async_chunk`, `talker2code2wav_async_chunk`
-
-4. **Schedulers**: Modified to handle chunk-based scheduling with async IO-compute overlap
-   - `OmniARScheduler`: For autoregressive stages
-   - `OmniGenerationScheduler`: For generation stages
-   - Both schedulers use **OmniChunkTransferAdapter** and **before/after** hooks around `super().schedule()`:
-     - **Before** `super().schedule()`: `process_pending_chunks(waiting, running)` moves requests waiting for chunks to `WAITING_FOR_CHUNK`, enqueues load tasks for background polling
-     - **After** `super().schedule()`: `restore_queues(waiting, running)` restores requests with ready chunks back to waiting/running, `postprocess_scheduler_output(scheduler_output)` attaches cached additional_information, clears chunk-ready flags
-   - **put_chunk** `save_async(pooler_output, request)`; **get_chunk** / **get_chunk_for_generation** `load_async(request)`
-
-5. **Model Runners**: Handle chunk processing
-   - `OmniGPUModelRunner`: Processes chunks in AR stages
-   - `GPUGenerationModelRunner`: Processes chunks in generation stages
-     - Uses `ubatch_slices` from `get_forward_context()` to track per-request sequence lengths in batched inference
-     - Reuses `ubatch_slices_padded` for code2wav batching to properly split batch outputs
-     - Handles list-type multimodal outputs: iterates through requests and assigns corresponding tensor to each
-     - Improved request state management: removes unscheduled and finished requests from input batch
-
-6. **Model Implementation**: Model-specific chunk handling
-   - `Qwen3OmniMoeForConditionalGeneration`: Main model with async_chunk support
-     - **Code2Wav stage batching**: Uses `ubatch_slices` to construct batched codec codes tensor `[batch_size, 16, max_seq_len]`
-     - **Batch output handling**: `generate_audio()` returns `list[torch.Tensor]`, one audio tensor per request
-     - **Multimodal outputs**: Returns list of audio tensors for batch processing instead of single concatenated tensor
-   - `Qwen3OmniCode2WavDecoder`: Audio generation model
-     - `chunked_decode()` and `chunked_decode_streaming()`: Return `list[torch.Tensor]` (one per request)
-     - Uses `ubatch_slices` to split batched waveform output into per-request audio chunks
-     - Each request gets correctly sized audio based on its code sequence length: `waveform[:, :, :code_seq_len * total_upsample]`
-
-7. **Request status**: `RequestStatus.WAITING_FOR_CHUNK` is added via patch (e.g. in `vllm_omni/patch.py`) so requests waiting for a chunk are not scheduled by the base vLLM scheduler until the chunk is ready.
-
-## Configuration
-
-Enable async_chunk in stage configuration 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
-
- `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)
- `max_batch_size: int`: Maximum batch size for the stage
-
-
-### Connector Configuration
-
-```yaml
-connectors:
-  - from_stage: 0
-    to_stage: 1
-    spec:
-      name: SharedMemoryConnector
-      extra:
-        stage_id: 0
-```
-
-### Code2Wav Batch Configuration
-
-For optimal performance with async_chunk, the code2wav stage should be configured with batching:
-
-```yaml
-stage_args:
-  - stage_id: 2  # code2wav stage
-    runtime:
-      devices: "1"
-      max_batch_size: 64  # Enables batched audio generation
-    engine_args:
-      model_stage: code2wav
-```
-
-## Related Files
-
- `vllm_omni/model_executor/stage_input_processors/qwen3_omni.py`: Chunk processing functions (receive `transfer_manager` as first param)
- `vllm_omni/distributed/omni_connectors/transfer_adapter/base.py`: OmniTransferAdapterBase (recv_loop, save_loop, load_async, save_async)
- `vllm_omni/distributed/omni_connectors/transfer_adapter/chunk_transfer_adapter.py`: OmniChunkTransferAdapter (process_pending_chunks, restore_queues, postprocess_scheduler_output)
- `vllm_omni/distributed/omni_connectors/connectors/shm_connector.py`: SharedMemoryConnector (transport-only put/get)
- `vllm_omni/core/sched/omni_ar_scheduler.py`: AR scheduler with chunk_transfer_adapter
- `vllm_omni/core/sched/omni_generation_scheduler.py`: Generation scheduler with same async chunk pattern
- `vllm_omni/worker/gpu_model_runner.py`: Model runner with chunk handling
- `vllm_omni/worker/gpu_generation_model_runner.py`: Generation model runner with batch output handling and ubatch_slices support
- `vllm_omni/model_executor/models/qwen3_omni/qwen3_omni.py`: Model implementation with code2wav batching
- `vllm_omni/model_executor/models/qwen3_omni/qwen3_omni_code2wav.py`: Code2wav decoder with batch support
- `vllm_omni/engine/arg_utils.py`: Configuration definitions (async_chunk, worker_type)
- `vllm_omni/config/model.py`: Model config with async_chunk field
@@ -1,286 +0,0 @@
-# Cache-DiT
-
-This section describes how to add cache-dit acceleration to a new diffusion pipeline. We use the Qwen-Image pipeline and LongCat-Image pipeline as reference implementations.
-
---
-
-## Table of Contents
-
- [Overview](#overview)
- [Standard Models: Automatic Support](#standard-models-automatic-support)
- [Custom Architectures: Writing Custom Implementation](#custom-architectures-writing-custom-implementation)
- [Testing](#testing)
- [Troubleshooting](#troubleshooting)
- [Reference Implementations](#reference-implementations)
- [Summary](#summary)
-
---
-
-## Overview
-
-### What is Cache-DiT?
-
-Cache-DiT is an acceleration library for Diffusion Transformers (DiT) that caches intermediate computation results across denoising steps. The core insight is that adjacent denoising steps often produce similar intermediate features, so we can skip redundant computations by reusing cached results.
-
-The library supports three main caching strategies:
-
- **DBCache:** Dynamic block-level caching that selectively computes or caches transformer blocks based on residual differences
- **TaylorSeer:** Calibration-based prediction that estimates block outputs using Taylor expansion
- **SCM (Step Computation Masking):** Dynamic step skipping based on configurable policies
-
-### Architecture
-
-vLLM-omni integrates cache-dit through the `CacheDiTBackend` class, which provides a unified interface for managing cache-dit acceleration on diffusion models.
-
-| Method/Class | Purpose | Behavior |
-|--------------|---------|----------|
-| [`CacheDiTBackend`](https://docs.vllm.ai/projects/vllm-omni/en/latest/api/vllm_omni/diffusion/cache/#vllm_omni.diffusion.cache.CacheBackend) | Unified backend interface | Automatically handles enabler selection and cache refresh |
-| [`enable_cache_for_dit()`](https://docs.vllm.ai/projects/vllm-omni/en/latest/api/vllm_omni/diffusion/cache/cache_dit_backend/#vllm_omni.diffusion.cache.cache_dit_backend.enable_cache_for_dit) | Apply caching to transformer | Configures DBCache on transformer blocks |
-
-**Key APIs from Cache-DiT:**
-
-[Cache-DiT API Reference](https://cache-dit.readthedocs.io/en/latest/user_guide/CACHE_API/)
-
-| API | Description |
-|-----|-------------|
-| `BlockAdapter` | Core abstraction for applying cache-dit to transformers. Specifies transformer module(s), block list(s), and forward signature pattern(s). |
-| `ForwardPattern` | Defines block forward signature patterns: `Pattern_0`, `Pattern_1`, `Pattern_2` |
-| `ParamsModifier` | Per-transformer or per-block-list cache configuration customization |
-| `DBCacheConfig` | Configuration for DBCache parameters (warmup steps, cached steps, thresholds) |
-| `refresh_context()` | Update cache context | Called when `num_inference_steps` changes |
-
---
-
-## Standard Models: Automatic Support
-
-Most DiT models follow this pattern:
- Single transformer with one `ModuleList` of blocks
- Standard forward signature
- Compatible with cache-dit's automatic detection
-
-**Examples:** Qwen-Image, Z-Image
-
-For standard single-transformer models, **no code changes are needed**. The `CacheDiTBackend` automatically uses `enable_cache_for_dit()`:
-
-```python
-from vllm_omni import Omni
-
-# Works automatically for standard models
-omni = Omni(
-    model="Qwen/Qwen-Image",  # Standard single-transformer model
-    cache_backend="cache_dit",
-    cache_config={
-        "Fn_compute_blocks": 1,
-        "Bn_compute_blocks": 0,
-        "max_warmup_steps": 4,
-    }
-)
-```
-
-**What happens automatically:**
-
-```python
-def enable_cache_for_dit(pipeline: Any, cache_config: Any) -> Callable[[int], None]:
-    """Default enabler for standard single-transformer DiT models."""
-
-    # Build cache configuration
-    db_cache_config = DBCacheConfig(
-        num_inference_steps=None,  # Will be set during first inference
-        Fn_compute_blocks=cache_config.Fn_compute_blocks,
-        Bn_compute_blocks=cache_config.Bn_compute_blocks,
-        max_warmup_steps=cache_config.max_warmup_steps,
-        max_cached_steps=cache_config.max_cached_steps,
-        max_continuous_cached_steps=cache_config.max_continuous_cached_steps,
-        residual_diff_threshold=cache_config.residual_diff_threshold,
-    )
-
-    # Enable cache-dit on transformer
-    cache_dit.enable_cache(
-        pipeline.transformer,
-        cache_config=db_cache_config,
-    )
-
-    # Return refresh function for dynamic num_inference_steps updates
-    def refresh_cache_context(pipeline: Any, num_inference_steps: int, verbose: bool = True):
-        cache_dit.refresh_context(pipeline.transformer, num_inference_steps=num_inference_steps, verbose=verbose)
-
-    return refresh_cache_context
-```
-
---
-
-## Custom Architectures: Writing Custom Implementation
-
-Some models require custom handling:
-
- **Dual-transformer:** Models with separate high-noise and low-noise transformers (e.g., Wan2.2)
- **Multi-block-list:** Models with multiple block lists in one transformer (e.g., LongCatImage with `transformer_blocks` + `single_transformer_blocks`)
- **Special forward patterns:** Models with non-standard block execution patterns
-
-### Example 1: Dual-Transformer Model (Wan2.2)
-
-Wan2.2 uses two transformers: one for high-noise steps and one for low-noise steps.
-
-**Key difference:** Use `BlockAdapter` to wrap multiple transformers with separate configurations.
-
-```python
-# Standard: cache_dit.enable_cache(pipeline.transformer, ...)
-# Custom: Use BlockAdapter to handle multiple transformers
-cache_dit.enable_cache(
-    BlockAdapter(
-        transformer=[pipeline.transformer, pipeline.transformer_2],  # Multiple transformers
-        blocks=[pipeline.transformer.blocks, pipeline.transformer_2.blocks],
-        forward_pattern=[ForwardPattern.Pattern_2, ForwardPattern.Pattern_2],
-        params_modifiers=[
-            ParamsModifier(...),  # Config for high-noise transformer
-            ParamsModifier(...),  # Config for low-noise transformer (different params)
-        ],
-    ),
-    cache_config=db_cache_config,
-)
-```
-
-**Key difference:** `refresh_context` must be called on each transformer separately.
-
-```python
-# Standard: cache_dit.refresh_context(pipeline.transformer, num_inference_steps=N)
-# Custom: Refresh each transformer with its own step count
-def refresh_cache_context(pipeline, num_inference_steps, verbose=True):
-    high_steps, low_steps = _split_inference_steps(num_inference_steps)
-    cache_dit.refresh_context(pipeline.transformer, num_inference_steps=high_steps, ...)
-    cache_dit.refresh_context(pipeline.transformer_2, num_inference_steps=low_steps, ...)
-```
-
-### Example 2: Multi-Block-List Model (LongCatImage)
-
-LongCatImage has a single transformer with two block lists: `transformer_blocks` and `single_transformer_blocks`.
-
-**Key difference:** Use `BlockAdapter` to specify multiple block lists within one transformer.
-
-```python
-# Standard: cache_dit.enable_cache(pipeline.transformer, ...)
-#   - Automatically detects single block list
-# Custom: Use BlockAdapter to specify multiple block lists
-cache_dit.enable_cache(
-    BlockAdapter(
-        transformer=pipeline.transformer,  # Single transformer
-        blocks=[
-            pipeline.transformer.transformer_blocks,        # Block list 1
-            pipeline.transformer.single_transformer_blocks, # Block list 2
-        ],
-        forward_pattern=[ForwardPattern.Pattern_1, ForwardPattern.Pattern_1],
-        params_modifiers=[modifier],
-    ),
-    cache_config=db_cache_config,
-)
-```
-
-> **Note:** For single transformer with multiple block lists, `refresh_context` works the same as standard models.
-
-### Registering Custom Implementations
-
-After writing your custom enabler, register it in `CUSTOM_DIT_ENABLERS` in `vllm_omni/diffusion/cache/cache_dit_backend.py`:
-
-```python
-CUSTOM_DIT_ENABLERS = {
-    "Wan22Pipeline": enable_cache_for_wan22,
-    "LongCatImagePipeline": enable_cache_for_longcat_image,
-    "YourCustomPipeline": enable_cache_for_your_model,  # Add here
-}
-```
-
---
-
-## Testing
-
-After adding cache-dit support, test with:
-
-```python
-from vllm_omni import Omni
-from vllm_omni.inputs.data import OmniDiffusionSamplingParams
-
-# Test your custom model
-omni = Omni(
-    model="your-model-name",
-    cache_backend="cache_dit",
-    cache_config={
-        "Fn_compute_blocks": 1,
-        "Bn_compute_blocks": 0,
-        "max_warmup_steps": 4,
-        "residual_diff_threshold": 0.24,
-    }
-)
-
-images = omni.generate(
-    "a beautiful landscape",
-    OmniDiffusionSamplingParams(num_inference_steps=50),
-)
-```
-
-**Verify:**
-
-1. Cache is applied (check logs for "Cache-dit enabled successfully on xxx")
-2. Performance improvement (should be around 1.5x-2x faster)
-3. Image quality (compare with `cache_backend=None`)
-
---
-
-## Troubleshooting
-
-### Issue: Cache not applied
-
-**Symptoms:** No speedup observed, no cache-related log messages.
-
-**Causes & Solutions:**
-
- **Enabler not registered:**
-
-**Problem:** Pipeline name not in `CUSTOM_DIT_ENABLERS` registry.
-
-**Solution:** Verify `pipeline.__class__.__name__` matches the registry key and add your enabler to `CUSTOM_DIT_ENABLERS`.
-
-### Issue: Quality degradation
-
-**Symptoms:** Generated images have artifacts or lower quality compared to non-cached inference.
-
-**Causes & Solutions:**
-
- **Cache parameters too aggressive:**
-
-**Solution:**
-```python
-cache_config={
-    "residual_diff_threshold": 0.12,  # Lower from 0.24 (try 0.12-0.18)
-    "max_warmup_steps": 6,            # Increase from 4 (try 6-8)
-    "max_continuous_cached_steps": 2, # Reduce if higher
-}
-```
-
-Check the [user guide for cache_dit](../../user_guide/diffusion/cache_dit_acceleration.md) for more adjustable parameters.
-
---
-
-## Reference Implementations
-
-Complete examples in the codebase:
-
-| Model | Path | Pattern | Notes |
-|-------|------|---------|-------|
-| **Standard DiT** | [`cache_dit_backend.py::enable_cache_for_dit`](https://docs.vllm.ai/projects/vllm-omni/en/latest/api/vllm_omni/diffusion/cache/cache_dit_backend/#vllm_omni.diffusion.cache.cache_dit_backend.enable_cache_for_dit) | Default enabler | Single transformer, automatic |
-| **Wan2.2** | [`cache_dit_backend.py::enable_cache_for_wan22`](https://docs.vllm.ai/projects/vllm-omni/en/latest/api/vllm_omni/diffusion/cache/cache_dit_backend/#vllm_omni.diffusion.cache.cache_dit_backend.enable_cache_for_wan22) | Dual-transformer | Separate high/low noise transformers |
-| **LongCat** | [`cache_dit_backend.py::enable_cache_for_longcat_image`](https://docs.vllm.ai/projects/vllm-omni/en/latest/api/vllm_omni/diffusion/cache/cache_dit_backend/#vllm_omni.diffusion.cache.cache_dit_backend.enable_cache_for_longcat_image) | Multi-block-list | Two block lists in one transformer |
-| **BAGEL** | [`cache_dit_backend.py::enable_cache_for_bagel`](https://docs.vllm.ai/projects/vllm-omni/en/latest/api/vllm_omni/diffusion/cache/cache_dit_backend/#vllm_omni.diffusion.cache.cache_dit_backend.enable_cache_for_bagel) | Omni model | Complex architecture |
-
---
-
-## Summary
-
-Adding cache-dit support:
-
-1. ✅ **Check model type** - Standard models work automatically, custom architectures need enablers
-2. ✅ **Write enabler** (if needed) - Use `BlockAdapter` for complex architectures
-3. ✅ **Register enabler** (if needed) - Add to `CUSTOM_DIT_ENABLERS` dictionary
-4. ✅ **Return refresh function** (if needed) - Handle `num_inference_steps` changes
-5. ✅ **Test** - Verify with `cache_backend="cache_dit"`
-
-For most models, the default enabler is sufficient. Only write custom enablers for complex architectures!
@@ -1,249 +0,0 @@
-# CFG-Parallel
-
-This section describes how to add CFG-Parallel (Classifier-Free Guidance Parallel) to a diffusion pipeline. We use the Qwen-Image pipeline as the reference implementation.
-
---
-
-## Table of Contents
-
- [Overview](#overview)
- [Step-by-Step Implementation](#step-by-step-implementation)
- [Customization](#customization)
- [Testing](#testing)
- [Troubleshooting](#troubleshooting)
- [Reference Implementations](#reference-implementations)
- [Summary](#summary)
-
---
-
-## Overview
-
-### What is CFG-Parallel?
-
-In standard Classifier-Free Guidance, each diffusion step requires two forward passes through the transformer:
-
-1. **Positive/Conditional**: Guided by the text prompt
-2. **Negative/Unconditional**: Typically using empty or negative prompt
-
-CFG-Parallel eliminates this bottleneck by distributing the two forward passes across different GPU ranks, allowing them to execute simultaneously rather than sequentially.
-
-### Architecture
-
-vLLM-omni provides `CFGParallelMixin` that encapsulates all CFG parallel logic. Pipelines inherit from this mixin and implement a `diffuse()` method that orchestrates the denoising loop.
-
-| Method | Purpose | Automatic Behavior |
-|--------|---------|-------------------|
-| [`predict_noise_maybe_with_cfg()`](https://docs.vllm.ai/projects/vllm-omni/en/latest/api/vllm_omni/diffusion/distributed/cfg_parallel/) | Predict noise with CFG | Detects parallel mode, distributes computation, gathers results |
-| [`scheduler_step_maybe_with_cfg()`](https://docs.vllm.ai/projects/vllm-omni/en/latest/api/vllm_omni/diffusion/distributed/cfg_parallel/) | Step scheduler with sync | Rank 0 steps, broadcasts latents to all ranks |
-| [`combine_cfg_noise()`](https://docs.vllm.ai/projects/vllm-omni/en/latest/api/vllm_omni/diffusion/distributed/cfg_parallel/) | Combine positive/negative | Applies CFG formula with optional normalization |
-| [`predict_noise()`](https://docs.vllm.ai/projects/vllm-omni/en/latest/api/vllm_omni/diffusion/distributed/cfg_parallel/) | Forward pass wrapper | Override for custom transformer calls |
-| [`cfg_normalize_function()`](https://docs.vllm.ai/projects/vllm-omni/en/latest/api/vllm_omni/diffusion/distributed/cfg_parallel/) | Normalize CFG output | Override for custom normalization |
-
-### How It Works
-
-`predict_noise_maybe_with_cfg()` automatically detects and switches between two execution modes:
-
- **CFG-Parallel mode** (when `cfg_world_size > 1`):
-  - Rank 0 computes positive prompt prediction
-  - Rank 1 computes negative prompt prediction
-  - Results are gathered via `all_gather()`
-  - Combined on rank 0 using CFG formula
-
- **Sequential mode** (when `cfg_world_size == 1`):
-  - Single rank computes both positive and negative predictions
-  - Directly combines them with CFG formula
-
-`scheduler_step_maybe_with_cfg()` ensures consistent latent states across all ranks:
-
- **CFG-Parallel mode**:
-  - Only rank 0 performs the scheduler step (applies noise prediction to update latents)
-  - Updated latents are broadcast to all other ranks via `broadcast()`
-  - All ranks maintain synchronized latent states for the next iteration
-
- **Sequential mode**:
-  - Single rank directly performs the scheduler step
-  - No synchronization needed
-
---
-
-## Step-by-Step Implementation
-
-### Step 1: Inherit `CFGParallelMixin`
-
-Allow your pipeline to inherit from `CFGParallelMixin` and implements the `diffuse()` method for your specific model.
-
-**Example (Qwen-Image):**
-
-```python
-from vllm_omni.diffusion.distributed.cfg_parallel import CFGParallelMixin
-import torch.nn as nn
-class YourModelPipeline(nn.Module, CFGParallelMixin):
-    def diffuse(self, ...) -> torch.Tensor:
-        for i, t in enumerate(timesteps):
-            # Prepare positive_kwargs (conditional) and negative_kwargs (unconditional)
-            positive_kwargs = {...}  # hidden_states, encoder_hidden_states, etc.
-            negative_kwargs = {...} if do_true_cfg else None
-
-            # Key method 1: Predict noise with automatic CFG parallel handling
-            noise_pred = self.predict_noise_maybe_with_cfg(
-                do_true_cfg=do_true_cfg,
-                true_cfg_scale=true_cfg_scale,
-                positive_kwargs=positive_kwargs,
-                negative_kwargs=negative_kwargs,
-            )
-
-            # Key method 2: Step scheduler with automatic CFG synchronization
-            latents = self.scheduler_step_maybe_with_cfg(
-                noise_pred, t, latents, do_true_cfg
-            )
-
-        return latents
-```
-
-**Key Points:**
-
- `positive_kwargs`: transformer arguments for conditional (text-guided) prediction
- `negative_kwargs`: transformer arguments for unconditional prediction (set to `None` if CFG disabled)
- For image editing pipelines, add `output_slice=image_seq_len` to extract the generative image portion
-
-### Step 2: Call `diffuse`
-
-Call `self.diffuse` in your pipeline's forward function:
-
-```python
-import torch.nn as nn
-class YourModelPipeline(nn.Module, CFGParallelMixin):
-    def forward(
-        self,
-        prompt: str,
-        negative_prompt: str | None = None,
-        guidance_scale: float = 3.5,
-        num_inference_steps: int = 50,
-        **kwargs,
-    ):
-        # Encode prompts, Initialize latents, Get timesteps
-        ...
-        # Run diffusion loop (calls the mixin's diffuse method)
-        latents = self.diffuse(
-            prompt_embeds=prompt_embeds,
-            prompt_embeds_mask=prompt_embeds_mask,
-            negative_prompt_embeds=negative_embeds,
-            negative_prompt_embeds_mask=negative_mask,
-            latents=latents,
-            timesteps=timesteps,
-            do_true_cfg=do_true_cfg,
-            true_cfg_scale=guidance_scale,
-            ...
-        )
-```
-
---
-
-## Customization
-
-### Override `predict_noise()` for Custom Transformer Calls
-
-If your transformer requires custom prediction function, you can rewrite `predict_noise` function. Taking Wan2.2 as an example, which has two transformer models. The actual transformer to be called is determined by `self.transformer`.
-
-```python
-class Wan22Pipeline(nn.Module, CFGParallelMixin):
-    def predict_noise(self, current_model: nn.Module | None = None, **kwargs: Any) -> torch.Tensor:
-        if current_model is None:
-            current_model = self.transformer
-        return current_model(**kwargs)[0]
-```
-
-
-### Override `cfg_normalize_function()` for Custom Normalization
-
-Some models have their own normalization function. Taking LongCat Image model as an example:
-
-```python
-class LongCatImagePipeline(nn.Module, CFGParallelMixin):
-    def cfg_normalize_function(self, noise_pred, comb_pred, cfg_renorm_min=0.0):
-        """
-        Normalize the combined noise prediction.
-        """
-        cond_norm = torch.norm(noise_pred, dim=-1, keepdim=True)
-        noise_norm = torch.norm(comb_pred, dim=-1, keepdim=True)
-        scale = (cond_norm / (noise_norm + 1e-8)).clamp(min=cfg_renorm_min, max=1.0)
-        noise_pred = comb_pred * scale
-        return noise_pred
-
-        # The original cfg_normalize_function function in CFGParallelMixin
-        # cond_norm = torch.norm(noise_pred, dim=-1, keepdim=True)
-        # noise_norm = torch.norm(comb_pred, dim=-1, keepdim=True)
-        # noise_pred = comb_pred * (cond_norm / noise_norm)
-        # return noise_pred
-```
-
---
-
-## Testing
-
-After adding CFG-Parallel support, test with:
-
-```bash
-cd examples/offline_inference/text_to_image
-python text_to_image.py \
-    --model Your-org/your-model \
-    --prompt "a cup of coffee on the table" \
-    --negative-prompt "ugly, unclear" \
-    --cfg-scale 4.0 \
-    --num-inference-steps 50 \
-    --output "cfg_enabled.png" \
-    --cfg-parallel-size 2
-```
-
-**Verify:**
-
-1. Check logs for CFG parallel being activated
-2. Record the `e2e_time_ms` in the log and compare with CFG-Parallel disabled
-3. Compare the generated result quality with baseline
-4. Record comparison results in your PR
-
---
-
-## Troubleshooting
-
-### Issue: CFG parallel not activating
-
-**Symptoms:** Generation still slow, logs don't show CFG parallel being used.
-
-**Causes & Solutions:**
-
- **CFG is not enabled:**
-
-**Problem:** Guidance scale too low or negative prompt not provided.
-
-**Solution:** Ensure `guidance_scale > 1.0` and negative prompt is provided:
-```python
-images = pipeline(
-    prompt="a cat",
-    negative_prompt="",  # Must provide (even if empty)
-    guidance_scale=3.5,   # Must be > 1.0
-)
-```
-
---
-
-## Reference Implementations
-
-Complete examples in the codebase:
-
-| Model | Path | Pattern | Notes |
-|-------|------|---------|-------|
-| **Qwen-Image** | `vllm_omni/diffusion/models/qwen_image/cfg_parallel.py` | Mixin | Dual-stream transformer |
-| **Qwen-Image-Edit** | `vllm_omni/diffusion/models/qwen_image/pipeline_qwen_image_edit.py` | Mixin | Image editing with `output_slice` |
-| **Wan2.2** | `vllm_omni/diffusion/models/wan2_2/pipeline_wan2_2.py` | Mixin | Dual-transformer architecture |
-| **CFGParallelMixin** | `vllm_omni/diffusion/distributed/cfg_parallel.py` | Base implementation | Core mixin class |
-
---
-
-## Summary
-
-Adding CFG-Parallel support:
-
-1. ✅ **Create mixin** - Inherit from `CFGParallelMixin` and implement `diffuse()` method
-2. ✅ **(Optional) Customize** - Override `predict_noise()` or `cfg_normalize_function()` for custom behavior
-3. ✅ **Test** - Verify with `--cfg-parallel-size 2` and compare performance
@@ -1,37 +1,27 @@
 # Disaggregated Inference for Omni-Modality Models

-This guide explains how to configure and use distributed connectors
-(`vllm_omni/distributed/omni_connectors`) in vllm-omni for multi-stage pipelines.
+This guide explains how to configure and use distributed connectors (vllm_omni/distributed/connectors) in vllm-omni for multi-stage pipelines.

-Backend-specific setup lives in separate docs:
-
- [SharedMemoryConnector](omni_connectors/shared_memory_connector.md)
- [MooncakeStoreConnector](omni_connectors/mooncake_store_connector.md)
- [MooncakeTransferEngineConnector](omni_connectors/mooncake_transfer_engine_connector.md)
- [YuanrongConnector](omni_connectors/yuanrong_connector.md)
-
-## Overview
+## 1. Overview

 Connectors enable data transfer between pipeline stages (e.g., Thinker -> Talker).
-Current connectors operate in D2H2D (device to host to device) mode.
+Currently supported connectors operate in **D2H2D (Device-to-Host-to-Device)** mode:
+1. **SharedMemoryConnector**: Uses system shared memory.
+2. **MooncakeConnector**: Uses [Mooncake](https://github.com/kvcache-ai/Mooncake).

-## Connector Choices
+*   **SharedMemoryConnector (Default)**: Zero-copy (on host), lowest latency. Best for **single-node** deployments. Auto-configured if no connectors are specified.
+*   **MooncakeConnector**: TCP/RDMA based. Best for **multi-node** distributed deployments. Requires a Mooncake Master service.

-| Use Case | Recommended Connector | Notes |
-| :--- | :--- | :--- |
-| Single node | SharedMemoryConnector | Auto-configured if no connector is specified. |
-| Multi node (Mooncake Store) | MooncakeStoreConnector | TCP-based, requires Mooncake Master + metadata server. |
-| Multi node (Mooncake RDMA) | MooncakeTransferEngineConnector | RDMA/TCP direct transfer with managed memory pool. Fastest. |
-| Multi node (Yuanrong) | YuanrongConnector | Requires Yuanrong Datasystem + etcd. |
+## 2. API Design

-## Core API
+The connector system is built around the `OmniConnectorBase` abstraction, which decouples data transport from stage logic.

-The connector system is built around `OmniConnectorBase`.
+### Core Interface

 ```python
 class OmniConnectorBase(ABC):
    @abstractmethod
-    def put(self, from_stage: str, to_stage: str, put_key: str, data: Any) -> tuple[bool, int, Optional[dict]]:
+    def put(self, from_stage: str, to_stage: str, request_id: str, data: Any) -> tuple[bool, int, Optional[dict]]:
        """
        Store data.
        Returns: (success, serialized_size, metadata)
@@ -39,23 +29,145 @@ class OmniConnectorBase(ABC):
        pass

    @abstractmethod
-    def get(self, from_stage: str, to_stage: str, get_key: str, metadata: Optional[dict] = None) -> Optional[tuple[Any, int]]:
+    def get(self, from_stage: str, to_stage: str, request_id: str, metadata: Optional[dict] = None) -> Optional[tuple[Any, int]]:
        """
        Retrieve data.
-        Args: metadata - transport-specific handles returned by put() (e.g., SHM name).
+        Args: metadata - Transport-specific handles returned by put() (e.g., SHM name).
        Returns: (object, serialized_size)
        """
        pass
 ```

-### Metadata Passing
+### Key Concept: Metadata Passing
+Unlike a pure key-value store, some connectors (like `SharedMemoryConnector`) generate transient resources (e.g., a shared memory block name) during `put()`. This `metadata` **must be passed via the control plane** (e.g., HTTP headers, queue messages) from the producer stage to the consumer stage so `get()` can locate the data.

-Some connectors (e.g., SharedMemoryConnector) generate transient resources during `put()`.
-This `metadata` must be passed through the control plane so `get()` can locate the data.
+## 3. Backends & Use Cases

-## Configuration Model
+### 3.1 SharedMemoryConnector
+**Best for:** Single-node, high-performance IPC.

-Define connectors in runtime:
+*   **Mechanism:**
+    *   **Small Payloads (< Threshold)**: Data is serialized and passed directly "inline" within the `metadata` dictionary. This avoids the overhead of creating SHM blocks for tiny messages.
+    *   **Large Payloads (>= Threshold)**: Data is written to a named System Shared Memory block. The block name is returned in `metadata`.
+*   **Configuration:**
+    *   `shm_threshold_bytes`: Size in bytes to switch from inline to SHM (default: 64KB).
+
+### 3.2 MooncakeConnector
+**Best for:** Multi-node distributed inference.
+
+*   **Mechanism:** Uses Mooncake's distributed KVCache store.
+    *   **Data Plane**: TCP or RDMA for high-bandwidth transfer.
+    *   **Control Plane**: Uses a centralized Mooncake Master and Metadata Server.
+    *   **Keying**: Deterministic keys based on `request_id/from_stage_to_stage`.
+*   **Requirements**: Requires a running Mooncake Master service.
+
+## 4. Relationship with vLLM
+
+vLLM provides specialized distributed mechanisms for specific artifacts:
+*   **KV Transfer** (`vllm.distributed.kv_transfer`): Optimized for transferring KV caches between prefill and decode instances (using NCCL, Mooncake, etc.).
+*   **EC Transfer** (`vllm.distributed.ec_transfer`): Optimized for sharing encoder embeddings.
+*   **Device Communicators** (`vllm.distributed.device_communicators`): Low-level primitives (NCCL, SHM) for Tensor/Pipeline Parallelism.
+
+`vllm-omni` complements this by introducing a **Generalized Connector Abstraction** (`OmniConnector`) for multimodal pipelines. While vLLM's connectors are artifact-specific, `vllm-omni`:
+
+1.  **Unifies Transport**: Provides a single API (`put`/`get`) to transport *any* stage artifact (Input Embeddings, Hidden States, Audio/Image Tensors, KV Cache, Final Output) between arbitrary pipeline stages (e.g., AudioEncoder -> LLM -> AudioGenerator).
+2.  **Extends Connectivity**: Enables flexible construction of complex DAGs (Directed Acyclic Graphs) where stages can run in the same process, same node, or across nodes, using the most appropriate backend (SHM, Mooncake, etc.) for each edge.
+3.  **Wraps & Adapts**: Can internally utilize vLLM's specialized `kv_transfer` for KV paths while using generic transports (SHM/Mooncake) for other data types, presenting a consistent interface to the application layer.
+
+## 5. Installation (Mooncake)
+
+If using `MooncakeConnector`, install the library first:
+
+```bash
+# For CUDA-enabled systems (Recommended)
+pip install mooncake-transfer-engine
+
+# For non-CUDA systems
+pip install mooncake-transfer-engine-non-cuda
+```
+
+## 6. Using MooncakeConnector
+
+### 6.1 Start Mooncake Master
+
+Start the master service on your primary node:
+
+```bash
+# if you use mooncake SSD storage
+mkdir -p ./mc_storage
+
+mooncake_master \
+  --rpc_port=50051 \
+  --enable_http_metadata_server=true \
+  --http_metadata_server_host=0.0.0.0 \
+  --http_metadata_server_port=8080 \
+  --metrics_port=9003 \
+  --root_fs_dir=./mc_storage/ \
+  --cluster_id=mc-local-1 &
+```
+
+### 6.2 Configuration (YAML)
+
+Edit your stage config (e.g., `qwen2_5_omni.yaml`).
+
+**Step 1: Define Connector in Global Runtime**
+
+```yaml
+runtime:
+  connectors:
+    connector_of_mooncake:
+      name: MooncakeConnector
+      extra:
+        host: "127.0.0.1"           # Local Worker IP
+        metadata_server: "http://<MASTER_IP>:8080/metadata"
+        master: "<MASTER_IP>:50051"
+        segment: 512000000          # 512MB segment
+        localbuf: 64000000          # 64MB buffer
+        proto: "tcp"                # "tcp" or "rdma"
+    ```
+
+**Mooncake Configuration Parameters:**
+
+*   **host**: The hostname or IP address of the local machine (worker). Mooncake uses this to register itself in the metadata server so other nodes can find it.
+*   **metadata_server**: The URL of the metadata server. This is used for service discovery and connection establishment (e.g., exchanging QP information for RDMA).
+*   **master**: The address of the Mooncake Master Server (e.g., `<MASTER_IP>:50051`). This is used for global state management and control plane operations.
+*   **segment**: The size of the global memory segment in bytes (default: ~512MB). This defines the shared memory region accessible by Mooncake for data transfer.
+*   **localbuf**: The size of the local buffer in bytes (default: ~64MB). Used for local data buffering during transfer operations.
+*   **proto**: The transport protocol to use. Options:
+    *   `tcp`: Standard TCP/IP (easier setup, universal compatibility).
+    *   `rdma`: Remote Direct Memory Access (higher performance, requires RDMA-capable hardware).
+
+For more details, refer to the [Mooncake Repository](https://github.com/kvcache-ai/Mooncake).
+
+    **Step 2: Reference in Stages**
+
+Explicitly link stages using `input_connectors` and `output_connectors`:
+
+```yaml
+stage_args:
+  - stage_id: 0
+    # ...
+    output_connectors:
+      to_stage_1: connector_of_mooncake
+
+  - stage_id: 1
+    # ...
+    input_connectors:
+      from_stage_0: connector_of_mooncake
+```
+
+## 7. Using SharedMemoryConnector (Auto-Mode)
+
+**Best for single-node.**
+
+The system will automatically create `SharedMemoryConnector`s for any pipeline edge that does not have an explicit connector defined. This is inferred from:
+1.  `runtime.edges` list in the config.
+2.  `engine_input_source` dependencies defined in `stage_args`.
+
+### Threshold Configuration
+By default, payloads larger than **64KB** (default threshold) are transferred via shared memory, while smaller ones use the control queue (inline).
+
+To adjust this threshold (e.g., to 1GB), add the following to your `runtime.connectors`:

 ```yaml
 runtime:
@@ -63,46 +175,26 @@ runtime:
    connector_of_shared_memory:
      name: SharedMemoryConnector
      extra:
-        shm_threshold_bytes: 65536
+        shm_threshold_bytes: 1024 # 1KB threshold
 ```

-Wire stages to connectors:
+## 8. Summary

-```yaml
-stage_args:
-  - stage_id: 0
-    output_connectors:
-      to_stage_1: connector_of_shared_memory
+| Use Case | Recommended Connector | Configuration |
+| :--- | :--- | :--- |
+| **Single Node** | `SharedMemoryConnector` | **None** (Automatic) or Custom Threshold |
+| **Multi Node** | `MooncakeConnector` | Explicit YAML + Mooncake Master |

-  - stage_id: 1
-    input_connectors:
-      from_stage_0: connector_of_shared_memory
-```
+## 9. Operational Notes (important)

-If a pipeline edge has no explicit connector, the system auto-creates a
-SharedMemoryConnector for that edge.
+- **Fail-fast config validation**: the loader raises if any expected edge is missing a connector. Define `input_connectors`/`output_connectors` or rely on auto-SHM filling; otherwise startup aborts.
+- **Missing payloads halt the stage**: workers expect connector payloads; if metadata or connector config is missing, the stage raises and stops. Verify connector wiring and metadata propagation before production.

-## Relationship with vLLM
+## 10. Future Roadmap: Device-to-Device (D2D) Transport

-vLLM provides specialized distributed mechanisms for specific artifacts:
+The current implementations (`SharedMemoryConnector`, `MooncakeConnector`) utilize a **D2H2D (Device-to-Host-to-Device)** data path. Tensors are moved to CPU memory (Host) for transport, which incurs PCIe overhead.

- KV Transfer (`vllm.distributed.kv_transfer`): optimized for KV caches.
- EC Transfer (`vllm.distributed.ec_transfer`): optimized for encoder embeddings.
- Device Communicators (`vllm.distributed.device_communicators`): low-level primitives (NCCL, SHM).
+As outlined in the design RFC, future versions will introduce **D2D (Device-to-Device)** connectors:

-vllm-omni complements this with a generalized connector abstraction:
-
-1. Unifies transport via a single `put`/`get` API for any stage artifact.
-2. Enables DAG-style pipelines across processes or nodes with per-edge transports.
-3. Can wrap vLLM-specific transfers for KV paths while keeping a consistent interface.
-
-## Operational Notes
-
- Fail-fast config validation: missing expected edges cause startup failures.
- Missing payloads halt stages: verify connector wiring and metadata propagation.
-
-## Future Roadmap: D2D Transport
-
-Current connectors use D2H2D paths. Future versions will introduce direct
-device-to-device connectors (NCCL, UCX, IPC) to reduce latency for large
-tensor payloads.
+*   **Goal**: Direct GPU-to-GPU transfer (via NCCL, UCX, or IPC) to minimize latency for large tensor payloads.
+*   **Mechanism**: The `OmniConnector` API allows `put()` to initiate a transfer and return a lightweight handle (metadata) via the control plane, while the heavy payload flows directly between devices.
@@ -1,146 +0,0 @@
-# HSDP
-
-This section describes how to add HSDP (Hybrid Sharded Data Parallel) support to a diffusion transformer model. We use the Wan2.2 transformer as the reference implementation.
-
---
-
-## Table of Contents
-
- [Overview](#overview)
- [Step-by-Step Implementation](#step-by-step-implementation)
- [Testing](#testing)
- [Reference Implementations](#reference-implementations)
-
---
-
-## Overview
-
-### What is HSDP?
-
-HSDP (Hybrid Sharded Data Parallel) is a memory optimization technique that **shards model weights** across multiple GPUs using PyTorch's FSDP2. Unlike Tensor Parallelism which splits computation, HSDP:
-
- Shards weights across GPUs to reduce per-GPU memory usage
- Gathers weights on-demand during forward passes
- Can work standalone or combined with other parallelism (e.g., Sequence Parallel)
-
-This enables inference of large models (e.g., Wan2.2 14B) on GPUs with limited memory.
-
-**Important constraints:**
- HSDP cannot be used with Tensor Parallelism
- For standalone HSDP (no other parallelism), `hsdp_shard_size` must be specified explicitly
-
-### Architecture
-
-HSDP implementation relies on:
-
-1. **`_hsdp_shard_conditions`**: Model attribute specifying which modules to shard
-2. **`apply_hsdp_to_model`**: Function that applies FSDP2 sharding
-3. **`HSDPInferenceConfig`**: Runtime configuration for HSDP
-
---
-
-## Step-by-Step Implementation
-
-### Step 1: Identify Modules to Shard
-
-Determine which modules in your transformer should be sharded. Typically, these are:
-
- Transformer blocks (e.g., `blocks.0`, `blocks.1`, ...)
- Large submodules with significant weight memory
-
-**Key questions:**
- Which modules have the largest weights?
- Which modules are repeated (like transformer blocks)?
-
-### Step 2: Define Shard Conditions
-
-Add `_hsdp_shard_conditions` to your model class. This is a list of functions that return `True` for modules that should be sharded.
-
-**Example (Transformer Blocks):**
-
-```python
-class MyTransformerModel(nn.Module):
-
-    @staticmethod
-    def _is_transformer_block(name: str, module) -> bool:
-        """Match transformer blocks for HSDP sharding.
-
-        Args:
-            name: Module name from named_modules() (e.g., "blocks.0", "blocks.0.attn")
-            module: The module instance
-
-        Returns:
-            True if this module should be sharded
-        """
-        return "blocks" in name and name.split(".")[-1].isdigit()
-
-    _hsdp_shard_conditions = [_is_transformer_block]
-```
-
-**Multiple Conditions Example:**
-
-```python
-class MyModel(nn.Module):
-
-    @staticmethod
-    def _is_transformer_block(name: str, module) -> bool:
-        return "blocks" in name and name.split(".")[-1].isdigit()
-
-    @staticmethod
-    def _is_moe_expert(name: str, module) -> bool:
-        # Also shard MoE expert layers
-        return "experts" in name and name.split(".")[-1].isdigit()
-
-    # Module is sharded if ANY condition returns True
-    _hsdp_shard_conditions = [_is_transformer_block, _is_moe_expert]
-```
-
---
-
-## Testing
-
-After adding HSDP support, test with:
-
-```python
-from vllm_omni import Omni
-from vllm_omni.diffusion.data import DiffusionParallelConfig
-from vllm_omni.inputs.data import OmniDiffusionSamplingParams
-
-parallel_config = DiffusionParallelConfig(
-    use_hsdp=True,
-    hsdp_shard_size=8,  # Shard across 8 GPUs
-)
-omni = Omni(model="your-model-name", parallel_config=parallel_config)
-
-output = omni.generate(
-    "a cup of coffee on the table",
-    OmniDiffusionSamplingParams(num_inference_steps=50),
-)
-```
-
-**Or via command line:**
-
-```bash
-vllm serve Your-org/your-model --omni --port 8091 --use-hsdp
-```
-
-**Verify:**
-
-1. Check logs for "HSDP Inference: replicate_size=..., shard_size=..."
-2. Check logs for "Sharded N modules + root"
-3. Verify memory usage is reduced proportionally
-4. Compare generated output quality with HSDP disabled
-
---
-
-## Reference Implementations
-
-Complete examples in the codebase:
-
-| Model | Path | Notes |
-|-------|------|-------|
-| **Wan2.2** | `vllm_omni/diffusion/models/wan2_2/wan2_2_transformer.py` | Reference implementation |
-| **HSDP Core** | `vllm_omni/diffusion/distributed/hsdp.py` | `apply_hsdp_to_model`, `shard_model` |
-| **HSDP Tests** | `tests/diffusion/distributed/test_hsdp.py` | Unit tests |
-
---
@@ -1,74 +0,0 @@
-# MooncakeStoreConnector
-
-## When to Use
-
-Best for multi-node distributed inference using Mooncake.
-
-## Installation
-
-```bash
-# For CUDA-enabled systems (recommended)
-pip install mooncake-transfer-engine
-
-# For non-CUDA systems
-pip install mooncake-transfer-engine-non-cuda
-```
-
-## Start Mooncake Master
-
-```bash
-# If you use Mooncake SSD storage
-mkdir -p ./mc_storage
-
-mooncake_master \
-  --rpc_port=50051 \
-  --enable_http_metadata_server=true \
-  --http_metadata_server_host=0.0.0.0 \
-  --http_metadata_server_port=8080 \
-  --metrics_port=9003 \
-  --root_fs_dir=./mc_storage/ \
-  --cluster_id=mc-local-1 &
-```
-
-## Configuration
-
-Define the connector in runtime:
-
-```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"
-```
-
-Wire stages to the connector:
-
-```yaml
-stage_args:
-  - stage_id: 0
-    output_connectors:
-      to_stage_1: connector_of_mooncake
-
-  - stage_id: 1
-    input_connectors:
-      from_stage_0: connector_of_mooncake
-```
-
-Parameters:
-
- host: local worker IP registered in the metadata server.
- metadata_server: metadata server URL for discovery and setup.
- master: Mooncake Master address.
- segment: global memory segment size in bytes.
- localbuf: local buffer size in bytes.
- proto: transport protocol ("tcp" or "rdma").
-
-For more details, refer to the
-[Mooncake repository](https://github.com/kvcache-ai/Mooncake).
@@ -1,207 +0,0 @@
-# MooncakeTransferEngineConnector
-
-## When to Use
-
-Best for high-performance multi-node data transfer between stages using Mooncake
-Transfer Engine. Supports both RDMA and TCP protocols with a managed memory pool,
-zero-copy deserialization, and optional GPUDirect RDMA. Applicable to any
-inter-stage data (KV caches, request payloads, etc.), not limited to KV cache transfer.
-
-Compared to `MooncakeStoreConnector` (TCP key-value store), this connector
-provides **~60x faster** data transfer via RDMA direct memory access.
-
-## Installation
-
-```bash
-pip install mooncake-transfer-engine
-```
-
-Ensure RDMA drivers are installed on all nodes (e.g., Mellanox OFED for
-InfiniBand/RoCE NICs).
-
-## Configuration
-
-Define the connector in runtime:
-
-```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: 2147483648  # 2GB memory pool
-        memory_pool_device: "cpu"     # "cpu" for pinned memory, "cuda" for GPUDirect RDMA
-```
-
-Wire stages to the connector:
-
-```yaml
-stage_args:
-  - stage_id: 0
-    output_connectors:
-      to_stage_1: rdma_connector
-
-  - stage_id: 1
-    input_connectors:
-      from_stage_0: rdma_connector
-```
-
-## Parameters
-
-### Required
-
-| Parameter | Description |
-|---|---|
-| `role` | **Internal, do not set manually.** Auto-injected by the orchestration layer (`"sender"` for `output_connectors`, `"receiver"` for `input_connectors`). Defaults to `"sender"` if omitted. |
-| `host` | Local IP address for RDMA. `"auto"` detects from network interfaces. |
-| `protocol` | Transport protocol: `"rdma"` (InfiniBand/RoCE) or `"tcp"`. |
-
-### Memory Pool
-
-| Parameter | Default | Description |
-|---|---|---|
-| `memory_pool_size` | 1 GB | Total size of the RDMA-registered memory pool in bytes. |
-| `memory_pool_device` | `"cpu"` | `"cpu"`: pinned host memory (recommended). `"cuda"`: GPU VRAM for GPUDirect RDMA (requires NIC-GPU direct PCIe connectivity). |
-
-### Networking
-
-| Parameter | Default | Description |
-|---|---|---|
-| `zmq_port` | 50051 | ZMQ **base** port. The orchestration layer computes the actual port as `base + purpose_offset + stage_offset` (see table below). Users only set this base value. |
-| `sender_host` | `None` | **Internal.** Receiver-side only — dynamically resolved via `update_sender_info()`. Not needed in YAML. |
-| `sender_zmq_port` | `None` | **Internal.** Receiver-side only — defaults to the sender's adjusted port. Not needed in YAML. |
-| `device_name` | `""` | RDMA device name (e.g., `"mlx5_0"`). Empty for auto-detect. Can also be set via `RDMA_DEVICE_NAME` env var. |
-
-#### ZMQ Port Offset Scheme
-
-To avoid port conflicts when multiple edges, purposes, DP replicas, or TP ranks share the same node, the actual ZMQ port is computed as:
-
-```
-side_channel_port = zmq_port + purpose_offset + stage_offset + dp_index * tp_size
-sender_listen     = side_channel_port + tp_rank
-receiver_connect  = remote_side_channel_port + tp_rank
-```
-
-| Component | Value | Description |
-|---|---|---|
-| `zmq_port` | 50051 (default) | Base port from YAML config |
-| `purpose_offset` | `request_forwarding` = 0, `kv_transfer` = 100 | Separates control-plane vs KV-cache connections |
-| `stage_offset` | `int(from_stage)` (0, 1, 2...) | Separates edges from different source stages |
-| `dp_index * tp_size` | e.g., DP1 × TP2 = 2 | Each DP replica reserves a port range of size `tp_size` (following vLLM convention: `VLLM_MOONCAKE_BOOTSTRAP_PORT + dp_index * tp_size`) |
-| `tp_rank` | 0, 1, 2... | Each TP rank within a DP replica uses its own port |
-| orchestrator | +200 | Extra offset when caller is the orchestrator (avoids collision with stage workers on the same node) |
-
-**Example** (base=50051, stage 0→1, DP=2, TP=2, kv_transfer):
-
-| Caller | DP | TP rank | Port |
-|---|---|---|---|
-| Stage worker | DP0 | rank 0 | `50051 + 100 + 0 + 0×2 + 0 = 50151` |
-| Stage worker | DP0 | rank 1 | `50051 + 100 + 0 + 0×2 + 1 = 50152` |
-| Stage worker | DP1 | rank 0 | `50051 + 100 + 0 + 1×2 + 0 = 50153` |
-| Stage worker | DP1 | rank 1 | `50051 + 100 + 0 + 1×2 + 1 = 50154` |
-| Orchestrator | — | — | `50051 + 200 + 0 = 50251` |
-
-## Memory Pool Modes
-
-| Mode | Config | Data Flow | Best For |
-|---|---|---|---|
-| CPU Pinned | `memory_pool_device: "cpu"` | GPU → CPU pool → RDMA → CPU pool → GPU | Most hardware topologies (recommended) |
-| GPUDirect | `memory_pool_device: "cuda"` | GPU → GPU pool → RDMA (NIC reads GPU BAR1) → GPU pool | NIC-GPU direct PCIe (PIX topology) |
-
-> **Note**: GPUDirect RDMA requires the NIC and GPU to share a direct PCIe
-> switch (PIX topology). On systems where they are connected via PXB or NODE,
-> CPU pinned memory is faster due to GPU BAR1 bandwidth limitations.
-
-## Environment Variables
-
-| Variable | Description |
-|---|---|
-| `RDMA_DEVICE_NAME` | Override RDMA device name (e.g., `mlx5_0`). |
-| `MC_IB_PCI_RELAXED_ORDERING` | Set to `1` to enable PCIe relaxed ordering for GPUDirect. |
-
-## Docker / Container Setup
-
-RDMA requires host-level device access:
-
-```bash
-docker run -it \
-    --cap-add=SYS_PTRACE \
-    --cap-add=IPC_LOCK \
-    --security-opt seccomp=unconfined \
-    --network=host \
-    --device=/dev/infiniband \
-    -v /sys/class/infiniband:/sys/class/infiniband:ro \
-    your-image:tag
-```
-
-## Performance
-
-Benchmark results on H800 GPUs with mlx5_0 RDMA NIC (~186 MB KV cache):
-
-| Metric | MooncakeStoreConnector | MooncakeTransferEngineConnector (CPU) |
-|---|---|---|
-| KV transfer wall time | ~810 ms | **~14 ms** |
-| RDMA throughput | N/A (TCP) | ~22 GB/s |
-| Speedup | 1x | **58x** |
-
-## Troubleshooting
-
-### Quick Diagnostics
-
-```bash
-# 1. Check RDMA devices and link status
-ibdev2netdev
-# Expected: "mlx5_X port 1 ==> <iface> (Up)"
-# RoCE devices map to Ethernet interfaces (e.g., enp75s0f0)
-# IB devices map to ib0, ib1, etc.
-
-# 2. Check InfiniBand device details
-ibstat
-
-# 3. Verify /dev/infiniband is accessible (required in containers)
-ls /dev/infiniband/
-
-# 4. Check Mooncake installation
-python -c "from mooncake.engine import TransferEngine; print('OK')"
-
-# 5. Check environment variables
-echo "RDMA_DEVICE_NAME=${RDMA_DEVICE_NAME:-<not set>}"
-echo "MC_IB_PCI_RELAXED_ORDERING=${MC_IB_PCI_RELAXED_ORDERING:-<not set>}"
-```
-
-### Common Issues
-
-| Symptom | Cause | Fix |
-|---|---|---|
-| `Failed to modify QP to RTR` | Cross-NIC QP handshake failure (multi-NIC DGX) | Set `device_name` to a single RoCE NIC (e.g., `mlx5_2`) or set `RDMA_DEVICE_NAME` env var |
-| `transport retry counter exceeded` | RDMA path between incompatible NICs | Same as above — restrict to one NIC |
-| `zmq.error.Again: Resource temporarily unavailable` | ZMQ recv timeout (transfer took too long) | Check NIC selection; increase data may need longer timeout |
-| `Mooncake Engine initialization failed` | Missing RDMA drivers or `/dev/infiniband` | Install Mellanox OFED; in Docker add `--device=/dev/infiniband` |
-| `MemoryError` in allocator | Memory pool too small for payload | Increase `memory_pool_size` |
-| GPU transfer slower than CPU | GPU BAR1 bandwidth limitation (PXB/NODE topology) | Use `memory_pool_device: "cpu"` instead of `"cuda"` |
-
-### Multi-NIC Environments (DGX)
-
-On DGX machines with 12+ RDMA NICs, only RoCE NICs (with a bound network
-interface) reliably support loopback. IB-only NICs may fail cross-NIC QP
-handshakes. To identify RoCE NICs:
-
-```bash
-ibdev2netdev | grep -v "ib[0-9]"
-# RoCE devices show Ethernet interface names like enp75s0f0
-```
-
-Then configure the connector:
-```yaml
-device_name: "mlx5_2"  # or set RDMA_DEVICE_NAME=mlx5_2
-```
-
-See the RDMA Test README in tests/distributed/omni_connectors/README.md
-for test-specific setup instructions.
-
-For more details on the underlying engine, refer to the
-[Mooncake repository](https://github.com/kvcache-ai/Mooncake).
@@ -1,27 +0,0 @@
-# SharedMemoryConnector
-
-## When to Use
-
-Best for single-node deployments where stages run on the same host. It is
-auto-configured when no explicit connector is specified for an edge.
-
-## How It Works
-
- Small payloads (< threshold): serialized and passed inline in metadata.
- Large payloads (>= threshold): stored in shared memory; the SHM name is
-  returned in metadata.
-
-## Configuration
-
-```yaml
-runtime:
-  connectors:
-    connector_of_shared_memory:
-      name: SharedMemoryConnector
-      extra:
-        shm_threshold_bytes: 65536
-```
-
-## Notes
-
- Auto-mode uses SharedMemoryConnector if no connector is declared for an edge.
@@ -1,101 +0,0 @@
-# YuanrongConnector
-
-## When to Use
-
-Best for multi-node distributed inference using Yuanrong Datasystem.
-
-## Mechanism
-
-Uses Yuanrong Datasystem's distributed KV store (`datasystem.kv_client`).
-
- Data Plane: TCP or RDMA for high-bandwidth transfer.
- Control Plane: Yuanrong Datasystem workers and etcd.
- Keying: deterministic keys based on `put_key` (often composed as `request_id:fromStage_toStage`).
-
-## Installation
-
-```bash
-pip install openyuanrong-datasystem
-```
-
-## Start etcd
-
-```bash
-# Download and install etcd (v3.5.12 or higher)
-ETCD_VERSION="v3.5.12"
-ETCD_ARCH="linux-arm64"
-wget https://github.com/etcd-io/etcd/releases/download/${ETCD_VERSION}/etcd-${ETCD_VERSION}-${ETCD_ARCH}.tar.gz
-tar -xvf etcd-${ETCD_VERSION}-${ETCD_ARCH}.tar.gz
-cd etcd-${ETCD_VERSION}-${ETCD_ARCH}
-sudo cp etcd etcdctl /usr/local/bin/
-
-# Start etcd
-etcd \
-  --name etcd-single \
-  --data-dir /tmp/etcd-data \
-  --listen-client-urls http://0.0.0.0:2379 \
-  --advertise-client-urls http://0.0.0.0:2379 \
-  --listen-peer-urls http://0.0.0.0:2380 \
-  --initial-advertise-peer-urls http://0.0.0.0:2380 \
-  --initial-cluster etcd-single=http://0.0.0.0:2380 &
-
-# Verify etcd is running
-etcdctl --endpoints "127.0.0.1:2379" put key "value"
-etcdctl --endpoints "127.0.0.1:2379" get key
-```
-
-For production environments, refer to the
-[official etcd clustering documentation](https://etcd.io/docs/current/op-guide/clustering/).
-
-## Start Datasystem Worker
-
-```bash
-# Replace ${ETCD_IP} with etcd node IP, ${WORKER_IP} with local node IP
-dscli start -w \
-  --worker_address "${WORKER_IP}:31501" \
-  --etcd_address "${ETCD_IP}:2379" \
-  --shared_memory_size_mb 20480
-```
-
-To stop the worker:
-
-```bash
-dscli stop --worker_address "${WORKER_IP}:31501"
-```
-
-## Configuration
-
-Define the connector in runtime:
-
-```yaml
-runtime:
-  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:
-  - stage_id: 0
-    output_connectors:
-      to_stage_1: connector_of_yuanrong
-
-  - stage_id: 1
-    input_connectors:
-      from_stage_0: connector_of_yuanrong
-```
-
-Parameters:
-
- host: datasystem worker host.
- port: datasystem worker port.
- get_sub_timeout_ms: get timeout in milliseconds (0 for no timeout).
-
-For more details, refer to the
-[Yuanrong Datasystem repository](https://atomgit.com/openeuler/yuanrong-datasystem).
@@ -45,7 +45,7 @@ ray start --address=<HEAD_NODE_IP>:6399

 When running on Ray, the system automatically adapts its communication strategy:

-*   **Cross-Node**: Recommended to use `MooncakeTransferEngineConnector` (RDMA, fastest) or `MooncakeStoreConnector` (TCP fallback).
+*   **Cross-Node**: Recommended to use `MooncakeConnector` (requires separate configuration).
 *   **Same-Node**: Can still use `SharedMemoryConnector` for efficiency, or Ray's native object store (plasma).
 *   **SHM threshold default differs**: when `worker_backend="ray"`, the SharedMemoryConnector default threshold is set to `sys.maxsize`, which forces payloads to go inline (no SHM). Override `shm_threshold_bytes` in the connector config if you want SHM for Ray runs.

@@ -1,456 +0,0 @@
-# Sequence Parallel
-
-This section describes how to add Sequence Parallel (SP) to a diffusion transformer model. We use the Qwen-Image transformer and Wan2.2 transformer as reference implementations.
-
---
-
-## Table of Contents
-
- [Overview](#overview)
- [Approach 1: Non-Intrusive `_sp_plan` (Recommended)](#approach-1-non-intrusive-_sp_plan-recommended)
- [Approach 2: Intrusive Modification (For Complex Cases)](#approach-2-intrusive-modification-for-complex-cases)
- [Testing](#testing)
- [Troubleshooting](#troubleshooting)
- [Reference Implementations](#reference-implementations)
- [Summary](#summary)
-
---
-
-## Overview
-
-
-### What is Sequence Parallel?
-
-**Terminology Note:** Our "Sequence Parallelism" (SP) corresponds to "Context Parallelism" (CP) in the [diffusers library](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/_modeling_parallel.py). We use "Sequence Parallelism" to align with vLLM-Omni's terminology.
-
-Diffusion transformers process long sequences of image patches or video frames. For high-resolution generation, these sequences can become very large. Enabling SP allows each GPU to process only a portion of the sequence, with attention mechanisms (Ulysses/Ring) handling cross-GPU communication transparently.
-
-### Architecture
-
-The major APIs for Sequence Parallel:
-
-```python
-from vllm_omni.diffusion.distributed.sp_plan import (
-    SequenceParallelInput,   # For sharding (splitting) tensors
-    SequenceParallelOutput,  # For gathering tensors
-)
-from vllm_omni.diffusion.distributed.sp_sharding import sp_shard, sp_gather
-```
-
-| Method/Class | Purpose | Behavior |
-|--------------|---------|----------|
-| `SequenceParallelInput` | Declare input sharding in `_sp_plan` | Auto-shards tensors at module input |
-| `SequenceParallelOutput` | Declare output gathering in `_sp_plan` | Auto-gathers tensors at module output |
-| `sp_shard()` | Manual tensor sharding | Splits tensor across SP workers |
-| `sp_gather()` | Manual tensor gathering | Gathers sharded tensors from all workers |
-
---
-
-## Approach 1: Non-Intrusive `_sp_plan` (Recommended)
-
-The `_sp_plan` mechanism allows SP **without modifying `forward()` logic**. The framework automatically registers hooks to shard inputs and gather outputs at module boundaries.
-
-**When to use:**
- Standard transformer architectures
- Tensor operations happen at `nn.Module` boundaries
- Predictable sharding/gathering patterns
-
-**How it works:**
-1. Declare `_sp_plan` dict in your transformer class
-2. Framework automatically applies hooks when `sequence_parallel_size > 1`
-3. Hooks shard/gather tensors at specified module boundaries
-4. Attention layers handle cross-GPU communication internally
-
-```python
-class StandardTransformer(nn.Module):
-    _sp_plan = {
-        # Shard hidden_states at first transformer block input
-        "blocks.0": {
-            "hidden_states": SequenceParallelInput(split_dim=1, expected_dims=3),
-        },
-        # Gather at final output projection
-        "proj_out": SequenceParallelOutput(gather_dim=1, expected_dims=3),
-    }
-```
-
-`StandardTransformer` has a transformer blocks list `self.blocks = nn.ModuleList([...])`, and a projection output layer `self.proj_out`. The `_sp_plan` above defines that when SP is enabled, sharding the input tensor to the first transformer block, and gathering the sharded tensor at the final output projection layer.
-
-**Requirements:**
- Tensor operations that need sharding/gathering must happen at **`nn.Module` boundaries**
- Inline Python operations (e.g., `torch.cat`, `pad_sequence`) **cannot be hooked**
-
-**Solution for inline operations:** Extract into a submodule (see Step 2 below).
-
-### Step 1: Understand Module Boundaries
-
-Identify where tensors need to be sharded or gathered in your model's forward pass:
-
-```python
-class MyTransformer(nn.Module):
-    def __init__(self):
-        self.patch_embed = PatchEmbed()      # ← Boundary 1
-        self.pos_embed = RoPE()              # ← Boundary 2
-        self.blocks = nn.ModuleList([...])   # ← Boundary 3
-        self.norm_out = LayerNorm()
-        self.proj_out = Linear()             # ← Boundary 4
-
-    def forward(self, x):
-        x = self.patch_embed(x)              # ← Shard before this?
-        pos = self.pos_embed(x)              # ← Shard RoPE outputs?
-        for block in self.blocks:
-            x = block(x, pos)                # ← Blocks process sharded x
-        x = self.norm_out(x)
-        output = self.proj_out(x)            # ← Gather after this?
-        return output
-```
-
-### Step 2: Handle Inline Operations
-
-If your `forward()` contains inline tensor operations, **extract them into submodules**.
-
-**Example: Z-Image concatenates image + text features inline**
-
-```python
-# ❌ BAD: Inline operation - hooks cannot intercept
-class ZImageTransformer(nn.Module):
-    def forward(self, x, cap_feats):
-        # This concatenation happens inline - _sp_plan can't shard it!
-        unified = torch.cat([x, cap_feats], dim=1)
-
-        for layer in self.layers:
-            unified = layer(unified)
-
-        return unified
-
-# ✅ GOOD: Extract into submodule
-class UnifiedPrepare(nn.Module):
-    """Submodule to concatenate image and text features."""
-    def forward(self, x, cap_feats):
-        return torch.cat([x, cap_feats], dim=1)
-
-class ZImageTransformer(nn.Module):
-    def __init__(self):
-        super().__init__()
-        self.unified_prepare = UnifiedPrepare()  # Now a module!
-        self.layers = nn.ModuleList([...])
-
-    def forward(self, x, cap_feats):
-        # Now _sp_plan can shard the output of unified_prepare!
-        unified = self.unified_prepare(x, cap_feats)
-
-        for layer in self.layers:
-            unified = layer(unified)
-
-        return unified
-```
-
-**Other common cases:**
- `pad_sequence()` → `PadSequenceModule`
- `torch.cat()` → `ConcatModule`
- `tensor.reshape()` → `ReshapeModule`
- Complex preprocessing → `PreprocessModule`
-
-### Step 3: Write `_sp_plan` for Your Model
-
-Create a class-level `_sp_plan` dictionary specifying where to shard/gather tensors.
-
-Typically, there are two patterns for diffusion models:
-
-**Pattern 1: Shard at first block, gather at output projection**
-
-Most common pattern for standard transformers:
-
-```python
-from vllm_omni.diffusion.distributed.sp_plan import (
-    SequenceParallelInput,   # For sharding (splitting) tensors
-    SequenceParallelOutput,  # For gathering tensors
-)
-class StandardTransformer(nn.Module):
-    _sp_plan = {
-        # Shard hidden_states at first transformer block input
-        "blocks.0": {
-            "hidden_states": SequenceParallelInput(split_dim=1, expected_dims=3),
-        },
-        # Gather at final output projection
-        "proj_out": SequenceParallelOutput(gather_dim=1, expected_dims=3),
-    }
-```
-
-**Pattern 2: Shard RoPE embeddings separately**
-
-When RoPE is computed in a separate module:
-
-```python
-from vllm_omni.diffusion.distributed.sp_plan import (
-    SequenceParallelInput,   # For sharding (splitting) tensors
-    SequenceParallelOutput,  # For gathering tensors
-)
-class TransformerWithRoPE(nn.Module):
-    _sp_plan = {
-        # Shard RoPE module OUTPUTS (returns tuple of cos, sin)
-        "rope": {
-            0: SequenceParallelInput(split_dim=1, expected_dims=4, split_output=True),  # cos
-            1: SequenceParallelInput(split_dim=1, expected_dims=4, split_output=True),  # sin
-        },
-        # Shard transformer block INPUT
-        "blocks.0": {
-            "hidden_states": SequenceParallelInput(split_dim=1, expected_dims=3),
-        },
-        # Gather at output
-        "proj_out": SequenceParallelOutput(gather_dim=1, expected_dims=3),
-    }
-```
-
-### API Reference
-
-**SequenceParallelInput Parameters:**
-
-| Parameter | Type | Description |
-|-----------|------|-------------|
-| `split_dim` | int | Dimension to split (usually `1` for sequence) |
-| `expected_dims` | int \| None | Expected tensor rank for validation (optional) |
-| `split_output` | bool | `False`: shard **input** params; `True`: shard **output** tensors |
-| `auto_pad` | bool | Auto-pad if sequence not divisible by world_size (default: `False`) |
-
-**SequenceParallelOutput Parameters:**
-
-| Parameter | Type | Description |
-|-----------|------|-------------|
-| `gather_dim` | int | Dimension to gather (usually `1` for sequence) |
-| `expected_dims` | int \| None | Expected tensor rank for validation (optional) |
-
-**Module Naming Conventions:**
-
-| Key | Meaning | Python equivalent |
-|-----|---------|-------------------|
-| `""` | Root model | `model` |
-| `"blocks.0"` | First element of ModuleList | `model.blocks[0]` |
-| `"blocks.*"` | All elements of ModuleList | `for b in model.blocks` |
-| `"rope"` | Named submodule | `model.rope` |
-| `"outputs.main"` | ModuleDict entry | `model.outputs["main"]` |
-
-**Dictionary Value Types:**
-
-| Key type | `split_output` | Description |
-|----------|----------------|-------------|
-| `"param_name"` (str) | `False` | Shard **input parameter** by name |
-| `0`, `1`, ... (int) | `True` | Shard **output tuple** by index |
-
---
-
-## Approach 2: Intrusive Modification (For Complex Cases)
-
-For models with dynamic sharding logic that cannot be expressed via `_sp_plan`, manually insert shard/gather calls.
-
-**When to use:**
- Dynamic/conditional sharding logic
- Complex tensor manipulations that can't be encapsulated
- Temporary workaround during development
-
-```python
-from vllm_omni.diffusion.distributed.sp_sharding import sp_shard, sp_gather
-
-def forward(self, hidden_states, ...):
-    if self.parallel_config.sequence_parallel_size > 1:
-        hidden_states = sp_shard(hidden_states, dim=1)
-
-    # ... computation ...
-
-    if self.parallel_config.sequence_parallel_size > 1:
-        output = sp_gather(output, dim=1)
-
-    return output
-```
-
---
-
-## Testing
-
-After implementing Sequence Parallel support, thoroughly test your implementation to ensure correctness and performance across different configurations.
-
-**Test Different `sp_size`:**
-
-Test your model with various sequence parallel world sizes to verify correctness and identify optimal configurations:
-
-```bash
-cd examples/offline_inference/text_to_image
-python text_to_image.py \
-    --model Your-org/your-model \
-    --prompt "a cup of coffee on the table" \
-    --num-inference-steps 50 \
-    --ulysses-degree 2 \
-    --ring-degree 2 \
-    --output sp_test_image_ulysses=2_ring=2.png
-```
-
-**Verify:**
-
-1. **Correctness:** Output should be identical across all `sp_size` values
-2. **Speed:** Throughput should remain stable or improve (especially for large sequences)
-3. **Logs:** Check for any shape mismatch or communication errors
-
-**Test with Tensor Parallel:**
-
-Sequence Parallel can be combined with other parallelism strategies:
-
-```bash
-cd examples/offline_inference/text_to_image
-python text_to_image.py \
-    --model Your-org/your-model \
-    --prompt "a cup of coffee on the table" \
-    --num-inference-steps 50 \
-    --ulysses-degree 2 \
-    --tensor-parallel-size 2 \
-    --output sp_test_image_ulysses=2_tp=2.png
-```
-
---
-
-## Troubleshooting
-
-### Issue: Shape mismatch errors
-
-**Symptoms:** `RuntimeError: shape mismatch` during forward pass.
-
-**Causes & Solutions:**
-
- **RoPE dimension mismatch:**
-
-**Problem:** RoPE embeddings not sharded, but hidden_states is sharded.
-
-**Solution:** Shard RoPE outputs in `_sp_plan`:
-```python
-_sp_plan = {
-    "rope": {
-        0: SequenceParallelInput(split_dim=1, expected_dims=4, split_output=True),
-        1: SequenceParallelInput(split_dim=1, expected_dims=4, split_output=True),
-    },
-    ...
-}
-```
-
- **Sequence Length not divisible by sp_size:**
-
-**Problem:** `SequenceParallelInput(auto_pad=False)` - auto_pad should be True to enable automatic sequence padding.
-
-**Solution:** In `SequenceParallelInput`, set `auto_pad=True` and add attention mask support.
-
-> **Experimental Feature:** `auto_pad=True` is an experimental feature and may be changed in the future. We plan to improve this solution to involve minimal changes to model files. More details are [here](https://github.com/vllm-project/vllm-omni/issues/1324).
-
-**Constraints of auto_pad:**
-
-| Constraint | Description |
-|------------|-------------|
-| **Attention Backend Compatibility** | The attention backends must support `attention_mask`. Currently only `TORCH_SDPA` and `FLASH_ATTN` (default for diffusion models) are supported. |
-| **Ring Attention Limitation** | Ring attention does not support `attention_mask`. Therefore, when using `auto_pad=True`, the combination of Ulysses + Ring attention is not feasible. |
-
-1. Enable `auto_pad=True` for all sequence-dimension inputs in `_sp_plan`:
-```python
-_sp_plan = {
-    "rope": {
-        0: SequenceParallelInput(split_dim=1, expected_dims=4, split_output=True, auto_pad=True),
-        1: SequenceParallelInput(split_dim=1, expected_dims=4, split_output=True, auto_pad=True),
-    },
-    "blocks.0": {
-        "hidden_states": SequenceParallelInput(split_dim=1, expected_dims=3, auto_pad=True)
-    },
-    ...
-}
-```
-
-2. Create attention mask dynamically when padding is applied:
-```python
-from vllm_omni.diffusion.forward_context import get_forward_context
-from vllm_omni.diffusion.attention.backends.abstract import AttentionMetadata
-
-# In model forward(), before transformer blocks:
-hidden_states_mask = None
-ctx = get_forward_context()
-if ctx.sp_original_seq_len is not None and ctx.sp_padding_size > 0:
-    batch_size = hidden_states.shape[0]
-    padded_seq_len = ctx.sp_original_seq_len + ctx.sp_padding_size
-    hidden_states_mask = torch.ones(batch_size, padded_seq_len, dtype=torch.bool, device=hidden_states.device)
-    hidden_states_mask[:, ctx.sp_original_seq_len:] = False
-
-# Pass mask to attention layers
-attn_metadata = AttentionMetadata(attn_mask=hidden_states_mask) if hidden_states_mask is not None else None
-output = self.attn(query, key, value, attn_metadata)
-```
-
-**Important Quality Considerations:**
-
-While `auto_pad` enables generation for irregular resolutions, be aware of potential quality impacts:
-
-| Aspect | Impact |
-|--------|--------|
-| **Training Distribution** | Models perform best on aspect ratios seen during training (e.g., 1:1, 16:9, 4:3). Unusual ratios like 700x400 (1.75:1) may produce lower quality results. |
-| **Padding Overhead** | Padded positions consume compute even when masked. For best efficiency, prefer resolutions divisible by `sp_size`. |
-
-**Recommendations for users:**
- Use standard aspect ratios when possible (e.g., 768x432 for 16:9 instead of 700x400)
- Ensure post-patch dimensions are divisible by `sp_size` for optimal quality
- Test generation quality when using unusual resolutions
-
-### Issue: Inline operations not sharded
-
-**Symptoms:** Some tensors remain full-sized, not sharded.
-
-**Causes & Solutions:**
-
- **Operations happen inline in `forward()`, not at module boundaries:**
-
-**Problem:**
-```python
-def forward(self, x, cap):
-    unified = torch.cat([x, cap], dim=1)  # ← Inline operation!
-    # _sp_plan can't hook this
-```
-
-**Solution:** Extract into submodule:
-```python
-class ConcatModule(nn.Module):
-    def forward(self, x, cap):
-        return torch.cat([x, cap], dim=1)
-
-class MyModel(nn.Module):
-    _sp_plan = {
-        "concat": {
-            0: SequenceParallelInput(split_dim=1, expected_dims=4, split_output=True),
-            1: SequenceParallelInput(split_dim=1, expected_dims=4, split_output=True),
-        },
-        ...
-    }
-    def __init__(self):
-        self.concat = ConcatModule()  # Now hookable!
-
-    def forward(self, x, cap):
-        unified = self.concat(x, cap)  # ← Can be sharded via _sp_plan
-```
-
---
-
-## Reference Implementations
-
-Complete examples in the codebase:
-
-| Model | Path | Pattern | Notes |
-|-------|------|---------|-------|
-| **Qwen-Image** | `vllm_omni/diffusion/models/qwen_image/qwen_image_transformer.py` | Dual-stream + preprocessing | auto_pad, separate RoPE |
-| **Wan2.2** | `vllm_omni/diffusion/models/wan2_2/wan2_2_transformer.py` | Dual-Transformer + RoPE | Video transformer |
-| **Z-Image** | `vllm_omni/diffusion/models/z_image/z_image_transformer.py` | Unified sequence | Concatenated input |
-| **SP Plan Types** | `vllm_omni/diffusion/distributed/sp_plan.py` | Type definitions | SequenceParallelInput/Output |
-| **Hook Implementation** | `vllm_omni/diffusion/hooks/sequence_parallel.py` | Hook mechanics | How hooks work |
-| **Tests** | `tests/diffusion/distributed/test_sp_plan_hooks.py` | Test examples | Validation patterns |
-
---
-
-## Summary
-
-Adding Sequence Parallel support to a transformer:
-
-1. ✅ **Choose approach** - Use `_sp_plan` for standard cases, intrusive modification for complex cases
-2. ✅ **Identify sharding boundaries** - Where should tensors be split/gathered?
-3. ✅ **Extract inline operations** - Move `torch.cat`, `pad_sequence`, etc. to submodules
-4. ✅ **Define `_sp_plan`** - Declare shard/gather points as class attribute
-5. ✅ **Use `auto_pad` for variable lengths** - Support non-uniform sequences
-6. ✅ **Test** - Verify with different `ulysses_degree` and `ring_degree` combinations
@@ -1,459 +0,0 @@
-# TeaCache
-
-This section describes how to add TeaCache to a diffusion transformer model. We use the Qwen-Image transformer as the reference implementation.
-
---
-
-## Table of Contents
-
- [Overview](#overview)
- [Step-by-Step Implementation](#step-by-step-implementation)
- [Customization](#customization)
- [Testing](#testing)
- [Troubleshooting](#troubleshooting)
- [Reference Implementations](#reference-implementations)
- [Summary](#summary)
-
---
-
-## Overview
-
-### What is TeaCache?
-
-TeaCache speeds up diffusion inference by caching transformer block computations when consecutive timesteps are similar. It provides **1.5x-2.0x speedup** with minimal quality loss.
-
-The core insight is that the modulated input (after normalization and timestep conditioning) changes gradually across timesteps. By measuring the L1 distance between consecutive modulated inputs and comparing it to a threshold, TeaCache decides whether to execute the full transformer blocks or reuse the cached residual from the previous step.
-
-vLLM-omni provides a **hook-based** TeaCache system that requires **zero changes to model code**. The hook completely intercepts the transformer's forward pass and implements adaptive caching transparently. This design allows easy integration with any transformer model by simply writing an extractor function.
-
-### Architecture
-
-The TeaCache system consists of three main components:
-
-| Component | Purpose | Location |
-|-----------|---------|----------|
-| [`CacheContext`](https://docs.vllm.ai/projects/vllm-omni/en/latest/api/vllm_omni/diffusion/cache/#vllm_omni.diffusion.cache.CacheContext) | Dataclass containing model-specific information for caching | `vllm_omni/diffusion/cache/teacache/context.py` |
-| [`EXTRACTOR_REGISTRY`](https://docs.vllm.ai/projects/vllm-omni/en/latest/api/vllm_omni/diffusion/cache/teacache/extractors/#vllm_omni.diffusion.cache.teacache.extractors.EXTRACTOR_REGISTRY) | Maps transformer class names to extractor functions | `vllm_omni/diffusion/cache/teacache/extractors.py` |
-| [`TeaCacheConfig`](https://docs.vllm.ai/projects/vllm-omni/en/latest/api/vllm_omni/diffusion/cache/#vllm_omni.diffusion.cache.TeaCacheConfig) | Configuration including thresholds and polynomial coefficients | `vllm_omni/diffusion/cache/teacache/config.py` |
-
-The hook handles all caching logic automatically, including:
-
- CFG-aware state management (separate states for positive/negative branches)
- CFG-parallel compatibility
- L1 distance computation with polynomial rescaling
- Residual caching and reuse
-
-
---
-
-## Step-by-Step Implementation
-
-To add TeaCache support for a new model, you need to:
-
-1. Write an **extractor function** that returns a `CacheContext` object
-2. Register the extractor in the `EXTRACTOR_REGISTRY`
-3. Add model-specific polynomial coefficients to `TeaCacheConfig`
-
-### Step 1: Model-Specific Preprocessing
-
-Extract and process model inputs. This typically involves:
- Embedding image/latent inputs
- Processing text encoder outputs (if dual-stream)
- Creating timestep embeddings
- Computing positional embeddings
-
-**Example (Qwen-Image):**
-
-```python
-def extract_qwen_context(
-    module: nn.Module,
-    hidden_states: torch.Tensor,
-    encoder_hidden_states: torch.Tensor,
-    encoder_hidden_states_mask: torch.Tensor,
-    timestep: torch.Tensor,
-    img_shapes: torch.Tensor,
-    txt_seq_lens: torch.Tensor,
-    guidance: torch.Tensor | None = None,
-    **kwargs: Any,
-) -> CacheContext:
-    # Validate model structure
-    if not hasattr(module, "transformer_blocks") or len(module.transformer_blocks) == 0:
-        raise ValueError("Module must have transformer_blocks")
-
-    # Preprocessing: embed inputs
-    hidden_states = module.img_in(hidden_states)
-    timestep = timestep.to(device=hidden_states.device, dtype=hidden_states.dtype)
-    encoder_hidden_states = module.txt_norm(encoder_hidden_states)
-    encoder_hidden_states = module.txt_in(encoder_hidden_states)
-
-    # Create timestep embedding
-    if guidance is not None:
-        guidance = guidance.to(hidden_states.dtype) * 1000
-    temb = (
-        module.time_text_embed(timestep, hidden_states)
-        if guidance is None
-        else module.time_text_embed(timestep, guidance, hidden_states)
-    )
-
-    # Compute position embeddings
-    image_rotary_emb = module.pos_embed(img_shapes, txt_seq_lens, device=hidden_states.device)
-```
-
-### Step 2: Extract Modulated Input
-
-The modulated input is used for cache decisions. Extract it from the **first transformer block** after normalization and modulation.
-
-**Example (Qwen-Image):**
-
-```python
-    # Extract modulated input from first transformer block
-    block = module.transformer_blocks[0]
-    img_mod_params = block.img_mod(temb)
-    img_mod1, _ = img_mod_params.chunk(2, dim=-1)
-    img_modulated, _ = block.img_norm1(hidden_states, img_mod1)
-```
-
-**Key Points:**
-
- Use the **first block** to extract modulated input early
- Apply the same normalization and modulation as the actual forward pass
- The tensor should represent the processed features that will change across timesteps
-
-### Step 3: Define Transformer Execution
-
-Create a callable that executes all transformer blocks. This encapsulates the main computation loop.
-
-**Example (Qwen-Image dual-stream):**
-
-```python
-    def run_transformer_blocks():
-        """Execute all Qwen transformer blocks."""
-        h = hidden_states
-        e = encoder_hidden_states
-
-        for block in module.transformer_blocks:
-            e, h = block(
-                hidden_states=h,
-                encoder_hidden_states=e,
-                encoder_hidden_states_mask=encoder_hidden_states_mask,
-                temb=temb,
-                image_rotary_emb=image_rotary_emb,
-            )
-        return (h, e)  # Return both image and text hidden states
-```
-
-**Example (Single-stream model like Flux):**
-
-```python
-    def run_transformer_blocks():
-        """Execute all Flux transformer blocks."""
-        h = hidden_states
-
-        for block in module.transformer_blocks:
-            h = block(h, temb=temb)
-        return (h,)  # Return only image hidden states
-```
-
-**Key Points:**
-
- Return format:
- For single-stream models: return `(hidden_states,)`
- For dual-stream models: return `(hidden_states, encoder_hidden_states)`
-
-### Step 4: Define Postprocessing
-
-Create a callable that applies final transformations to produce the model output.
-
-**Example (Qwen-Image):**
-
-```python
-    return_dict = kwargs.get("return_dict", True)
-
-    def postprocess(h):
-        """Apply Qwen-specific output postprocessing."""
-        h = module.norm_out(h, temb)
-        output = module.proj_out(h)
-        if not return_dict:
-            return (output,)
-        return Transformer2DModelOutput(sample=output)
-```
-
-### Step 5: Return CacheContext
-
-Package all information into a `CacheContext` object.
-
-```python
-    return CacheContext(
-        modulated_input=img_modulated,
-        hidden_states=hidden_states,
-        encoder_hidden_states=encoder_hidden_states,  # or None for single-stream
-        temb=temb,
-        run_transformer_blocks=run_transformer_blocks,
-        postprocess=postprocess,
-    )
-```
-
-**CacheContext Fields:**
-
-| Field | Type | Purpose |
-|-------|------|---------|
-| `modulated_input` | `torch.Tensor` | Tensor used for cache decision (similarity comparison) |
-| `hidden_states` | `torch.Tensor` | Current hidden states (will be modified by caching) |
-| `encoder_hidden_states` | `torch.Tensor | None` | Encoder states for dual-stream models, `None` for single-stream |
-| `temb` | `torch.Tensor` | Timestep embedding tensor |
-| `run_transformer_blocks` | `Callable[[], tuple]` | Executes transformer blocks, returns `(hidden_states, [encoder_hidden_states])` |
-| `postprocess` | `Callable[[torch.Tensor], Any]` | Applies final transformations to produce model output |
-| `extra_states` | `dict | None` | Optional dict for additional model-specific state |
-
-### Step 6: Register the Extractor
-
-Add your extractor to the `EXTRACTOR_REGISTRY` in `vllm_omni/diffusion/cache/teacache/extractors.py`:
-
-```python
-EXTRACTOR_REGISTRY: dict[str, Callable] = {
-    "QwenImageTransformer2DModel": extract_qwen_context,
-    "Bagel": extract_bagel_context,
-    "ZImageTransformer2DModel": extract_zimage_context,
-    "YourModelTransformer2DModel": extract_your_model_context,  # Add here
-}
-```
-
-**Key:** Use the transformer class name (`module.__class__.__name__`)
-
-### Step 7: Add Model Coefficients
-
-Add polynomial rescaling coefficients to `vllm_omni/diffusion/cache/teacache/config.py`:
-
-```python
-_MODEL_COEFFICIENTS = {
-    "QwenImageTransformer2DModel": [
-        -4.50000000e02,
-        2.80000000e02,
-        -4.50000000e01,
-        3.20000000e00,
-        -2.00000000e-02,
-    ],
-    "YourModelTransformer2DModel": [  # Add your model's coefficients
-        # 5 polynomial coefficients (can reuse similar model's coefficients initially)
-    ],
-}
-```
-
-
-**Initial approach:** Start with coefficients from a similar model architecture, then tune empirically following [Customization](#customization) section.
-
---
-
-## Customization
-
-### Coefficient Estimation
-
-While you can start with coefficients from a similar model architecture, estimating custom coefficients for your specific model typically improves TeaCache performance.
-
-**Why Estimate Coefficients?**
-
-The polynomial coefficients rescale L1 distances between consecutive modulated inputs to better predict when cached residuals can be reused. Model-specific coefficients account for:
-
- Architecture differences (layer count, hidden size, attention patterns)
- Training data characteristics
- Noise prediction behavior across timesteps
-
-| Approach | Performance | Effort |
-|----------|-------------|--------|
-| Using defaults from similar model | Within 5-10% of optimal | Low |
-| Estimating custom coefficients | Best performance | Medium |
-
-#### Implement Data Collection Adapter
-
-Add an adapter in `vllm_omni/diffusion/cache/teacache/coefficient_estimator.py`:
-
-```python
-class YourModelAdapter:
-    """Adapter for coefficient estimation on your model."""
-
-    @staticmethod
-    def load_pipeline(model_path: str, device: str, dtype: torch.dtype) -> Any:
-        """Load your diffusion pipeline."""
-        from your_model_package import YourModelPipeline
-
-        pipeline = YourModelPipeline.from_pretrained(
-            model_path,
-            torch_dtype=dtype,
-        )
-        pipeline = pipeline.to(device)
-        return pipeline
-
-    @staticmethod
-    def get_transformer(pipeline: Any) -> tuple[Any, str]:
-        """Extract transformer from pipeline."""
-        return pipeline.transformer, "YourTransformer2DModel"
-
-    @staticmethod
-    def install_hook(transformer: Any, hook: DataCollectionHook) -> None:
-        """Install data collection hook on transformer."""
-        from vllm_omni.diffusion.hooks import HookRegistry
-
-        registry = HookRegistry.get_or_create(transformer)
-        registry.register_hook(hook._HOOK_NAME, hook)
-
-
-# Register your adapter
-_MODEL_ADAPTERS["YourModel"] = YourModelAdapter
-```
-
-#### Collect Data and Estimate
-
-```python
-from vllm_omni.diffusion.cache.teacache.coefficient_estimator import (
-    TeaCacheCoefficientEstimator,
-)
-from datasets import load_dataset
-from tqdm import tqdm
-
-# Initialize estimator
-estimator = TeaCacheCoefficientEstimator(
-    model_path="/path/to/your/model",
-    model_type="YourModel",
-)
-
-# Load diverse prompts (paper recommends ~70 prompts)
-dataset = load_dataset("nateraw/parti-prompts", split="train")
-prompts = dataset["Prompt"][:70]
-
-# Collect data
-for prompt in tqdm(prompts, desc="Collecting data"):
-    estimator.collect_from_prompt(prompt=prompt, num_inference_steps=50)
-
-# Estimate coefficients
-coeffs = estimator.estimate(poly_order=4)
-print(f"Estimated coefficients: {coeffs.tolist()}")
-```
-
-**Data Statistics Guide:**
-
-| Metric | Good Range | Warning Signs |
-|--------|------------|---------------|
-| **Count** | 2000-5000+ | < 1000: too few prompts |
-| **Input Diffs (x)** | 0.01-0.10 | Very small (<0.001): model may not modulate properly |
-| **Output Diffs (y)** | Should correlate with x | No correlation: check extractor |
-| **Coefficient magnitude** | -1e6 to 1e6 | > 1e8: numerical instability |
-
---
-
-## Testing
-
-After adding TeaCache support, test with:
-
-```python
-from vllm_omni import Omni
-from vllm_omni.inputs.data import OmniDiffusionSamplingParams
-
-omni = Omni(
-    model="your-model-name",
-    cache_backend="tea_cache",
-    cache_config={
-        "rel_l1_thresh": 0.2,
-        "coefficients": [1.33e6, -1.69e5, 7.95e3, -1.64e2, 1.26],  # Your coefficients
-    }
-)
-
-images = omni.generate(
-    "a beautiful landscape",
-    OmniDiffusionSamplingParams(num_inference_steps=50),
-)
-```
-
-**Verify:**
-
-1. **Check logs** - Look for TeaCache initialization messages
-2. **Compare performance** - Measure speedup vs baseline (expect 1.5x-2.0x)
-3. **Verify output quality** - Visually compare cached vs uncached outputs (should be nearly identical)
-
-See more detailed examples in [user guide for teacache](../../user_guide/diffusion/teacache.md).
-
---
-
-## Troubleshooting
-
-### Issue: "Unknown model type"
-
-**Symptoms:** Error message indicating the model type is not recognized when enabling TeaCache.
-
-**Causes & Solutions:**
-
- **Extractor not registered:**
-
-**Problem:** The transformer class name doesn't exist in `EXTRACTOR_REGISTRY`.
-
-**Solution:** Check the class name and add to registry:
-```python
-# Check transformer class name
-print(pipeline.transformer.__class__.__name__)
-
-# Add to EXTRACTOR_REGISTRY
-EXTRACTOR_REGISTRY["YourTransformer2DModel"] = extract_your_context
-```
-
- **Transformer class name mismatch:**
-
-**Solution:** Ensure the registry key matches exactly with `module.__class__.__name__`.
-
-### Issue: "Cannot find coefficients"
-
-**Symptoms:** Error when initializing TeaCache about missing model coefficients.
-
-**Causes & Solutions:**
-
- **Missing coefficients in config:**
-
-**Solution:** Add coefficients to `_MODEL_COEFFICIENTS` in `config.py`, or pass custom coefficients:
-```python
-omni = Omni(
-    model="your-model",
-    cache_backend="tea_cache",
-    cache_config={"coefficients": [1.0, -0.5, 0.1, -0.01, 0.001]}
-)
-```
-
-### Issue: Quality Degradation
-
-**Symptoms:** Output images look noticeably different or have artifacts compared to baseline.
-
-**Causes & Solutions:**
-
- **Threshold too high:**
-
-**Problem:** `rel_l1_thresh` is too aggressive, causing cache reuse when outputs differ significantly.
-
-**Solution:** Lower the threshold:
-```python
-cache_config={"rel_l1_thresh": 0.1}  # Try 0.1-0.2
-```
-
- **Coefficients not tuned:**
-
-**Solution:** Estimate model-specific coefficients using the coefficient estimation process described above.
-
---
-
-## Reference Implementations
-
-Complete examples in the codebase:
-
-| Model | Path | Pattern | Notes |
-|-------|------|---------|-------|
-| **Qwen-Image** | `vllm_omni/diffusion/cache/teacache/extractors.py` | Dual-stream | `extract_qwen_context` |
-| **Bagel** | `vllm_omni/diffusion/cache/teacache/extractors.py` | Omni model | `extract_bagel_context` |
-| **TeaCache Core** | `vllm_omni/diffusion/cache/teacache/` | Base implementation | Hook and config |
-| **Coefficient Estimator** | `vllm_omni/diffusion/cache/teacache/coefficient_estimator.py` | Estimation tool | Adapter pattern |
-
---
-
-## Summary
-
-Adding TeaCache support:
-
-1. ✅ **Write extractor** - Create function returning `CacheContext` with model-specific preprocessing
-2. ✅ **Register extractor** - Add to `EXTRACTOR_REGISTRY` with transformer class name
-3. ✅ **Add coefficients** - Add polynomial coefficients to `_MODEL_COEFFICIENTS`
-4. ✅ **Test** - Verify with `cache_backend="tea_cache"`
@@ -1,279 +0,0 @@
-# Tensor Parallel
-
-This section describes how to add Tensor Parallel (TP) to a diffusion transformer model. We use the Z-Image transformer as the reference implementation.
-
---
-
-## Table of Contents
-
- [Overview](#overview)
- [Step-by-Step Implementation](#step-by-step-implementation)
- [Testing](#testing)
- [Troubleshooting](#troubleshooting)
- [Reference Implementations](#reference-implementations)
- [Summary](#summary)
-
---
-
-## Overview
-
-### What is Tensor Parallel?
-
-Tensor Parallel (TP) is a model parallelism technique that **shards model weights** across multiple GPUs. Each GPU holds only a portion of the model's parameters and computes only part of each layer's output.
-
-Diffusion transformers contain large attention and MLP layers. We can use Tensor Parallel to shard the model dimension across multiple GPUs, allowing larger models to fit in memory while achieving near-linear speedup.
-
-### Architecture
-
-The Tensor Parallel implementation relies vLLM's Parallel Layers:
-
-[vLLM Parallel Layers API Reference](https://docs.vllm.ai/en/latest/contributing/model/basic/?h=column#3-optional-implement-tensor-parallelism-and-quantization-support)
-
-**Parallel Layer Types:**
-
-| Layer Type | Purpose | Weight Partitioning |
-|------------|---------|---------------------|
-| `ColumnParallelLinear` | First FFN layer, separated QKV | Columns (output dimension) |
-| `RowParallelLinear` | Second FFN layer, attention output | Rows (input dimension) |
-| `QKVParallelLinear` | Multi-head/grouped-query attention QKV | Handles head replication automatically |
-| `ReplicatedLinear` | Layers that shouldn't be sharded | No partitioning (replicated) |
-
---
-
-## Step-by-Step Implementation
-
-
-### Step 1: Identify Linear Layers
-
-Find all `nn.Linear` layers in your transformer that need to be sharded.
-
-**Key questions:**
- Which layers should be column parallel (weight split by columns)?
- Which layers should be row parallel (weight split by rows)?
-
-### Step 2: Replace Linear Layers with Parallel Equivalents
-
-Replace `nn.Linear` with parallel layers from `vllm.model_executor.layers.linear`.
-
-**Example (MLP Block - Up-Down Pattern):**
-
-```python
-class FeedForward(nn.Module):
-    def __init__(self, dim: int, hidden_dim: int):
-        super().__init__()
-        # Column parallel: weight split by columns [hidden_dim/N, dim]
-        self.w1 = ColumnParallelLinear(
-            dim,
-            hidden_dim,
-            bias=False,
-            return_bias=False,
-        )
-        self.act = nn.GELU()
-
-        self.w2 = RowParallelLinear(
-            hidden_dim,
-            dim,
-            bias=False,
-            input_is_parallel=True,  # Input already sharded from w1
-            return_bias=False,
-        )
-
-    def forward(self, x):
-        # x: [batch, seq, dim] (replicated on all GPUs)
-        # w1 outputs sharded [batch, seq, hidden_dim/N]
-        x = self.w1(x)
-        # act operates on sharded tensors (no communication)
-        x = self.act(x)
-        # w2 outputs full dim [batch, seq, dim] via all-reduce
-        x = self.w2(x)
-        return x
-```
-
-**Example (Attention - QKV-Out Pattern):**
-
-```python
-from vllm_omni.diffusion.attention.layer import Attention
-class YourModelAttention(nn.Module):
-    def __init__(self, dim: int, num_heads: int, num_kv_heads: int):
-        super().__init__()
-        self.head_dim = dim // num_heads
-
-        # Column parallel: QKV weight split by columns
-        # Each GPU gets num_heads/N heads
-        self.to_qkv = QKVParallelLinear(
-            hidden_size=dim,
-            head_size=self.head_dim,
-            total_num_heads=num_heads,
-            total_num_kv_heads=num_kv_heads,
-            bias=False,
-            return_bias=False,
-        )
-
-        # Row parallel: output weight split by rows
-        self.to_out = RowParallelLinear(
-            dim,
-            dim,
-            bias=False,
-            input_is_parallel=True,  # Input sharded from attention
-            return_bias=False,
-        )
-
-        self.attn = Attention(
-            num_heads=self.to_qkv.num_heads, # Each GPU gets num_heads/N heads
-            head_size=self.head_dim,
-            softmax_scale=1.0 / (self.head_dim**0.5),
-            causal=False,
-            num_kv_heads=self.to_qkv.num_kv_heads,
-        )
-
-    def forward(self, x):
-        # x: [batch, seq, dim] (replicated)
-        # to_qkv outputs sharded [batch, seq, (q+k+v) * head_dim/N]
-        qkv = self.to_qkv(x)
-        # Split into Q, K, V (each sharded on heads)
-        q, k, v = qkv.split([...], dim=-1)
-        # Attention computed independently on each GPU
-        out = self.attn(q, k, v)
-        # to_out all-reduces to full dim
-        out = self.to_out(out)
-        return out
-```
-
-**Key Points:**
-
- `ColumnParallelLinear` → `RowParallelLinear` is the standard pairing
- Set `input_is_parallel=True` on `RowParallelLinear` when input comes from `ColumnParallelLinear`
- Use `QKVParallelLinear` for attention projections (handles head replication automatically)
-
-### Step 3: Validate TP Constraints
-
-For correct TP operation, these dimensions **must be divisible** by `tensor_parallel_size`:
-
-| Dimension | Reason | Example Error |
-|-----------|--------|---------------|
-| `num_heads` | Heads sharded by QKVParallelLinear | `num_heads=30, tp=4` ❌ (30 % 4 ≠ 0) |
-| `num_kv_heads` | KV heads sharded by QKVParallelLinear | `num_kv_heads=30, tp=4` ❌ (30 % 4 ≠ 0) |
-
---
-
-## Testing
-
-After adding Tensor Parallel support, test with:
-
-```python
-from vllm_omni import Omni
-from vllm_omni.diffusion.data import DiffusionParallelConfig
-from vllm_omni.inputs.data import OmniDiffusionSamplingParams
-
-parallel_config = DiffusionParallelConfig(tensor_parallel_size=2)
-omni = Omni(model="your-model-name", parallel_config=parallel_config)
-
-output = omni.generate(
-    "a cup of coffee on the table",
-    OmniDiffusionSamplingParams(num_inference_steps=50),
-)
-```
-
-**Or via command line:**
-
-```bash
-cd examples/offline_inference/text_to_image
-python text_to_image.py \
-    --model Your-org/your-model \
-    --prompt "a cup of coffee on the table" \
-    --negative-prompt "ugly, unclear" \
-    --cfg-scale 4.0 \
-    --num-inference-steps 50 \
-    --output "tp_enabled.png" \
-    --tensor-parallel-size 2
-```
-
-**Verify:**
-
-1. Check the `e2e_time_ms` in the log for speedup
-2. Compare generated image quality with TP disabled
-3. Verify memory usage is reduced proportionally
-4. Record comparison results in your PR
-
---
-
-## Troubleshooting
-
-### Issue: TP not activating
-
-**Symptoms:** Model runs on single GPU, no memory savings or speedup.
-
-**Causes & Solutions:**
-
- **Still using `nn.Linear`:**
-
-**Problem:** Linear layers not replaced with parallel equivalents.
-
-**Solution:** Replace with parallel layers:
-```python
-# ❌ BAD
-self.proj = nn.Linear(dim, dim)
-
-# ✅ GOOD
-self.proj = RowParallelLinear(dim, dim, input_is_parallel=True)
-```
-
-### Issue: Dimension mismatch errors
-
-**Symptoms:** `RuntimeError: shape mismatch` during forward pass.
-
-**Causes & Solutions:**
-
- **Missing `input_is_parallel=True`:**
-
-**Problem:** RowParallelLinear expects sharded input but receives full tensor.
-
-**Solution:** Set `input_is_parallel=True` when input comes from ColumnParallelLinear:
-```python
-# ✅ GOOD: Correct pairing
-self.w1 = ColumnParallelLinear(dim, hidden_dim, return_bias=False,)
-self.w2 = RowParallelLinear(
-    hidden_dim,
-    dim,
-    input_is_parallel=True,  # Input sharded from w1
-    return_bias=False,
-)
-```
-
- **Incorrect split dimensions:**
-
-**Problem:** QKV split sizes don't match sharded dimensions.
-
-**Solution:** Use `self.to_qkv.num_heads` (local heads per GPU):
-```python
-# ❌ BAD: Uses total heads
-q_size = self.total_num_heads * self.head_dim
-
-# ✅ GOOD: Uses local heads
-q_size = self.to_qkv.num_heads * self.head_dim
-```
-
---
-
-## Reference Implementations
-
-Complete examples in the codebase:
-
-| Model | Path | Pattern | Notes |
-|-------|------|---------|-------|
-| **Z-Image** | `vllm_omni/diffusion/models/z_image/z_image_transformer.py` | Standard TP | Full implementation with validation |
-| **FLUX** | `vllm_omni/diffusion/models/flux/flux_transformer.py` | Dual-stream | Image + text streams |
-| **Qwen-Image** | `vllm_omni/diffusion/models/qwen_image/qwen_image_transformer.py` | Standard TP | With RoPE |
-| **TP Tests** | `tests/e2e/offline_inference/test_zimage_parallelism.py` | E2E testing | TP correctness and performance |
-| **Constraint Tests** | `tests/diffusion/models/z_image/test_zimage_tp_constraints.py` | Unit testing | Validation logic |
-
---
-
-## Summary
-
-Adding Tensor Parallel support to a transformer:
-
-1. ✅ **Identify linear layers** - Which layers should be sharded?
-2. ✅ **Replace with parallel layers** - Use QKVParallelLinear, ColumnParallelLinear, RowParallelLinear
-3. ✅ **Validate TP constraints** - Ensure dimensions divisible by TP size
-4. ✅ **Test** - Verify with `tensor_parallel_size=N`, check memory, speed, and quality
@@ -1,70 +0,0 @@
-# vLLM-Omni ComfyUI Integration
-
-vLLM-Omni offers a ComfyUI integration on top of its online serving API.
-It can send model inference requests to either a locally running vLLM-Omni service or a remote one.
-
-## Requirement
-
- Python 3.12 or above
- [ComfyUI installed](https://docs.comfy.org/installation/system_requirements)
- [vLLM-Omni installed](https://docs.vllm.ai/projects/vllm-omni/en/latest/getting_started/installation/) on either the same device or another device discoverable via the internet.
- No need to install additional packages apart from those already required by ComfyUI.
-
-!!! tip
-    If you run both ComfyUI and vLLM-Omni on the same device, you can create separate virtual environments and use different Python versions for them.
-
-
-## Installation
-
-Copy the `apps/ComfyUI-vLLM-Omni` folder to the `custom_nodes` subfolder of your ComfyUI installation. Your directory should look like `ComfyUI/custom_nodes/ComfyUI-vLLM-Omni`.
-
-If you are running ComfyUI during copying, you should restart ComfyUI to load this extension.
-
-!!! tip
-    You can use utility websites such as https://download-directory.github.io/ to download a subdirectory of a repo. Also checkout community discussions (e.g., https://stackoverflow.com/questions/7106012/download-a-single-folder-or-directory-from-a-github-repository) for more info.
-
-On the device and virtual environment you run ComfyUI, launch ComfyUI with
-```bash
-cd ComfyUI
-
-# The regular way
-python main.py
-
-# If you are mainly using this node, launch it faster with
-python main.py --cpu
-```
-
-On the device and virtual environment you run vLLM-Omni, start a model service with
-```bash
-vllm serve The_Model_ID_to_Serve --omni --port 8000
-```
-
-Check **ComfyUI's sidebar -> Node Library**. There should be a new folder named **vLLM-Omni**.
-If no, check your shell running the ComfyUI process. There may be some error messages before the line `Import times for custom nodes:` and the line `To see the GUI go to: http://127.0.0.1:8188`.
-
-## Quickstart
-
-This extension offers the following nodes based on the output modalities:
-
- **Generate Image** for text-to-image and image-to-image tasks
- **Multimodality Comprehension** for multimodality-to-text and multimodality-to-audio tasks
- **TTS** and **TTS Voice Clone** for TTS tasks
-
-This extension also offers example workflows (at **ComfyUI sidebar -> Templates -> vLLM-Omni**)
-
-!!! info
-    The node UI and feature designs are intended to match vLLM-Omni online serving interfaces. It cannot offer more than what the interfaces support.
-
-To build a simple workflow yourself,
-
- Drag a generation node onto the canvas.
- Depending on your need, grab built-in multimedia file loader nodes, such as **image->Load Image**, **image->video->Load Video**, **audio->Load Audio**
- Depending on your need, grab built-in multimedia file preview nodes, such as **image->Preview Image**, **image->video->Save Video**, **audio->Preview Audio**. For text output, you can install [ComfyUI-Custom-Scripts plugin](https://github.com/pythongosssss/ComfyUI-Custom-Scripts/) and grab its **utils->Show Text 🐍** node.
- If you want to tune sampling parameters, grab corresponding nodes from **vLLM-Omni-> Sampling Params**.
-    - For multi-stage models, you can connect multiple **AR Sampling Params** and **Diffusion Sampling Params** nodes to a **Multi-Stage Sampling Params List** node, and connect this node to the generation node.
-    - For some multi-stage models like BAGEL, [only one stage's sampling parameters are exposed and tunable via vLLM-Omni's online serving API](https://docs.vllm.ai/projects/vllm-omni/en/latest/user_guide/examples/online_serving/bagel/). Thus, these models are treated as single-stage ones. Please check the vLLM-Omni documentation on how to correctly set each model's sampling parameters.
-    - For multi-stage models where all stages are either autoregression or diffusion, you can also connect only a single Sampling Params node, indicating that this set of sampling parameters will be used for all stages.
-
-## Examples & Screenshots
-
-Please read the [ComfyUI integration's README](https://github.com/vllm-project/vllm-omni/tree/main/apps/ComfyUI-vLLM-Omni) for more info.
@@ -1,151 +0,0 @@
-# Custom Pipeline Extension Guide
-
-Transformer already support Custom Pipeline via
-https://github.com/huggingface/diffusers/blob/main/docs/source/en/using-diffusers/custom_pipeline_overview.md
-
-This guide demonstrates how to use the newly added features for extending vLLM-Omni's diffusion pipeline with custom functionality.
-
-## Overview
-
-Three main features enable custom pipeline extension:
-
-1. **`WorkerWrapperBase`**: A wrapper class that enables dynamic worker extension with custom functionality
-2. **`load_format`**: A parameter that controls how diffusion models are loaded, including support for custom pipelines
-3. **`CustomPipelineWorkerExtension`**: An extension class that enables pipeline re-initialization with custom implementations
-
-## Features
-
-### WorkerWrapperBase
-
-`WorkerWrapperBase` is a wrapper class that creates `DiffusionWorker` instances with optional extension support. It enables dynamic inheritance, allowing you to add custom methods and functionality to workers without modifying the base worker class.
-
-**Key capabilities:**
- Dynamic worker class extension via `worker_extension_cls`
- Support for custom pipeline initialization via `custom_pipeline_args`
- Method delegation to underlying worker
- Attribute access forwarding
-
-**Location:** `vllm_omni/diffusion/worker/diffusion_worker.py`
-
-### load_format Parameter
-
-The `load_format` parameter controls how diffusion models are loaded. It supports the following values:
-
- **`"default"`**: Standard model loading using the model registry (default behavior)
- **`"custom_pipeline"`**: Load a custom pipeline class specified by `custom_pipeline_name`
- **`"dummy"`**: Skip model loading (useful for testing or when pipeline will be initialized separately)
-
-**Location:** `vllm_omni/diffusion/model_loader/diffusers_loader.py`
-
-### CustomPipelineWorkerExtension
-
-`CustomPipelineWorkerExtension` is a mixin class that extends `DiffusionWorker` with the ability to re-initialize the pipeline with a custom implementation.
-
-**Key method:**
- `re_init_pipeline(custom_pipeline_args)`: Re-initializes the pipeline with custom arguments, properly cleaning up the old pipeline first
-
-**Location:** `vllm_omni/diffusion/worker/diffusion_worker.py`
-
-## Usage Example
-
-### Step 1: Create a Custom Pipeline
-
-Create a custom pipeline class that extends an existing pipeline. In this example, we extend `QwenImageEditPipeline` to add trajectory tracking:
-
-```python
-# custom_pipeline.py
-from vllm_omni.diffusion.data import DiffusionOutput, OmniDiffusionConfig
-from vllm_omni.diffusion.models.qwen_image.pipeline_qwen_image_edit import QwenImageEditPipeline
-import torch
-
-class CustomPipeline(QwenImageEditPipeline):
-    def __init__(self, *, od_config: OmniDiffusionConfig, prefix: str = ""):
-        super().__init__(od_config=od_config, prefix=prefix)
-
-    def forward(self, req, prompt=None, negative_prompt=None, **kwargs):
-        # Call parent's forward to get normal output
-        output = super().forward(req=req, prompt=prompt, negative_prompt=negative_prompt, **kwargs)
-
-        # Add custom trajectory data
-        actual_num_steps = req.sampling_params.num_inference_steps or kwargs.get('num_inference_steps', 50)
-        output.trajectory_timesteps = torch.linspace(1000, 0, actual_num_steps, dtype=torch.float32)
-        output.trajectory_latents = torch.randn(actual_num_steps, 1, 16, 64, 64, dtype=torch.float32)
-
-        return output
-```
-
-### Step 2: Use the Custom Pipeline with Omni
-
-Initialize the `Omni` engine with custom pipeline configuration:
-
-```python
-from vllm_omni.entrypoints.omni import Omni
-from vllm_omni.inputs.data import OmniDiffusionSamplingParams
-
-# Initialize with custom pipeline
-omni = Omni(
-    model="Qwen/Qwen-Image-Edit",
-    diffusion_load_format="dummy",  # Skip initial loading
-    custom_pipeline_args={
-        "pipeline_class": "custom_pipeline.CustomPipeline"
-    },
-)
-
-# Generate with the custom pipeline
-outputs = omni.generate(
-    ...
-)
-
-# Access custom trajectory data
-output = outputs[0].request_output[0]
-print(f"Trajectory timesteps shape: {output.metrics['trajectory_timesteps'].shape}")
-print(f"Trajectory latents shape: {output.latents.shape}")
-```
-
-### Step 3: Run the Example
-
-The example provided in this directory demonstrates the complete workflow:
-
-```bash
-cd examples/offline_inference/custom_pipeline/image_to_image
-
-# Run with custom pipeline
-python image_edit.py \
-    --model Qwen/Qwen-Image-Edit-2511 \
-    --image cherry_blossom.jpg \
-    --prompt "Let this mascot dance under the moon, surrounded by floating stars" \
-    --output output_image_edit.png \
-    --num_inference_steps 10
-```
-
-## Advanced Usage
-
-### Custom Worker Extension
-
-You can create custom worker extensions to add new methods beyond pipeline re-initialization:
-
-```python
-from typing import Any
-from vllm_omni.diffusion.worker.diffusion_worker import CustomPipelineWorkerExtension
-
-class MyCustomExtension(CustomPipelineWorkerExtension):
-    def custom_method(self):
-        """Your custom worker method."""
-        return "custom_result"
-
-    def another_method(self, data: Any):
-        """Another custom method."""
-        # Access worker internals via self
-        return self.model_runner.some_operation(data)
-
-omni = Omni(
-    model="Qwen/Qwen-Image-Edit",
-    diffusion_load_format="dummy",
-    custom_pipeline_args={
-        "pipeline_class": "custom_pipeline.CustomPipeline"
-    },
-    worker_extension_cls=MyCustomExtension,
-    # Note: worker_extension_cls is an internal parameter
-    # CustomPipelineWorkerExtension will automatically init pipeline when custom_pipeline_args is provided
-)
-```
@@ -5,5 +5,4 @@ vLLM-Omni supports the following hardware platforms:
 - [GPU](gpu.md)
    - [NVIDIA CUDA](gpu.md)
    - [AMD ROCm](gpu.md)
-    - [Intel XPU](gpu.md)
 - [NPU](npu.md)
@@ -18,10 +18,6 @@ vLLM-Omni is a Python library that supports the following GPU variants. The libr

    --8<-- "docs/getting_started/installation/gpu/rocm.inc.md:requirements"

-=== "Intel XPU"
-
-    --8<-- "docs/getting_started/installation/gpu/xpu.inc.md:requirements"
-
 ## Set up using Python

 ### Create a new Python environment
@@ -30,20 +26,15 @@ vLLM-Omni is a Python library that supports the following GPU variants. The libr

 ### Pre-built wheels

-Note: Pre-built wheels are currently only available for vLLM-Omni 0.11.0rc1, 0.12.0rc1, 0.14.0rc1, 0.14.0. For the latest version, please [build from source](https://docs.vllm.ai/projects/vllm-omni/en/latest/getting_started/installation/gpu/#build-wheel-from-source).
-
 === "NVIDIA CUDA"

    --8<-- "docs/getting_started/installation/gpu/cuda.inc.md:pre-built-wheels"

+
 === "AMD ROCm"

    --8<-- "docs/getting_started/installation/gpu/rocm.inc.md:pre-built-wheels"

-=== "Intel XPU"
-
-    --8<-- "docs/getting_started/installation/gpu/xpu.inc.md:pre-built-wheels"
-
 [](){ #build-from-source }

 ### Build wheel from source
@@ -56,10 +47,6 @@ Note: Pre-built wheels are currently only available for vLLM-Omni 0.11.0rc1, 0.1

    --8<-- "docs/getting_started/installation/gpu/rocm.inc.md:build-wheel-from-source"

-=== "Intel XPU"
-
-    --8<-- "docs/getting_started/installation/gpu/xpu.inc.md:build-wheel-from-source"
-
 ## Set up using Docker

 ### Pre-built images
@@ -72,16 +59,8 @@ Note: Pre-built wheels are currently only available for vLLM-Omni 0.11.0rc1, 0.1

    --8<-- "docs/getting_started/installation/gpu/rocm.inc.md:pre-built-images"

-=== "Intel XPU"
-
-    --8<-- "docs/getting_started/installation/gpu/xpu.inc.md:pre-built-images"
-
 ### Build your own docker image

 === "AMD ROCm"

    --8<-- "docs/getting_started/installation/gpu/rocm.inc.md:build-docker"
-
-=== "Intel XPU"
-
-    --8<-- "docs/getting_started/installation/gpu/xpu.inc.md:build-docker"
@@ -17,10 +17,12 @@ Therefore, it is recommended to install vLLM and vLLM-Omni with a **fresh new**
 # --8<-- [start:pre-built-wheels]

 #### Installation of vLLM
+Note: Pre-built wheels are currently only available for vLLM-Omni 0.11.0rc1 and 0.12.0rc1. For the latest version, please [build from source](https://docs.vllm.ai/projects/vllm-omni/en/latest/getting_started/installation/gpu/#build-wheel-from-source).
+

 vLLM-Omni is built based on vLLM. Please install it with command below.
 ```bash
-uv pip install vllm --torch-backend=auto
+uv pip install vllm==0.14.0 --torch-backend=auto
 ```

 #### Installation of vLLM-Omni
@@ -34,10 +36,10 @@ uv pip install vllm-omni
 # --8<-- [start:build-wheel-from-source]

 #### Installation of vLLM
-If you do not need to modify source code of vLLM, you can directly install the stable 0.16.0 release version of the library
+If you do not need to modify source code of vLLM, you can directly install the stable 0.14.0 release version of the library

 ```bash
-uv pip install vllm==0.16.0 --torch-backend=auto
+uv pip install vllm==0.14.0 --torch-backend=auto
 ```

 The release 0.14.0 of vLLM is based on PyTorch 2.9.0 which requires CUDA 12.9 environment.
@@ -56,11 +58,11 @@ If you want to check, modify or debug with source code of vLLM, install the libr
 ```bash
 git clone https://github.com/vllm-project/vllm.git
 cd vllm
-git checkout v0.16.0
+git checkout v0.14.0
 ```
 Set up environment variables to get pre-built wheels. If there are internet problems, just download the whl file manually. And set `VLLM_PRECOMPILED_WHEEL_LOCATION` as your local absolute path of whl file.
 ```bash
-export VLLM_PRECOMPILED_WHEEL_LOCATION=https://github.com/vllm-project/vllm/releases/download/v0.16.0/vllm-0.16.0-cp38-abi3-manylinux_2_31_x86_64.whl
+export VLLM_PRECOMPILED_WHEEL_LOCATION=https://github.com/vllm-project/vllm/releases/download/v0.14.0/vllm-0.14.0-cp38-abi3-manylinux_2_31_x86_64.whl
 ```
 Install vllm with command below (If you have no existing PyTorch).
 ```bash
@@ -9,60 +9,10 @@ vLLM-Omni current recommends the steps in under setup through Docker Images.

 # --8<-- [start:pre-built-wheels]

-#### Installation of vLLM
-
-vLLM-Omni is built based on vLLM. Please install it with command below.
-```bash
-uv pip install vllm==0.16.0+rocm700 --extra-index-url https://wheels.vllm.ai/rocm/0.16.0/rocm700
-```
-
-#### Installation of vLLM-Omni
-
-```bash
-# we need to add --no-build-isolation as the torch
-# is not obtained from pypi, we have to install using the
-# torch installed in our environment
-uv pip install vllm-omni
-
-# Optional if want to run Qwen3 TTS
-uv pip uninstall onnxruntime # should be removed before we can install onnxruntime-rocm
-uv pip install onnxruntime-rocm sox
-```
-
 # --8<-- [end:pre-built-wheels]

 # --8<-- [start:build-wheel-from-source]

-#### Installation of vLLM
-If you do not need to modify source code of vLLM, you can directly install the stable 0.16.0 release version of the library
-
-```bash
-uv pip install vllm==0.16.0+rocm700 --extra-index-url https://wheels.vllm.ai/rocm/0.16.0/rocm700
-```
-
-The release 0.16.0 of vLLM requires ROCm 7.0 environment.
-
-#### Installation of vLLM-Omni
-Since vllm-omni is rapidly evolving, it's recommended to install it from source
-```bash
-git clone https://github.com/vllm-project/vllm-omni.git
-cd vllm-omni
-VLLM_OMNI_TARGET_DEVICE=rocm uv pip install -e .
-# OR
-uv pip install -e . --no-build-isolation
-```
-
-<details><summary>(Optional) Installation of vLLM from source</summary>
-If you want to check, modify or debug with source code of vLLM, install the library from source with the following instructions:
-
-```bash
-git clone https://github.com/vllm-project/vllm.git
-cd vllm
-git checkout v0.16.0
-python3 -m pip install -r requirements/rocm.txt
-python3 setup.py develop
-```
-
 # --8<-- [end:build-wheel-from-source]

 # --8<-- [start:build-docker]
@@ -73,6 +23,15 @@ python3 setup.py develop
 DOCKER_BUILDKIT=1 docker build -f docker/Dockerfile.rocm -t vllm-omni-rocm .
 ```

+If you want to specify which GPU Arch to build for to cutdown build time:
+
+```bash
+DOCKER_BUILDKIT=1 docker build \
+  -f docker/Dockerfile.rocm \
+  --build-arg PYTORCH_ROCM_ARCH="gfx942;gfx950" \
+  -t vllm-omni-rocm .
+```
+
 #### Launch the docker image

 ##### Launch with OpenAI API Server
@@ -1,48 +0,0 @@
-# --8<-- [start:requirements]
-
- GPU: Validated on Intel® Arc™ B-Series.
-
-# --8<-- [end:requirements]
-# --8<-- [start:set-up-using-python]
-
-vLLM-Omni currently recommends using the Docker image setup steps below.
-
-# --8<-- [start:pre-built-wheels]
-
-# --8<-- [end:pre-built-wheels]
-
-# --8<-- [start:build-wheel-from-source]
-
-# --8<-- [end:build-wheel-from-source]
-
-# --8<-- [start:build-docker]
-
-#### Build docker image
-
-```bash
-DOCKER_BUILDKIT=1 docker build -f docker/Dockerfile.xpu -t vllm-omni-xpu --shm-size=4g .
-```
-
-#### Launch the docker image
-
-##### Launch with OpenAI API Server
-
-```
-docker run -it -d --shm-size 10g \
-  --name {container_name} \
-  --net=host \
-  --ipc=host \
-  --privileged \
-  -v /dev/dri/by-path:/dev/dri/by-path \
-  --device /dev/dri:/dev/dri \
-  -v ~/.cache/huggingface:/root/.cache/huggingface \
-  --env "HF_TOKEN=$HF_TOKEN" \
-  vllm-omni-xpu \
-  --model Qwen/Qwen2.5-Omni-3B --port 8091
-```
-
-# --8<-- [end:build-docker]
-
-# --8<-- [start:pre-built-images]
-
-# --8<-- [end:pre-built-images]
@@ -16,18 +16,8 @@ vLLM-Omni supports NPU through the vLLM Ascend Plugin (vllm-ascend). This is a c

 ## Installation

-### Set up using Docker
+### Recommended

 === "NPU"

-    --8<-- "docs/getting_started/installation/npu/npu.inc.md:pre-built-images"
-
-### Build wheel from source
-
-=== "NPU release"
-
-    --8<-- "docs/getting_started/installation/npu/npu.inc.md:installation-release"
-
-=== "NPU from main"
-
-    --8<-- "docs/getting_started/installation/npu/npu.inc.md:installation-main"
+    --8<-- "docs/getting_started/installation/npu/npu.inc.md:installation"
@@ -3,24 +3,25 @@
 For detailed hardware and software requirements, please refer to the [vllm-ascend installation documentation](https://docs.vllm.ai/projects/ascend/en/latest/installation.html).

 # --8<-- [end:requirements]
-# --8<-- [start:installation-release]
+# --8<-- [start:installation]

 The recommended way to use vLLM-Omni on NPU is through the vllm-ascend pre-built Docker images:

 ```bash
+# Update DEVICE according to your NPUs (/dev/davinci[0-7])
+export DEVICE0=/dev/davinci0
+export DEVICE1=/dev/davinci1
 # Update the vllm-ascend image
 # Atlas A2:
-# export IMAGE=quay.io/ascend/vllm-ascend:v0.14.0rc1
+# export IMAGE=quay.io/ascend/vllm-ascend:v0.14.0
 # Atlas A3:
-# export IMAGE=quay.io/ascend/vllm-ascend:v0.14.0rc1-a3
-export IMAGE=quay.io/ascend/vllm-ascend:v0.14.0rc1
+# export IMAGE=quay.io/ascend/vllm-ascend:v0.14.0-a3
+export IMAGE=quay.io/ascend/vllm-ascend:v0.14.0
 docker run --rm \
    --name vllm-omni-npu \
    --shm-size=1g \
-    --device /dev/davinci0 \
-    --device /dev/davinci1 \
-    --device /dev/davinci2 \
-    --device /dev/davinci3 \
+    --device $DEVICE0 \
+    --device $DEVICE1 \
    --device /dev/davinci_manager \
    --device /dev/devmm_svm \
    --device /dev/hisi_hdc \
@@ -33,109 +34,26 @@ docker run --rm \
    -p 8000:8000 \
    -it $IMAGE bash

-# Because vllm-ascend will release v0.16.0rc1 after vllm-omni 0.16.0,
-# we have to pin vllm-ascend at the current commit.
-cd /vllm-workspace/vllm-ascend
-git checkout e2175d9c7e62b437391dfee996b1375674ba7c18
-pip install -v -e .
+# Install the missing dependency of mooncake in the origin image.
+apt update
+apt install libjemalloc2
+echo "export LD_PRELOAD=/usr/lib/$(uname -m)-linux-gnu/libjemalloc.so.2:$LD_PRELOAD" >> ~/.bashrc
+source ~/.bashrc

 # Inside the container, install vLLM-Omni from source
 cd /vllm-workspace
-git clone -b v0.16.0 https://github.com/vllm-project/vllm-omni.git
-
+git clone -b v0.14.0 https://github.com/vllm-project/vllm-omni.git
 cd vllm-omni
 pip install -v -e .
 export VLLM_WORKER_MULTIPROC_METHOD=spawn
+
+# (Optional) Disable mooncake for stable capability
+mv /usr/local/Ascend/ascend-toolkit/latest/python/site-packages/mooncake \
+   /usr/local/Ascend/ascend-toolkit/latest/python/site-packages/mooncake.disabled
 ```

 The default workdir is `/workspace`, with vLLM, vLLM-Ascend and vLLM-Omni code placed in `/vllm-workspace` installed in development mode.

 For other installation methods (pip installation, building from source, custom Docker builds), please refer to the [vllm-ascend installation guide](https://docs.vllm.ai/projects/ascend/en/latest/installation.html).

-We are keeping [issue #886](https://github.com/vllm-project/vllm-omni/issues/886) up to date with the aligned versions of vLLM, vLLM-Ascend, and vLLM-Omni, and also outlining the Q1 roadmap there.
-
-# --8<-- [end:installation-release]
-
-# --8<-- [start:installation-main]
-
-You can also build vLLM-Omni from the latest main branch if you want to use the latest features or bug fixes. (But sometimes it will break for a while. You can check [issue #886](https://github.com/vllm-project/vllm-omni/issues/886) for the status of the latest commit of vLLM-Omni main branch on NPU.)
-
-```bash
-# Pin vLLM version to 0.16.0
-cd /vllm-workspace/vllm
-git pull origin main
-git fetch origin --tags
-git checkout v0.16.0
-VLLM_TARGET_DEVICE=empty pip install -v -e .
-
-# Because vllm-ascend has not yet entered continuous development and has not been officially released, we need to pin it to a specific commit. Please note that this commit may change over time.
-cd ../vllm-ascend
-git pull origin main
-git fetch origin --tags
-git checkout e2175d9c7e62b437391dfee996b1375674ba7c18
-pip install -v -e .
-
-# Install vLLM-Omni from the latest main branch
-cd ../vllm-omni
-git clone https://github.com/vllm-project/vllm-omni.git
-pip install -v -e . --no-build-isolation
-# or VLLM_OMNI_TARGET_DEVICE=npu pip install -v -e .
-export VLLM_WORKER_MULTIPROC_METHOD=spawn
-```
-
-# --8<-- [end:installation-main]
-
-# --8<-- [start:pre-built-images]
-
-vLLM-Omni offers Docker images for Ascend NPU deployment. You can just pull the **prebuilt image** from the image repository [ascend/vllm-omni](https://quay.io/repository/ascend/vllm-omni?tab=tags) and run it with bash.
-
-Supported images as following.
-
-| image name | Hardware | OS |
-|-|-|-|
-| image-tag | Atlas A2 | Ubuntu |
-| image-tag-a3 | Atlas A3 | Ubuntu |
-
-Here's an example deployment command that has been verified on 4 x NPUs:
-
-```bash
-# Atlas A2:
-# export IMAGE=quay.io/ascend/vllm-omni:v0.16.0
-# Atlas A3:
-# export IMAGE=quay.io/ascend/vllm-omni:v0.16.0-a3
-export IMAGE=quay.io/ascend/vllm-omni:v0.16.0
-docker run --rm \
-    --name vllm-omni-npu \
-    --shm-size=1g \
-    --device /dev/davinci0 \
-    --device /dev/davinci1 \
-    --device /dev/davinci2 \
-    --device /dev/davinci3 \
-    --device /dev/davinci_manager \
-    --device /dev/devmm_svm \
-    --device /dev/hisi_hdc \
-    -v /usr/local/dcmi:/usr/local/dcmi \
-    -v /usr/local/bin/npu-smi:/usr/local/bin/npu-smi \
-    -v /usr/local/Ascend/driver/lib64/:/usr/local/Ascend/driver/lib64/ \
-    -v /usr/local/Ascend/driver/version.info:/usr/local/Ascend/driver/version.info \
-    -v /etc/ascend_install.info:/etc/ascend_install.info \
-    -v /root/.cache:/root/.cache \
-    -p 8000:8000 \
-    -it $IMAGE bash
-```
-
-!!! tip
-    You can use this docker image to serve models the same way you would with in vLLM! To do so, make sure you overwrite the default entrypoint (`vllm serve --omni`) which works only for models supported in the vLLM-Omni project.
-
-Or build IMAGE from **source code**:
-
-```bash
-git clone https://github.com/vllm-project/vllm-omni.git
-cd vllm-omni
-# A2
-docker build -t vllm-omni-dev-image:latest -f ./docker/Dockerfile.npu .
-# A3
-# docker build -t vllm-omni-dev-image:latest -f ./docker/Dockerfile.npu.a3 .
-```
-
-# --8<-- [end:pre-built-images]
+# --8<-- [end:installation]
@@ -19,10 +19,10 @@ uv venv --python 3.12 --seed
 source .venv/bin/activate

 # On CUDA
-uv pip install vllm==0.16.0 --torch-backend=auto
+uv pip install vllm==0.14.0 --torch-backend=auto

 # On ROCm
-uv pip install vllm==0.16.0 --extra-index-url https://wheels.vllm.ai/rocm/0.16.0/rocm700
+uv pip install vllm==0.14.0 --extra-index-url https://wheels.vllm.ai/rocm/0.14.0/rocm700

 git clone https://github.com/vllm-project/vllm-omni.git
 cd vllm-omni
@@ -46,46 +46,6 @@ if __name__ == "__main__":
    images[0].save("coffee.png")
 ```

-You can pass a list of prompts and wait for them to process altogether, shown below.
-
-!!! info
-
-    However, it is not currently recommended to do so
-    because not all models support batch inference,
-    and batch requesting mostly does not provide significant performance improvement (despite the impression that it does).
-    This feature is primarily for the sake of interface compatibility with vLLM and to allow for future improvements.
-
-```python
-from vllm_omni.entrypoints.omni import Omni
-
-if __name__ == "__main__":
-    omni = Omni(
-        model="Tongyi-MAI/Z-Image-Turbo",
-        # stage_configs_path="./stage-config.yaml",  # See below
-    )
-    prompts = [
-        "a cup of coffee on a table",
-        "a toy dinosaur on a sandy beach",
-        "a fox waking up in bed and yawning",
-    ]
-    omni_outputs = omni.generate(prompts)
-    for i_prompt, prompt_output in enumerate(omni_outputs):
-        this_request_output = prompt_output.request_output[0]
-        this_images = this_request_output.images
-        for i_image, image in enumerate(this_images):
-            image.save(f"p{i_prompt}-img{i_image}.jpg")
-            print("saved to", f"p{i_prompt}-img{i_image}.jpg")
-            # saved to p0-img0.jpg
-            # saved to p1-img0.jpg
-            # saved to p2-img0.jpg
-```
-
-!!! info
-
-    For diffusion pipelines, the stage config field `stage_args.[].runtime.max_batch_size` is 1 by default, and the input
-    list is sliced into single-item requests before feeding into the diffusion pipeline. For models that do internally support
-    batched inputs, you can [modify this configuration](../configuration/stage_configs.md) to let the model accept a longer batch of prompts.
-
 For more usages, please refer to [offline inference](../user_guide/examples/offline_inference/qwen2_5_omni.md)

 ## Online Serving with OpenAI-Completions API
@@ -1,258 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
-
-import ast
-import logging
-import re
-import sys
-from argparse import SUPPRESS, Action, ArgumentParser, HelpFormatter, _ArgumentGroup
-from collections.abc import Iterable
-from importlib.machinery import ModuleSpec
-from pathlib import Path
-from typing import Literal
-from unittest.mock import MagicMock
-
-from pydantic_core import core_schema
-
-
-class _FlexibleArgumentParser(ArgumentParser):
-    """Fallback parser for docs when vllm is unavailable.
-
-    Accepts the 'deprecated' kwarg used by vllm CLI and emits warnings
-    if a deprecated argument is actually provided.
-    """
-
-    _deprecated: set[Action] = set()
-    _deprecated_warned: set[str] = set()
-
-    if sys.version_info < (3, 13):
-
-        def parse_known_args(self, args=None, namespace=None):
-            namespace, args = super().parse_known_args(args, namespace)
-            for action in _FlexibleArgumentParser._deprecated:
-                if (
-                    hasattr(namespace, dest := action.dest)
-                    and getattr(namespace, dest) != action.default
-                    and dest not in _FlexibleArgumentParser._deprecated_warned
-                ):
-                    _FlexibleArgumentParser._deprecated_warned.add(dest)
-                    logger.warning("argument '%s' is deprecated", dest)
-            return namespace, args
-
-    def add_argument(self, *args, **kwargs):
-        deprecated = kwargs.pop("deprecated", False)
-        action = super().add_argument(*args, **kwargs)
-        if deprecated:
-            _FlexibleArgumentParser._deprecated.add(action)
-        return action
-
-    class _FlexibleArgumentGroup(_ArgumentGroup):
-        def add_argument(self, *args, **kwargs):
-            deprecated = kwargs.pop("deprecated", False)
-            action = super().add_argument(*args, **kwargs)
-            if deprecated:
-                _FlexibleArgumentParser._deprecated.add(action)
-            return action
-
-    def add_argument_group(self, *args, **kwargs):
-        group = self._FlexibleArgumentGroup(self, *args, **kwargs)
-        self._action_groups.append(group)
-        return group
-
-
-logger = logging.getLogger("mkdocs")
-
-# Define root and doc output directories
-ROOT_DIR = Path(__file__).parent.parent.parent.parent
-ARGPARSE_DOC_DIR = ROOT_DIR / "docs/generated/argparse_omni"
-
-# Ensure the repo root is in sys.path for dynamic imports
-sys.path.insert(0, str(ROOT_DIR))
-
-
-class PydanticMagicMock(MagicMock):
-    """`MagicMock` that's able to generate pydantic-core schemas and avoids _mock_name=None errors."""
-
-    def __init__(self, *args, **kwargs):
-        name = kwargs.pop("name", None)
-        super().__init__(*args, **kwargs)
-        self.__spec__ = ModuleSpec(name, None)
-        self._mock_name = name or "mock"
-
-    def __get_pydantic_core_schema__(self, source_type, handler):
-        return core_schema.any_schema()
-
-
-# --- Static extraction for CLI argument docs ---
-
-
-def extract_omni_serve_subparser_init():
-    """
-    Statically parse vllm_omni/entrypoints/cli/serve.py to extract the subparser_init method
-    and return a callable that adds arguments to a parser. This avoids import and mock issues.
-    """
-    serve_path = ROOT_DIR / "vllm_omni" / "entrypoints" / "cli" / "serve.py"
-    with open(serve_path, encoding="utf-8") as f:
-        source = f.read()
-    tree = ast.parse(source, filename=str(serve_path))
-    # Find class OmniServeCommand
-    for node in tree.body:
-        if isinstance(node, ast.ClassDef) and node.name == "OmniServeCommand":
-            for item in node.body:
-                if isinstance(item, ast.FunctionDef) and item.name == "subparser_init":
-                    # We'll exec this function body in a dummy context
-                    func_code = ast.Module(body=[item], type_ignores=[])
-                    code = compile(func_code, filename=str(serve_path), mode="exec")
-                    # Prepare dummy context
-                    local_vars = {}
-
-                    # Provide a dummy subparsers with add_parser returning our parser
-                    class DummySubparsers:
-                        def add_parser(self, name, **kwargs):
-                            return _FlexibleArgumentParser(prog=name)
-
-                    dummy_subparsers = DummySubparsers()
-                    # Provide globals for exec
-                    # Extract DESCRIPTION from the source file (assume it's a triple-quoted string at the top)
-                    m = re.search(r'DESCRIPTION\s*=\s*([ru]?""".*?""")', source, re.DOTALL)
-                    DESCRIPTION = m.group(1)[3:-3] if m else ""
-                    exec_globals = {
-                        "_FlexibleArgumentParser": _FlexibleArgumentParser,
-                        "FlexibleArgumentParser": _FlexibleArgumentParser,
-                        "make_arg_parser": lambda parser: parser,  # no-op for doc
-                        "VLLM_SUBCMD_PARSER_EPILOG": "",
-                        "logger": logger,
-                        "DummySubparsers": DummySubparsers,
-                        "argparse": __import__("argparse"),
-                        "json": __import__("json"),
-                        "DESCRIPTION": DESCRIPTION,
-                    }
-                    exec(code, exec_globals, local_vars)
-                    # Get the function
-                    subparser_init = local_vars["subparser_init"]
-
-                    # Return a callable that mimics OmniServeCommand().subparser_init
-                    def parser_factory():
-                        class DummySelf:
-                            name = "serve"
-
-                        return subparser_init(DummySelf(), dummy_subparsers)
-
-                    return parser_factory
-    raise RuntimeError("Could not statically extract OmniServeCommand.subparser_init")
-
-
-OmniServeCommand = type("OmniServeCommand", (), {"subparser_init": staticmethod(extract_omni_serve_subparser_init())})
-
-
-class MarkdownFormatter(HelpFormatter):
-    """Custom formatter that generates markdown for argument groups."""
-
-    def __init__(self, prog: str, starting_heading_level: int = 3):
-        super().__init__(prog, max_help_position=sys.maxsize, width=sys.maxsize)
-
-        self._section_heading_prefix = "#" * starting_heading_level
-        self._argument_heading_prefix = "#" * (starting_heading_level + 1)
-        self._markdown_output = []
-
-    def start_section(self, heading: str):
-        if heading not in {"positional arguments", "options"}:
-            heading_md = f"\n{self._section_heading_prefix} {heading}\n\n"
-            self._markdown_output.append(heading_md)
-
-    def end_section(self):
-        pass
-
-    def add_text(self, text: str):
-        if text:
-            self._markdown_output.append(f"{text.strip()}\n\n")
-
-    def add_usage(self, usage, actions, groups, prefix=None):
-        pass
-
-    def add_arguments(self, actions: Iterable[Action]):
-        for action in actions:
-            if len(action.option_strings) == 0 or "--help" in action.option_strings:
-                continue
-
-            option_strings = f"`{'`, `'.join(action.option_strings)}`"
-            heading_md = f"{self._argument_heading_prefix} {option_strings}\n\n"
-            self._markdown_output.append(heading_md)
-
-            if choices := action.choices:
-                choices = f"`{'`, `'.join(str(c) for c in choices)}`"
-                self._markdown_output.append(f"Possible choices: {choices}\n\n")
-            elif (metavar := action.metavar) and isinstance(metavar, (list, tuple)):
-                metavar = f"`{'`, `'.join(str(m) for m in metavar)}`"
-                self._markdown_output.append(f"Possible choices: {metavar}\n\n")
-
-            if action.help:
-                self._markdown_output.append(f"{action.help}\n\n")
-
-            if (default := action.default) != SUPPRESS:
-                # Make empty string defaults visible
-                if default == "":
-                    default = '""'
-                self._markdown_output.append(f"Default: `{default}`\n\n")
-
-    def format_help(self):
-        """Return the formatted help as markdown."""
-        return "".join(self._markdown_output)
-
-
-# Function to create parser using subparser_init style CLI class
-
-
-def create_parser_subparser_init(subcmd_class):
-    """
-    Create an argparse parser using subparser_init style CLI class, with MarkdownFormatter.
-    Compatible with both AST-extracted static parser_factory and real class instance method.
-    """
-
-    class DummySubparsers:
-        def add_parser(self, name, **kwargs):
-            return _FlexibleArgumentParser(prog=name)
-
-    dummy_subparsers = DummySubparsers()
-    # If subparser_init is a zero-arg static function (AST-extracted), call it directly
-    subparser_init = getattr(subcmd_class, "subparser_init", None)
-    if subparser_init is not None:
-        import inspect
-
-        sig = inspect.signature(subparser_init)
-        if len(sig.parameters) == 0:
-            parser = subparser_init()
-        else:
-            parser = subcmd_class().subparser_init(dummy_subparsers)
-    else:
-        parser = subcmd_class().subparser_init(dummy_subparsers)
-    parser.formatter_class = MarkdownFormatter
-    return parser
-
-
-def on_startup(command: Literal["build", "gh-deploy", "serve"], dirty: bool):
-    """
-    Entry point for doc generation. Builds doc directory and outputs markdown for each CLI command.
-    """
-    logger.info("Generating vllm-omni argparse documentation")
-    logger.debug("Root directory: %s", ROOT_DIR.resolve())
-    logger.debug("Output directory: %s", ARGPARSE_DOC_DIR.resolve())
-    if not ARGPARSE_DOC_DIR.exists():
-        ARGPARSE_DOC_DIR.mkdir(parents=True)
-
-    # Register all CLI parsers; you can easily add more commands here
-    parsers = {
-        "omni_serve": create_parser_subparser_init(OmniServeCommand),
-        # "another_cmd": create_parser_subparser_init(AnotherCommandClass),
-    }
-
-    for stem, parser in parsers.items():
-        doc_path = ARGPARSE_DOC_DIR / f"{stem}.inc.md"
-        with open(doc_path, "w", encoding="utf-8") as f:
-            f.write(super(type(parser), parser).format_help())
-        logger.info("Argparse generated: %s", doc_path.relative_to(ROOT_DIR))
-
-
-if __name__ == "__main__":
-    logging.basicConfig(level=logging.INFO)
-    on_startup("build", False)
@@ -20,13 +20,11 @@ th {
 | `Qwen3OmniMoeForConditionalGeneration` | Qwen3-Omni | `Qwen/Qwen3-Omni-30B-A3B-Instruct` |
 | `Qwen2_5OmniForConditionalGeneration` | Qwen2.5-Omni | `Qwen/Qwen2.5-Omni-7B`, `Qwen/Qwen2.5-Omni-3B` |
 | `BagelForConditionalGeneration` | BAGEL (DiT-only) | `ByteDance-Seed/BAGEL-7B-MoT` |
-| `HunyuanImage3ForCausalMM` | HunyuanImage3.0 (DiT-only) | `tencent/HunyuanImage-3.0`, `tencent/HunyuanImage-3.0-Instruct` |
 | `QwenImagePipeline` | Qwen-Image | `Qwen/Qwen-Image` |
 | `QwenImagePipeline` | Qwen-Image-2512 | `Qwen/Qwen-Image-2512` |
 | `QwenImageEditPipeline` | Qwen-Image-Edit | `Qwen/Qwen-Image-Edit` |
 | `QwenImageEditPlusPipeline` | Qwen-Image-Edit-2509 | `Qwen/Qwen-Image-Edit-2509` |
 | `QwenImageLayeredPipeline` | Qwen-Image-Layered | `Qwen/Qwen-Image-Layered` |
-| `GlmImagePipeline` | GLM-Image | `zai-org/GLM-Image` |
 |`ZImagePipeline` | Z-Image | `Tongyi-MAI/Z-Image-Turbo` |
 | `WanPipeline` | Wan2.2-T2V, Wan2.2-TI2V | `Wan-AI/Wan2.2-T2V-A14B-Diffusers`, `Wan-AI/Wan2.2-TI2V-5B-Diffusers` |
 | `WanImageToVideoPipeline` | Wan2.2-I2V | `Wan-AI/Wan2.2-I2V-A14B-Diffusers` |
@@ -35,15 +33,10 @@ th {
 |`LongCatImageEditPipeline` | LongCat-Image-Edit | `meituan-longcat/LongCat-Image-Edit` |
 |`StableDiffusion3Pipeline` | Stable-Diffusion-3 | `stabilityai/stable-diffusion-3.5-medium` |
 |`Flux2KleinPipeline` | FLUX.2-klein | `black-forest-labs/FLUX.2-klein-4B`, `black-forest-labs/FLUX.2-klein-9B` |
-|`FluxPipeline` | FLUX.1-dev | `black-forest-labs/FLUX.1-dev` |
-|`OmniGen2Pipeline` | OmniGen2 | `OmniGen2/OmniGen2` |
 |`StableAudioPipeline` | Stable-Audio-Open | `stabilityai/stable-audio-open-1.0` |
 |`Qwen3TTSForConditionalGeneration` | Qwen3-TTS-12Hz-1.7B-CustomVoice | `Qwen/Qwen3-TTS-12Hz-1.7B-CustomVoice` |
 |`Qwen3TTSForConditionalGeneration` | Qwen3-TTS-12Hz-1.7B-VoiceDesign | `Qwen/Qwen3-TTS-12Hz-1.7B-VoiceDesign` |
 |`Qwen3TTSForConditionalGeneration` | Qwen3-TTS-12Hz-1.7B-Base | `Qwen/Qwen3-TTS-12Hz-0.6B-Base` |
-|`GlmImageForConditionalGeneration` | GLM-Image | `zai-org/GLM-Image` |
-|`NextStep11Pipeline` | NextStep-1.1 | `stepfun-ai/NextStep-1.1` |
-|`MiMoAudioForConditionalGeneration` | MiMo-Audio-7B-Instruct | `XiaomiMiMo/MiMo-Audio-7B-Instruct` |


 ## List of Supported Models for NPU
@@ -57,7 +50,6 @@ th {

 | Architecture | Models | Example HF Models |
 |--------------|--------|-------------------|
-| `Qwen3OmniMoeForConditionalGeneration` | Qwen3-Omni | `Qwen/Qwen3-Omni-30B-A3B-Instruct` |
 | `Qwen2_5OmniForConditionalGeneration` | Qwen2.5-Omni | `Qwen/Qwen2.5-Omni-7B`, `Qwen/Qwen2.5-Omni-3B`|
 | `QwenImagePipeline` | Qwen-Image | `Qwen/Qwen-Image` |
 | `QwenImagePipeline` | Qwen-Image-2512 | `Qwen/Qwen-Image-2512` |
@@ -66,11 +58,3 @@ th {
 | `QwenImageLayeredPipeline` | Qwen-Image-Layered | `Qwen/Qwen-Image-Layered` |
 | `QwenImageEditPlusPipeline` | Qwen-Image-Edit-2511 | `Qwen/Qwen-Image-Edit-2511` |
 |`ZImagePipeline` | Z-Image | `Tongyi-MAI/Z-Image-Turbo` |
-| `WanPipeline` | Wan2.2-T2V, Wan2.2-TI2V | `Wan-AI/Wan2.2-T2V-A14B-Diffusers`, `Wan-AI/Wan2.2-TI2V-5B-Diffusers` |
-| `WanImageToVideoPipeline` | Wan2.2-I2V | `Wan-AI/Wan2.2-I2V-A14B-Diffusers` |
-|`LongcatImagePipeline` | LongCat-Image | `meituan-longcat/LongCat-Image` |
-|`LongCatImageEditPipeline` | LongCat-Image-Edit | `meituan-longcat/LongCat-Image-Edit` |
-|`Flux2KleinPipeline` | FLUX.2-klein | `black-forest-labs/FLUX.2-klein-4B`, `black-forest-labs/FLUX.2-klein-9B` |
-|`Qwen3TTSForConditionalGeneration` | Qwen3-TTS-12Hz-1.7B-CustomVoice | `Qwen/Qwen3-TTS-12Hz-1.7B-CustomVoice` |
-|`Qwen3TTSForConditionalGeneration` | Qwen3-TTS-12Hz-1.7B-VoiceDesign | `Qwen/Qwen3-TTS-12Hz-1.7B-VoiceDesign` |
-|`Qwen3TTSForConditionalGeneration` | Qwen3-TTS-12Hz-1.7B-Base | `Qwen/Qwen3-TTS-12Hz-0.6B-Base` |
@@ -1,255 +0,0 @@
-# VLLM-Omni PR Reviewer
-
-## Overview
-
-The VLLM-Omni PR Reviewer is an automated code review bot powered by GLM-4.7 AI model. It helps maintain code quality by providing intelligent feedback on pull requests.
-
-## Features
-
- **Intelligent Code Analysis**: Leverages GLM-4.7 for understanding code context and providing meaningful feedback
- **Comprehensive Reviews**: Covers code quality, architecture, security, testing, and documentation
- **Structured Output**: Provides well-formatted reviews with clear sections and actionable suggestions
- **Rate Limiting**: Built-in cooldown mechanism to prevent excessive API usage
- **Retry Logic**: Automatic retries with exponential backoff for transient API failures
- **Defensive Parsing**: Robust validation of API responses to handle malformed data
- **Cost Control**: Only repository members/collaborators/owners can trigger reviews
-
-## How to Use
-
-### Triggering a Review
-
-To trigger an automated PR review, mention the bot in a PR comment:
-
-```
-@vllm-omni-reviewer please review
-```
-
-Or include in your PR description:
-
-```
-@vllm-omni-reviewer
-```
-
-The bot will automatically review your changes and post a detailed comment.
-
-## What Gets Reviewed
-
- **vLLM Architecture Compatibility**: Ensures changes align with vLLM's design patterns
- **Multi-modal Integration**: Reviews audio, vision, and text processing implementations
- **Performance Implications**: Analyzes impact on inference latency and throughput
- **Code Quality**: Checks Python best practices, type hints, and documentation
- **Security Considerations**: Identifies potential security vulnerabilities
- **Testing Coverage**: Recommends additional test cases when needed
-
-## Review Output
-
-The bot posts a structured review comment with:
-
- **Overview**: Brief summary of the PR's purpose
- **Critical Issues (Must Fix)**: Blocking issues that need to be addressed
- **Important Issues (Should Fix)**: Significant concerns that should be resolved
- **Minor Issues & Suggestions**: Small improvements and optional suggestions
- **Positive Aspects**: Highlights well-implemented features
- **Performance Considerations**: Analysis of performance impact
- **Testing Recommendations**: Suggestions for additional tests
- **Overall Assessment**: Final recommendation (Approve/Request Changes/Needs Major Work)
-
-## Rate Limiting and Cooldown
-
-The bot includes a cooldown mechanism to prevent excessive API usage:
-
- **Default cooldown**: 5 minutes between reviews per PR
- **Configurable**: Can be adjusted via `PR_REVIEWER_COOLDOWN_MINUTES` environment variable
- **Smart detection**: Checks for previous bot comments before starting a review
-
-If you trigger a review within the cooldown period, the bot will log a message and skip the review.
-
-## Architecture
-
-```
-┌─────────────────┐
-│  PR Comment     │
-│  @vllm-omni-    │
-│  reviewer       │
-└────────┬────────┘
-         │
-         ▼
-┌─────────────────────────────────┐
-│  GitHub Actions Workflow        │
-│  (.github/workflows/            │
-│   pr-reviewer.yml)              │
-│                                 │
-│  - Python 3.11                  │
-│  - requests==2.31.0             │
-│  - pyyaml==6.0.1                │
-└────────┬────────────────────────┘
-         │
-         ▼
-┌─────────────────────────────────┐
-│  PR Reviewer Script             │
-│  (.github/scripts/              │
-│   pr_reviewer.py)               │
-│                                 │
-│  1. Check cooldown              │
-│  2. Fetch PR details & diff     │
-│  3. Build review prompt         │
-│  4. Call GLM-4.7 API            │
-│     (with retry logic)          │
-│  5. Validate response           │
-│  6. Post review comment         │
-└────────┬────────────────────────┘
-         │
-         ▼
-┌─────────────────────────────────┐
-│  GLM-4.7 API                    │
-│  (open.bigmodel.cn)             │
-└─────────────────────────────────┘
-```
-
-1. **GitHub Actions Workflow** (`.github/workflows/pr-reviewer.yml`): Triggers on @mention
-2. **Python Script** (`.github/scripts/pr_reviewer.py`): Fetches PR data and calls GLM-4.7 API
-3. **GLM-4.7 API**: Provides intelligent code analysis
-
-## Testing
-
-### Testing the PR Reviewer Bot
-
-To test the PR reviewer bot before deploying to production:
-
-1. **Create a test PR** - Make a small, safe change (e.g., documentation update)
-2. **Open the PR** - Create a pull request with a descriptive title
-3. **Trigger the review** - Comment `@vllm-omni-reviewer` on the PR
-4. **Monitor results** - Check the Actions tab for workflow execution logs
-
-### Running Unit Tests
-
-The bot includes comprehensive unit tests that can be run locally:
-
-```bash
-# Run all tests
-pytest .github/tests/test_pr_reviewer.py -v
-
-# Run specific test
-pytest .github/tests/test_pr_reviewer.py::TestCheckTrigger -v
-
-# Run with coverage
-pytest .github/tests/test_pr_reviewer.py --cov=.github/scripts/pr_reviewer.py --cov-report=term-missing
-```
-
-### What to Look For
-
-When testing, verify that:
- [ ] The workflow triggers on the `@vllm-omni-reviewer` comment
- [ ] The cooldown mechanism works correctly
- [ ] The GLM API call completes without errors (with retry if needed)
- [ ] A review comment is posted to the PR
- [ ] The review content is meaningful and well-structured
- [ ] The cost is within the expected range (0.50-5 CNY)
-
-### Safe Test Changes
-
-For testing, consider making these types of safe changes:
- Documentation updates (like adding this Testing section)
- Comment improvements
- README enhancements
- Non-functional file additions
-
-### Example Test PR
-
-A good test PR might:
- Update a documentation file
- Add explanatory comments
- Improve code formatting
- Fix a minor typo
-
-These changes are safe to merge if the test is successful and won't affect functionality.
-
-## Troubleshooting
-
-### Bot Doesn't Respond
-
-1. **Check permissions** - Verify you have Owner/Member/Collaborator access
-2. **Check Actions tab** - Look for workflow execution and view logs
-3. **Check cooldown** - If another review was posted recently, wait for the cooldown period
-4. **Check API key** - Ensure `GLM_API_KEY` is configured in repository secrets
-
-### API Errors
-
-If the GLM API call fails:
- Check the Actions tab for detailed error logs
- Verify the `GLM_API_KEY` secret is correctly configured
- Ensure sufficient API quota is available
- The bot will automatically retry up to 3 times with exponential backoff
-
-### Review Seems Truncated
-
-If the review appears incomplete:
- Large diffs may be truncated at 100,000 characters
- Check the logs for truncation warnings
- Consider breaking large PRs into smaller chunks
-
-## Configuration
-
-### Required Secrets
-
-The following secret must be configured in the repository settings:
-
- `GLM_API_KEY` - Your GLM (BigModel) API key for accessing the GLM-4.7 API
-
-To add the secret:
-1. Go to repository Settings → Secrets and variables → Actions
-2. Click "New repository secret"
-3. Name: `GLM_API_KEY`
-4. Value: Your GLM API key
-
-### Optional Configuration
-
-The following optional environment variables can be set in the workflow file:
-
-| Variable | Default | Description |
-|----------|---------|-------------|
-| `GLM_API_URL` | `https://open.bigmodel.cn/api/paas/v4/chat/completions` | GLM API endpoint |
-| `GLM_MODEL` | `glm-4.7` | Model to use for reviews |
-| `PR_REVIEWER_COOLDOWN_MINUTES` | `5` | Cooldown period between reviews |
-| `PR_REVIEWER_MAX_RETRIES` | `3` | Maximum API retry attempts |
-| `PR_REVIEWER_RETRY_DELAY` | `1.0` | Base delay for retry backoff (seconds) |
-| `PR_REVIEWER_MAX_DIFF_SIZE` | `100000` | Maximum diff size before truncation |
-
-### Workflow Customization
-
-The workflow can be customized in `.github/workflows/pr-reviewer.yml`:
- Change Python version (default: 3.11)
- Adjust timeout value (default: 10 minutes)
- Modify trigger conditions
- Add additional dependencies
-
-## Code Quality
-
-The PR reviewer script follows vllm-omni coding standards:
-
- **Type hints**: All functions have complete type hints following mypy strict mode
- **Logging**: Uses Python's logging module for structured logging
- **Testing**: Comprehensive unit tests with pytest
- **Pre-commit**: Script is checked by pre-commit hooks (flake8)
-
-## Cost Estimate
-
-| Component | Cost |
-|-----------|------|
-| GitHub Actions (public repo) | Free |
-| GLM API (glm-4.7) | ~0.50-5 CNY per PR (varies by size) |
-| Total (20 PRs/month) | ~10-100 CNY/month (~$2-15 USD) |
-
-## Contributing
-
-To improve the PR reviewer bot:
-
-1. Edit `.github/scripts/pr_reviewer.py` for logic changes
-2. Edit `.github/workflows/pr-reviewer.yml` for workflow changes
-3. Add tests to `.github/tests/test_pr_reviewer.py`
-4. Run `pre-commit run --files .github/scripts/pr_reviewer.py` to check code quality
-5. Test thoroughly with a test PR before deploying to production
-
-## License
-
-This bot is part of the VLLM-Omni project and follows the same license terms.
@@ -1,205 +0,0 @@
-# Image Edit API
-
-vLLM-Omni provides an OpenAI DALL-E compatible API for image edit using diffusion models.
-
-Each server instance runs a single model (specified at startup via `vllm serve <model> --omni`).
-
-## Quick Start
-
-### Start the Server
-
-For example...
-
-```bash
-# Qwen-Image
-vllm serve Qwen/Qwen-Image-Edit-2511 --omni --port 8000
-
-
-### Generate Images
-
-**Using curl:**
-
-```bash
-curl -s -D >(grep -i x-request-id >&2) \
-  -o >(jq -r '.data[0].b64_json' | base64 --decode > gift-basket.png) \
-  -X POST "http://localhost:8000/v1/images/edits" \
-  -F "model=xxx" \
-  -F "image=@./xx.png" \
-  -F "prompt='this bear is wearing sportwear. holding a basketball, and bending one leg.'" \
-  -F "size=1024x1024" \
-  -F "output_format=png"
-```
-
-
-**Using OpenAI SDK:**
-
-```python
-import base64
-from openai import OpenAI
-from pathlib import Path
-client = OpenAI(
-    api_key="None",
-    base_url="http://localhost:8000/v1"
-)
-
-input_image_url = "https://vllm-public-assets.s3.us-west-2.amazonaws.com/omni-assets/qwen-bear.png"
-
-result = client.images.edit(
-    image=[],
-    model="Qwen-Image-Edit-2511",
-    prompt="Change the bears in the two input images into walking together.",
-    size='512x512',
-    stream=False,
-    output_format='jpeg',
-    # url格式
-    extra_body={
-        "url": [input_image_url1,input_image_url],
-        "num_inference_steps": 50,
-        "guidance_scale": 1,
-        "seed": 777,
-    }
-)
-
-image_base64 = result.data[0].b64_json
-image_bytes = base64.b64decode(image_base64)
-
-# Save the image to a file
-with open("edit_out_http.jpeg", "wb") as f:
-    f.write(image_bytes)
-```
-
-## API Reference
-
-### Endpoint
-
-```
-POST /v1/images/edits
-Content-Type: multipart/form-data
-```
-
-### Request Parameters
-
-#### OpenAI Standard Parameters
-
-| Parameter | Type | Default | Description |
-|-----------|------|---------|-------------|
-| `prompt` | string | **required** | A text description of the desired image |
-| `model` | string | server's model | Model to use (optional, should match server if specified) |
-| `image` | string or array | **required** | The image(s) to edit. |
-| `n` | integer | 1 | Number of images to generate (1-10) |
-| `size` | string | "auto" | Image dimensions in WxH format (e.g., "1024x1024", "512x512"), when set to auto, it decide size from first input image. |
-| `response_format` | string | "b64_json" | Response format (only "b64_json" supported) |
-| `user` | string | null | User identifier for tracking |
-| `output_format` | string | "png" | The format in which the generated images are returned. Must be one of "png", "jpg", "jpeg", "webp". |
-| `output_compression` | integer | 100 | The compression level (0-100%) for the generated images. |
-| `background` | string or null | "auto" | Allows to set transparency for the background of the generated image(s).
-
-#### vllm-omni Extension Parameters
-
-| Parameter | Type | Default | Description |
-|-----------|------|---------|-------------|
-| `url` | string or array | None | The image(s) to edit. |
-| `negative_prompt` | string | null | Text describing what to avoid in the image |
-| `num_inference_steps` | integer | model defaults | Number of diffusion steps |
-| `guidance_scale` | float | model defaults | Classifier-free guidance scale (typically 0.0-20.0) |
-| `true_cfg_scale` | float | model defaults | True CFG scale (model-specific parameter, may be ignored if not supported) |
-| `seed` | integer | null | Random seed for reproducibility |
-
-### Response Format
-
-```json
-{
-  "created": 1701234567,
-  "data": [
-    {
-      "b64_json": "<base64-encoded PNG>",
-      "url": null,
-      "revised_prompt": null
-    }
-  ],
-  "output_format": null,
-  "size": null,
-}
-```
-
-## Examples
-
-### Multiple Images input
-
-```bash
-curl -s -D >(grep -i x-request-id >&2) \
-  -o >(jq -r '.data[0].b64_json' | base64 --decode > gift-basket.png) \
-  -X POST "http://localhost:8000/v1/images/edits" \
-  -F "model=xxx" \
-  -F "image=@xx.png" \
-  -F "image=@xx.png"
-  -F "prompt='this bear is wearing sportwear. holding a basketball, and bending one leg.'" \
-  -F "size=1024x1024" \
-  -F "output_format=png"
-```
-
-
-## Parameter Handling
-
-The API passes parameters directly to the diffusion pipeline without model-specific transformation:
-
- **Default values**: When parameters are not specified, the underlying model uses its own defaults
- **Pass-through design**: User-provided values are forwarded directly to the diffusion engine
- **Minimal validation**: Only basic type checking and range validation at the API level
-
-### Parameter Compatibility
-
-The API passes parameters directly to the diffusion pipeline without model-specific validation.
-
- Unsupported parameters may be silently ignored by the model
- Incompatible values will result in errors from the underlying pipeline
- Recommended values vary by model - consult model documentation
-
-**Best Practice:** Start with the model's recommended parameters, then adjust based on your needs.
-
-## Error Responses
-
-### 400 Bad Request
-
-Invalid parameters (e.g., model mismatch):
-
-```json
-{
-  "detail": "Invalid size format: '1024x'. Expected format: 'WIDTHxHEIGHT' (e.g., '1024x1024')."
-}
-```
-
-### 422 Unprocessable Entity
-
-Validation errors (missing required fields):
-
-```json
-{
-  "detail": "Field 'image' or 'url' is required"
-}
-```
-
-## Troubleshooting
-
-### Server Not Running
-
-```bash
-# Check if server is responding
-curl -X http://localhost:8000/v1/images/edit \
-  -F "prompt='test'"
-```
-
-### Out of Memory
-
-If you encounter OOM errors:
-1. Reduce image size: `"size": "512x512"`
-2. Reduce inference steps: `"num_inference_steps": 25`
-
-## Development
-
-Enable debug logging to see prompts and generation details:
-
-```bash
-vllm serve Qwen/Qwen-Image-Edit-2511 --omni \
-  --uvicorn-log-level debug
-```
@@ -1,267 +0,0 @@
-# Speech API
-
-vLLM-Omni provides an OpenAI-compatible API for text-to-speech (TTS) generation using Qwen3-TTS models.
-
-Each server instance runs a single model (specified at startup via `vllm serve <model> --omni`).
-
-## Quick Start
-
-### Start the Server
-
-```bash
-# CustomVoice model (predefined speakers)
-vllm serve Qwen/Qwen3-TTS-12Hz-1.7B-CustomVoice \
-    --stage-configs-path vllm_omni/model_executor/stage_configs/qwen3_tts.yaml \
-    --omni \
-    --port 8091 \
-    --trust-remote-code \
-    --enforce-eager
-```
-
-### Generate Speech
-
-**Using curl:**
-
-```bash
-curl -X POST http://localhost:8091/v1/audio/speech \
-    -H "Content-Type: application/json" \
-    -d '{
-        "input": "Hello, how are you?",
-        "voice": "vivian",
-        "language": "English"
-    }' --output output.wav
-```
-
-**Using Python:**
-
-```python
-import httpx
-
-response = httpx.post(
-    "http://localhost:8091/v1/audio/speech",
-    json={
-        "input": "Hello, how are you?",
-        "voice": "vivian",
-        "language": "English",
-    },
-    timeout=300.0,
-)
-
-with open("output.wav", "wb") as f:
-    f.write(response.content)
-```
-
-**Using OpenAI SDK:**
-
-```python
-from openai import OpenAI
-
-client = OpenAI(base_url="http://localhost:8091/v1", api_key="none")
-
-response = client.audio.speech.create(
-    model="Qwen/Qwen3-TTS-12Hz-1.7B-CustomVoice",
-    voice="vivian",
-    input="Hello, how are you?",
-)
-
-response.stream_to_file("output.wav")
-```
-
-## API Reference
-
-### Endpoint
-
-```
-POST /v1/audio/speech
-Content-Type: application/json
-```
-
-### Request Parameters
-
-#### OpenAI Standard Parameters
-
-| Parameter | Type | Default | Description |
-|-----------|------|---------|-------------|
-| `input` | string | **required** | The text to synthesize into speech |
-| `model` | string | server's model | Model to use (optional, should match server if specified) |
-| `voice` | string | "vivian" | Speaker name (e.g., vivian, ryan, aiden) |
-| `response_format` | string | "wav" | Audio format: wav, mp3, flac, pcm, aac, opus |
-| `speed` | float | 1.0 | Playback speed (0.25-4.0) |
-
-#### vLLM-Omni Extension Parameters
-
-| Parameter | Type | Default | Description |
-|-----------|------|---------|-------------|
-| `task_type` | string | "CustomVoice" | TTS task type: CustomVoice, VoiceDesign, or Base |
-| `language` | string | "Auto" | Language (see supported languages below) |
-| `instructions` | string | "" | Voice style/emotion instructions |
-| `max_new_tokens` | integer | 2048 | Maximum tokens to generate |
-
-**Supported languages:** Auto, Chinese, English, Japanese, Korean, German, French, Russian, Portuguese, Spanish, Italian
-
-#### Voice Clone Parameters (Base task)
-
-| Parameter | Type | Default | Description |
-|-----------|------|---------|-------------|
-| `ref_audio` | string | null | Reference audio (URL or base64 data URL) |
-| `ref_text` | string | null | Transcript of reference audio |
-| `x_vector_only_mode` | bool | null | Use speaker embedding only (no ICL) |
-
-### Response Format
-
-Returns binary audio data with appropriate `Content-Type` header (e.g., `audio/wav`).
-
-### Voices Endpoint
-
-```
-GET /v1/audio/voices
-```
-
-Lists available voices for the loaded model.
-
-```json
-{
-    "voices": ["aiden", "dylan", "eric", "ono_anna", "ryan", "serena", "sohee", "uncle_fu", "vivian"]
-}
-```
-
-## Examples
-
-### CustomVoice with Style Instruction
-
-```bash
-curl -X POST http://localhost:8091/v1/audio/speech \
-    -H "Content-Type: application/json" \
-    -d '{
-        "input": "I am so excited!",
-        "voice": "vivian",
-        "instructions": "Speak with great enthusiasm"
-    }' --output excited.wav
-```
-
-### VoiceDesign (Natural Language Voice Description)
-
-```bash
-# Start server with VoiceDesign model first
-vllm serve Qwen/Qwen3-TTS-12Hz-1.7B-VoiceDesign \
-    --stage-configs-path vllm_omni/model_executor/stage_configs/qwen3_tts.yaml \
-    --omni \
-    --port 8091 \
-    --trust-remote-code \
-    --enforce-eager
-```
-
-```bash
-curl -X POST http://localhost:8091/v1/audio/speech \
-    -H "Content-Type: application/json" \
-    -d '{
-        "input": "Hello world",
-        "task_type": "VoiceDesign",
-        "instructions": "A warm, friendly female voice with a gentle tone"
-    }' --output designed.wav
-```
-
-### Base (Voice Cloning)
-
-```bash
-# Start server with Base model first
-vllm serve Qwen/Qwen3-TTS-12Hz-1.7B-Base \
-    --stage-configs-path vllm_omni/model_executor/stage_configs/qwen3_tts.yaml \
-    --omni \
-    --port 8091 \
-    --trust-remote-code \
-    --enforce-eager
-```
-
-```bash
-curl -X POST http://localhost:8091/v1/audio/speech \
-    -H "Content-Type: application/json" \
-    -d '{
-        "input": "Hello, this is a cloned voice",
-        "task_type": "Base",
-        "ref_audio": "https://example.com/reference.wav",
-        "ref_text": "Original transcript of the reference audio"
-    }' --output cloned.wav
-```
-
-## Supported Models
-
-| Model | Task Type | Description |
-|-------|-----------|-------------|
-| `Qwen/Qwen3-TTS-12Hz-1.7B-CustomVoice` | CustomVoice | Predefined speaker voices with optional style control |
-| `Qwen/Qwen3-TTS-12Hz-1.7B-VoiceDesign` | VoiceDesign | Natural language voice style description |
-| `Qwen/Qwen3-TTS-12Hz-1.7B-Base` | Base | Voice cloning from reference audio |
-| `Qwen/Qwen3-TTS-12Hz-0.6B-CustomVoice` | CustomVoice | Smaller/faster variant |
-| `Qwen/Qwen3-TTS-12Hz-0.6B-Base` | Base | Smaller/faster variant for voice cloning |
-
-## Error Responses
-
-### 400 Bad Request
-
-Invalid parameters:
-
-```json
-{
-    "error": {
-        "message": "Input text cannot be empty",
-        "type": "BadRequestError",
-        "param": null,
-        "code": 400
-    }
-}
-```
-
-### 404 Not Found
-
-Model not found:
-
-```json
-{
-    "error": {
-        "message": "The model `xxx` does not exist.",
-        "type": "NotFoundError",
-        "param": "model",
-        "code": 404
-    }
-}
-```
-
-## Troubleshooting
-
-### "TTS model did not produce audio output"
-
-Ensure you're using the correct model variant for your task type:
- CustomVoice task → CustomVoice model
- VoiceDesign task → VoiceDesign model
- Base task → Base model
-
-### Server Not Running
-
-```bash
-# Check if server is responding
-curl http://localhost:8091/v1/audio/voices
-```
-
-### Out of Memory
-
-If you encounter OOM errors:
-1. Use smaller model variant: `Qwen/Qwen3-TTS-12Hz-0.6B-CustomVoice`
-2. Reduce `--gpu-memory-utilization`
-
-### Unsupported Speaker
-
-Use `/v1/audio/voices` to list available voices for the loaded model.
-
-## Development
-
-Enable debug logging:
-
-```bash
-vllm serve Qwen/Qwen3-TTS-12Hz-1.7B-CustomVoice \
-    --stage-configs-path vllm_omni/model_executor/stage_configs/qwen3_tts.yaml \
-    --omni \
-    --port 8091 \
-    --trust-remote-code \
-    --enforce-eager \
-    --uvicorn-log-level debug
-```
				`@@ -1 +0,0 @@`
				`# noqa: N999 # This is not a python library intended to be imported`
				`@@ -1 +0,0 @@`
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				`@@ -1 +0,0 @@`
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