delete review-PR

Signed-off-by: hsliuustc0106 <liuhongsheng4@huawei.com>
Simplify review-pr workflow
2026-04-13 13:47:41 +08:00 · 2026-04-13 09:04:40 +08:00 · 2026-04-13 09:04:40 +08:00 · 2026-04-13 08:38:21 +08:00 · 2026-04-13 08:27:48 +08:00
689 changed files with 16724 additions and 65938 deletions
@@ -44,19 +44,11 @@ steps:
    agents:
      queue: "cpu_queue_premerge"

-  # L4 Test — main+NIGHTLY=1 (scheduled), or PR with specific label (e.g. add label then Rebuild)
+  # L4 Test — main+NIGHTLY=1 (scheduled), or PR with label nightly-test (e.g. add label then Rebuild)
  - label: "Upload Nightly Pipeline"
    depends_on: image-build
    key: upload-nightly-pipeline
-    if: >-
-      (build.branch == "main" && build.env("NIGHTLY") == "1") ||
-      (build.branch != "main" && (
-        build.pull_request.labels includes "nightly-test" ||
-        build.pull_request.labels includes "omni-test" ||
-        build.pull_request.labels includes "tts-test" ||
-        build.pull_request.labels includes "diffusion-x2iat-test" ||
-        build.pull_request.labels includes "diffusion-x2v-test"
-      ))
+    if: '(build.branch == "main" && build.env("NIGHTLY") == "1") || (build.branch != "main" && build.pull_request.labels includes "nightly-test")'
    commands:
      - buildkite-agent pipeline upload .buildkite/test-nightly.yml
    agents:
@@ -64,13 +64,12 @@ while true; do
 done

 echo "--- Pulling container"
-## Temporary change to use AMD Docker Hub to store the vllm-omni image
+## Temporary change to use AMD Docker Hub to store the vllm-ci image
 # to bypass the rate limit issue with ECR Public Gallery.
-# Images are now stored in a separate repository for vllm-omni, instead of vllm-ci.
 # TODO: @tjtanaa point back to ECR Public Gallery
 # once the amd agents are configured to use ECR Public Gallery.
 # image_name="public.ecr.aws/q9t5s3a7/vllm-ci-test-repo:${BUILDKITE_COMMIT}-rocm-omni"
-image_name="rocm/vllm-omni:${BUILDKITE_COMMIT}"
+image_name="rocm/vllm-ci:${BUILDKITE_COMMIT}-rocm-omni"
 container_name="rocm_${BUILDKITE_COMMIT}_$(tr -dc A-Za-z0-9 < /dev/urandom | head -c 10; echo)"

 # TODO: @tjtanaa uncomment this once the amd agents are configured to use ECR Public Gallery.
@@ -126,7 +126,7 @@ if skip:
 else:
    rep = "'true'"
    ready_rep = "'build.branch != \"main\" && build.pull_request.labels includes \"ready\"'"
-    merge_rep = "'(build.branch == \"main\" && build.env(\"NIGHTLY\") != \"1\") || (build.branch != \"main\" && build.pull_request.labels includes \"merge-test\")'"
+    merge_rep = "'build.branch == \"main\" && build.env(\"NIGHTLY\") != \"1\"'"
 rendered = (
    continuation
    .replace("__IMAGE_BUILD_IF__", rep)
@@ -32,6 +32,7 @@ steps:
  mirror_hardwares: [amdproduction]
  grade: Blocking
  commands:
+    - export GPU_ARCHS=gfx942
    - export VLLM_LOGGING_LEVEL=DEBUG
    - export VLLM_WORKER_MULTIPROC_METHOD=spawn
    - |
@@ -54,7 +55,7 @@ steps:
 #     - export GPU_ARCHS=gfx942
 #     - export VLLM_LOGGING_LEVEL=DEBUG
 #     - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-#     - timeout 20m pytest -s -v tests/e2e/offline_inference/test_stable_audio_expansion.py -m "advanced_model and diffusion and L4" --run-level advanced_model
+#     - timeout 20m pytest -s -v tests/e2e/offline_inference/test_stable_audio_model.py

 - label: "Diffusion Cache Backend Test"
  agent_pool: mi325_1
@@ -62,12 +63,13 @@ steps:
  mirror_hardwares: [amdproduction]
  grade: Blocking
  commands:
+    - export GPU_ARCHS=gfx942
    - export VLLM_LOGGING_LEVEL=DEBUG
    - export VLLM_WORKER_MULTIPROC_METHOD=spawn
    - timeout 15m pytest -s -v -m "core_model and cache and diffusion and not distributed_cuda and L4"

- label: "Diffusion Sequence Parallelism Test (Need 4 GPUs)"
-  agent_pool: mi325_4
+- label: "Diffusion Sequence Parallelism Test"
+  agent_pool: mi325_2
  depends_on: amd-build
  mirror_hardwares: [amdproduction]
  grade: Blocking
@@ -75,7 +77,6 @@ steps:
    - export VLLM_LOGGING_LEVEL=DEBUG
    - export VLLM_WORKER_MULTIPROC_METHOD=spawn
    - timeout 20m pytest -s -v tests/e2e/offline_inference/test_sequence_parallel.py
-    - timeout 20m pytest -s -v tests/diffusion/distributed/test_ulysses_uaa_perf.py

 # merge-only tests
 - label: "Diffusion Tensor Parallelism Test"
@@ -94,14 +95,22 @@ steps:
  commands:
    - timeout 20m pytest -s -v tests/diffusion/test_diffusion_worker.py

- label: "Engine Test"
-  agent_pool: mi325_1
+- label: "Benchmark & Engine Test"
+  agent_pool: mi325_2
  depends_on: amd-build
  mirror_hardwares: [amdproduction]
  grade: Blocking
  commands:
    - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-    - timeout 20m pytest -s -v tests/engine/test_async_omni_engine_abort.py
+    - |
+      timeout 20m bash -c '
+        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))
+      '

 - label: "Omni Model Test Qwen2-5-Omni"
  agent_pool: mi325_2
@@ -112,7 +121,6 @@ steps:
    - export VLLM_LOGGING_LEVEL=DEBUG
    - export VLLM_WORKER_MULTIPROC_METHOD=spawn
    - timeout 20m pytest -s -v tests/e2e/offline_inference/test_qwen2_5_omni.py
-    - timeout 20m pytest -s -v tests/e2e/online_serving/test_qwen2_5_omni.py -m "advanced_model" --run-level "advanced_model"

 - label: "Omni Model Test Qwen3-Omni"
  agent_pool: mi325_2
@@ -123,10 +131,11 @@ steps:
    - export VLLM_LOGGING_LEVEL=DEBUG
    - export VLLM_WORKER_MULTIPROC_METHOD=spawn
    - export VLLM_TEST_CLEAN_GPU_MEMORY=1
-    - timeout 30m pytest -s -v tests/e2e/offline_inference/test_qwen3_omni.py tests/e2e/online_serving/test_qwen3_omni.py tests/e2e/online_serving/test_mimo_audio.py -m "advanced_model" --run-level "advanced_model"
+    - timeout 10m pytest -s -v tests/e2e/offline_inference/test_qwen3_omni.py
+    - timeout 20m pytest -s -v tests/e2e/online_serving/test_qwen3_omni.py -m "advanced_model" --run-level "advanced_model"

 - label: "Qwen3-TTS CustomVoice E2E Test"
-  agent_pool: mi325_1
+  agent_pool: mi325_2
  depends_on: amd-build
  mirror_hardwares: [amdproduction]
  grade: Blocking
@@ -136,21 +145,21 @@ steps:
        export VLLM_LOGGING_LEVEL=DEBUG
        export VLLM_WORKER_MULTIPROC_METHOD=spawn
        export VLLM_ALLOW_LONG_MAX_MODEL_LEN="1"
-        pytest -s -v tests/e2e/online_serving/test_qwen3_tts_customvoice.py tests/e2e/offline_inference/test_qwen3_tts_customvoice.py -m "advanced_model" --run-level "advanced_model"
+        pytest -s -v tests/e2e/online_serving/test_qwen3_tts_customvoice.py -m "advanced_model" --run-level "advanced_model" && pytest -s -v tests/e2e/offline_inference/test_qwen3_tts_customvoice.py
      '

 - label: "Qwen3-TTS Base E2E Test"
-  agent_pool: mi325_1
+  agent_pool: mi325_2
  depends_on: amd-build
  mirror_hardwares: [amdproduction]
  grade: Blocking
  commands:
    - |
-      timeout 30m bash -c '
+      timeout 20m bash -c '
        export VLLM_LOGGING_LEVEL=DEBUG
        export VLLM_WORKER_MULTIPROC_METHOD=spawn
        export VLLM_ALLOW_LONG_MAX_MODEL_LEN="1"
-        pytest -s -v tests/e2e/online_serving/test_qwen3_tts_base.py tests/e2e/offline_inference/test_qwen3_tts_base.py -m "advanced_model" --run-level "advanced_model"
+        pytest -s -v tests/e2e/online_serving/test_qwen3_tts_base.py -m "advanced_model" --run-level "advanced_model" && pytest -s -v tests/e2e/offline_inference/test_qwen3_tts_base.py
      '

 - label: "Diffusion Image Edit Test"
@@ -164,58 +173,43 @@ steps:
    - export VLLM_WORKER_MULTIPROC_METHOD=spawn
    - timeout 20m pytest -s -v tests/e2e/online_serving/test_image_gen_edit.py

-# TODO: Bagel test on ROCm is very unstable. @tjtanaa
-# Need to debug before reneable numerical changes across large PRs
-# # split Bagel Model Test with H100 (Real Weights) into three tests
-# - label: "Bagel Text2Img Model Test (1/3)"
-#   agent_pool: mi325_1
-#   depends_on: amd-build
-#   mirror_hardwares: [amdproduction]
-#   grade: Blocking
-#   commands:
-#     - export GPU_ARCHS=gfx942
-#     - export VLLM_TEST_CLEAN_GPU_MEMORY=1
-#     - export VLLM_LOGGING_LEVEL=DEBUG
-#     - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-#     - export VLLM_ROCM_USE_AITER_RMSNORM=0
-#     - timeout 30m pytest -s -v tests/e2e/offline_inference/test_bagel_text2img.py -m "advanced_model" --run-level "advanced_model" -k "shared_memory" -k "rocm"
-
-# - label: "Bagel Img2Img Model Test (2/3)"
-#   agent_pool: mi325_1
-#   depends_on: amd-build
-#   mirror_hardwares: [amdproduction]
-#   grade: Blocking
-#   commands:
-#     - export GPU_ARCHS=gfx942
-#     - export VLLM_TEST_CLEAN_GPU_MEMORY=1
-#     - export VLLM_LOGGING_LEVEL=DEBUG
-#     - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-#     - export VLLM_ROCM_USE_AITER_RMSNORM=0
-#     - timeout 30m pytest -s -v tests/e2e/offline_inference/test_bagel_img2img.py -m "advanced_model" --run-level "advanced_model" -k "rocm"
-
-# - label: "Bagel Online Serving Test (3/3)"
-#   agent_pool: mi325_1
-#   depends_on: amd-build
-#   mirror_hardwares: [amdproduction]
-#   grade: Blocking
-#   commands:
-#     - export GPU_ARCHS=gfx942
-#     - export VLLM_TEST_CLEAN_GPU_MEMORY=1
-#     - export VLLM_IMAGE_FETCH_TIMEOUT=60
-#     - export VLLM_LOGGING_LEVEL=DEBUG
-#     - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-#     - export VLLM_ROCM_USE_AITER_RMSNORM=0
-#     - timeout 40m pytest -s -v tests/e2e/online_serving/test_bagel_online.py -m "advanced_model" --run-level "advanced_model" -k "rocm"
-
- label: "Voxtral-TTS E2E Test"
+# split Bagel Model Test with H100 (Real Weights) into three tests
+- label: "Bagel Text2Img Model Test"
  agent_pool: mi325_1
  depends_on: amd-build
  mirror_hardwares: [amdproduction]
  grade: Blocking
  commands:
-    - |
-      timeout 20m bash -c '
-        export VLLM_LOGGING_LEVEL=DEBUG
-        export VLLM_WORKER_MULTIPROC_METHOD=spawn
-        pytest -s -v tests/e2e/online_serving/test_voxtral_tts.py tests/e2e/offline_inference/test_voxtral_tts.py -m "advanced_model" --run-level "advanced_model"
-      '
+    - export GPU_ARCHS=gfx942
+    - export VLLM_TEST_CLEAN_GPU_MEMORY=1
+    - export VLLM_LOGGING_LEVEL=DEBUG
+    - export VLLM_WORKER_MULTIPROC_METHOD=spawn
+    - export VLLM_ROCM_USE_AITER_RMSNORM=0
+    - timeout 30m pytest -s -v tests/e2e/offline_inference/test_bagel_text2img.py -m "advanced_model" --run-level "advanced_model" -k "shared_memory" -k "rocm"
+
+- label: "Bagel Img2Img Model Test"
+  agent_pool: mi325_1
+  depends_on: amd-build
+  mirror_hardwares: [amdproduction]
+  grade: Blocking
+  commands:
+    - export GPU_ARCHS=gfx942
+    - export VLLM_TEST_CLEAN_GPU_MEMORY=1
+    - export VLLM_LOGGING_LEVEL=DEBUG
+    - export VLLM_WORKER_MULTIPROC_METHOD=spawn
+    - export VLLM_ROCM_USE_AITER_RMSNORM=0
+    - timeout 30m pytest -s -v tests/e2e/offline_inference/test_bagel_img2img.py -m "advanced_model" --run-level "advanced_model" -k "rocm"
+
+- label: "Bagel Online Serving Test"
+  agent_pool: mi325_1
+  depends_on: amd-build
+  mirror_hardwares: [amdproduction]
+  grade: Blocking
+  commands:
+    - export GPU_ARCHS=gfx942
+    - export VLLM_TEST_CLEAN_GPU_MEMORY=1
+    - export VLLM_IMAGE_FETCH_TIMEOUT=60
+    - export VLLM_LOGGING_LEVEL=DEBUG
+    - export VLLM_WORKER_MULTIPROC_METHOD=spawn
+    - export VLLM_ROCM_USE_AITER_RMSNORM=0
+    - timeout 40m pytest -s -v tests/e2e/online_serving/test_bagel_online.py -m "advanced_model" --run-level "advanced_model" -k "rocm"
@@ -9,37 +9,13 @@ steps:
    - export VLLM_ROCM_USE_AITER=0
    - "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"

- label: "Voxtral TTS CUDA Unit Test"
-  agent_pool: mi325_1
-  depends_on: amd-build
-  mirror_hardwares: [amdproduction]
-  grade: Blocking
-  commands:
-    - timeout 10m pytest -s -v tests/model_executor/models/voxtral_tts/test_cuda_graph_acoustic_transformer.py
-
 - label: "Diffusion Model Test"
-  agent_pool: mi325_1
+  agent_pool: mi325_2
  depends_on: amd-build
  mirror_hardwares: [amdproduction]
  grade: Blocking
  commands:
-    - timeout 30m pytest -s -v tests/e2e/offline_inference/test_t2i_model.py -m "core_model and diffusion" --run-level "core_model"
-
- label: "Diffusion Batching Test"
-  agent_pool: mi325_1
-  depends_on: amd-build
-  mirror_hardwares: [amdproduction]
-  grade: Blocking
-  commands:
-    - timeout 20m pytest -s -v tests/e2e/offline_inference/test_qwen_image_diffusion_batching.py -m "core_model and diffusion" --run-level "core_model"
-
- label: "Custom Pipeline Test"
-  agent_pool: mi325_1
-  depends_on: amd-build
-  mirror_hardwares: [amdproduction]
-  grade: Blocking
-  commands:
-    - timeout 20m pytest -s -v tests/e2e/offline_inference/custom_pipeline/ -m "core_model"
+    - timeout 20m pytest -s -v tests/e2e/offline_inference/test_t2i_model.py -m "core_model and diffusion" --run-level "core_model"

 - label: "Diffusion Model CPU offloading Test"
  agent_pool: mi325_1
@@ -47,6 +23,7 @@ steps:
  mirror_hardwares: [amdproduction]
  grade: Blocking
  commands:
+    - export GPU_ARCHS=gfx942
    - export VLLM_LOGGING_LEVEL=DEBUG
    - export VLLM_WORKER_MULTIPROC_METHOD=spawn
    - |
@@ -69,7 +46,7 @@ steps:
 #     - export GPU_ARCHS=gfx942
 #     - export VLLM_LOGGING_LEVEL=DEBUG
 #     - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-#     - timeout 20m pytest -s -v tests/e2e/offline_inference/test_stable_audio_expansion.py -m "advanced_model and diffusion and L4" --run-level advanced_model
+#     - timeout 20m pytest -s -v tests/e2e/offline_inference/test_stable_audio_model.py

 - label: "Diffusion Cache Backend Test"
  agent_pool: mi325_1
@@ -100,58 +77,47 @@ steps:
  commands:
    - timeout 20m pytest -s -v tests/diffusion/test_diffusion_worker.py

- label: "Engine Test"
-  agent_pool: mi325_1
+- label: "Benchmark & Engine Test"
+  agent_pool: mi325_2
  depends_on: amd-build
  mirror_hardwares: [amdproduction]
  grade: Blocking
  commands:
    - export VLLM_WORKER_MULTIPROC_METHOD=spawn
    - |
-      timeout 15m bash -c '
-              pytest -s -v tests/engine/test_async_omni_engine_abort.py
+      timeout 30m bash -c '
+        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))
      '

-
-# NOTE: This test is not running any thing. It is skipped and deselected.
-# Currently it is = 1 skipped, 1 deselected, 17 warnings in 0.03s ======
-# - label: "Omni Model Test Qwen2-5-Omni"
-#   agent_pool: mi325_2
-#   depends_on: amd-build
-#   mirror_hardwares: [amdproduction]
-#   grade: Blocking
-#   commands:
-#     - export VLLM_LOGGING_LEVEL=DEBUG
-#     - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-#     - timeout 20m pytest -s -v tests/e2e/offline_inference/test_qwen2_5_omni.py -m "core_model" --run-level "core_model"
-
-# - label: "Omni Model Test Qwen3-Omni"
-#   agent_pool: mi325_2
-#   depends_on: amd-build
-#   mirror_hardwares: [amdproduction]
-#   grade: Blocking
-#   commands:
-#     - export VLLM_LOGGING_LEVEL=DEBUG
-#     - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-#     - export VLLM_TEST_CLEAN_GPU_MEMORY=1
-#     - timeout 10m pytest -s -v tests/e2e/offline_inference/test_qwen3_omni.py
-#     - timeout 20m pytest -s -v tests/e2e/online_serving/test_qwen3_omni.py -m "core_model" --run-level "core_model"
-
- label: "MiMo-Audio E2E Test with H100"
-  agent_pool: mi325_1
+- label: "Omni Model Test Qwen2-5-Omni"
+  agent_pool: mi325_2
  depends_on: amd-build
  mirror_hardwares: [amdproduction]
  grade: Blocking
  commands:
-    - |
-      timeout 30m bash -c '
-        export VLLM_LOGGING_LEVEL=DEBUG
-        export VLLM_WORKER_MULTIPROC_METHOD=spawn
-        pytest -s -v tests/e2e/online_serving/test_mimo_audio.py -m "core_model" --run-level "core_model"
-      '
+    - export VLLM_LOGGING_LEVEL=DEBUG
+    - export VLLM_WORKER_MULTIPROC_METHOD=spawn
+    - timeout 17m pytest -s -v tests/e2e/offline_inference/test_qwen2_5_omni.py
+
+- label: "Omni Model Test Qwen3-Omni"
+  agent_pool: mi325_2
+  depends_on: amd-build
+  mirror_hardwares: [amdproduction]
+  grade: Blocking
+  commands:
+    - export VLLM_LOGGING_LEVEL=DEBUG
+    - export VLLM_WORKER_MULTIPROC_METHOD=spawn
+    - export VLLM_TEST_CLEAN_GPU_MEMORY=1
+    - timeout 10m pytest -s -v tests/e2e/offline_inference/test_qwen3_omni.py
+    - timeout 10m pytest -s -v tests/e2e/online_serving/test_qwen3_omni.py -m "core_model" --run-level "core_model"

 - label: "Qwen3-TTS E2E Test"
-  agent_pool: mi325_1
+  agent_pool: mi325_2
  depends_on: amd-build
  mirror_hardwares: [amdproduction]
  grade: Blocking
@@ -159,21 +125,7 @@ steps:
    - export VLLM_LOGGING_LEVEL=DEBUG
    - export VLLM_WORKER_MULTIPROC_METHOD=spawn
    - export VLLM_ALLOW_LONG_MAX_MODEL_LEN="1"
-    - timeout 30m pytest -s -v tests/e2e/online_serving/test_qwen3_tts_customvoice.py -m "core_model" --run-level "core_model"
-
- label: "Voxtral-TTS E2E Test"
-  agent_pool: mi325_1
-  depends_on: amd-build
-  mirror_hardwares: [amdproduction]
-  grade: Blocking
-  commands:
-    - |
-      timeout 20m bash -c '
-        export VLLM_LOGGING_LEVEL=DEBUG
-        export VLLM_WORKER_MULTIPROC_METHOD=spawn
-        pytest -s -v tests/e2e/online_serving/test_voxtral_tts.py -m "advanced_model" --run-level "advanced_model"
-        pytest -s -v tests/e2e/offline_inference/test_voxtral_tts.py -m "advanced_model" --run-level "advanced_model"
-      '
+    - timeout 20m pytest -s -v tests/e2e/online_serving/test_qwen3_tts_customvoice.py -m "core_model" --run-level "core_model"

 - label: "Diffusion Image Edit Test"
  agent_pool: mi325_1
@@ -186,55 +138,42 @@ steps:
    - export VLLM_WORKER_MULTIPROC_METHOD=spawn
    - timeout 20m pytest -s -v tests/e2e/online_serving/test_image_gen_edit.py

-# TODO: Bagel test on ROCm is very unstable. @tjtanaa
-# Need to debug before reneable numerical changes across large PRs
-# - label: "Bagel Text2Img Model Test"
-#   agent_pool: mi325_1
-#   depends_on: amd-build
-#   mirror_hardwares: [amdproduction]
-#   grade: Blocking
-#   commands:
-#     - export GPU_ARCHS=gfx942
-#     - export VLLM_TEST_CLEAN_GPU_MEMORY=1
-#     - export VLLM_LOGGING_LEVEL=DEBUG
-#     - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-#     - export VLLM_ROCM_USE_AITER_RMSNORM=0
-#     - timeout 30m pytest -s -v tests/e2e/offline_inference/test_bagel_text2img.py -m "core_model" --run-level "core_model" -k "rocm"
-
-# - label: "Bagel Img2Img Model Test"
-#   agent_pool: mi325_1
-#   depends_on: amd-build
-#   mirror_hardwares: [amdproduction]
-#   grade: Blocking
-#   commands:
-#     - export GPU_ARCHS=gfx942
-#     - export VLLM_TEST_CLEAN_GPU_MEMORY=1
-#     - export VLLM_LOGGING_LEVEL=DEBUG
-#     - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-#     - export VLLM_ROCM_USE_AITER_RMSNORM=0
-#     - timeout 30m pytest -s -v tests/e2e/offline_inference/test_bagel_img2img.py -m "core_model" --run-level "core_model" -k "rocm"
-
-# - label: "Bagel Online Serving Test"
-#   agent_pool: mi325_1
-#   depends_on: amd-build
-#   mirror_hardwares: [amdproduction]
-#   grade: Blocking
-#   commands:
-#     - export GPU_ARCHS=gfx942
-#     - export VLLM_TEST_CLEAN_GPU_MEMORY=1
-#     - export VLLM_IMAGE_FETCH_TIMEOUT=60
-#     - export VLLM_LOGGING_LEVEL=DEBUG
-#     - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-#     - export VLLM_ROCM_USE_AITER_RMSNORM=0
-#     - timeout 40m pytest -s -v tests/e2e/online_serving/test_bagel_online.py -m "core_model" --run-level "core_model" -k "rocm"
-
- label: "CosyVoice3-TTS E2E Test"
+- label: "Bagel Text2Img Model Test"
  agent_pool: mi325_1
  depends_on: amd-build
  mirror_hardwares: [amdproduction]
  grade: Blocking
  commands:
-    - |
-      timeout 20m bash -c '
-        pytest -s -v tests/e2e/online_serving/test_cosyvoice3_tts.py -m "core_model" --run-level "core_model"
-      '
+    - export GPU_ARCHS=gfx942
+    - export VLLM_TEST_CLEAN_GPU_MEMORY=1
+    - export VLLM_LOGGING_LEVEL=DEBUG
+    - export VLLM_WORKER_MULTIPROC_METHOD=spawn
+    - export VLLM_ROCM_USE_AITER_RMSNORM=0
+    - timeout 30m pytest -s -v tests/e2e/offline_inference/test_bagel_text2img.py -m "core_model" --run-level "core_model" -k "rocm"
+
+- label: "Bagel Img2Img Model Test"
+  agent_pool: mi325_1
+  depends_on: amd-build
+  mirror_hardwares: [amdproduction]
+  grade: Blocking
+  commands:
+    - export GPU_ARCHS=gfx942
+    - export VLLM_TEST_CLEAN_GPU_MEMORY=1
+    - export VLLM_LOGGING_LEVEL=DEBUG
+    - export VLLM_WORKER_MULTIPROC_METHOD=spawn
+    - export VLLM_ROCM_USE_AITER_RMSNORM=0
+    - timeout 30m pytest -s -v tests/e2e/offline_inference/test_bagel_img2img.py -m "core_model" --run-level "core_model" -k "rocm"
+
+- label: "Bagel Online Serving Test"
+  agent_pool: mi325_1
+  depends_on: amd-build
+  mirror_hardwares: [amdproduction]
+  grade: Blocking
+  commands:
+    - export GPU_ARCHS=gfx942
+    - export VLLM_TEST_CLEAN_GPU_MEMORY=1
+    - export VLLM_IMAGE_FETCH_TIMEOUT=60
+    - export VLLM_LOGGING_LEVEL=DEBUG
+    - export VLLM_WORKER_MULTIPROC_METHOD=spawn
+    - export VLLM_ROCM_USE_AITER_RMSNORM=0
+    - timeout 40m pytest -s -v tests/e2e/online_serving/test_bagel_online.py -m "core_model" --run-level "core_model" -k "rocm"
@@ -117,17 +117,3 @@ steps:
    - export VLLM_LOGGING_LEVEL=DEBUG
    - export VLLM_WORKER_MULTIPROC_METHOD=spawn
    - pytest -s -v tests/e2e/online_serving/test_image_gen_edit.py
-
-
- label: "Omni Sleep Mode Test"
-  timeout_in_minutes: 40
-  agent_pool: mi325_2
-  depends_on: amd-build
-  mirror_hardwares: [amdproduction]
-  grade: Blocking
-  commands:
-    - export GPU_ARCHS=gfx942
-    - export VLLM_LOGGING_LEVEL=DEBUG
-    - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-    - export VLLM_TEST_CLEAN_GPU_MEMORY="1"
-    - pytest -s -v tests/e2e/offline_inference/test_omni_sleep_mode.py -m "advanced_model and omni and MI325" --run-level "advanced_model"
@@ -1,8 +1,3 @@
-env:
-  VLLM_WORKER_MULTIPROC_METHOD: spawn
-  HF_HUB_DOWNLOAD_TIMEOUT: 300
-  HF_HUB_ETAG_TIMEOUT: 60
-
 steps:
  - label: "Simple Unit Test"
    depends_on: upload-merge-pipeline
@@ -76,6 +71,24 @@ steps:
          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"
+            - "HF_TOKEN"
+          volumes:
+            - "/fsx/hf_cache:/fsx/hf_cache"
+
  - label: "Diffusion Cache Backend Test"
    timeout_in_minutes: 15
    depends_on: upload-merge-pipeline
@@ -95,7 +108,7 @@ steps:
            - "/fsx/hf_cache:/fsx/hf_cache"

  - label: "Diffusion Sequence Parallelism Test"
-    timeout_in_minutes: 25
+    timeout_in_minutes: 20
    depends_on: upload-merge-pipeline
    commands:
      - pytest -s -v tests/e2e/offline_inference/test_sequence_parallel.py tests/diffusion/distributed/test_ulysses_uaa_perf.py
@@ -156,6 +169,7 @@ steps:
    commands:
      - |
        timeout 15m bash -c '
+                export VLLM_WORKER_MULTIPROC_METHOD=spawn
                pytest -s -v tests/engine/test_async_omni_engine_abort.py
        '
    agents:
@@ -177,6 +191,7 @@ steps:
    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 tests/e2e/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
@@ -197,6 +212,7 @@ steps:
      - |
        timeout 20m bash -c '
          export VLLM_LOGGING_LEVEL=DEBUG
+          export VLLM_WORKER_MULTIPROC_METHOD=spawn
          export VLLM_ALLOW_LONG_MAX_MODEL_LEN="1"
          pytest -s -v tests/e2e/online_serving/test_qwen3_tts_customvoice.py tests/e2e/offline_inference/test_qwen3_tts_customvoice.py -m "advanced_model" --run-level "advanced_model"
        '
@@ -219,6 +235,7 @@ steps:
      - |
        timeout 20m bash -c '
          export VLLM_LOGGING_LEVEL=DEBUG
+          export VLLM_WORKER_MULTIPROC_METHOD=spawn
          export VLLM_ALLOW_LONG_MAX_MODEL_LEN="1"
          pytest -s -v tests/e2e/online_serving/test_qwen3_tts_base.py tests/e2e/offline_inference/test_qwen3_tts_base.py -m "advanced_model" --run-level "advanced_model"
        '
@@ -239,6 +256,7 @@ steps:
    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 tests/e2e/online_serving/test_qwen3_omni.py tests/e2e/online_serving/test_mimo_audio.py -m "advanced_model" --run-level "advanced_model"
    agents:
@@ -279,6 +297,7 @@ steps:
    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"
@@ -321,6 +340,7 @@ steps:
      - |
        timeout 55m bash -c '
          set -e
+          export VLLM_WORKER_MULTIPROC_METHOD=spawn
          export VLLM_TEST_CLEAN_GPU_MEMORY=1
          export VLLM_IMAGE_FETCH_TIMEOUT=60
          pytest -s -v tests/e2e/offline_inference/test_bagel_text2img.py -m "advanced_model" --run-level "advanced_model" -k "shared_memory"
@@ -360,46 +380,6 @@ steps:
                  path: /mnt/hf-cache
                  type: DirectoryOrCreate

-  - label: "Omni Sleep Mode Test with H100"
-    timeout_in_minutes: 30
-    depends_on: upload-merge-pipeline
-    commands:
-      - export VLLM_TEST_CLEAN_GPU_MEMORY="1"
-      - pytest -s -v tests/e2e/offline_inference/test_omni_sleep_mode.py -m "advanced_model and H100 and omni" --run-level "advanced_model"
-    agents:
-      queue: "mithril-h100-pool"
-    plugins:
-      - kubernetes:
-          podSpec:
-            containers:
-              - image: 936637512419.dkr.ecr.us-west-2.amazonaws.com/vllm-ci-pull-through-cache/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
-                resources:
-                  limits:
-                    nvidia.com/gpu: 2
-                volumeMounts:
-                  - name: devshm
-                    mountPath: /dev/shm
-                  - name: hf-cache
-                    mountPath: /root/.cache/huggingface
-                env:
-                  - name: HF_HOME
-                    value: /root/.cache/huggingface
-                  - name: HF_TOKEN
-                    valueFrom:
-                      secretKeyRef:
-                        name: hf-token-secret
-                        key: token
-            nodeSelector:
-              node.kubernetes.io/instance-type: gpu-h100-sxm
-            volumes:
-              - name: devshm
-                emptyDir:
-                  medium: Memory
-              - name: hf-cache
-                hostPath:
-                  path: /mnt/hf-cache
-                  type: DirectoryOrCreate
-
  - label: "Voxtral-TTS E2E Test"
    timeout_in_minutes: 20
    depends_on: upload-merge-pipeline
@@ -407,6 +387,7 @@ steps:
      - |
        timeout 20m bash -c '
          export VLLM_LOGGING_LEVEL=DEBUG
+          export VLLM_WORKER_MULTIPROC_METHOD=spawn
          pytest -s -v tests/e2e/online_serving/test_voxtral_tts.py tests/e2e/offline_inference/test_voxtral_tts.py -m "advanced_model" --run-level "advanced_model"
        '
    agents:
@@ -0,0 +1,367 @@
+# Nightly diffusion GPU tests — appended to the main nightly build via
+#   buildkite-agent pipeline upload .buildkite/test-nightly-diffusion.yml
+# from test-nightly.yml (step key: nightly-diffusion-model-test). Top-level groups are
+# foldable in the Buildkite UI (Other / Wan / Qwen-Image).
+steps:
+  - group: ":card_index_dividers: Other Model Test"
+    key: nightly-other-model-test-group
+    steps:
+      - label: ":full_moon: Diffusion · Other · Function Test with H100"
+        timeout_in_minutes: 120
+        if: build.env("NIGHTLY") == "1" || build.pull_request.labels includes "nightly-test"
+        commands:
+          - export VLLM_WORKER_MULTIPROC_METHOD=spawn
+          - pytest -s -v tests/e2e/online_serving/test_*_expansion.py -k "not test_wan22_expansion and not test_wan_2_1_vace_expansion and not test_qwen_image" -m "advanced_model and diffusion and H100" --run-level "advanced_model"
+        agents:
+          queue: "mithril-h100-pool"
+        plugins:
+          - kubernetes:
+              podSpec:
+                containers:
+                  - image: 936637512419.dkr.ecr.us-west-2.amazonaws.com/vllm-ci-pull-through-cache/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
+                    resources:
+                      limits:
+                        nvidia.com/gpu: 2
+                    volumeMounts:
+                      - name: devshm
+                        mountPath: /dev/shm
+                      - name: hf-cache
+                        mountPath: /root/.cache/huggingface
+                    env:
+                      - name: HF_HOME
+                        value: /root/.cache/huggingface
+                      - name: HF_TOKEN
+                        valueFrom:
+                          secretKeyRef:
+                            name: hf-token-secret
+                            key: token
+                nodeSelector:
+                  node.kubernetes.io/instance-type: gpu-h100-sxm
+                volumes:
+                  - name: devshm
+                    emptyDir:
+                      medium: Memory
+                  - name: hf-cache
+                    hostPath:
+                      path: /mnt/hf-cache
+                      type: DirectoryOrCreate
+
+      - label: ":full_moon: Diffusion · Other · Function Test with L4"
+        timeout_in_minutes: 60
+        if: build.env("NIGHTLY") == "1" || build.pull_request.labels includes "nightly-test"
+        commands:
+          - export VLLM_WORKER_MULTIPROC_METHOD=spawn
+          - pytest -s -v tests/e2e/online_serving/test_*_expansion.py -m "advanced_model and diffusion and L4" --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:$BUILDKITE_COMMIT
+              always-pull: true
+              propagate-environment: true
+              shm-size: "8gb"
+              environment:
+                - "HF_HOME=/fsx/hf_cache"
+                - "HF_TOKEN"
+              volumes:
+                - "/fsx/hf_cache:/fsx/hf_cache"
+
+      - label: ":full_moon: Diffusion · Other · Doc Test"
+        timeout_in_minutes: 60
+        if: build.env("NIGHTLY") == "1" || build.pull_request.labels includes "nightly-test"
+        commands:
+          - export VLLM_WORKER_MULTIPROC_METHOD=spawn
+          - export VLLM_TEST_CLEAN_GPU_MEMORY="1"
+          - pytest -s -v tests/examples/online_serving/test_text_to_image.py tests/examples/offline_inference/test_text_to_image.py -m "advanced_model and example and H100" --run-level "advanced_model"
+        agents:
+          queue: "mithril-h100-pool"
+        plugins:
+          - kubernetes:
+              podSpec:
+                containers:
+                  - image: 936637512419.dkr.ecr.us-west-2.amazonaws.com/vllm-ci-pull-through-cache/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
+                    resources:
+                      limits:
+                        nvidia.com/gpu: 2
+                    volumeMounts:
+                      - name: devshm
+                        mountPath: /dev/shm
+                      - name: hf-cache
+                        mountPath: /root/.cache/huggingface
+                    env:
+                      - name: HF_HOME
+                        value: /root/.cache/huggingface
+                      - name: HF_TOKEN
+                        valueFrom:
+                          secretKeyRef:
+                            name: hf-token-secret
+                            key: token
+                nodeSelector:
+                  node.kubernetes.io/instance-type: gpu-h100-sxm
+                volumes:
+                  - name: devshm
+                    emptyDir:
+                      medium: Memory
+                  - name: hf-cache
+                    hostPath:
+                      path: /mnt/hf-cache
+                      type: DirectoryOrCreate
+
+  - group: ":card_index_dividers: Wan Series Model Test"
+    key: nightly-wan-model-test-group
+    steps:
+      - label: ":full_moon: Diffusion · Wan · Function Test"
+        timeout_in_minutes: 90
+        if: build.env("NIGHTLY") == "1" || build.pull_request.labels includes "nightly-test"
+        commands:
+          - export VLLM_WORKER_MULTIPROC_METHOD=spawn
+          - pytest -s -v tests/e2e/online_serving/test_wan22_expansion.py tests/e2e/online_serving/test_wan_2_1_vace_expansion.py -m "advanced_model" --run-level "advanced_model"
+        agents:
+          queue: "mithril-h100-pool"
+        plugins:
+          - kubernetes:
+              podSpec:
+                containers:
+                  - image: 936637512419.dkr.ecr.us-west-2.amazonaws.com/vllm-ci-pull-through-cache/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
+                    resources:
+                      limits:
+                        nvidia.com/gpu: 2
+                    volumeMounts:
+                      - name: devshm
+                        mountPath: /dev/shm
+                      - name: hf-cache
+                        mountPath: /root/.cache/huggingface
+                    env:
+                      - name: HF_HOME
+                        value: /root/.cache/huggingface
+                      - name: HF_TOKEN
+                        valueFrom:
+                          secretKeyRef:
+                            name: hf-token-secret
+                            key: token
+                nodeSelector:
+                  node.kubernetes.io/instance-type: gpu-h100-sxm
+                volumes:
+                  - name: devshm
+                    emptyDir:
+                      medium: Memory
+                  - name: hf-cache
+                    hostPath:
+                      path: /mnt/hf-cache
+                      type: DirectoryOrCreate
+
+      - label: ":full_moon: Diffusion · Wan · Accuracy Test"
+        key: nightly-wan22-i2v-accuracy
+        timeout_in_minutes: 180
+        if: build.env("NIGHTLY") == "1" || build.pull_request.labels includes "nightly-test"
+        commands:
+          - export VLLM_WORKER_MULTIPROC_METHOD=spawn
+          - pytest -s -v tests/e2e/accuracy/wan22_i2v/test_wan22_i2v_video_similarity.py --run-level advanced_model
+        agents:
+          queue: "mithril-h100-pool"
+        plugins:
+          - kubernetes:
+              podSpec:
+                containers:
+                  - image: 936637512419.dkr.ecr.us-west-2.amazonaws.com/vllm-ci-pull-through-cache/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
+                    resources:
+                      limits:
+                        nvidia.com/gpu: 2
+                    volumeMounts:
+                      - name: devshm
+                        mountPath: /dev/shm
+                      - name: hf-cache
+                        mountPath: /root/.cache/huggingface
+                    env:
+                      - name: HF_HOME
+                        value: /root/.cache/huggingface
+                      - name: HF_TOKEN
+                        valueFrom:
+                          secretKeyRef:
+                            name: hf-token-secret
+                            key: token
+                nodeSelector:
+                  node.kubernetes.io/instance-type: gpu-h100-sxm
+                volumes:
+                  - name: devshm
+                    emptyDir:
+                      medium: Memory
+                  - name: hf-cache
+                    hostPath:
+                      path: /mnt/hf-cache
+                      type: DirectoryOrCreate
+
+  - group: ":card_index_dividers: Qwen-Image Series Model Test"
+    key: nightly-qwen-image-edit-group
+    steps:
+      - label: ":full_moon: Diffusion · Qwen-Image · Function Test with H100"
+        timeout_in_minutes: 120
+        if: build.env("NIGHTLY") == "1" || build.pull_request.labels includes "nightly-test"
+        commands:
+          - export VLLM_WORKER_MULTIPROC_METHOD=spawn
+          - pytest -s -v tests/e2e/online_serving/test_qwen_image*_expansion.py -m "advanced_model and diffusion and H100" --run-level "advanced_model"
+        agents:
+          queue: "mithril-h100-pool"
+        plugins:
+          - kubernetes:
+              podSpec:
+                containers:
+                  - image: 936637512419.dkr.ecr.us-west-2.amazonaws.com/vllm-ci-pull-through-cache/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
+                    resources:
+                      limits:
+                        nvidia.com/gpu: 2
+                    volumeMounts:
+                      - name: devshm
+                        mountPath: /dev/shm
+                      - name: hf-cache
+                        mountPath: /root/.cache/huggingface
+                    env:
+                      - name: HF_HOME
+                        value: /root/.cache/huggingface
+                      - name: HF_TOKEN
+                        valueFrom:
+                          secretKeyRef:
+                            name: hf-token-secret
+                            key: token
+                nodeSelector:
+                  node.kubernetes.io/instance-type: gpu-h100-sxm
+                volumes:
+                  - name: devshm
+                    emptyDir:
+                      medium: Memory
+                  - name: hf-cache
+                    hostPath:
+                      path: /mnt/hf-cache
+                      type: DirectoryOrCreate
+
+      - label: ":full_moon: Diffusion · Qwen-Image · GEBench Accuracy Test"
+        key: nightly-gebench-accuracy
+        timeout_in_minutes: 60
+        if: build.env("NIGHTLY") == "1" || build.pull_request.labels includes "nightly-test"
+        commands:
+          - export VLLM_WORKER_MULTIPROC_METHOD=spawn
+          - pytest -s -v tests/e2e/accuracy/test_gebench_h100_smoke.py --run-level advanced_model --gebench-model Qwen/Qwen-Image-2512 --accuracy-judge-model QuantTrio/Qwen3-VL-30B-A3B-Instruct-AWQ --accuracy-gpu 0 --gebench-port 8093 --accuracy-workers 1
+          - buildkite-agent artifact upload "tests/e2e/accuracy/artifacts/gebench_qwen-image-2512/summary*.json"
+        agents:
+          queue: "mithril-h100-pool"
+        plugins:
+          - kubernetes:
+              podSpec:
+                containers:
+                  - image: 936637512419.dkr.ecr.us-west-2.amazonaws.com/vllm-ci-pull-through-cache/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
+                    resources:
+                      limits:
+                        nvidia.com/gpu: 1
+                    volumeMounts:
+                      - name: devshm
+                        mountPath: /dev/shm
+                      - name: hf-cache
+                        mountPath: /root/.cache/huggingface
+                    env:
+                      - name: HF_HOME
+                        value: /root/.cache/huggingface
+                      - name: HF_TOKEN
+                        valueFrom:
+                          secretKeyRef:
+                            name: hf-token-secret
+                            key: token
+                nodeSelector:
+                  node.kubernetes.io/instance-type: gpu-h100-sxm
+                volumes:
+                  - name: devshm
+                    emptyDir:
+                      medium: Memory
+                  - name: hf-cache
+                    hostPath:
+                      path: /mnt/hf-cache
+                      type: DirectoryOrCreate
+
+      - label: ":full_moon: Diffusion · Qwen-Image · GEdit-Bench Accuracy Test"
+        key: nightly-gedit-bench-accuracy
+        timeout_in_minutes: 60
+        if: build.env("NIGHTLY") == "1" || build.pull_request.labels includes "nightly-test"
+        commands:
+          - export VLLM_WORKER_MULTIPROC_METHOD=spawn
+          - pytest -s -v tests/e2e/accuracy/test_gedit_bench_h100_smoke.py --run-level advanced_model --gedit-model Qwen/Qwen-Image-Edit --accuracy-judge-model QuantTrio/Qwen3-VL-30B-A3B-Instruct-AWQ --accuracy-gpu 0 --gedit-port 8093 --gedit-samples-per-group 20 --accuracy-workers 1
+          - buildkite-agent artifact upload "tests/e2e/accuracy/artifacts/gedit_scores_qwen-image-edit/qwen-image-edit_all_all_vie_score_*.csv"
+          - buildkite-agent artifact upload "tests/e2e/accuracy/artifacts/gedit_scores_qwen-image-edit/qwen-image-edit_all_all_summary_*.json"
+        agents:
+          queue: "mithril-h100-pool"
+        plugins:
+          - kubernetes:
+              podSpec:
+                containers:
+                  - image: 936637512419.dkr.ecr.us-west-2.amazonaws.com/vllm-ci-pull-through-cache/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
+                    resources:
+                      limits:
+                        nvidia.com/gpu: 1
+                    volumeMounts:
+                      - name: devshm
+                        mountPath: /dev/shm
+                      - name: hf-cache
+                        mountPath: /root/.cache/huggingface
+                    env:
+                      - name: HF_HOME
+                        value: /root/.cache/huggingface
+                      - name: VLLM_HTTP_TIMEOUT_KEEP_ALIVE
+                        value: "120"
+                      - name: HF_TOKEN
+                        valueFrom:
+                          secretKeyRef:
+                            name: hf-token-secret
+                            key: token
+                nodeSelector:
+                  node.kubernetes.io/instance-type: gpu-h100-sxm
+                volumes:
+                  - name: devshm
+                    emptyDir:
+                      medium: Memory
+                  - name: hf-cache
+                    hostPath:
+                      path: /mnt/hf-cache
+                      type: DirectoryOrCreate
+
+      - label: ":full_moon: Diffusion · Qwen-Image · Perf Test"
+        key: nightly-qwen-image-performance
+        timeout_in_minutes: 180
+        if: build.env("NIGHTLY") == "1" || build.pull_request.labels includes "nightly-test"
+        commands:
+          - export VLLM_WORKER_MULTIPROC_METHOD=spawn
+          - export DIFFUSION_BENCHMARK_DIR=tests/dfx/perf/results
+          - export CACHE_DIT_VERSION=1.3.0
+          - pytest -s -v tests/dfx/perf/scripts/run_diffusion_benchmark.py --config-file tests/dfx/perf/tests/test_qwen_image_vllm_omni.json
+          - buildkite-agent artifact upload "tests/dfx/perf/results/benchmark_results_*.json"
+          - buildkite-agent artifact upload "tests/dfx/perf/results/logs/*.log"
+        agents:
+          queue: "mithril-h100-pool"
+        plugins:
+          - kubernetes:
+              podSpec:
+                containers:
+                  - image: 936637512419.dkr.ecr.us-west-2.amazonaws.com/vllm-ci-pull-through-cache/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
+                    resources:
+                      limits:
+                        nvidia.com/gpu: 4
+                    volumeMounts:
+                      - name: devshm
+                        mountPath: /dev/shm
+                      - name: hf-cache
+                        mountPath: /root/.cache/huggingface
+                    env:
+                      - name: HF_HOME
+                        value: /root/.cache/huggingface
+                      - name: HF_TOKEN
+                        valueFrom:
+                          secretKeyRef:
+                            name: hf-token-secret
+                            key: token
+                nodeSelector:
+                  node.kubernetes.io/instance-type: gpu-h100-sxm
+                volumes:
+                  - name: devshm
+                    emptyDir:
+                      medium: Memory
+                  - name: hf-cache
+                    hostPath:
+                      path: /mnt/hf-cache
+                      type: DirectoryOrCreate
@@ -1,18 +1,14 @@
-env:
-  VLLM_WORKER_MULTIPROC_METHOD: spawn
-  HF_HUB_DOWNLOAD_TIMEOUT: 300
-  HF_HUB_ETAG_TIMEOUT: 60
-
 steps:
  # Group: collapses under one heading in the Buildkite UI; child steps still run in parallel.
  - group: ":card_index_dividers: Omni Model Test"
    key: nightly-omni-test-group
-    depends_on: upload-nightly-pipeline
-    if: build.env("NIGHTLY") == "1" || build.pull_request.labels includes "nightly-test" || build.pull_request.labels includes "omni-test"
    steps:
-      - label: ":full_moon: Omni · Function Test"
+      - label: ":full_moon: Omni · Function Test with H100"
        timeout_in_minutes: 90
+        depends_on: upload-nightly-pipeline
+        if: build.env("NIGHTLY") == "1" || build.pull_request.labels includes "nightly-test"
        commands:
+          - export VLLM_WORKER_MULTIPROC_METHOD=spawn
          - pytest -s -v tests/e2e/online_serving/test_*_expansion.py -m "advanced_model and H100 and omni" --run-level "advanced_model"
        agents:
          queue: "mithril-h100-pool"
@@ -48,9 +44,34 @@ steps:
                      path: /mnt/hf-cache
                      type: DirectoryOrCreate

+      - label: ":full_moon: Omni · Function Test with L4"
+        timeout_in_minutes: 90
+        depends_on: upload-nightly-pipeline
+        if: build.env("NIGHTLY") == "1" || build.pull_request.labels includes "nightly-test"
+        commands:
+          - export VLLM_WORKER_MULTIPROC_METHOD=spawn
+          - export VLLM_ALLOW_LONG_MAX_MODEL_LEN="1"
+          - pytest -s -v tests/e2e/online_serving/test_*_expansion.py -m "advanced_model and L4 and omni" --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
+              propagate-environment: true
+              shm-size: "8gb"
+              environment:
+                - "HF_HOME=/fsx/hf_cache"
+                - "HF_TOKEN"
+              volumes:
+                - "/fsx/hf_cache:/fsx/hf_cache"
+
      - label: ":full_moon: Omni · Doc Test with L4"
        timeout_in_minutes: 90
+        depends_on: upload-nightly-pipeline
+        if: build.env("NIGHTLY") == "1" || build.pull_request.labels includes "nightly-test"
        commands:
+          - export VLLM_WORKER_MULTIPROC_METHOD=spawn
          - export VLLM_ALLOW_LONG_MAX_MODEL_LEN="1"
          - pytest -s -v tests/examples/ -m "advanced_model and omni and L4" --run-level "advanced_model"
        agents:
@@ -69,7 +90,10 @@ steps:

      - label: ":full_moon: Omni · Doc Test with H100"
        timeout_in_minutes: 90
+        depends_on: upload-nightly-pipeline
+        if: build.env("NIGHTLY") == "1" || build.pull_request.labels includes "nightly-test"
        commands:
+          - export VLLM_WORKER_MULTIPROC_METHOD=spawn
          - pytest -s -v tests/examples/ -m "advanced_model and omni and H100" --run-level "advanced_model"
        agents:
          queue: "mithril-h100-pool"
@@ -108,85 +132,15 @@ steps:
      - label: ":full_moon: Omni · Perf Test"
        key: nightly-omni-performance
        timeout_in_minutes: 180
+        depends_on: upload-nightly-pipeline
+        if: build.env("NIGHTLY") == "1" || build.pull_request.labels includes "nightly-test"
        commands:
-          - export BENCHMARK_DIR=tests/dfx/perf/results
-          - |
-            set +e
-            pytest -s -v tests/dfx/perf/scripts/run_benchmark.py --test-config-file tests/dfx/perf/tests/test_qwen_omni.json
-            EXIT=$$?
-            buildkite-agent artifact upload "tests/dfx/perf/results/*.json"
-            exit $$EXIT
-        agents:
-          queue: "mithril-h100-pool"
-        plugins:
-          - kubernetes:
-              podSpec:
-                containers:
-                  - image: 936637512419.dkr.ecr.us-west-2.amazonaws.com/vllm-ci-pull-through-cache/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
-                    resources:
-                      limits:
-                        nvidia.com/gpu: 2
-                    volumeMounts:
-                      - name: devshm
-                        mountPath: /dev/shm
-                      - name: hf-cache
-                        mountPath: /root/.cache/huggingface
-                    env:
-                      - name: HF_HOME
-                        value: /root/.cache/huggingface
-                      - name: HF_TOKEN
-                        valueFrom:
-                          secretKeyRef:
-                            name: hf-token-secret
-                            key: token
-                nodeSelector:
-                  node.kubernetes.io/instance-type: gpu-h100-sxm
-                volumes:
-                  - name: devshm
-                    emptyDir:
-                      medium: Memory
-                  - name: hf-cache
-                    hostPath:
-                      path: /mnt/hf-cache
-                      type: DirectoryOrCreate
-
-
-  - group: ":card_index_dividers: TTS Model Test"
-    key: nightly-tts-test-group
-    depends_on: upload-nightly-pipeline
-    if: build.env("NIGHTLY") == "1" || build.pull_request.labels includes "nightly-test" || build.pull_request.labels includes "tts-test"
-    steps:
-      - label: ":full_moon: TTS · Function Test"
-        timeout_in_minutes: 90
-        commands:
-          - export VLLM_ALLOW_LONG_MAX_MODEL_LEN="1"
-          - pytest -s -v tests/e2e/online_serving/test_*_expansion.py -m "advanced_model and L4 and omni" --run-level "advanced_model"
-        agents:
-          queue: "gpu_1_queue" # g6.12xlarge instance on AWS, has 4 L4 GPU
-        plugins:
-          - docker#v5.2.0:
-              image: public.ecr.aws/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
-              always-pull: true
-              propagate-environment: true
-              shm-size: "8gb"
-              environment:
-                - "HF_HOME=/fsx/hf_cache"
-                - "HF_TOKEN"
-              volumes:
-                - "/fsx/hf_cache:/fsx/hf_cache"
-
-      - label: ":full_moon: TTS · Perf Test"
-        key: nightly-tts-performance
-        timeout_in_minutes: 180
-        commands:
+          - export VLLM_WORKER_MULTIPROC_METHOD=spawn
          - export BENCHMARK_DIR=tests/dfx/perf/results
          - export VLLM_ALLOW_LONG_MAX_MODEL_LEN="1"
-          - |
-            set +e
-            pytest -s -v tests/dfx/perf/scripts/run_benchmark.py --test-config-file tests/dfx/perf/tests/test_tts.json
-            EXIT=$$?
-            buildkite-agent artifact upload "tests/dfx/perf/results/*.json"
-            exit $$EXIT
+          - pytest -s -v tests/dfx/perf/scripts/run_benchmark.py
+          - buildkite-agent artifact upload "tests/dfx/perf/results/*.json"
+          - buildkite-agent artifact upload "tests/dfx/perf/results/*.html"
        agents:
          queue: "mithril-h100-pool"
        plugins:
@@ -196,7 +150,7 @@ steps:
                  - 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
+                        nvidia.com/gpu: 2
                    volumeMounts:
                      - name: devshm
                        mountPath: /dev/shm
@@ -221,371 +175,17 @@ steps:
                      path: /mnt/hf-cache
                      type: DirectoryOrCreate

-  # Diffusion X2I suite: x2i / x2a / x2t and related non-video paths; x2v is only in "Diffusion X2V Model Test" below.
-  - group: ":card_index_dividers: Diffusion X2I(&A&T) Model Test"
-    key: nightly-diffusion-x2iat-group
+  # Dynamically appends steps from test-nightly-diffusion.yml into this build (same mechanism as
+  # pipeline.yml → test-ready.yml / test-merge.yml / test-nightly.yml). Foldable groups stay in the
+  # uploaded YAML (Other / Wan / Qwen-Image).
+  - label: ":card_index_dividers: Diffusion Model Test"
+    key: nightly-diffusion-model-test
    depends_on: upload-nightly-pipeline
-    if: >-
-      build.env("NIGHTLY") == "1" ||
-      build.pull_request.labels includes "nightly-test" ||
-      build.pull_request.labels includes "diffusion-x2iat-test"
-    steps:
-      - label: ":full_moon: Diffusion X2I(&A&T) · Function Test with H100"
-        timeout_in_minutes: 120
-        commands:
-          - pytest -s -v tests/e2e/online_serving/test_*_expansion.py -k "not test_wan22_expansion and not test_wan_2_1_vace_expansion and not hunyuan" -m "advanced_model and diffusion and H100" --run-level "advanced_model"
-        agents:
-          queue: "mithril-h100-pool"
-        plugins:
-          - kubernetes:
-              podSpec:
-                containers:
-                  - image: 936637512419.dkr.ecr.us-west-2.amazonaws.com/vllm-ci-pull-through-cache/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
-                    resources:
-                      limits:
-                        nvidia.com/gpu: 2
-                    volumeMounts:
-                      - name: devshm
-                        mountPath: /dev/shm
-                      - name: hf-cache
-                        mountPath: /root/.cache/huggingface
-                    env:
-                      - name: HF_HOME
-                        value: /root/.cache/huggingface
-                      - name: HF_TOKEN
-                        valueFrom:
-                          secretKeyRef:
-                            name: hf-token-secret
-                            key: token
-                nodeSelector:
-                  node.kubernetes.io/instance-type: gpu-h100-sxm
-                volumes:
-                  - name: devshm
-                    emptyDir:
-                      medium: Memory
-                  - name: hf-cache
-                    hostPath:
-                      path: /mnt/hf-cache
-                      type: DirectoryOrCreate
-
-      - label: ":full_moon: Diffusion X2I(&A&T) · Function Test with L4"
-        timeout_in_minutes: 60
-        commands:
-          - pytest -s -v tests/e2e/online_serving/test_*_expansion.py -k "not test_wan22_expansion and not test_wan_2_1_vace_expansion and not hunyuan" -m "advanced_model and diffusion and L4" --run-level "advanced_model"
-        agents:
-          queue: "gpu_4_queue" # g6.12xlarge instance on AWS, has 4 L4 GPU
-        plugins:
-          - docker#v5.2.0:
-              image: public.ecr.aws/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
-              always-pull: true
-              propagate-environment: true
-              shm-size: "8gb"
-              environment:
-                - "HF_HOME=/fsx/hf_cache"
-                - "HF_TOKEN"
-              volumes:
-                - "/fsx/hf_cache:/fsx/hf_cache"
-
-      - label: ":full_moon: Diffusion X2I(&A&T) · Doc Test"
-        timeout_in_minutes: 60
-        commands:
-          - export VLLM_TEST_CLEAN_GPU_MEMORY="1"
-          - pytest -s -v tests/examples/*/test_text_to_image.py -m "advanced_model and example and H100" --run-level "advanced_model"
-        agents:
-          queue: "mithril-h100-pool"
-        plugins:
-          - kubernetes:
-              podSpec:
-                containers:
-                  - image: 936637512419.dkr.ecr.us-west-2.amazonaws.com/vllm-ci-pull-through-cache/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
-                    resources:
-                      limits:
-                        nvidia.com/gpu: 2
-                    volumeMounts:
-                      - name: devshm
-                        mountPath: /dev/shm
-                      - name: hf-cache
-                        mountPath: /root/.cache/huggingface
-                    env:
-                      - name: HF_HOME
-                        value: /root/.cache/huggingface
-                      - name: HF_TOKEN
-                        valueFrom:
-                          secretKeyRef:
-                            name: hf-token-secret
-                            key: token
-                nodeSelector:
-                  node.kubernetes.io/instance-type: gpu-h100-sxm
-                volumes:
-                  - name: devshm
-                    emptyDir:
-                      medium: Memory
-                  - name: hf-cache
-                    hostPath:
-                      path: /mnt/hf-cache
-                      type: DirectoryOrCreate
-
-      - label: ":full_moon: Diffusion X2I(&A&T) · GEBench Accuracy Test"
-        timeout_in_minutes: 60
-        commands:
-          - pytest -s -v tests/e2e/accuracy/test_gebench_h100_smoke.py --run-level advanced_model --gebench-model Qwen/Qwen-Image-2512 --accuracy-judge-model QuantTrio/Qwen3-VL-30B-A3B-Instruct-AWQ --accuracy-gpu 0 --gebench-port 8093 --accuracy-workers 1
-          - buildkite-agent artifact upload "tests/e2e/accuracy/artifacts/gebench_qwen-image-2512/summary*.json"
-        agents:
-          queue: "mithril-h100-pool"
-        plugins:
-          - kubernetes:
-              podSpec:
-                containers:
-                  - image: 936637512419.dkr.ecr.us-west-2.amazonaws.com/vllm-ci-pull-through-cache/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
-                    resources:
-                      limits:
-                        nvidia.com/gpu: 1
-                    volumeMounts:
-                      - name: devshm
-                        mountPath: /dev/shm
-                      - name: hf-cache
-                        mountPath: /root/.cache/huggingface
-                    env:
-                      - name: HF_HOME
-                        value: /root/.cache/huggingface
-                      - name: HF_TOKEN
-                        valueFrom:
-                          secretKeyRef:
-                            name: hf-token-secret
-                            key: token
-                nodeSelector:
-                  node.kubernetes.io/instance-type: gpu-h100-sxm
-                volumes:
-                  - name: devshm
-                    emptyDir:
-                      medium: Memory
-                  - name: hf-cache
-                    hostPath:
-                      path: /mnt/hf-cache
-                      type: DirectoryOrCreate
-
-      - label: ":full_moon: Diffusion X2I(&A&T) · GEdit-Bench Accuracy Test"
-        timeout_in_minutes: 60
-        commands:
-          - pytest -s -v tests/e2e/accuracy/test_gedit_bench_h100_smoke.py --run-level advanced_model --gedit-model Qwen/Qwen-Image-Edit --accuracy-judge-model QuantTrio/Qwen3-VL-30B-A3B-Instruct-AWQ --accuracy-gpu 0 --gedit-port 8093 --gedit-samples-per-group 20 --accuracy-workers 1
-          - buildkite-agent artifact upload "tests/e2e/accuracy/artifacts/gedit_scores_qwen-image-edit/qwen-image-edit_all_all_vie_score_*.csv"
-          - buildkite-agent artifact upload "tests/e2e/accuracy/artifacts/gedit_scores_qwen-image-edit/qwen-image-edit_all_all_summary_*.json"
-        agents:
-          queue: "mithril-h100-pool"
-        plugins:
-          - kubernetes:
-              podSpec:
-                containers:
-                  - image: 936637512419.dkr.ecr.us-west-2.amazonaws.com/vllm-ci-pull-through-cache/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
-                    resources:
-                      limits:
-                        nvidia.com/gpu: 1
-                    volumeMounts:
-                      - name: devshm
-                        mountPath: /dev/shm
-                      - name: hf-cache
-                        mountPath: /root/.cache/huggingface
-                    env:
-                      - name: HF_HOME
-                        value: /root/.cache/huggingface
-                      - name: VLLM_HTTP_TIMEOUT_KEEP_ALIVE
-                        value: "120"
-                      - name: HF_TOKEN
-                        valueFrom:
-                          secretKeyRef:
-                            name: hf-token-secret
-                            key: token
-                nodeSelector:
-                  node.kubernetes.io/instance-type: gpu-h100-sxm
-                volumes:
-                  - name: devshm
-                    emptyDir:
-                      medium: Memory
-                  - name: hf-cache
-                    hostPath:
-                      path: /mnt/hf-cache
-                      type: DirectoryOrCreate
-
-      - label: ":full_moon: Diffusion X2I(&A&T) · Accuracy Test"
-        timeout_in_minutes: 180
-        commands:
-          - pytest -s -v tests/e2e/accuracy/test_qwen_image*.py --run-level advanced_model
-        agents:
-          queue: "mithril-h100-pool"
-        plugins:
-          - kubernetes:
-              podSpec:
-                containers:
-                  - image: 936637512419.dkr.ecr.us-west-2.amazonaws.com/vllm-ci-pull-through-cache/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
-                    resources:
-                      limits:
-                        nvidia.com/gpu: 1
-                    volumeMounts:
-                      - name: devshm
-                        mountPath: /dev/shm
-                      - name: hf-cache
-                        mountPath: /root/.cache/huggingface
-                    env:
-                      - name: HF_HOME
-                        value: /root/.cache/huggingface
-                      - name: VLLM_HTTP_TIMEOUT_KEEP_ALIVE
-                        value: "120"
-                      - name: HF_TOKEN
-                        valueFrom:
-                          secretKeyRef:
-                            name: hf-token-secret
-                            key: token
-                nodeSelector:
-                  node.kubernetes.io/instance-type: gpu-h100-sxm
-                volumes:
-                  - name: devshm
-                    emptyDir:
-                      medium: Memory
-                  - name: hf-cache
-                    hostPath:
-                      path: /mnt/hf-cache
-                      type: DirectoryOrCreate
-
-      - label: ":full_moon: Diffusion X2I(&A&T) · Perf Test"
-        key: nightly-diffusion-x2iat-performance
-        timeout_in_minutes: 180
-        commands:
-          - export DIFFUSION_BENCHMARK_DIR=tests/dfx/perf/results
-          - export DIFFUSION_ATTENTION_BACKEND=FLASH_ATTN
-          - export CACHE_DIT_VERSION=1.3.0
-          # [HACK]: run upload in the same command block as pytest.
-          # Because `exit` aborts the entire commands list.
-          - |
-            set +e
-            pytest -s -v tests/dfx/perf/scripts/run_diffusion_benchmark.py --test-config-file tests/dfx/perf/tests/test_qwen_image_vllm_omni.json
-            EXIT1=$$?
-            pytest -s -v tests/dfx/perf/scripts/run_diffusion_benchmark.py --test-config-file tests/dfx/perf/tests/test_qwen_image_edit_vllm_omni.json
-            EXIT2=$$?
-            pytest -s -v tests/dfx/perf/scripts/run_diffusion_benchmark.py --test-config-file tests/dfx/perf/tests/test_qwen_image_edit_2509_vllm_omni.json
-            EXIT3=$$?
-            pytest -s -v tests/dfx/perf/scripts/run_diffusion_benchmark.py --test-config-file tests/dfx/perf/tests/test_qwen_image_layered_vllm_omni.json
-            EXIT4=$$?
-            buildkite-agent artifact upload "tests/dfx/perf/results/diffusion_result_*.json"
-            buildkite-agent artifact upload "tests/dfx/perf/results/logs/*.log"
-            exit $$((EXIT1 | EXIT2 | EXIT3 | EXIT4))
-        agents:
-          queue: "mithril-h100-pool"
-        plugins:
-          - kubernetes:
-              podSpec:
-                containers:
-                  - image: 936637512419.dkr.ecr.us-west-2.amazonaws.com/vllm-ci-pull-through-cache/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
-                    resources:
-                      limits:
-                        nvidia.com/gpu: 4
-                    volumeMounts:
-                      - name: devshm
-                        mountPath: /dev/shm
-                      - name: hf-cache
-                        mountPath: /root/.cache/huggingface
-                    env:
-                      - name: HF_HOME
-                        value: /root/.cache/huggingface
-                      - name: HF_TOKEN
-                        valueFrom:
-                          secretKeyRef:
-                            name: hf-token-secret
-                            key: token
-                nodeSelector:
-                  node.kubernetes.io/instance-type: gpu-h100-sxm
-                volumes:
-                  - name: devshm
-                    emptyDir:
-                      medium: Memory
-                  - name: hf-cache
-                    hostPath:
-                      path: /mnt/hf-cache
-                      type: DirectoryOrCreate
-
-  # Diffusion x2v only (Wan, HunyuanVideo, …). x2i/x2a/x2t live in the X2I group above, not here.
-  - group: ":card_index_dividers: Diffusion X2V Model Test"
-    key: nightly-diffusion-x2v-group
-    depends_on: upload-nightly-pipeline
-    if: >-
-      build.env("NIGHTLY") == "1" ||
-      build.pull_request.labels includes "nightly-test" ||
-      build.pull_request.labels includes "diffusion-x2v-test"
-    steps:
-      - label: ":full_moon: Diffusion X2V · Function Test"
-        timeout_in_minutes: 90
-        commands:
-          - pytest -s -v tests/e2e/online_serving/test_wan22_expansion.py tests/e2e/online_serving/test_wan_2_1_vace_expansion.py tests/e2e/online_serving/test_hunyuan_video_15_expansion.py -m "advanced_model" --run-level "advanced_model"
-        agents:
-          queue: "mithril-h100-pool"
-        plugins:
-          - kubernetes:
-              podSpec:
-                containers:
-                  - image: 936637512419.dkr.ecr.us-west-2.amazonaws.com/vllm-ci-pull-through-cache/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
-                    resources:
-                      limits:
-                        nvidia.com/gpu: 2
-                    volumeMounts:
-                      - name: devshm
-                        mountPath: /dev/shm
-                      - name: hf-cache
-                        mountPath: /root/.cache/huggingface
-                    env:
-                      - name: HF_HOME
-                        value: /root/.cache/huggingface
-                      - name: HF_TOKEN
-                        valueFrom:
-                          secretKeyRef:
-                            name: hf-token-secret
-                            key: token
-                nodeSelector:
-                  node.kubernetes.io/instance-type: gpu-h100-sxm
-                volumes:
-                  - name: devshm
-                    emptyDir:
-                      medium: Memory
-                  - name: hf-cache
-                    hostPath:
-                      path: /mnt/hf-cache
-                      type: DirectoryOrCreate
-
-      - label: ":full_moon: Diffusion X2V · Accuracy Test"
-        timeout_in_minutes: 180
-        commands:
-          - pytest -s -v tests/e2e/accuracy/wan22_i2v/test_wan22_i2v_video_similarity.py -m advanced_model --run-level advanced_model
-        agents:
-          queue: "mithril-h100-pool"
-        plugins:
-          - kubernetes:
-              podSpec:
-                containers:
-                  - image: 936637512419.dkr.ecr.us-west-2.amazonaws.com/vllm-ci-pull-through-cache/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
-                    resources:
-                      limits:
-                        nvidia.com/gpu: 2
-                    volumeMounts:
-                      - name: devshm
-                        mountPath: /dev/shm
-                      - name: hf-cache
-                        mountPath: /root/.cache/huggingface
-                    env:
-                      - name: HF_HOME
-                        value: /root/.cache/huggingface
-                      - name: HF_TOKEN
-                        valueFrom:
-                          secretKeyRef:
-                            name: hf-token-secret
-                            key: token
-                nodeSelector:
-                  node.kubernetes.io/instance-type: gpu-h100-sxm
-                volumes:
-                  - name: devshm
-                    emptyDir:
-                      medium: Memory
-                  - name: hf-cache
-                    hostPath:
-                      path: /mnt/hf-cache
-                      type: DirectoryOrCreate
+    if: build.env("NIGHTLY") == "1" || build.pull_request.labels includes "nightly-test"
+    commands:
+      - buildkite-agent pipeline upload .buildkite/test-nightly-diffusion.yml
+    agents:
+      queue: "cpu_queue_premerge"

  - label: ":bar_chart: Testcase Statistics"
    key: nightly-testcase-statistics
@@ -593,6 +193,7 @@ steps:
    depends_on: upload-nightly-pipeline
    if: build.env("NIGHTLY") == "1" || build.pull_request.labels includes "nightly-test"
    commands:
+      - export VLLM_WORKER_MULTIPROC_METHOD=spawn
      - python tools/nightly/buildkite_testcase_statistics.py -o tests/dfx/perf/results/buildkite_testcase_statistics.html
      - buildkite-agent artifact upload "tests/dfx/perf/results/*.html"
    agents:
@@ -634,18 +235,16 @@ steps:
    key: nightly-perf-distribution
    depends_on:
      - nightly-omni-performance
-      - nightly-tts-performance
-      - nightly-diffusion-x2iat-performance
+      - nightly-qwen-image-performance
      - nightly-testcase-statistics
    if: build.env("NIGHTLY") == "1"
    commands:
      - pip install openpyxl
      - export DEFAULT_INPUT_DIR=tests/dfx/perf/results
      - export DEFAULT_OUTPUT_DIR=tests/dfx/perf/results
-      - buildkite-agent artifact download "tests/dfx/perf/results/*.json" . --step nightly-tts-performance
      - buildkite-agent artifact download "tests/dfx/perf/results/*.json" . --step nightly-omni-performance
-      - buildkite-agent artifact download "tests/dfx/perf/results/*.json" . --step nightly-diffusion-x2iat-performance
-      - buildkite-agent artifact download "tests/dfx/perf/results/*.html" . --step nightly-testcase-statistics
+      - buildkite-agent artifact download "tests/dfx/perf/results/*.json" . --step nightly-qwen-image-performance
+      - buildkite-agent artifact download "tests/dfx/perf/results/*.html" . --step nightly-omni-performance
      - python tools/nightly/generate_nightly_perf_excel.py
      - python tools/nightly/generate_nightly_perf_html.py
      - python tools/nightly/send_nightly_email.py --report-file "tests/dfx/perf/results/*.xlsx, tests/dfx/perf/results/*.html"
@@ -1,8 +1,3 @@
-env:
-  VLLM_WORKER_MULTIPROC_METHOD: spawn
-  HF_HUB_DOWNLOAD_TIMEOUT: 300
-  HF_HUB_ETAG_TIMEOUT: 60
-
 steps:
  - label: "Simple Unit Test"
    depends_on: upload-ready-pipeline
@@ -123,7 +118,7 @@ steps:
  - label: "Audio Generation Model Test"
    depends_on: upload-ready-pipeline
    commands:
-      - timeout 20m pytest -s -v tests/e2e/offline_inference/test_stable_audio_expansion.py -m "advanced_model and diffusion and L4" --run-level advanced_model
+      - 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:
@@ -178,6 +173,7 @@ steps:
    commands:
      - |
        timeout 15m bash -c '
+                export VLLM_WORKER_MULTIPROC_METHOD=spawn
                pytest -s -v tests/engine/test_async_omni_engine_abort.py
        '
    agents:
@@ -194,11 +190,35 @@ steps:
          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/online_serving/test_qwen2_5_omni.py -m "core_model" --run-level "core_model"
+        '
+    agents:
+      queue: "gpu_4_queue" # g6.12xlarge instance on AWS, has 4 L4 GPU
+    plugins:
+      - docker#v5.2.0:
+          image: public.ecr.aws/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
+          always-pull: true
+          propagate-environment: true
+          environment:
+            - "HF_HOME=/fsx/hf_cache"
+            - "HF_TOKEN"
+          volumes:
+            - "/fsx/hf_cache:/fsx/hf_cache"
+
  - label: "Omni Model Test with H100"
    depends_on: upload-ready-pipeline
    commands:
      - |
        timeout 20m bash -c '
+          export VLLM_WORKER_MULTIPROC_METHOD=spawn
          pytest -s -v tests/e2e/online_serving/test_qwen3_omni.py -m "core_model" --run-level "core_model"
        '
    agents:
@@ -236,6 +256,7 @@ steps:
      - |
        timeout 30m bash -c '
          export VLLM_LOGGING_LEVEL=DEBUG
+          export VLLM_WORKER_MULTIPROC_METHOD=spawn
          pytest -s -v tests/e2e/online_serving/test_mimo_audio.py -m "core_model" --run-level "core_model"
        '
    agents:
@@ -278,6 +299,7 @@ steps:
      - |
        timeout 20m bash -c '
          export VLLM_LOGGING_LEVEL=DEBUG
+          export VLLM_WORKER_MULTIPROC_METHOD=spawn
          export VLLM_ALLOW_LONG_MAX_MODEL_LEN="1"
          pytest -s -v tests/e2e/online_serving/test_qwen3_tts_customvoice.py -m "core_model" --run-level "core_model"
        '
@@ -295,56 +317,6 @@ steps:
          volumes:
            - "/fsx/hf_cache:/fsx/hf_cache"

-  - label: "VoxCPM E2E Test"
-    timeout_in_minutes: 20
-    depends_on: upload-ready-pipeline
-    commands:
-      - |
-        timeout 20m bash -c '
-          pip install voxcpm
-          export VLLM_LOGGING_LEVEL=DEBUG
-          export VLLM_WORKER_MULTIPROC_METHOD=spawn
-          pytest -s -v tests/e2e/offline_inference/test_voxcpm.py -m "core_model" --run-level "core_model"
-        '
-    agents:
-      queue: "gpu_1_queue"
-    plugins:
-      - docker#v5.2.0:
-          image: public.ecr.aws/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
-          always-pull: true
-          propagate-environment: true
-          shm-size: "8gb"
-          environment:
-            - "HF_HOME=/fsx/hf_cache"
-            - "HF_TOKEN"
-          volumes:
-            - "/fsx/hf_cache:/fsx/hf_cache"
-
-  - label: "VoxCPM2 Native AR E2E Test"
-    timeout_in_minutes: 20
-    depends_on: upload-ready-pipeline
-    commands:
-      - |
-        timeout 20m bash -c '
-          pip install voxcpm
-          export VLLM_LOGGING_LEVEL=DEBUG
-          export VLLM_WORKER_MULTIPROC_METHOD=spawn
-          pytest -s -v tests/e2e/offline_inference/test_voxcpm2.py -m "core_model" --run-level "core_model"
-        '
-    agents:
-      queue: "gpu_1_queue"
-    plugins:
-      - docker#v5.2.0:
-          image: public.ecr.aws/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
-          always-pull: true
-          propagate-environment: true
-          shm-size: "8gb"
-          environment:
-            - "HF_HOME=/fsx/hf_cache"
-            - "HF_TOKEN"
-          volumes:
-            - "/fsx/hf_cache:/fsx/hf_cache"
-
  - label: "OmniVoice E2E Test"
    timeout_in_minutes: 20
    depends_on: upload-ready-pipeline
@@ -352,6 +324,7 @@ steps:
      - |
        timeout 20m bash -c '
          export VLLM_LOGGING_LEVEL=DEBUG
+          export VLLM_WORKER_MULTIPROC_METHOD=spawn
          pytest -s -v tests/e2e/online_serving/test_omnivoice.py -m "core_model" --run-level "core_model"
        '
    agents:
@@ -367,33 +340,6 @@ steps:
          volumes:
            - "/fsx/hf_cache:/fsx/hf_cache"

-  - label: "Qwen3-TTS Base E2E Test (ModelRunner V2)"
-    depends_on: upload-ready-pipeline
-    soft_fail:
-      - exit_status: 1
-    commands:
-      - |
-        timeout 20m bash -c '
-          export VLLM_LOGGING_LEVEL=DEBUG
-          export VLLM_WORKER_MULTIPROC_METHOD=spawn
-          export VLLM_ALLOW_LONG_MAX_MODEL_LEN="1"
-          export VLLM_OMNI_USE_V2_RUNNER="1"
-          pytest -s -v tests/e2e/online_serving/test_qwen3_tts_base.py -m "core_model" --run-level "core_model"
-        '
-    agents:
-      queue: "gpu_1_queue"
-    plugins:
-      - docker#v5.2.0:
-          image: public.ecr.aws/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
-          always-pull: true
-          propagate-environment: true
-          shm-size: "8gb"
-          environment:
-            - "HF_HOME=/fsx/hf_cache"
-            - "HF_TOKEN"
-          volumes:
-            - "/fsx/hf_cache:/fsx/hf_cache"
-
  - label: "Voxtral-TTS E2E Test"
    timeout_in_minutes: 20
    depends_on: upload-ready-pipeline
@@ -401,6 +347,7 @@ steps:
      - |
        timeout 20m bash -c '
          export VLLM_LOGGING_LEVEL=DEBUG
+          export VLLM_WORKER_MULTIPROC_METHOD=spawn
          pytest -s -v tests/e2e/online_serving/test_voxtral_tts.py -m "core_model" --run-level "core_model"
        '
    agents:
@@ -437,6 +384,7 @@ steps:
  #   commands:
  #     - |
  #       timeout 20m bash -c '
+  #         export VLLM_WORKER_MULTIPROC_METHOD=spawn
  #         pytest -s -v tests/e2e/online_serving/test_image_gen_edit.py
  #       '
  #   agents:
@@ -473,6 +421,7 @@ steps:
    commands:
      - |
        timeout 30m bash -c '
+          export VLLM_WORKER_MULTIPROC_METHOD=spawn
          export VLLM_TEST_CLEAN_GPU_MEMORY=1
          pytest -s -v tests/e2e/offline_inference/test_bagel_text2img.py -m "core_model" --run-level "core_model"
        '
@@ -515,6 +464,7 @@ steps:
    commands:
      - |
        timeout 30m bash -c '
+          export VLLM_WORKER_MULTIPROC_METHOD=spawn
          export VLLM_TEST_CLEAN_GPU_MEMORY=1
          pytest -s -v tests/e2e/offline_inference/test_bagel_img2img.py -m "core_model" --run-level "core_model"
        '
@@ -557,6 +507,7 @@ steps:
    commands:
      - |
        timeout 40m bash -c '
+          export VLLM_WORKER_MULTIPROC_METHOD=spawn
          export VLLM_TEST_CLEAN_GPU_MEMORY=1
          export VLLM_IMAGE_FETCH_TIMEOUT=60
          pytest -s -v tests/e2e/online_serving/test_bagel_online.py -m "core_model" --run-level "core_model"
@@ -601,6 +552,7 @@ steps:
    commands:
      - |
        timeout 20m bash -c '
+          export VLLM_WORKER_MULTIPROC_METHOD=spawn
          pytest -s -v tests/e2e/online_serving/test_cosyvoice3_tts.py -m "core_model" --run-level "core_model"
        '
    agents:
@@ -3,7 +3,7 @@
   Last synced: 2025-12-15
   Modifications: Removed unused CUDA/NVIDIA logic, keeping only AMD tests
 #}
-{% set docker_image_amd = "rocm/vllm-omni:$BUILDKITE_COMMIT" %}
+{% set docker_image_amd = "rocm/vllm-ci:$BUILDKITE_COMMIT-rocm-omni" %}
 {% set default_working_dir = "/app/vllm-omni" %}

  - group: "AMD Tests"
@@ -48,9 +48,6 @@
          DOCKER_BUILDKIT: "1"
          TEST_COMMAND: |-
            (command rocm-smi || true) && cd {{ (step.working_dir or default_working_dir) | safe }}
-{% if "mi250" in step.agent_pool %}
-            python3 -m pip uninstall -y amd-aiter
-{% endif %}
            {{ indented_cmd | safe }}
        priority: 100
        {% if step.grade and step.grade == "Blocking" %}
@@ -1,300 +0,0 @@
---
-name: vllm-omni-npu-model-runner-upgrade
-description: "Upgrade vllm-omni NPU model runners (OmniNPUModelRunner, NPUARModelRunner, NPUGenerationModelRunner) to align with the latest vllm-ascend NPUModelRunner while preserving omni-specific logic."
---
-
-# vLLM-Omni NPU Model Runner Upgrade Skill
-
-## Overview
-
-This skill guides the process of upgrading vllm-omni's NPU model runners to align with the latest vllm-ascend codebase while preserving omni-specific enhancements. The NPU runners are designed to run omni multimodal models (like Qwen3-Omni, Bagel, MiMoAudio) on Ascend NPUs.
-
-## File Structure
-
-### NPU Model Runner Files
-```
-vllm-omni/vllm_omni/platforms/npu/worker/
-├── __init__.py
-├── npu_model_runner.py           # OmniNPUModelRunner (base class)
-├── npu_ar_model_runner.py        # NPUARModelRunner (autoregressive)
-├── npu_ar_worker.py              # AR worker
-├── npu_generation_model_runner.py # NPUGenerationModelRunner (diffusion/non-AR)
-└── npu_generation_worker.py      # Generation worker
-```
-
-### GPU Reference Files (for omni-specific logic sync)
-```
-vllm-omni/vllm_omni/worker/
-├── __init__.py
-├── gpu_model_runner.py           # OmniGPUModelRunner
-├── gpu_ar_model_runner.py        # GPUARModelRunner
-├── gpu_ar_worker.py
-├── gpu_generation_model_runner.py
-├── gpu_generation_worker.py
-├── mixins.py
-├── base.py
-└── gpu_memory_utils.py
-```
-
-### vllm-ascend Reference Files
-```
-vllm-ascend/vllm_ascend/worker/
-├── model_runner_v1.py            # NPUModelRunner (base class to copy from)
-├── npu_input_batch.py
-├── block_table.py
-├── pcp_utils.py
-└── worker.py
-```
-
-## Inheritance Hierarchy
-
-```
-                    GPUModelRunner (vllm)
-                         |
-        +----------------+----------------+
-        |                                 |
-  OmniGPUModelRunner              NPUModelRunner (vllm-ascend)
-  (vllm_omni/worker)              (vllm_ascend/worker)
-        |                                 |
-        +----------- OmniNPUModelRunner --+
-                     (multiple inheritance)
-                            |
-            +---------------+---------------+
-            |                               |
-    NPUARModelRunner            NPUGenerationModelRunner
-    (autoregressive)            (non-autoregressive/diffusion)
-```
-
-## Omni-Specific Comment Markers
-
-Omni-specific logic is marked with comment blocks:
-```python
-# -------------------------------------- Omni-new -------------------------------------------------
-# ... omni-specific code ...
-# -------------------------------------- Omni-new -------------------------------------------------
-```
-
-Or simpler variations:
-```python
-#  -------------------------------------- Omni-new -------------------------------------------------
-#  ------------------------------------------------------------------------------------------------
-```
-
-**Important**:
- Always preserve and add these markers when modifying code.
- **The reference documents (`references/omni-specific-blocks.md`) may not be up-to-date.** Always grep for `Omni-new` in the GPU implementations to find the authoritative list of omni-specific blocks.
- When you discover new omni-specific code that is not documented in the references, please update the reference files.
-
-## Key Methods Requiring Attention
-
-### OmniNPUModelRunner (npu_model_runner.py)
-
-| Method | Description | Omni-Specific Logic |
-|--------|-------------|---------------------|
-| `load_model` | Load model and initialize talker_mtp | Uses `ACLGraphWrapper` instead of `CUDAGraphWrapper`, initializes talker buffers |
-| `_dummy_run` | Warmup/profiling run | talker_mtp dummy forward, `extract_multimodal_outputs` |
-| `_model_forward` | Forward pass wrapper | Injects `model_kwargs_extra`, wraps with `OmniOutput`, NPU-specific graph updates |
-| `_talker_mtp_forward` | Talker MTP forward for Qwen3-Omni | Uses `set_ascend_forward_context` |
-
-### NPUARModelRunner (npu_ar_model_runner.py)
-
-| Method | Description | Omni-Specific Logic |
-|--------|-------------|---------------------|
-| `__init__` | Initialize with KV transfer manager | `OmniKVTransferManager` setup |
-| `execute_model` | Main inference entry | KV transfer handling, `_update_states` override, `extract_multimodal_outputs` |
-| `sample_tokens` | Token sampling | Hidden states extraction, multimodal outputs processing, `OmniModelRunnerOutput` |
-| `_resolve_global_request_id` | Request ID resolution | For disaggregated inference |
-
-### NPUGenerationModelRunner (npu_generation_model_runner.py)
-
-| Method | Description | Omni-Specific Logic |
-|--------|-------------|---------------------|
-| `_update_request_states` | Update request states for async chunk | async_chunk handling |
-| `execute_model` | Generation forward | async_chunk, `seq_token_counts`, `_run_generation_model` |
-| `sample_tokens` | Output processing | multimodal output packaging to `OmniModelRunnerOutput` |
-| `_dummy_run` | Dummy run override | model_kwargs initialization, multimodal extraction |
-| `_run_generation_model` | Run generation model | Calls `_model_forward` with sampler |
-
-## Upgrade Workflow
-
-### Step 1: Preparation
-
-1. **Identify target versions**(Use gh cli to check):
-   - We're using vllm-omni main branch
-   - Check the last release of vllm-omni
-   - Target vllm-ascend version(Just directly use the local latest vllm-ascend code)
-
-2. **Check GPU-side changes** (since last release):
-   ```bash
-   cd /root/vllm-workspace/vllm-omni
-   git log --oneline --since="<last-release-date>" -- vllm_omni/worker/
-   ```
-
-3. **Read latest vllm-ascend code**:
-   - We don't track vllm-ascend changes - just directly use the latest code from `/root/vllm-workspace/vllm-ascend/vllm_ascend/worker/model_runner_v1.py`
-   - Copy the relevant methods and re-insert omni-specific blocks
-
-### Step 2: Analyze Omni-Specific Logic
-
-For each NPU model runner file:
-
-1. **Extract existing omni-specific blocks**:
-   ```bash
-   grep -n "Omni-new" vllm_omni/platforms/npu/worker/npu_model_runner.py
-   ```
-
-2. **Document each omni block**:
-   - Which method it belongs to
-   - What functionality it provides
-   - Dependencies on other omni code
-
-### Step 3: Update Base Class (OmniNPUModelRunner)
-
-**Note**: Always check the GPU implementation `gpu_model_runner.py` for any new omni logic not yet documented in references.
-
-1. **Read the latest vllm-ascend `NPUModelRunner.load_model`**
-2. **Copy the method, keeping the structure**
-3. **Re-insert omni-specific logic** (check GPU `gpu_model_runner.py` for authoritative list):
-   - Replace `CUDAGraphWrapper` with `ACLGraphWrapper`
-   - Keep talker_mtp initialization
-   - Preserve buffer allocations for talker
-   - Check for any new omni blocks added since last sync
-
-4. **Update `_dummy_run`**:
-   - Copy from vllm-ascend
-   - Compare with GPU `_dummy_run` for omni-specific blocks
-   - Re-insert all `Omni-new` marked code from GPU version
-
-5. **Update `_model_forward`**:
-   - Keep the omni wrapper logic
-   - Update NPU-specific parts (graph params, SP all-gather)
-   - Check GPU version for any new omni logic
-
-### Step 4: Update AR Model Runner
-
-1. **Compare with GPU `gpu_ar_model_runner.py`** for any new omni features
-2. **Copy `execute_model` from vllm-ascend**
-3. **Re-insert omni blocks** (reference `references/omni-specific-blocks.md`, but note it may be incomplete):
-   - **IMPORTANT**: Always check the GPU implementation `gpu_ar_model_runner.py` for all `Omni-new` marked code blocks
-   - The reference doc may not include newly added omni logic - treat it as a starting point, not exhaustive
-   - When discovering new omni code blocks, please update `references/omni-specific-blocks.md`
-   - Common omni blocks include but are not limited to: KV transfer, multimodal outputs, sampling_metadata handling, etc.
-
-4. **Update `sample_tokens`** (also compare with GPU implementation):
-   - Compare with `gpu_ar_model_runner.py`'s `sample_tokens` method
-   - Identify all `Omni-new` marked code blocks
-   - Ensure NPU version includes all omni-specific logic
-
-### Step 5: Update Generation Model Runner
-
-**Note**: Generation model runner may have unique omni logic for diffusion/non-AR models.
-
-1. **Compare with GPU `gpu_generation_model_runner.py`** - grep for all `Omni-new` blocks
-2. **Update `execute_model`**:
-   - Check GPU version for all omni-specific blocks
-   - Keep async_chunk handling
-   - Keep `seq_token_counts` injection
-   - Update forward/context setup from vllm-ascend
-   - Look for any new omni logic not documented in references
-
-3. **Update `_dummy_run`**:
-   - Copy from vllm-ascend base
-   - Compare with GPU `_dummy_run` if exists
-   - Re-insert all omni-specific logic
-
-### Step 6: Update Imports
-
-Check and update imports at the top of each file:
-
-```python
-# Common vllm-ascend imports
-from vllm_ascend.ascend_forward_context import get_forward_context, set_ascend_forward_context
-from vllm_ascend.attention.attention_v1 import AscendAttentionState
-from vllm_ascend.attention.utils import using_paged_attention
-from vllm_ascend.compilation.acl_graph import ACLGraphWrapper, update_full_graph_params
-from vllm_ascend.ops.rotary_embedding import update_cos_sin
-from vllm_ascend.utils import enable_sp, lmhead_tp_enable
-from vllm_ascend.worker.model_runner_v1 import SEQ_LEN_WITH_MAX_PA_WORKSPACE, NPUModelRunner
-
-# Omni-specific imports
-from vllm_omni.model_executor.models.output_templates import OmniOutput
-from vllm_omni.worker.gpu_model_runner import OmniGPUModelRunner
-from vllm_omni.outputs import OmniModelRunnerOutput
-from vllm_omni.distributed.omni_connectors.kv_transfer_manager import OmniKVTransferManager
-```
-
-### Step 7: Sync GPU-Side Omni Changes
-
-1. **Check recent GPU worker changes**:
-   ```bash
-   git diff <from-tag>..<to-tag> -- vllm_omni/worker/gpu_model_runner.py
-   git diff <from-tag>..<to-tag> -- vllm_omni/worker/gpu_ar_model_runner.py
-   ```
-
-2. **Identify new omni features** that need to be ported to NPU
-
-3. **Apply corresponding changes** to NPU runners
-
-### Step 8: Validation
-
-1. **Run type checking**:
-   ```bash
-   cd /root/vllm-workspace/vllm-omni
-   python -m py_compile vllm_omni/platforms/npu/worker/npu_model_runner.py
-   python -m py_compile vllm_omni/platforms/npu/worker/npu_ar_model_runner.py
-   python -m py_compile vllm_omni/platforms/npu/worker/npu_generation_model_runner.py
-   ```
-
-2. **Run import test**:
-   ```bash
-   python -c "from vllm_omni.platforms.npu.worker import *"
-   ```
-
-3. **Run model serving test** (if hardware available):
-   ```bash
-   vllm serve <model-path> --trust-remote-code
-   ```
-
-## Common Pitfalls
-
-### 1. Forward Context Differences
- GPU uses `set_forward_context`
- NPU uses `set_ascend_forward_context`
- Parameters may differ slightly
-
-### 2. Graph Wrapper Differences
- GPU: `CUDAGraphWrapper`
- NPU: `ACLGraphWrapper`
- Constructor parameters may differ
-
-### 3. Buffer Creation
- GPU: `_make_buffer` returns different structure
- NPU: May need numpy=True/False parameter
-
-### 4. Attention Metadata
- GPU: Uses vllm attention metadata builders
- NPU: Uses `AscendCommonAttentionMetadata`
-
-### 5. Sampling
- GPU: Uses vllm sampler
- NPU: Uses `AscendSampler`
-
-## Checklist Before Commit
-
- [ ] All omni-specific comment markers preserved
- [ ] New omni logic from GPU side synced
- [ ] Imports updated to latest vllm-ascend
- [ ] No `CUDAGraphWrapper` references in NPU code
- [ ] `set_ascend_forward_context` used instead of `set_forward_context`
- [ ] `ACLGraphWrapper` used for talker_mtp wrapping
- [ ] Type hints match vllm-ascend signatures
- [ ] No duplicate code blocks
- [ ] Python syntax valid (py_compile passes)
-
-## Reference Files for Comparison
-
-When upgrading, keep these files open for reference:
-
-1. **vllm-ascend NPUModelRunner**: `/root/vllm-workspace/vllm-ascend/vllm_ascend/worker/model_runner_v1.py`
-2. **vllm GPUModelRunner**: `/root/vllm-workspace/vllm/vllm/v1/worker/gpu_model_runner.py`
-3. **vllm-omni OmniGPUModelRunner**: `/root/vllm-workspace/vllm-omni/vllm_omni/worker/gpu_model_runner.py`
@@ -1,335 +0,0 @@
-# GPU to NPU Translation Patterns
-
-This document provides a quick reference for translating GPU code patterns to NPU equivalents when porting omni-specific logic.
-
-## Import Translations
-
-### Forward Context
-```python
-# GPU
-from vllm.forward_context import set_forward_context
-
-# NPU
-from vllm_ascend.ascend_forward_context import set_ascend_forward_context
-```
-
-### Graph Wrapper
-```python
-# GPU
-from vllm.compilation.cuda_graph import CUDAGraphWrapper
-
-# NPU
-from vllm_ascend.compilation.acl_graph import ACLGraphWrapper
-```
-
-### Attention State
-```python
-# GPU (no equivalent - uses FlashAttention states directly)
-
-# NPU
-from vllm_ascend.attention.attention_v1 import AscendAttentionState
-```
-
-### Utilities
-```python
-# GPU
-# (directly use torch.cuda functions)
-
-# NPU
-from vllm_ascend.utils import enable_sp, lmhead_tp_enable
-from vllm_ascend.ops.rotary_embedding import update_cos_sin
-```
-
-## Context Manager Translations
-
-### Forward Context Setup
-```python
-# GPU
-with set_forward_context(
-    attn_metadata,
-    self.vllm_config,
-    num_tokens=num_tokens_padded,
-    num_tokens_across_dp=num_tokens_across_dp,
-    cudagraph_runtime_mode=cudagraph_mode,
-    batch_descriptor=batch_desc,
-):
-    # forward pass
-
-# NPU
-with set_ascend_forward_context(
-    attn_metadata,
-    self.vllm_config,
-    num_tokens=num_tokens_padded,
-    num_tokens_across_dp=num_tokens_across_dp,
-    aclgraph_runtime_mode=cudagraph_mode,  # Note: 'aclgraph' not 'cudagraph'
-    batch_descriptor=batch_desc,
-    num_actual_tokens=scheduler_output.total_num_scheduled_tokens,
-    model_instance=self.model,
-):
-    # forward pass
-```
-
-### Graph Capture Context
-```python
-# GPU
-from vllm.compilation.cuda_graph import graph_capture as cuda_graph_capture
-with cuda_graph_capture(self.device):
-    # capture
-
-# NPU
-from vllm_ascend.worker.model_runner_v1 import graph_capture
-with graph_capture(self.device):
-    # capture
-```
-
-## Graph Wrapper Usage
-
-### Creating Graph Wrapper
-```python
-# GPU
-if cudagraph_mode.has_full_cudagraphs() and has_separate_talker:
-    self.talker_mtp = CUDAGraphWrapper(
-        talker_mtp,
-        self.vllm_config,
-        runtime_mode=CUDAGraphMode.FULL
-    )
-
-# NPU
-if cudagraph_mode.has_full_cudagraphs() and has_separate_talker:
-    self.talker_mtp = ACLGraphWrapper(
-        talker_mtp,
-        self.vllm_config,
-        runtime_mode=CUDAGraphMode.FULL
-    )
-```
-
-### Checking Graph Wrapper Type
-```python
-# GPU
-if not isinstance(self.talker_mtp, CUDAGraphWrapper):
-    _cudagraph_mode = CUDAGraphMode.NONE
-
-# NPU
-if not isinstance(self.talker_mtp, ACLGraphWrapper):
-    _cudagraph_mode = CUDAGraphMode.NONE
-```
-
-## Device Operations
-
-### Synchronization
-```python
-# GPU
-torch.cuda.synchronize()
-
-# NPU
-torch.npu.synchronize()
-```
-
-### Stream Operations
-```python
-# GPU
-stream = torch.cuda.Stream(device=device)
-torch.cuda.current_stream()
-
-# NPU
-stream = torch.npu.Stream(device=device)
-torch.npu.current_stream()
-```
-
-## Attention Metadata
-
-### State Setting (NPU-specific)
-```python
-# GPU - handled internally by attention backends
-
-# NPU - explicit state setting required
-self.attn_state = AscendAttentionState.DecodeOnly
-if self.speculative_config and self.speculative_config.method == "mtp":
-    if self.vllm_config.model_config.use_mla:
-        self.attn_state = AscendAttentionState.SpecDecoding
-    else:
-        self.attn_state = AscendAttentionState.ChunkedPrefill
-```
-
-### Building Attention Metadata
-```python
-# GPU - uses vllm attention builders
-
-# NPU - may need additional parameters
-(attn_metadata, spec_decode_common_attn_metadata) = self._build_attention_metadata(
-    num_tokens=num_tokens_unpadded,
-    num_tokens_padded=num_tokens_padded,
-    num_reqs=num_reqs,
-    num_reqs_padded=num_reqs_padded,
-    max_query_len=max_num_scheduled_tokens,
-    ubatch_slices=ubatch_slices_attn,
-    logits_indices=logits_indices,
-    use_spec_decode=use_spec_decode,
-    num_scheduled_tokens=scheduler_output.num_scheduled_tokens,
-    num_scheduled_tokens_np=num_scheduled_tokens_np,
-    cascade_attn_prefix_lens=cascade_attn_prefix_lens,
-)
-```
-
-## Rotary Embedding
-
-### Update Cos/Sin Cache
-```python
-# GPU - typically handled inside attention
-
-# NPU - explicit update required before forward
-from vllm_ascend.ops.rotary_embedding import update_cos_sin
-update_cos_sin(positions)
-```
-
-## Sequence Parallelism
-
-### Enable SP Check
-```python
-# GPU - use vllm distributed utilities
-
-# NPU - use vllm-ascend wrapper
-from vllm_ascend.utils import enable_sp
-
-if enable_sp():
-    # sequence parallelism enabled
-```
-
-## Sampler
-
-### Sampler Type
-```python
-# GPU - uses vllm sampler
-self.sampler = Sampler()
-
-# NPU - uses AscendSampler
-from vllm_ascend.sample.sampler import AscendSampler
-self.sampler = AscendSampler()
-```
-
-## Input Batch
-
-### Batch Class
-```python
-# GPU
-from vllm.v1.worker.gpu_input_batch import InputBatch
-
-# NPU
-from vllm_ascend.worker.npu_input_batch import NPUInputBatch
-```
-
-## Graph Parameter Updates
-
-### Full Graph Params Update (NPU-specific)
-```python
-# GPU - not needed
-
-# NPU - required for FULL graph mode
-from vllm_ascend.compilation.acl_graph import update_full_graph_params
-
-forward_context = get_forward_context()
-if (
-    forward_context.cudagraph_runtime_mode == CUDAGraphMode.FULL
-    and not forward_context.capturing
-    and not self.use_sparse
-):
-    update_full_graph_params(
-        self.attn_backend,
-        self.update_stream,
-        forward_context,
-        num_tokens_padded,
-        self.vllm_config,
-        self.speculative_config,
-        positions.shape[0],
-    )
-```
-
-## Paged Attention Check
-
-```python
-# GPU - not typically needed
-
-# NPU
-from vllm_ascend.attention.utils import using_paged_attention
-
-if is_graph_capturing and using_paged_attention(num_tokens, self.vllm_config):
-    seq_lens = SEQ_LEN_WITH_MAX_PA_WORKSPACE
-```
-
-## Common Method Signature Differences
-
-### _dummy_run Parameters
-```python
-# GPU (v0.17.0)
-def _dummy_run(
-    self,
-    num_tokens: int,
-    cudagraph_runtime_mode: CUDAGraphMode | None = None,
-    force_attention: bool = False,
-    uniform_decode: bool = False,
-    allow_microbatching: bool = True,
-    skip_eplb: bool = False,
-    is_profile: bool = False,
-    create_mixed_batch: bool = False,
-    remove_lora: bool = True,
-    is_graph_capturing: bool = False,
-    num_active_loras: int = 0,
-) -> tuple[torch.Tensor, torch.Tensor]:
-
-# NPU (v0.17.0) - adds with_prefill, activate_lora
-def _dummy_run(
-    self,
-    num_tokens: int,
-    with_prefill: bool = False,
-    cudagraph_runtime_mode: CUDAGraphMode | None = None,
-    force_attention: bool = False,
-    uniform_decode: bool = False,
-    is_profile: bool = False,
-    create_mixed_batch: bool = False,
-    allow_microbatching: bool = True,
-    skip_eplb: bool = False,
-    remove_lora: bool = True,
-    activate_lora: bool = False,
-    is_graph_capturing: bool = False,
-    num_active_loras: int = 0,
-) -> tuple[torch.Tensor, torch.Tensor]:
-```
-
-### _model_forward Parameters
-```python
-# GPU - no num_tokens_padded
-def _model_forward(
-    self,
-    input_ids: torch.Tensor | None = None,
-    positions: torch.Tensor | None = None,
-    intermediate_tensors: IntermediateTensors | None = None,
-    inputs_embeds: torch.Tensor | None = None,
-    **model_kwargs: dict[str, Any],
-):
-
-# NPU - has num_tokens_padded as first parameter
-def _model_forward(
-    self,
-    num_tokens_padded: int,
-    input_ids: torch.Tensor | None = None,
-    positions: torch.Tensor | None = None,
-    intermediate_tensors: IntermediateTensors | None = None,
-    inputs_embeds: torch.Tensor | None = None,
-    **model_kwargs: dict[str, Any],
-):
-```
-
-## Quick Reference Table
-
-| Feature | GPU | NPU |
-|---------|-----|-----|
-| Graph wrapper | `CUDAGraphWrapper` | `ACLGraphWrapper` |
-| Forward context | `set_forward_context` | `set_ascend_forward_context` |
-| Runtime mode param | `cudagraph_runtime_mode` | `aclgraph_runtime_mode` |
-| Device sync | `torch.cuda.synchronize()` | `torch.npu.synchronize()` |
-| Stream | `torch.cuda.Stream` | `torch.npu.Stream` |
-| Current stream | `torch.cuda.current_stream()` | `torch.npu.current_stream()` |
-| Input batch | `InputBatch` | `NPUInputBatch` |
-| Sampler | `Sampler` | `AscendSampler` |
-| Attention state | N/A | `AscendAttentionState` |
-| RoPE update | N/A | `update_cos_sin()` |
@@ -1,374 +0,0 @@
-# Omni-Specific Code Blocks Reference
-
-This document catalogs omni-specific code blocks in the NPU model runners, making it easier to identify what needs to be preserved during upgrades.
-
-> **IMPORTANT**: This document may not be complete or up-to-date!
->
-> - Always grep for `Omni-new` in the GPU implementations (`vllm_omni/worker/`) to find the authoritative list
-> - New omni features may be added that are not yet documented here
-> - When you discover new omni-specific blocks during an upgrade, please update this document
-> - Last verified: Check git history for this file
-
-## OmniNPUModelRunner (npu_model_runner.py)
-
-### load_model - Talker MTP Initialization
-
-```python
-def load_model(self, *args, **kwargs) -> None:
-    NPUModelRunner.load_model(self, *args, **kwargs)
-    # Initialize enable_sp cache to avoid get_current_vllm_config() error
-    # in _pad_for_sequence_parallelism during execute_model.
-    # This is a workaround for vllm-ascend not passing vllm_config to enable_sp().
-    enable_sp(self.vllm_config)
-    # TODO move this model specific logic to a separate class
-    # TTS model IS the talker (no .talker sub-attr); use getattr to support both Omni and TTS.
-    talker_mtp = getattr(self.model, "talker_mtp", None)
-    if talker_mtp is not None:
-        self.talker_mtp = talker_mtp  # type: ignore[assignment]
-        cudagraph_mode = self.compilation_config.cudagraph_mode
-        assert cudagraph_mode is not None
-        # Only wrap talker_mtp in CUDAGraphWrapper for Omni models that
-        # have a separate .talker sub-module.  TTS models' code predictor
-        # has internal AR loops / torch.multinomial — not graph-safe.
-        has_separate_talker = getattr(self.model, "talker", None) is not None
-        if cudagraph_mode.has_full_cudagraphs() and has_separate_talker:
-            # NOTE: Use ACLGraphWrapper on NPU, not CUDAGraphWrapper
-            self.talker_mtp = ACLGraphWrapper(talker_mtp, self.vllm_config, runtime_mode=CUDAGraphMode.FULL)
-        # TTS exposes mtp_hidden_size; Omni uses hf_text_config.hidden_size.
-        hidden_size = int(
-            getattr(self.model, "mtp_hidden_size", 0) or getattr(self.model_config.hf_text_config, "hidden_size")
-        )
-        max_batch_size = max(self.max_num_reqs, self.compilation_config.max_cudagraph_capture_size)
-        self.talker_mtp_input_ids = self._make_buffer(max_batch_size, dtype=torch.int32)
-        self.talker_mtp_inputs_embeds = self._make_buffer(
-            max_batch_size, hidden_size, dtype=self.dtype, numpy=False
-        )
-        self.last_talker_hidden = self._make_buffer(max_batch_size, hidden_size, dtype=self.dtype, numpy=False)
-        self.text_step = self._make_buffer(max_batch_size, hidden_size, dtype=self.dtype, numpy=False)
-```
-
-### _dummy_run - Talker MTP Dummy Forward
-
-Location: Inside `set_ascend_forward_context` block, before main model forward
-
-```python
-# ---------------------------------------Omni-new----------------------------------------------
-if getattr(self.model, "talker", None) is not None and hasattr(self.model, "talker_mtp"):
-    num_tokens_padded_talker_mtp = num_tokens_padded
-    if num_tokens_padded_talker_mtp == self.max_num_tokens:
-        num_tokens_padded_talker_mtp = self.talker_mtp_input_ids.gpu.shape[0]
-    outputs = self.talker_mtp(
-        self.talker_mtp_input_ids.gpu[:num_tokens_padded_talker_mtp],
-        self.talker_mtp_inputs_embeds.gpu[:num_tokens_padded_talker_mtp],
-        self.last_talker_hidden.gpu[:num_tokens_padded_talker_mtp],
-        self.text_step.gpu[:num_tokens_padded_talker_mtp],
-    )
-    self.compilation_config.cache_dir = None
-# ---------------------------------------Omni-new----------------------------------------------
-```
-
-### _dummy_run - Extract Multimodal Outputs
-
-Location: After model forward, before dummy_compute_logits
-
-```python
-# ---------------------------------------Omni-new----------------------------------------------
-hidden_states, multimodal_outputs = self.extract_multimodal_outputs(hidden_states)
-# ---------------------------------------Omni-new----------------------------------------------
-```
-
-### _model_forward - Omni Output Wrapping
-
-```python
-def _model_forward(
-    self,
-    num_tokens_padded: int,
-    input_ids: torch.Tensor | None = None,
-    positions: torch.Tensor | None = None,
-    intermediate_tensors: IntermediateTensors | None = None,
-    inputs_embeds: torch.Tensor | None = None,
-    **model_kwargs: dict[str, Any],
-):
-    """Override to combine NPUModelRunner's signature with OmniGPUModelRunner's logic."""
-    # Omni-specific: build and inject extra model kwargs
-    model_kwargs_extra = self._build_model_kwargs_extra()
-
-    # Call the model forward (same as NPUModelRunner)
-    assert self.model is not None
-    model_output = self.model(
-        input_ids=input_ids,
-        positions=positions,
-        intermediate_tensors=intermediate_tensors,
-        inputs_embeds=inputs_embeds,
-        **model_kwargs,
-        **model_kwargs_extra,
-    )
-
-    # Omni-specific: wrap output if needed
-    if not isinstance(model_output, OmniOutput) and hasattr(self.model, "make_omni_output"):
-        model_output = self.model.make_omni_output(model_output, **model_kwargs_extra)
-
-    # Omni-specific: cache model output for later sample_tokens
-    self._omni_last_model_output = model_output
-
-    # NPU-specific: update full graph params (keep from vllm-ascend)
-    forward_context = get_forward_context()
-    # ... NPU graph update logic ...
-
-    # NPU-specific: all-gather for sequence parallelism (keep from vllm-ascend)
-    if get_forward_context().sp_enabled and not isinstance(model_output, IntermediateTensors):
-        model_output = self._all_gather_hidden_states_and_aux(model_output)
-
-    return model_output
-```
-
---
-
-## NPUARModelRunner (npu_ar_model_runner.py)
-
-### __init__ - KV Transfer Manager
-
-```python
-def __init__(self, *args, **kwargs):
-    super().__init__(*args, **kwargs)
-    self.input_ids = self._make_buffer(self.max_num_tokens, dtype=torch.int32)
-    # each model stage has their own hidden size
-    self.hidden_size = self.model_config.hf_text_config.hidden_size
-    self.inputs_embeds = self._make_buffer(self.max_num_tokens, self.hidden_size, dtype=self.dtype, numpy=False)
-    # Initialize KV cache manager (preserve vllm_config fallback behavior)
-    self.kv_transfer_manager = OmniKVTransferManager.from_vllm_config(self.vllm_config, self.model_config)
-```
-
-### execute_model - KV Transfer Before Update States
-
-Location: At the very beginning of execute_model
-
-```python
-#  -------------------------------------- Omni-new -------------------------------------------------
-# [Omni] Handle KV transfer BEFORE updating states (which removes finished requests)
-self.kv_extracted_req_ids = self.kv_transfer_manager.handle_finished_requests_kv_transfer(
-    finished_reqs=getattr(scheduler_output, "finished_requests_needing_kv_transfer", {}),
-    kv_caches=self.kv_caches,
-    block_size=self.cache_config.block_size,
-    cache_dtype=str(self.cache_config.cache_dtype),
-    request_id_resolver=self._resolve_global_request_id,
-)
-#  -------------------------------------- Omni-new -------------------------------------------------
-```
-
-### execute_model - Custom _update_states Call
-
-Location: Inside synchronize_input_prep context
-
-```python
-#  -------------------------------------- Omni-new -------------------------------------------------
-self._update_states(scheduler_output)
-#  ------------------------------------------------------------------------------------------------
-```
-
-### execute_model - Extract Multimodal Outputs
-
-Location: In post process section, after hidden_states assignment
-
-```python
-#  -------------------------------------- Omni-new -------------------------------------------------
-hidden_states, multimodal_outputs = self.extract_multimodal_outputs(hidden_states)
-
-if multimodal_outputs is not None:
-    keys_or_type = (
-        list(multimodal_outputs.keys())
-        if isinstance(multimodal_outputs, dict)
-        else type(multimodal_outputs)
-    )
-    logger.debug(f"[AR] execute_model: multimodal_outputs keys = {keys_or_type}")
-else:
-    logger.debug("[AR] execute_model: multimodal_outputs is None")
-#  -------------------------------------- Omni-new -------------------------------------------------
-```
-
-### execute_model - Compute Logits with sampling_metadata
-
-Location: In both broadcast_pp_output True and False branches
-
-```python
-#  -------------------------------------- Omni-new -------------------------------------------------
-# Try with sampling_metadata first; fall back to without for models that don't support it
-try:
-    logits = self.model.compute_logits(
-        sample_hidden_states, sampling_metadata=self.input_batch.sampling_metadata
-    )
-except TypeError:
-    logits = self.model.compute_logits(sample_hidden_states)
-#  -------------------------------------- Omni-new -------------------------------------------------
-```
-
-### sample_tokens - KV Extracted Req IDs
-
-Location: At the beginning of sample_tokens
-
-```python
-#  -------------------------------------- Omni-new -------------------------------------------------
-kv_extracted_req_ids = getattr(self, "kv_extracted_req_ids", None)
-self.kv_extracted_req_ids = None
-#  -------------------------------------- Omni-new -------------------------------------------------
-```
-
-### sample_tokens - Process Additional Information and Build Output
-
-Location: After bookkeeping sync, replacing the original output construction
-
-```python
-#  -------------------------------------- Omni-new -------------------------------------------------
-hidden_states_cpu = hidden_states.detach().to("cpu").contiguous()
-num_scheduled_tokens_np = getattr(self, "_omni_num_scheduled_tokens_np", None)
-if num_scheduled_tokens_np is None:
-    req_ids = self.input_batch.req_ids
-    num_scheduled_tokens_np = np.array(
-        [scheduler_output.num_scheduled_tokens[rid] for rid in req_ids],
-        dtype=np.int32,
-    )
-
-self._process_additional_information_updates(
-    hidden_states, multimodal_outputs, num_scheduled_tokens_np, scheduler_output
-)
-
-pooler_output: list[dict[str, object]] = []
-for rid in req_ids_output_copy:
-    idx = req_id_to_index_output_copy[rid]
-    start = int(self.query_start_loc.cpu[idx])
-    sched = int(num_scheduled_tokens_np[idx])
-    end = start + sched
-    hidden_slice = hidden_states_cpu[start:end]
-    payload: dict[str, object] = {"hidden": hidden_slice}
-    if isinstance(multimodal_outputs, dict) and multimodal_outputs:
-        # ... multimodal output slicing logic ...
-    pooler_output.append(payload)
-
-model_runner_output = OmniModelRunnerOutput(
-    req_ids=req_ids_output_copy,
-    req_id_to_index=req_id_to_index_output_copy,
-    sampled_token_ids=valid_sampled_token_ids,
-    logprobs=logprobs_lists,
-    prompt_logprobs_dict=prompt_logprobs_dict,
-    pooler_output=(pooler_output if self.vllm_config.model_config.engine_output_type != "text" else None),
-    kv_connector_output=kv_connector_output,
-)
-model_runner_output.kv_extracted_req_ids = kv_extracted_req_ids
-#  -------------------------------------- Omni-new -------------------------------------------------
-```
-
---
-
-## NPUGenerationModelRunner (npu_generation_model_runner.py)
-
-### execute_model - Async Chunk Update
-
-Location: Inside prepare input section, before synchronize_input_prep
-
-```python
-#  -------------------------------------- Omni-new -------------------------------------------------
-if self.model_config.async_chunk and num_scheduled_tokens:
-    self._update_request_states(scheduler_output)
-#  -------------------------------------- Omni-new -------------------------------------------------
-```
-
-### execute_model - Seq Token Counts
-
-Location: After _preprocess call
-
-```python
-# [Omni] Pass token counts per request for code2wav output slicing
-model_kwargs["seq_token_counts"] = tokens
-```
-
-### execute_model - Run Generation Model
-
-Location: Inside forward context
-
-```python
-#  -------------------------------------- Omni-new -------------------------------------------------
-outputs = self._run_generation_model(
-    num_tokens_padded=num_tokens_padded,
-    input_ids=input_ids,
-    positions=positions,
-    intermediate_tensors=intermediate_tensors,
-    inputs_embeds=inputs_embeds,
-    model_kwargs=model_kwargs,
-    logits_indices=logits_indices,
-)
-_, multimodal_outputs = self.extract_multimodal_outputs(outputs)
-# -------------------------------------- Omni-new -------------------------------------------------
-```
-
-### sample_tokens - Multimodal Output Processing
-
-The entire sample_tokens method body is omni-specific for generation models:
-
-```python
-#  -------------------------------------- Omni-new -------------------------------------------------
-pooler_output: list[object] = []
-if isinstance(multimodal_outputs, torch.Tensor):
-    # ... tensor handling ...
-elif isinstance(multimodal_outputs, list):
-    # ... list handling ...
-elif isinstance(multimodal_outputs, dict):
-    # ... dict handling per request ...
-else:
-    raise RuntimeError("Unsupported diffusion output type")
-# [Omni] Copy req_id mappings to avoid async scheduling mutation.
-req_ids_output_copy = self.input_batch.req_ids.copy()
-req_id_to_index_output_copy = self.input_batch.req_id_to_index.copy()
-output = OmniModelRunnerOutput(
-    req_ids=req_ids_output_copy,
-    req_id_to_index=req_id_to_index_output_copy,
-    sampled_token_ids=[],
-    logprobs=None,
-    prompt_logprobs_dict={},
-    pooler_output=pooler_output,
-    kv_connector_output=kv_connector_output,
-    num_nans_in_logits={},
-    ec_connector_output=ec_connector_output if self.supports_mm_inputs else None,
-)
-#  -------------------------------------- Omni-new -------------------------------------------------
-```
-
-### _dummy_run - Model Kwargs Init and Multimodal Extract
-
-Location: Before model forward and after
-
-```python
-model_kwargs = self._init_model_kwargs()  # Before forward
-
-# ... forward ...
-
-# -------------------------------------- Omni-new -------------------------------------------------
-hidden_states, _ = self.extract_multimodal_outputs(hidden_states)
-# -------------------------------------------------------------------------------------------------
-```
-
---
-
-## ExecuteModelState Extension
-
-The `ExecuteModelState` NamedTuple is extended for omni:
-
-```python
-class ExecuteModelState(NamedTuple):
-    """Ephemeral cached state transferred between execute_model() and
-    sample_tokens(), after execute_model() returns None."""
-
-    scheduler_output: SchedulerOutput
-    logits: torch.Tensor
-    spec_decode_metadata: SpecDecodeMetadata | None
-    spec_decode_common_attn_metadata: AscendCommonAttentionMetadata | None
-    hidden_states: torch.Tensor
-    sample_hidden_states: torch.Tensor
-    aux_hidden_states: list[torch.Tensor] | None
-    attn_metadata: PerLayerAttnMetadata
-    positions: torch.Tensor
-    ec_connector_output: ECConnectorOutput | None
-    cudagraph_stats: CUDAGraphStat | None
-    multimodal_outputs: Any  # <-- Omni extension
-```
-
-This extended state must be imported from `npu_ar_model_runner` in `npu_generation_model_runner`.
@@ -1,222 +0,0 @@
-# NPU Model Runner Upgrade Workflow Checklist
-
-> **Note**: Reference documents (`omni-specific-blocks.md`) may not be complete. Always grep for `Omni-new` in GPU implementations to find all omni-specific code blocks. Update the reference docs when discovering new blocks.
-
-## Pre-Upgrade Preparation
-
-### 1. Version Information
- [ ] Identify current vllm-omni version: `_________`
- [ ] Identify target vllm-ascend version: `_________`
- [ ] Identify target vllm version: `_________`
- [ ] Last release date for GPU worker changes: `_________`
-
-### 2. Gather Git History
-```bash
-# GPU-side omni changes since last release
-cd /root/vllm-workspace/vllm-omni
-git log --oneline --since="YYYY-MM-DD" -- vllm_omni/worker/
-
-# vllm-ascend NPUModelRunner changes
-cd /root/vllm-workspace/vllm-ascend
-git log --oneline <from-tag>..<to-tag> -- vllm_ascend/worker/model_runner_v1.py
-```
-
-### 3. Backup Current Files
- [ ] Create backup of current NPU runners:
-  ```bash
-  cp -r vllm_omni/platforms/npu/worker vllm_omni/platforms/npu/worker.backup
-  ```
-
---
-
-## OmniNPUModelRunner (npu_model_runner.py)
-
-### Read and Understand
- [ ] Read current `npu_model_runner.py`
- [ ] Read latest `vllm_ascend/worker/model_runner_v1.py`
- [ ] Read latest `vllm_omni/worker/gpu_model_runner.py`
-
-### Method: load_model
- [ ] Document existing omni-specific logic
- [ ] Copy latest NPUModelRunner.load_model structure
- [ ] Re-insert: `enable_sp(self.vllm_config)` call
- [ ] Re-insert: talker_mtp detection and setup
- [ ] Replace: `CUDAGraphWrapper` → `ACLGraphWrapper`
- [ ] Re-insert: Buffer allocations (talker_mtp_input_ids, etc.)
-
-### Method: _dummy_run
- [ ] Document existing omni-specific logic locations
- [ ] Copy latest NPUModelRunner._dummy_run
- [ ] Re-insert: talker_mtp dummy forward block (inside context)
- [ ] Re-insert: `extract_multimodal_outputs` call
- [ ] Verify: Comment markers are present
-
-### Method: _model_forward
- [ ] Copy latest NPUModelRunner._model_forward structure
- [ ] Re-insert: `_build_model_kwargs_extra()` call
- [ ] Re-insert: OmniOutput wrapping logic
- [ ] Re-insert: `_omni_last_model_output` caching
- [ ] Keep: NPU graph params update
- [ ] Keep: SP all-gather logic
-
-### Method: _talker_mtp_forward
- [ ] Verify: Uses `set_ascend_forward_context`
- [ ] Verify: Uses `ACLGraphWrapper` check
- [ ] Sync any changes from GPU `_talker_mtp_forward`
-
-### Imports
- [ ] Update vllm-ascend imports to latest paths
- [ ] Verify all omni imports are present
- [ ] Remove any deprecated imports
-
---
-
-## NPUARModelRunner (npu_ar_model_runner.py)
-
-### Read and Understand
- [ ] Read current `npu_ar_model_runner.py`
- [ ] Read latest `vllm_ascend/worker/model_runner_v1.py` execute_model
- [ ] Read latest `vllm_omni/worker/gpu_ar_model_runner.py`
-
-### Method: __init__
- [ ] Sync any new initialization from GPU side
- [ ] Keep: `OmniKVTransferManager` setup
- [ ] Keep: Custom buffer allocations
-
-### Method: execute_model
- [ ] Document all omni blocks with line numbers
- [ ] Copy latest NPUModelRunner.execute_model structure
- [ ] Re-insert: KV transfer handling (beginning)
- [ ] Re-insert: Custom `_update_states` call
- [ ] Re-insert: `extract_multimodal_outputs`
- [ ] Re-insert: `compute_logits` with sampling_metadata try/except
- [ ] Update: ExecuteModelState to include multimodal_outputs
-
-### Method: sample_tokens
- [ ] Document all omni blocks
- [ ] Copy latest NPUModelRunner.sample_tokens structure
- [ ] Re-insert: `kv_extracted_req_ids` handling
- [ ] Re-insert: Hidden states CPU copy
- [ ] Re-insert: `_process_additional_information_updates`
- [ ] Re-insert: `OmniModelRunnerOutput` construction
-
-### ExecuteModelState
- [ ] Verify: `multimodal_outputs` field is present
- [ ] Verify: Imported/used correctly in execute_model
-
-### Imports
- [ ] Update all vllm-ascend imports
- [ ] Keep omni-specific imports
-
---
-
-## NPUGenerationModelRunner (npu_generation_model_runner.py)
-
-### Read and Understand
- [ ] Read current `npu_generation_model_runner.py`
- [ ] Read latest GPU `gpu_generation_model_runner.py`
-
-### Method: _update_request_states
- [ ] Verify: async_chunk handling is correct
- [ ] Sync any changes from GPU side
-
-### Method: execute_model
- [ ] Document all omni blocks
- [ ] Copy latest NPUModelRunner.execute_model base structure
- [ ] Re-insert: async_chunk update logic
- [ ] Re-insert: `seq_token_counts` injection
- [ ] Re-insert: `_run_generation_model` call
- [ ] Re-insert: `extract_multimodal_outputs`
- [ ] Use: ExecuteModelState from npu_ar_model_runner
-
-### Method: sample_tokens
- [ ] Keep: Entire omni multimodal output processing
- [ ] Update: Any new output fields needed
- [ ] Keep: `OmniModelRunnerOutput` construction
-
-### Method: _run_generation_model
- [ ] Sync any changes from GPU side
- [ ] Keep: `_model_forward` call with sampler
-
-### Method: _dummy_run
- [ ] Copy latest NPUModelRunner._dummy_run
- [ ] Re-insert: `model_kwargs = self._init_model_kwargs()`
- [ ] Re-insert: `extract_multimodal_outputs` at end
-
-### Imports
- [ ] Import ExecuteModelState from npu_ar_model_runner
- [ ] Update vllm-ascend imports
-
---
-
-## Post-Upgrade Validation
-
-### Syntax Validation
- [ ] `python -m py_compile vllm_omni/platforms/npu/worker/npu_model_runner.py`
- [ ] `python -m py_compile vllm_omni/platforms/npu/worker/npu_ar_model_runner.py`
- [ ] `python -m py_compile vllm_omni/platforms/npu/worker/npu_generation_model_runner.py`
-
-### Import Validation
- [ ] `python -c "from vllm_omni.platforms.npu.worker.npu_model_runner import OmniNPUModelRunner"`
- [ ] `python -c "from vllm_omni.platforms.npu.worker.npu_ar_model_runner import NPUARModelRunner"`
- [ ] `python -c "from vllm_omni.platforms.npu.worker.npu_generation_model_runner import NPUGenerationModelRunner"`
-
-### Comment Markers
- [ ] Grep for "Omni-new" in all three files
- [ ] Verify all omni blocks have closing markers
-
-### Code Review
- [ ] No `CUDAGraphWrapper` references
- [ ] All `set_forward_context` replaced with `set_ascend_forward_context`
- [ ] Parameter names correct (`aclgraph_runtime_mode` not `cudagraph_runtime_mode`)
- [ ] No duplicate code blocks
- [ ] No missing imports
-
---
-
-## Git Commit
-
-### Commit Message Template
-```
-[NPU] Upgrade model runners to align with vllm-ascend vX.Y.Z
-
- Update OmniNPUModelRunner with latest NPUModelRunner base
- Update NPUARModelRunner execute_model and sample_tokens
- Update NPUGenerationModelRunner for async_chunk changes
- Sync GPU-side omni changes from vX.Y.Z release
- Preserve all omni-specific logic (marked with Omni-new comments)
-
-Changes from vllm-ascend:
- <list key changes>
-
-Changes synced from GPU:
- <list key GPU-side omni changes>
-```
-
-### Files to Stage
- [ ] `vllm_omni/platforms/npu/worker/npu_model_runner.py`
- [ ] `vllm_omni/platforms/npu/worker/npu_ar_model_runner.py`
- [ ] `vllm_omni/platforms/npu/worker/npu_generation_model_runner.py`
- [ ] Any other modified files
-
---
-
-## Troubleshooting
-
-### Import Errors
- Check if vllm-ascend module paths have changed
- Verify PYTHONPATH includes both vllm-ascend and vllm-omni
-
-### Type Errors
- Check method signatures match between GPU and NPU
- Verify NamedTuple fields match expected structure
-
-### Runtime Errors
- Enable debug logging: `export VLLM_LOGGING_LEVEL=DEBUG`
- Check graph capture issues: try `--enforce-eager`
- Check attention issues: verify AscendAttentionState usage
-
-### Performance Regression
- Compare with previous version on same model
- Check if graph capture is working: look for ACLGraph logs
- Verify SP/EP configurations are correct
@@ -0,0 +1,629 @@
+#!/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())
@@ -0,0 +1,62 @@
+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@v6.0.2
+        with:
+          fetch-depth: 0
+
+      - name: Set up Python
+        uses: actions/setup-python@v6.2.0
+        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@v7.0.0
+        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
@@ -174,7 +174,6 @@ CLAUDE.md

 # Codex
 AGENTS.md
-.codex
 .codex/

 # cursor
@@ -204,7 +203,6 @@ checkpoints/
 # Cache directories
 cache/
 !vllm_omni/diffusion/cache/
-!tests/diffusion/cache/
 .cache/
 diffusion_cache/
 kv_cache/
@@ -264,6 +262,3 @@ tmp_test
 vllm_omni/_version.py
 # output files
 *.wav
-.worktrees/
-# CI overlay yamls materialized from tests/utils.py:_CI_OVERLAYS at test time
-tests/.ci_generated/
@@ -27,7 +27,7 @@ pip install gdown
 Download the dataset from Google Drive:

 ```bash
-gdown 1GlSjVfSHkW3-leKKBlfrjuuTGqQ_xaLP
+gdown --id 1GlSjVfSHkW3-leKKBlfrjuuTGqQ_xaLP
 ```

 ### 4. Extract the Dataset
@@ -74,7 +74,7 @@ rm meta.lst
 # Full setup and benchmark
 cd benchmarks/build_dataset
 pip install gdown
-gdown  1GlSjVfSHkW3-leKKBlfrjuuTGqQ_xaLP
+gdown --id 1GlSjVfSHkW3-leKKBlfrjuuTGqQ_xaLP
 tar -xf seedtts_testset.tar
 cp seedtts_testset/en/meta.lst meta.lst
 python extract_tts_prompts.py -i meta.lst -o top100.txt -n 100
@@ -306,8 +306,6 @@ async def async_request_v1_videos(
            video_bytes = await content_response.read()
            output.response_body = video_bytes
            output.success = True
-            if "stage_durations" in poll_json:
-                output.stage_durations = poll_json["stage_durations"] or {}
            if "peak_memory_mb" in poll_json:
                output.peak_memory_mb = poll_json["peak_memory_mb"]
            elif "peak_memory_mb" in resp_json:
@@ -558,7 +558,6 @@ class RandomDataset(BaseDataset):
        super().__init__(args, api_url, model)
        self.num_prompts = args.num_prompts
        self.enable_negative_prompt = enable_negative_prompt
-        self.num_input_images = max(1, args.num_input_images)
        self.random_request_config = getattr(args, "random_request_config", None)
        if self.random_request_config:
            self.random_request_config = json.loads(self.random_request_config)
@@ -581,7 +580,11 @@ class RandomDataset(BaseDataset):

        # Random image generate
        if self.args.task in ["i2v", "ti2v", "ti2i", "i2i"]:
-            self._random_image_path = self._generate_random_image_paths()
+            img = Image.new("RGB", (512, 512), (255, 255, 255))
+
+            image_path = os.path.join(tempfile.gettempdir(), "diffusion_benchmark_random_image.png")
+            self._random_image_path = [image_path]
+            img.save(image_path)
        else:
            self._random_image_path = None

@@ -616,18 +619,6 @@ class RandomDataset(BaseDataset):
    def get_requests(self) -> list[RequestFuncInput]:
        return [self[i] for i in range(len(self))]

-    def _generate_random_image_paths(self) -> list[str]:
-        image_paths: list[str] = []
-        for image_idx in range(self.num_input_images):
-            img = Image.new("RGB", (512, 512), (255, 255, 255))
-            image_path = os.path.join(
-                tempfile.gettempdir(),
-                f"diffusion_benchmark_random_image_{image_idx}.png",
-            )
-            img.save(image_path)
-            image_paths.append(image_path)
-        return image_paths
-

 def _compute_expected_latency_ms_from_base(req: RequestFuncInput, args, base_time_ms: float | None) -> float | None:
    """Compute expected execution time (ms) based on a base per-step-per-frame unit time.
@@ -1124,15 +1115,6 @@ if __name__ == "__main__":
            '{"width":768,"height":768,"num_inference_steps":20,"weight":0.85}]'
        ),
    )
-    parser.add_argument(
-        "--num-input-images",
-        type=int,
-        default=1,
-        help=(
-            "Number of synthetic input images to attach for image-conditioned tasks "
-            "(i2v, ti2v, ti2i, i2i) when using random dataset."
-        ),
-    )

    args = parser.parse_args()

@@ -1,290 +0,0 @@
-"""Benchmark Fish Speech voice cache: inline ref_audio vs uploaded voice.
-
-Measures TTFP improvement from DAC-code caching when using uploaded voices.
-
-Setup:
-  1. Start vllm-omni with Fish Speech S2 Pro (use our feat branch)
-  2. Provide a reference audio file for voice cloning
-
-Usage:
-    python bench_voice_cache.py \
-        --ref-audio /path/to/reference.wav \
-        --ref-text "Transcript of the reference audio." \
-        --num-prompts 20 \
-        --port 8091
-
-The script runs two rounds:
-  A) Inline ref_audio: every request sends base64 audio (no cache)
-  B) Uploaded voice: upload once, then use voice name (cache hits after 1st)
-"""
-
-import argparse
-import asyncio
-import base64
-import json
-import os
-import sys
-import time
-from pathlib import Path
-
-import aiohttp
-
-# Allow imports from benchmarks/fish-speech/
-sys.path.insert(0, str(Path(__file__).resolve().parent))
-
-from fish_bench_utils import (  # noqa: E402
-    BenchmarkResult,
-    RequestResult,
-    compute_stats,
-    print_benchmark_results,
-    send_streaming_request,
-)
-
-SAMPLE_RATE = 44100
-SAMPLE_WIDTH = 2
-
-PROMPTS = [
-    "Hello, welcome to the voice synthesis benchmark test.",
-    "She said she would be here by noon, but nobody showed up.",
-    "The quick brown fox jumps over the lazy dog near the riverbank.",
-    "I can't believe how beautiful the sunset looks from up here.",
-    "Please remember to bring your identification documents tomorrow morning.",
-    "Have you ever wondered what it would be like to travel through time?",
-    "The restaurant on the corner serves the best pasta I have ever tasted.",
-    "After the meeting, we should discuss the quarterly results.",
-    "Learning a new language takes patience and genuine curiosity.",
-    "The train leaves at half past seven, so we need to arrive early.",
-    "Could you please turn down the music, I'm trying to concentrate.",
-    "It was a dark and stormy night when the keeper heard a knock.",
-]
-
-
-def encode_audio_to_base64(audio_path: str) -> str:
-    """Encode a local audio file to base64 data URL."""
-    ext = audio_path.lower().rsplit(".", 1)[-1]
-    mime_map = {"wav": "audio/wav", "mp3": "audio/mpeg", "flac": "audio/flac"}
-    mime_type = mime_map.get(ext, "audio/wav")
-    with open(audio_path, "rb") as f:
-        audio_b64 = base64.b64encode(f.read()).decode("utf-8")
-    return f"data:{mime_type};base64,{audio_b64}"
-
-
-async def upload_voice(
-    host: str,
-    port: int,
-    audio_path: str,
-    ref_text: str,
-    voice_name: str = "bench_voice",
-) -> dict:
-    """Upload a voice via POST /v1/audio/voices."""
-    url = f"http://{host}:{port}/v1/audio/voices"
-    data = aiohttp.FormData()
-    data.add_field("name", voice_name)
-    data.add_field("consent", "true")
-    if ref_text:
-        data.add_field("ref_text", ref_text)
-    data.add_field(
-        "audio_sample",
-        open(audio_path, "rb"),
-        filename=os.path.basename(audio_path),
-        content_type="audio/wav",
-    )
-
-    async with aiohttp.ClientSession() as session:
-        async with session.post(url, data=data) as resp:
-            result = await resp.json()
-            print(f"  Upload response ({resp.status}): {json.dumps(result, indent=2)}")
-            return result
-
-
-async def delete_voice(host: str, port: int, voice_name: str) -> None:
-    """Delete an uploaded voice."""
-    url = f"http://{host}:{port}/v1/audio/voices/{voice_name}"
-    async with aiohttp.ClientSession() as session:
-        async with session.delete(url) as resp:
-            if resp.status == 200:
-                print(f"  Deleted voice '{voice_name}'")
-
-
-async def run_round(
-    host: str,
-    port: int,
-    num_prompts: int,
-    create_payload_fn,
-    label: str,
-    num_warmups: int = 2,
-    timeout_s: float = 120.0,
-) -> BenchmarkResult:
-    """Run one benchmark round and return results."""
-    api_url = f"http://{host}:{port}/v1/audio/speech"
-    connector = aiohttp.TCPConnector(limit=1, limit_per_host=1)
-    session = aiohttp.ClientSession(
-        connector=connector,
-        timeout=aiohttp.ClientTimeout(total=timeout_s),
-    )
-
-    try:
-        # Warmup.
-        if num_warmups > 0:
-            print(f"  [{label}] Warming up ({num_warmups} requests)...")
-            for i in range(num_warmups):
-                payload = create_payload_fn(PROMPTS[i % len(PROMPTS)])
-                r = await send_streaming_request(
-                    session,
-                    api_url,
-                    payload,
-                    SAMPLE_RATE,
-                    SAMPLE_WIDTH,
-                )
-                status = "OK" if r.success else f"FAIL: {r.error[:80]}"
-                print(f"    warmup {i + 1}: ttfp={r.ttfp * 1000:.0f}ms  {status}")
-
-        # Benchmark.
-        print(f"  [{label}] Running {num_prompts} requests (concurrency=1)...")
-        results: list[RequestResult] = []
-        start = time.perf_counter()
-        for i in range(num_prompts):
-            prompt = PROMPTS[i % len(PROMPTS)]
-            payload = create_payload_fn(prompt)
-            r = await send_streaming_request(
-                session,
-                api_url,
-                payload,
-                SAMPLE_RATE,
-                SAMPLE_WIDTH,
-            )
-            results.append(r)
-            tag = "HIT" if i > 0 and label == "uploaded_voice" else ""
-            print(
-                f"    req {i + 1:3d}: ttfp={r.ttfp * 1000:7.1f}ms  "
-                f"e2e={r.e2e * 1000:7.1f}ms  "
-                f"{'OK' if r.success else 'FAIL'} {tag}"
-            )
-        wall_time = time.perf_counter() - start
-    finally:
-        await session.close()
-
-    bench = compute_stats(results, wall_time)
-    bench.concurrency = 1
-    bench.num_prompts = num_prompts
-    bench.config_name = label
-    return bench
-
-
-async def main():
-    parser = argparse.ArgumentParser(
-        description="Benchmark Fish Speech voice cache (inline vs uploaded)",
-    )
-    parser.add_argument("--host", default="127.0.0.1")
-    parser.add_argument("--port", type=int, default=8091)
-    parser.add_argument("--ref-audio", required=True, help="Path to reference audio file")
-    parser.add_argument("--ref-text", required=True, help="Transcript of reference audio")
-    parser.add_argument("--num-prompts", type=int, default=20)
-    parser.add_argument("--num-warmups", type=int, default=2)
-    parser.add_argument("--voice-name", default="bench_voice")
-    args = parser.parse_args()
-
-    if not os.path.exists(args.ref_audio):
-        print(f"Error: ref_audio not found: {args.ref_audio}")
-        sys.exit(1)
-
-    ref_audio_b64 = encode_audio_to_base64(args.ref_audio)
-    print(f"Reference audio: {args.ref_audio} ({len(ref_audio_b64) // 1024}KB base64)")
-
-    # ---- Round A: Inline ref_audio (no cache) ----
-    print(f"\n{'=' * 60}")
-    print("Round A: INLINE ref_audio (every request sends full audio)")
-    print(f"{'=' * 60}")
-
-    def make_inline_payload(prompt: str) -> dict:
-        return {
-            "input": prompt,
-            "voice": "default",
-            "stream": True,
-            "response_format": "pcm",
-            "ref_audio": ref_audio_b64,
-            "ref_text": args.ref_text,
-            "max_new_tokens": 2048,
-        }
-
-    bench_inline = await run_round(
-        args.host,
-        args.port,
-        args.num_prompts,
-        make_inline_payload,
-        "inline_ref_audio",
-        num_warmups=args.num_warmups,
-    )
-    print_benchmark_results(bench_inline)
-
-    # ---- Upload voice ----
-    print(f"\n{'=' * 60}")
-    print("Uploading voice for cache test...")
-    print(f"{'=' * 60}")
-    await delete_voice(args.host, args.port, args.voice_name)
-    await upload_voice(
-        args.host,
-        args.port,
-        args.ref_audio,
-        args.ref_text,
-        args.voice_name,
-    )
-
-    # ---- Round B: Uploaded voice (cache hits after 1st request) ----
-    print(f"\n{'=' * 60}")
-    print("Round B: UPLOADED VOICE (cache hits after 1st request)")
-    print(f"{'=' * 60}")
-
-    def make_uploaded_payload(prompt: str) -> dict:
-        return {
-            "input": prompt,
-            "voice": args.voice_name,
-            "stream": True,
-            "response_format": "pcm",
-            "ref_text": args.ref_text,
-            "max_new_tokens": 2048,
-        }
-
-    bench_cached = await run_round(
-        args.host,
-        args.port,
-        args.num_prompts,
-        make_uploaded_payload,
-        "uploaded_voice",
-        num_warmups=args.num_warmups,
-    )
-    print_benchmark_results(bench_cached)
-
-    # ---- Comparison ----
-    print(f"\n{'=' * 60}")
-    print("COMPARISON: Inline ref_audio vs Uploaded voice (cached)")
-    print(f"{'=' * 60}")
-    print(f"{'Metric':<30} {'Inline':>12} {'Cached':>12} {'Speedup':>10}")
-    print(f"{'-' * 64}")
-
-    def fmt_speedup(inline_val: float, cached_val: float) -> str:
-        if cached_val > 0 and inline_val > 0:
-            ratio = inline_val / cached_val
-            return f"{ratio:.2f}x"
-        return "N/A"
-
-    rows = [
-        ("Mean TTFP (ms)", bench_inline.mean_ttfp_ms, bench_cached.mean_ttfp_ms),
-        ("Median TTFP (ms)", bench_inline.median_ttfp_ms, bench_cached.median_ttfp_ms),
-        ("P99 TTFP (ms)", bench_inline.p99_ttfp_ms, bench_cached.p99_ttfp_ms),
-        ("Mean E2E (ms)", bench_inline.mean_e2e_ms, bench_cached.mean_e2e_ms),
-        ("Median E2E (ms)", bench_inline.median_e2e_ms, bench_cached.median_e2e_ms),
-        ("Mean RTF", bench_inline.mean_rtf, bench_cached.mean_rtf),
-    ]
-    for label, a, b in rows:
-        print(f"{label:<30} {a:>12.1f} {b:>12.1f} {fmt_speedup(a, b):>10}")
-
-    print("\nNote: Round B request #1 is a cache MISS (cold start).")
-    print("      Requests #2+ are cache HITs (skip DAC encoding).")
-
-    # Cleanup.
-    await delete_voice(args.host, args.port, args.voice_name)
-
-
-if __name__ == "__main__":
-    asyncio.run(main())
@@ -1,501 +0,0 @@
-"""Shared benchmark infrastructure for Fish Speech serving benchmarks.
-
-Provides common dataclasses, metrics computation, streaming HTTP client,
-and result formatting used by model-specific benchmark scripts.
-
-Model-specific scripts supply a ``create_payload_fn(prompt) -> dict``
-callback and audio parameters; everything else is handled here.
-"""
-
-import asyncio
-import base64
-import json
-import time
-from collections.abc import Callable
-from dataclasses import asdict, dataclass, field
-from datetime import datetime
-from pathlib import Path
-
-import aiohttp
-import numpy as np
-from tqdm.asyncio import tqdm
-
-# ---------------------------------------------------------------------------
-# Shared test prompts (varying length for realistic workload)
-# ---------------------------------------------------------------------------
-PROMPTS = [
-    "Hello, welcome to the voice synthesis benchmark test.",
-    "She said she would be here by noon, but nobody showed up.",
-    "The quick brown fox jumps over the lazy dog near the riverbank.",
-    "I can't believe how beautiful the sunset looks from up here on the mountain.",
-    "Please remember to bring your identification documents to the appointment tomorrow morning.",
-    "Have you ever wondered what it would be like to travel through time and visit ancient civilizations?",
-    "The restaurant on the corner serves the best pasta I have ever tasted in my entire life.",
-    "After the meeting, we should discuss the quarterly results and plan for the next phase.",
-    "Learning a new language takes patience, practice, and a genuine curiosity about other cultures.",
-    "The train leaves at half past seven, so we need to arrive at the station before then.",
-    "Could you please turn down the music a little bit, I'm trying to concentrate on my work.",
-    "It was a dark and stormy night when the old lighthouse keeper heard a knock at the door.",
-]
-
-
-# ---------------------------------------------------------------------------
-# Dataclasses
-# ---------------------------------------------------------------------------
-@dataclass
-class RequestResult:
-    success: bool = False
-    ttfp: float = 0.0  # Time to first audio packet (seconds)
-    e2e: float = 0.0  # End-to-end latency (seconds)
-    audio_bytes: int = 0  # Total audio bytes received
-    audio_duration: float = 0.0  # Audio duration in seconds
-    rtf: float = 0.0  # Real-time factor = e2e / audio_duration
-    prompt: str = ""
-    error: str = ""
-
-
-@dataclass
-class BenchmarkResult:
-    config_name: str = ""
-    concurrency: int = 0
-    num_prompts: int = 0
-    completed: int = 0
-    failed: int = 0
-    duration_s: float = 0.0
-    # TTFP stats (ms)
-    mean_ttfp_ms: float = 0.0
-    median_ttfp_ms: float = 0.0
-    std_ttfp_ms: float = 0.0
-    p90_ttfp_ms: float = 0.0
-    p95_ttfp_ms: float = 0.0
-    p99_ttfp_ms: float = 0.0
-    # E2E stats (ms)
-    mean_e2e_ms: float = 0.0
-    median_e2e_ms: float = 0.0
-    std_e2e_ms: float = 0.0
-    p90_e2e_ms: float = 0.0
-    p95_e2e_ms: float = 0.0
-    p99_e2e_ms: float = 0.0
-    # RTF stats
-    mean_rtf: float = 0.0
-    median_rtf: float = 0.0
-    std_rtf: float = 0.0
-    p99_rtf: float = 0.0
-    # Audio stats
-    mean_audio_duration_s: float = 0.0
-    total_audio_duration_s: float = 0.0
-    audio_throughput: float = 0.0  # audio_duration / wall_time
-    request_throughput: float = 0.0  # requests / second
-    # Per-request details
-    per_request: list = field(default_factory=list)
-
-
-# ---------------------------------------------------------------------------
-# Audio helpers
-# ---------------------------------------------------------------------------
-def pcm_bytes_to_duration(
-    num_bytes: int,
-    sample_rate: int = 24000,
-    sample_width: int = 2,
-) -> float:
-    """Convert raw PCM byte count to duration in seconds."""
-    return num_bytes / sample_width / sample_rate
-
-
-def _is_sse_response(response: aiohttp.ClientResponse) -> bool:
-    content_type = (response.headers.get("Content-Type") or "").lower()
-    return "text/event-stream" in content_type
-
-
-async def _read_raw_audio_stream(
-    response: aiohttp.ClientResponse,
-    *,
-    start_time: float,
-) -> tuple[int, float]:
-    first_audio_at = 0.0
-    total_bytes = 0
-
-    async for chunk in response.content.iter_any():
-        if chunk and first_audio_at <= 0:
-            first_audio_at = time.perf_counter() - start_time
-        total_bytes += len(chunk)
-
-    return total_bytes, first_audio_at
-
-
-def _extract_sse_payload(raw_event: bytes) -> bytes | None:
-    data_lines: list[bytes] = []
-    for raw_line in raw_event.splitlines():
-        line = raw_line.rstrip(b"\r")
-        if line.startswith(b"data: "):
-            data_lines.append(line[6:])
-        elif line.startswith(b"data:"):
-            data_lines.append(line[5:].lstrip())
-
-    if not data_lines:
-        return None
-    return b"\n".join(data_lines).strip()
-
-
-async def _read_sse_audio_stream(
-    response: aiohttp.ClientResponse,
-    *,
-    start_time: float,
-) -> tuple[int, float]:
-    """Decode SSE events and count raw audio bytes from base64 payloads."""
-    first_audio_at = 0.0
-    total_bytes = 0
-    pending = b""
-
-    async for chunk in response.content.iter_any():
-        if not chunk:
-            continue
-        pending += chunk
-        pending = pending.replace(b"\r\n", b"\n")
-
-        while b"\n\n" in pending:
-            raw_event, pending = pending.split(b"\n\n", 1)
-            payload_bytes = _extract_sse_payload(raw_event)
-            if payload_bytes is None:
-                continue
-            if payload_bytes == b"[DONE]":
-                return total_bytes, first_audio_at
-
-            try:
-                payload = json.loads(payload_bytes)
-            except json.JSONDecodeError as exc:
-                raise ValueError(f"Invalid SSE JSON payload: {exc}") from exc
-
-            audio = payload.get("audio")
-            if not isinstance(audio, dict):
-                continue
-
-            audio_b64 = audio.get("data")
-            if not audio_b64:
-                continue
-
-            try:
-                audio_bytes = base64.b64decode(audio_b64)
-            except Exception as exc:
-                raise ValueError(f"Invalid base64 audio chunk: {exc}") from exc
-
-            if audio_bytes and first_audio_at <= 0:
-                first_audio_at = time.perf_counter() - start_time
-            total_bytes += len(audio_bytes)
-
-    return total_bytes, first_audio_at
-
-
-# ---------------------------------------------------------------------------
-# Metrics
-# ---------------------------------------------------------------------------
-def compute_stats(
-    results: list[RequestResult],
-    wall_time: float,
-) -> BenchmarkResult:
-    """Compute aggregate statistics from per-request results."""
-    successful = [r for r in results if r.success]
-    failed = [r for r in results if not r.success]
-
-    bench = BenchmarkResult(
-        completed=len(successful),
-        failed=len(failed),
-        duration_s=wall_time,
-    )
-
-    if not successful:
-        return bench
-
-    ttfps = [r.ttfp * 1000 for r in successful]
-    e2es = [r.e2e * 1000 for r in successful]
-    rtfs = [r.rtf for r in successful]
-    audio_durs = [r.audio_duration for r in successful]
-
-    bench.mean_ttfp_ms = float(np.mean(ttfps))
-    bench.median_ttfp_ms = float(np.median(ttfps))
-    bench.std_ttfp_ms = float(np.std(ttfps))
-    bench.p90_ttfp_ms = float(np.percentile(ttfps, 90))
-    bench.p95_ttfp_ms = float(np.percentile(ttfps, 95))
-    bench.p99_ttfp_ms = float(np.percentile(ttfps, 99))
-
-    bench.mean_e2e_ms = float(np.mean(e2es))
-    bench.median_e2e_ms = float(np.median(e2es))
-    bench.std_e2e_ms = float(np.std(e2es))
-    bench.p90_e2e_ms = float(np.percentile(e2es, 90))
-    bench.p95_e2e_ms = float(np.percentile(e2es, 95))
-    bench.p99_e2e_ms = float(np.percentile(e2es, 99))
-
-    bench.mean_rtf = float(np.mean(rtfs))
-    bench.median_rtf = float(np.median(rtfs))
-    bench.std_rtf = float(np.std(rtfs))
-    bench.p99_rtf = float(np.percentile(rtfs, 99))
-
-    bench.mean_audio_duration_s = float(np.mean(audio_durs))
-    bench.total_audio_duration_s = float(np.sum(audio_durs))
-    bench.audio_throughput = bench.total_audio_duration_s / wall_time
-    bench.request_throughput = len(successful) / wall_time
-
-    bench.per_request = [
-        {
-            "ttfp_ms": r.ttfp * 1000,
-            "e2e_ms": r.e2e * 1000,
-            "rtf": r.rtf,
-            "audio_duration_s": r.audio_duration,
-            "prompt": r.prompt,
-        }
-        for r in successful
-    ]
-
-    return bench
-
-
-# ---------------------------------------------------------------------------
-# Output formatting
-# ---------------------------------------------------------------------------
-def print_benchmark_results(bench: BenchmarkResult) -> None:
-    """Print benchmark results in standardized format."""
-    W = 50
-    print("")
-    print(f"{'=' * W}")
-    print(f"{'Serving Benchmark Result':^{W}}")
-    print(f"{'=' * W}")
-    print(f"{'Successful requests:':<40}{bench.completed:<10}")
-    print(f"{'Failed requests:':<40}{bench.failed:<10}")
-    print(f"{'Maximum request concurrency:':<40}{bench.concurrency:<10}")
-    print(f"{'Benchmark duration (s):':<40}{bench.duration_s:<10.2f}")
-    print(f"{'Request throughput (req/s):':<40}{bench.request_throughput:<10.2f}")
-    print(f"{'-' * W}")
-    print(f"{'End-to-end Latency':^{W}}")
-    print(f"{'-' * W}")
-    print(f"{'Mean E2EL (ms):':<40}{bench.mean_e2e_ms:<10.2f}")
-    print(f"{'Median E2EL (ms):':<40}{bench.median_e2e_ms:<10.2f}")
-    print(f"{'P99 E2EL (ms):':<40}{bench.p99_e2e_ms:<10.2f}")
-    print(f"{'=' * W}")
-    print(f"{'Audio Result':^{W}}")
-    print(f"{'=' * W}")
-    print(f"{'Total audio duration generated (s):':<40}{bench.total_audio_duration_s:<10.2f}")
-    print(f"{'Audio throughput (audio duration/s):':<40}{bench.audio_throughput:<10.2f}")
-    print(f"{'-' * W}")
-    print(f"{'Time to First Packet':^{W}}")
-    print(f"{'-' * W}")
-    print(f"{'Mean AUDIO_TTFP (ms):':<40}{bench.mean_ttfp_ms:<10.2f}")
-    print(f"{'Median AUDIO_TTFP (ms):':<40}{bench.median_ttfp_ms:<10.2f}")
-    print(f"{'P99 AUDIO_TTFP (ms):':<40}{bench.p99_ttfp_ms:<10.2f}")
-    print(f"{'-' * W}")
-    print(f"{'Real Time Factor':^{W}}")
-    print(f"{'-' * W}")
-    print(f"{'Mean AUDIO_RTF:':<40}{bench.mean_rtf:<10.3f}")
-    print(f"{'Median AUDIO_RTF:':<40}{bench.median_rtf:<10.3f}")
-    print(f"{'P99 AUDIO_RTF:':<40}{bench.p99_rtf:<10.3f}")
-    print(f"{'=' * W}")
-    print("")
-
-
-def save_results(
-    all_results: list[dict],
-    result_dir: str,
-    config_name: str,
-) -> Path:
-    """Save benchmark results as JSON and return the file path."""
-    out = Path(result_dir)
-    out.mkdir(parents=True, exist_ok=True)
-    timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
-    result_file = out / f"bench_{config_name}_{timestamp}.json"
-
-    with open(result_file, "w") as f:
-        json.dump(all_results, f, indent=2)
-    print(f"Results saved to {result_file}")
-    return result_file
-
-
-# ---------------------------------------------------------------------------
-# Streaming HTTP client
-# ---------------------------------------------------------------------------
-async def send_streaming_request(
-    session: aiohttp.ClientSession,
-    api_url: str,
-    payload: dict,
-    sample_rate: int,
-    sample_width: int,
-    pbar: tqdm | None = None,
-) -> RequestResult:
-    """Send a streaming TTS request and measure latency metrics."""
-    result = RequestResult(prompt=payload.get("input", ""))
-    st = time.perf_counter()
-
-    try:
-        async with session.post(api_url, json=payload) as response:
-            if response.status != 200:
-                result.error = f"HTTP {response.status}: {await response.text()}"
-            else:
-                if _is_sse_response(response):
-                    total_bytes, result.ttfp = await _read_sse_audio_stream(
-                        response,
-                        start_time=st,
-                    )
-                else:
-                    total_bytes, result.ttfp = await _read_raw_audio_stream(
-                        response,
-                        start_time=st,
-                    )
-
-                result.e2e = time.perf_counter() - st
-                result.audio_bytes = total_bytes
-                result.audio_duration = pcm_bytes_to_duration(total_bytes, sample_rate, sample_width)
-
-                if total_bytes <= 0 or result.ttfp <= 0:
-                    result.error = "HTTP 200 but no audio bytes were received"
-                else:
-                    if result.audio_duration > 0:
-                        result.rtf = result.e2e / result.audio_duration
-                    result.success = True
-
-    except Exception as e:
-        result.error = str(e)
-        result.e2e = time.perf_counter() - st
-
-    finally:
-        if pbar:
-            pbar.update(1)
-    return result
-
-
-# ---------------------------------------------------------------------------
-# Benchmark runner
-# ---------------------------------------------------------------------------
-async def run_benchmark(
-    host: str,
-    port: int,
-    num_prompts: int,
-    max_concurrency: int,
-    create_payload_fn: Callable[[str], dict],
-    sample_rate: int,
-    sample_width: int = 2,
-    num_warmups: int = 3,
-    request_timeout_s: float = 120.0,
-) -> BenchmarkResult:
-    """Run a TTS streaming benchmark at a given concurrency level.
-
-    Args:
-        create_payload_fn: Model-specific function that takes a prompt string
-            and returns the request JSON payload dict.
-        sample_rate: PCM sample rate for audio duration calculation.
-        sample_width: PCM sample width in bytes (default 2 for 16-bit).
-    """
-    api_url = f"http://{host}:{port}/v1/audio/speech"
-
-    connector = aiohttp.TCPConnector(
-        limit=max_concurrency,
-        limit_per_host=max_concurrency,
-        keepalive_timeout=60,
-    )
-    session = aiohttp.ClientSession(
-        connector=connector,
-        timeout=aiohttp.ClientTimeout(
-            total=request_timeout_s,
-            connect=min(10.0, request_timeout_s),
-            sock_connect=min(10.0, request_timeout_s),
-            sock_read=request_timeout_s,
-        ),
-    )
-
-    try:
-        # Warmup
-        if num_warmups > 0:
-            print(f"  Warming up with {num_warmups} requests...")
-            warmup_tasks = [
-                send_streaming_request(
-                    session,
-                    api_url,
-                    create_payload_fn(PROMPTS[i % len(PROMPTS)]),
-                    sample_rate,
-                    sample_width,
-                )
-                for i in range(num_warmups)
-            ]
-            warmup_results = await asyncio.gather(*warmup_tasks)
-            warmup_ok = sum(1 for r in warmup_results if r.success)
-            if warmup_ok == 0:
-                print("  WARNING: All warmup requests failed!")
-                for r in warmup_results:
-                    if r.error:
-                        print(f"    {r.error[:200]}")
-            print(f"  Warmup done ({warmup_ok}/{num_warmups} succeeded).")
-
-        # Build request list
-        request_prompts = [PROMPTS[i % len(PROMPTS)] for i in range(num_prompts)]
-
-        # Run
-        print(f"  Running {num_prompts} requests with concurrency={max_concurrency}...")
-        semaphore = asyncio.Semaphore(max_concurrency)
-        pbar = tqdm(total=num_prompts, desc=f"  concurrency={max_concurrency}")
-
-        async def limited_request(prompt: str) -> RequestResult:
-            async with semaphore:
-                return await send_streaming_request(
-                    session,
-                    api_url,
-                    create_payload_fn(prompt),
-                    sample_rate,
-                    sample_width,
-                    pbar,
-                )
-
-        start_time = time.perf_counter()
-        tasks = [asyncio.create_task(limited_request(p)) for p in request_prompts]
-        results: list[RequestResult] = await asyncio.gather(*tasks)
-        wall_time = time.perf_counter() - start_time
-        pbar.close()
-
-    finally:
-        await session.close()
-
-    # Compute stats
-    bench = compute_stats(results, wall_time)
-    bench.concurrency = max_concurrency
-    bench.num_prompts = num_prompts
-
-    print_benchmark_results(bench)
-
-    # Print sample errors
-    failed = [r for r in results if not r.success]
-    if failed:
-        for r in failed[:3]:
-            print(f"  [ERROR] {r.error[:200]}")
-
-    return bench
-
-
-async def run_benchmark_sweep(
-    host: str,
-    port: int,
-    num_prompts: int,
-    concurrency_levels: list[int],
-    create_payload_fn: Callable[[str], dict],
-    sample_rate: int,
-    sample_width: int = 2,
-    num_warmups: int = 3,
-    request_timeout_s: float = 120.0,
-    config_name: str = "benchmark",
-    result_dir: str = "results",
-) -> list[dict]:
-    """Run benchmarks across multiple concurrency levels and save results."""
-    all_results = []
-
-    for concurrency in concurrency_levels:
-        result = await run_benchmark(
-            host=host,
-            port=port,
-            num_prompts=num_prompts,
-            max_concurrency=concurrency,
-            create_payload_fn=create_payload_fn,
-            sample_rate=sample_rate,
-            sample_width=sample_width,
-            num_warmups=num_warmups,
-            request_timeout_s=request_timeout_s,
-        )
-        result.config_name = config_name
-        all_results.append(asdict(result))
-
-    save_results(all_results, result_dir, config_name)
-    return all_results
@@ -9,7 +9,7 @@ cd benchmarks/build_dataset
 pip install gdown

 # Download SeedTTS test set from Google Drive
-gdown  1GlSjVfSHkW3-leKKBlfrjuuTGqQ_xaLP
+gdown --id 1GlSjVfSHkW3-leKKBlfrjuuTGqQ_xaLP

 # Extract
 tar -xf seedtts_testset.tar
@@ -35,8 +35,8 @@ MODEL=Qwen/Qwen3-TTS-12Hz-1.7B-CustomVoice bash run_benchmark.sh --async-only
 # Use a Voice Clone model
 MODEL=Qwen/Qwen3-TTS-12Hz-1.7B-Base TASK_TYPE=Base bash run_benchmark.sh --async-only

-# Use batch size 16 for higher throughput
-BATCH_SIZE=16 bash run_benchmark.sh --async-only
+# Use bs16 config for higher throughput
+STAGE_CONFIG=vllm_omni/configs/qwen3_tts_bs16.yaml bash run_benchmark.sh --async-only

 # Custom GPU, prompt count, concurrency levels
 GPU_DEVICE=1 NUM_PROMPTS=20 CONCURRENCY="1 4" bash run_benchmark.sh
@@ -50,8 +50,7 @@ GPU_DEVICE=1 NUM_PROMPTS=20 CONCURRENCY="1 4" bash run_benchmark.sh
 CUDA_VISIBLE_DEVICES=0 python -m vllm_omni.entrypoints.cli.main serve \
    "Qwen/Qwen3-TTS-12Hz-0.6B-CustomVoice" \
    --omni --host 127.0.0.1 --port 8000 \
-    --deploy-config vllm_omni/deploy/qwen3_tts.yaml \
-    --stage-overrides '{"0":{"max_num_seqs":1,"gpu_memory_utilization":0.3,"max_num_batched_tokens":512},"1":{"max_num_seqs":1,"gpu_memory_utilization":0.3,"max_num_batched_tokens":8192}}' \
+    --stage-configs-path benchmarks/qwen3-tts/vllm_omni/configs/qwen3_tts_bs1.yaml \
    --trust-remote-code
 ```

@@ -85,19 +84,16 @@ python benchmarks/qwen3-tts/plot_results.py \
    --output results/comparison.png
 ```

-## Batch-size presets
+## Stage Configs

-The bench script loads the bundled production deploy (`vllm_omni/deploy/qwen3_tts.yaml`) and layers per-stage budgets on top via `--stage-overrides`, driven by the `BATCH_SIZE` env var. Each batch size picks compatible per-stage `max_num_seqs`, `max_num_batched_tokens`, and `gpu_memory_utilization` defaults:
+| Config | max_num_seqs | Description |
+|--------|:------------:|-------------|
+| `vllm_omni/configs/qwen3_tts_bs1.yaml` | 1 | Single-request processing (lowest latency) |
+| `vllm_omni/configs/qwen3_tts_bs16.yaml` | 16 | High-throughput concurrent processing |

-| `BATCH_SIZE` | Description |
-|:--:|-------------|
-| `1` (default) | Single-request processing (lowest latency) |
-| `4`  | Moderate-throughput concurrent processing |
-| `16` | High-throughput concurrent processing |
+All configs use a 2-stage pipeline (Talker -> Code2Wav) with `async_chunk` streaming enabled. The `SharedMemoryConnector` streams codec frames (25-frame chunks with 25-frame context overlap) between stages.

-The 2-stage pipeline (Talker -> Code2Wav) runs with `async_chunk` streaming enabled via the prod deploy; the `SharedMemoryConnector` streams codec frames (25-frame chunks with 25-frame context overlap) between stages.
-
-The model is specified via the CLI `--model` flag (or `MODEL` env var), so the same bench script works for both the 0.6B and 1.7B model variants.
+The model is specified via the CLI `--model` flag (or `MODEL` env var), so the same configs work for both the 0.6B and 1.7B model variants.

 ## Metrics

@@ -26,8 +26,8 @@
 #   # Use Voice Clone model
 #   MODEL=Qwen/Qwen3-TTS-12Hz-1.7B-Base TASK_TYPE=Base bash run_benchmark.sh --async-only
 #
-#   # Use batch_size=4:
-#   BATCH_SIZE=4 bash run_benchmark.sh --async-only
+#   # Use batch_size=4 config:
+#   STAGE_CONFIG=vllm_omni/configs/qwen3_tts_bs4.yaml bash run_benchmark.sh --async-only
 #
 # Environment variables:
 #   GPU_DEVICE       - GPU index to use (default: 0)
@@ -35,9 +35,9 @@
 #   CONCURRENCY      - Space-separated concurrency levels (default: "1 4 10")
 #   MODEL            - Model name (default: Qwen/Qwen3-TTS-12Hz-0.6B-CustomVoice)
 #   PORT             - Server port (default: 8000)
-#   BATCH_SIZE       - Per-stage ``max_num_seqs`` for both talker and code2wav (default: 1)
-#   GPU_MEM_TALKER   - gpu_memory_utilization for talker stage (default: 0.3 at bs=1, else 0.2)
-#   GPU_MEM_CODE2WAV - gpu_memory_utilization for code2wav stage (default: 0.3 at bs=1, else 0.2)
+#   GPU_MEM_TALKER   - gpu_memory_utilization for talker stage (default: 0.3)
+#   GPU_MEM_CODE2WAV - gpu_memory_utilization for code2wav stage (default: 0.2)
+#   STAGE_CONFIG     - Path to stage config YAML (default: configs/qwen3_tts_bs1.yaml)
 #   TASK_TYPE        - Task type: CustomVoice, VoiceDesign, Base (default: CustomVoice)

 set -euo pipefail
@@ -51,36 +51,14 @@ NUM_PROMPTS="${NUM_PROMPTS:-50}"
 CONCURRENCY="${CONCURRENCY:-1 4 10}"
 MODEL="${MODEL:-Qwen/Qwen3-TTS-12Hz-0.6B-CustomVoice}"
 PORT="${PORT:-8000}"
-BATCH_SIZE="${BATCH_SIZE:-1}"
-DEFAULT_MEM=$([ "${BATCH_SIZE}" = "1" ] && echo "0.3" || echo "0.2")
-GPU_MEM_TALKER="${GPU_MEM_TALKER:-${DEFAULT_MEM}}"
-GPU_MEM_CODE2WAV="${GPU_MEM_CODE2WAV:-${DEFAULT_MEM}}"
+GPU_MEM_TALKER="${GPU_MEM_TALKER:-0.3}"
+GPU_MEM_CODE2WAV="${GPU_MEM_CODE2WAV:-0.2}"
 NUM_WARMUPS="${NUM_WARMUPS:-3}"
-DEPLOY_CONFIG="vllm_omni/deploy/qwen3_tts.yaml"
+STAGE_CONFIG="${STAGE_CONFIG:-vllm_omni/configs/qwen3_tts_bs1.yaml}"
 RESULT_DIR="${SCRIPT_DIR}/results"
 TIMESTAMP="$(date +%Y%m%d_%H%M%S)"
 TASK_TYPE="${TASK_TYPE:-CustomVoice}"

-# Build --stage-overrides JSON from BATCH_SIZE + GPU_MEM_*.
-STAGE_OVERRIDES=$(
-    BATCH_SIZE="${BATCH_SIZE}" \
-    GPU_MEM_TALKER="${GPU_MEM_TALKER}" \
-    GPU_MEM_CODE2WAV="${GPU_MEM_CODE2WAV}" \
-    python - <<'PYEOF'
-import json, os
-bs = int(os.environ["BATCH_SIZE"])
-mem_t = float(os.environ["GPU_MEM_TALKER"])
-mem_c = float(os.environ["GPU_MEM_CODE2WAV"])
-# Prefill budget grows with batch size on both stages.
-talker_batched = 512 if bs <= 4 else 4096
-code2wav_batched = 8192 if bs <= 4 else 32768
-print(json.dumps({
-    "0": {"max_num_seqs": bs, "gpu_memory_utilization": mem_t, "max_num_batched_tokens": talker_batched},
-    "1": {"max_num_seqs": bs, "gpu_memory_utilization": mem_c, "max_num_batched_tokens": code2wav_batched},
-}))
-PYEOF
-)
-
 # Parse args
 RUN_ASYNC=true
 RUN_HF=true
@@ -97,27 +75,41 @@ mkdir -p "${RESULT_DIR}"
 echo "============================================================"
 echo " Qwen3-TTS Benchmark"
 echo "============================================================"
-echo " GPU:            ${GPU_DEVICE}"
-echo " Model:          ${MODEL}"
-echo " Prompts:        ${NUM_PROMPTS}"
-echo " Concurrency:    ${CONCURRENCY}"
-echo " Port:           ${PORT}"
-echo " Deploy config:  ${DEPLOY_CONFIG}"
-echo " Batch size:     ${BATCH_SIZE}"
-echo " GPU mem T/C:    ${GPU_MEM_TALKER} / ${GPU_MEM_CODE2WAV}"
-echo " Results:        ${RESULT_DIR}"
-echo " Task type:      ${TASK_TYPE}"
+echo " GPU:          ${GPU_DEVICE}"
+echo " Model:        ${MODEL}"
+echo " Prompts:      ${NUM_PROMPTS}"
+echo " Concurrency:  ${CONCURRENCY}"
+echo " Port:         ${PORT}"
+echo " Stage config: ${STAGE_CONFIG}"
+echo " Results:      ${RESULT_DIR}"
+echo " Task type:    ${TASK_TYPE}"
 echo "============================================================"

+# Prepare stage config with correct GPU device and memory settings
+prepare_config() {
+    local config_template="$1"
+    local config_name="$2"
+    local output_path="${RESULT_DIR}/${config_name}_stage_config.yaml"
+
+    # Use sed to patch GPU device and memory utilization
+    sed \
+        -e "s/devices: \"0\"/devices: \"${GPU_DEVICE}\"/g" \
+        -e "s/gpu_memory_utilization: 0.3/gpu_memory_utilization: ${GPU_MEM_TALKER}/g" \
+        -e "s/gpu_memory_utilization: 0.2/gpu_memory_utilization: ${GPU_MEM_CODE2WAV}/g" \
+        "${config_template}" > "${output_path}"
+
+    echo "${output_path}"
+}
+
 # Start server and wait for it to be ready
 start_server() {
-    local config_name="$1"
+    local stage_config="$1"
+    local config_name="$2"
    local log_file="${RESULT_DIR}/server_${config_name}_${TIMESTAMP}.log"

    echo ""
    echo "Starting server with config: ${config_name}"
-    echo "  Deploy config: ${DEPLOY_CONFIG}"
-    echo "  Stage overrides: ${STAGE_OVERRIDES}"
+    echo "  Stage config: ${stage_config}"
    echo "  Log file: ${log_file}"

    VLLM_WORKER_MULTIPROC_METHOD=spawn \
@@ -126,8 +118,7 @@ start_server() {
        --omni \
        --host 127.0.0.1 \
        --port "${PORT}" \
-        --deploy-config "${DEPLOY_CONFIG}" \
-        --stage-overrides "${STAGE_OVERRIDES}" \
+        --stage-configs-path "${stage_config}" \
        --stage-init-timeout 120 \
        --trust-remote-code \
        --disable-log-stats \
@@ -184,13 +175,17 @@ trap 'stop_server' EXIT
 # Run benchmark for a given config
 run_bench() {
    local config_name="$1"
+    local config_template="$2"

    echo ""
    echo "============================================================"
    echo " Benchmarking: ${config_name}"
    echo "============================================================"

-    start_server "${config_name}"
+    local stage_config
+    stage_config=$(prepare_config "${config_template}" "${config_name}")
+
+    start_server "${stage_config}" "${config_name}"

    # Convert concurrency string to args
    local conc_args=""
@@ -217,7 +212,7 @@ run_bench() {

 # Run vllm-omni benchmark
 if [ "${RUN_ASYNC}" = true ]; then
-    run_bench "async_chunk"
+    run_bench "async_chunk" "${SCRIPT_DIR}/${STAGE_CONFIG}"
 fi

 # Run HuggingFace baseline benchmark
@@ -1,3 +1,5 @@
+# Qwen3-TTS batch_size=1 config (streaming with async_chunk)
+# 2-stage pipeline: Talker -> Code2Wav
 async_chunk: true
 stage_args:
  - stage_id: 0
@@ -5,85 +7,87 @@ stage_args:
    is_comprehension: true
    runtime:
      devices: "0"
-      max_batch_size: 1
    engine_args:
-      dtype: bfloat16
-      model_stage: latent_generator
-      model_arch: VoxCPMForConditionalGeneration
-      # Optional persistent HF-compatible config dir for native VoxCPM models.
-      hf_config_path: ${oc.env:VLLM_OMNI_VOXCPM_HF_CONFIG_PATH,}
+      max_num_seqs: 1
+      model_stage: qwen3_tts
+      model_arch: Qwen3TTSTalkerForConditionalGeneration
      worker_type: ar
      scheduler_cls: vllm_omni.core.sched.omni_ar_scheduler.OmniARScheduler
-      enforce_eager: true
+      enforce_eager: false
      trust_remote_code: true
-      async_scheduling: false
+      async_scheduling: true
      enable_prefix_caching: false
      engine_output_type: latent
-      gpu_memory_utilization: 0.75
+      gpu_memory_utilization: 0.3
      distributed_executor_backend: "mp"
-      max_num_batched_tokens: 4096
+      max_num_batched_tokens: 512
      max_model_len: 4096
-      custom_process_next_stage_input_func: vllm_omni.model_executor.stage_input_processors.voxcpm.latent2vae_async_chunk
+      custom_process_next_stage_input_func: vllm_omni.model_executor.stage_input_processors.qwen3_tts.talker2code2wav_async_chunk
    output_connectors:
      to_stage_1: connector_of_shared_memory
    default_sampling_params:
-      temperature: 0.0
-      top_p: 1.0
-      top_k: -1
+      temperature: 0.9
+      top_k: 50
      max_tokens: 4096
      seed: 42
      detokenize: false
-      repetition_penalty: 1.0
-    final_output: false
+      repetition_penalty: 1.05
+      stop_token_ids: [2150]

  - stage_id: 1
    stage_type: llm
    runtime:
      devices: "0"
-      max_batch_size: 1
    engine_args:
-      dtype: float32
-      model_stage: vae
-      model_arch: VoxCPMForConditionalGeneration
-      hf_config_path: ${oc.env:VLLM_OMNI_VOXCPM_HF_CONFIG_PATH,}
+      max_num_seqs: 1
+      model_stage: code2wav
+      model_arch: Qwen3TTSCode2Wav
      worker_type: generation
      scheduler_cls: vllm_omni.core.sched.omni_generation_scheduler.OmniGenerationScheduler
      enforce_eager: true
      trust_remote_code: true
-      async_scheduling: false
+      async_scheduling: true
      enable_prefix_caching: false
      engine_output_type: audio
-      gpu_memory_utilization: 0.1
+      gpu_memory_utilization: 0.3
      distributed_executor_backend: "mp"
      max_num_batched_tokens: 8192
-      max_model_len: 4096
+      max_model_len: 32768
    engine_input_source: [0]
-    input_connectors:
-      from_stage_0: connector_of_shared_memory
    final_output: true
    final_output_type: audio
+    input_connectors:
+      from_stage_0: connector_of_shared_memory
+    tts_args:
+      max_instructions_length: 500
    default_sampling_params:
      temperature: 0.0
      top_p: 1.0
      top_k: -1
-      max_tokens: 1
+      max_tokens: 65536
      seed: 42
      detokenize: true
      repetition_penalty: 1.0

 runtime:
  enabled: true
+  defaults:
+    window_size: -1
+    max_inflight: 1

  connectors:
    connector_of_shared_memory:
      name: SharedMemoryConnector
      extra:
        shm_threshold_bytes: 65536
-        codec_streaming: false
+        codec_streaming: true
        connector_get_sleep_s: 0.01
        connector_get_max_wait_first_chunk: 3000
        connector_get_max_wait: 300
+        codec_chunk_frames: 25
+        codec_left_context_frames: 25

  edges:
    - from: 0
      to: 1
+      window_size: -1
@@ -0,0 +1,94 @@
+# Qwen3-TTS max_num_seqs=16 config (streaming with async_chunk)
+# High-throughput concurrent request processing
+# 2-stage pipeline: Talker -> Code2Wav
+async_chunk: true
+stage_args:
+  - stage_id: 0
+    stage_type: llm
+    is_comprehension: true
+    runtime:
+      devices: "0"
+    engine_args:
+      max_num_seqs: 16
+      model_stage: qwen3_tts
+      model_arch: Qwen3TTSTalkerForConditionalGeneration
+      worker_type: ar
+      scheduler_cls: vllm_omni.core.sched.omni_ar_scheduler.OmniARScheduler
+      enforce_eager: false
+      trust_remote_code: true
+      async_scheduling: true
+      enable_prefix_caching: false
+      engine_output_type: latent
+      gpu_memory_utilization: 0.3
+      distributed_executor_backend: "mp"
+      max_num_batched_tokens: 4096
+      max_model_len: 4096
+      custom_process_next_stage_input_func: vllm_omni.model_executor.stage_input_processors.qwen3_tts.talker2code2wav_async_chunk
+    output_connectors:
+      to_stage_1: connector_of_shared_memory
+    default_sampling_params:
+      temperature: 0.9
+      top_k: 50
+      max_tokens: 4096
+      seed: 42
+      detokenize: false
+      repetition_penalty: 1.05
+      stop_token_ids: [2150]
+
+  - stage_id: 1
+    stage_type: llm
+    runtime:
+      devices: "0"
+    engine_args:
+      max_num_seqs: 16
+      model_stage: code2wav
+      model_arch: Qwen3TTSCode2Wav
+      worker_type: generation
+      scheduler_cls: vllm_omni.core.sched.omni_generation_scheduler.OmniGenerationScheduler
+      enforce_eager: true
+      trust_remote_code: true
+      async_scheduling: true
+      enable_prefix_caching: false
+      engine_output_type: audio
+      gpu_memory_utilization: 0.2
+      distributed_executor_backend: "mp"
+      max_num_batched_tokens: 16384
+      max_model_len: 32768
+    engine_input_source: [0]
+    final_output: true
+    final_output_type: audio
+    input_connectors:
+      from_stage_0: connector_of_shared_memory
+    tts_args:
+      max_instructions_length: 500
+    default_sampling_params:
+      temperature: 0.0
+      top_p: 1.0
+      top_k: -1
+      max_tokens: 65536
+      seed: 42
+      detokenize: true
+      repetition_penalty: 1.0
+
+runtime:
+  enabled: true
+  defaults:
+    window_size: -1
+    max_inflight: 16
+
+  connectors:
+    connector_of_shared_memory:
+      name: SharedMemoryConnector
+      extra:
+        shm_threshold_bytes: 65536
+        codec_streaming: true
+        connector_get_sleep_s: 0.01
+        connector_get_max_wait_first_chunk: 3000
+        connector_get_max_wait: 300
+        codec_chunk_frames: 25
+        codec_left_context_frames: 25
+
+  edges:
+    - from: 0
+      to: 1
+      window_size: -1
@@ -0,0 +1,94 @@
+# Qwen3-TTS batch_size=4 config (streaming with async_chunk)
+# Enables concurrent request processing
+# 2-stage pipeline: Talker -> Code2Wav
+async_chunk: true
+stage_args:
+  - stage_id: 0
+    stage_type: llm
+    is_comprehension: true
+    runtime:
+      devices: "0"
+    engine_args:
+      max_num_seqs: 4
+      model_stage: qwen3_tts
+      model_arch: Qwen3TTSTalkerForConditionalGeneration
+      worker_type: ar
+      scheduler_cls: vllm_omni.core.sched.omni_ar_scheduler.OmniARScheduler
+      enforce_eager: false
+      trust_remote_code: true
+      async_scheduling: true
+      enable_prefix_caching: false
+      engine_output_type: latent
+      gpu_memory_utilization: 0.3
+      distributed_executor_backend: "mp"
+      max_num_batched_tokens: 512
+      max_model_len: 4096
+      custom_process_next_stage_input_func: vllm_omni.model_executor.stage_input_processors.qwen3_tts.talker2code2wav_async_chunk
+    output_connectors:
+      to_stage_1: connector_of_shared_memory
+    default_sampling_params:
+      temperature: 0.9
+      top_k: 50
+      max_tokens: 4096
+      seed: 42
+      detokenize: false
+      repetition_penalty: 1.05
+      stop_token_ids: [2150]
+
+  - stage_id: 1
+    stage_type: llm
+    runtime:
+      devices: "0"
+    engine_args:
+      max_num_seqs: 4
+      model_stage: code2wav
+      model_arch: Qwen3TTSCode2Wav
+      worker_type: generation
+      scheduler_cls: vllm_omni.core.sched.omni_generation_scheduler.OmniGenerationScheduler
+      enforce_eager: true
+      trust_remote_code: true
+      async_scheduling: true
+      enable_prefix_caching: false
+      engine_output_type: audio
+      gpu_memory_utilization: 0.2
+      distributed_executor_backend: "mp"
+      max_num_batched_tokens: 8192
+      max_model_len: 32768
+    engine_input_source: [0]
+    final_output: true
+    final_output_type: audio
+    input_connectors:
+      from_stage_0: connector_of_shared_memory
+    tts_args:
+      max_instructions_length: 500
+    default_sampling_params:
+      temperature: 0.0
+      top_p: 1.0
+      top_k: -1
+      max_tokens: 65536
+      seed: 42
+      detokenize: true
+      repetition_penalty: 1.0
+
+runtime:
+  enabled: true
+  defaults:
+    window_size: -1
+    max_inflight: 4
+
+  connectors:
+    connector_of_shared_memory:
+      name: SharedMemoryConnector
+      extra:
+        shm_threshold_bytes: 65536
+        codec_streaming: true
+        connector_get_sleep_s: 0.01
+        connector_get_max_wait_first_chunk: 3000
+        connector_get_max_wait: 300
+        codec_chunk_frames: 25
+        codec_left_context_frames: 25
+
+  edges:
+    - from: 0
+      to: 1
+      window_size: -1
@@ -31,11 +31,8 @@ PORT_OFF="${PORT_OFF:-8001}"
 RESULT_DIR="${SCRIPT_DIR}/results"
 TIMESTAMP="$(date +%Y%m%d_%H%M%S)"

-# The bundled ``vllm_omni/deploy/qwen3_tts.yaml`` is auto-loaded by the model
-# registry; no ``--deploy-config`` flag needed on the default (ON) path.
-# async_chunk OFF is selected by the ``--no-async-chunk`` CLI flag —
-# the single ``qwen3_tts`` pipeline dispatches to the end-to-end codec
-# processor when ``deploy.async_chunk`` is false.
+STAGE_CONFIG_ON="vllm_omni/model_executor/stage_configs/qwen3_tts.yaml"
+STAGE_CONFIG_OFF="vllm_omni/model_executor/stage_configs/qwen3_tts_no_async_chunk.yaml"

 mkdir -p "${RESULT_DIR}"

@@ -80,6 +77,7 @@ wait_for_server() {
 echo ""
 echo "[Phase 1] Starting async_chunk ON server on port ${PORT_ON}..."
 CUDA_VISIBLE_DEVICES=${GPU_DEVICE} vllm-omni serve "${MODEL}" \
+    --stage-configs-path "${STAGE_CONFIG_ON}" \
    --host 0.0.0.0 --port "${PORT_ON}" \
    --trust-remote-code --enforce-eager --omni \
    > "${RESULT_DIR}/server_on_${TIMESTAMP}.log" 2>&1 &
@@ -106,7 +104,7 @@ sleep 5
 echo ""
 echo "[Phase 2] Starting async_chunk OFF server on port ${PORT_OFF}..."
 CUDA_VISIBLE_DEVICES=${GPU_DEVICE} vllm-omni serve "${MODEL}" \
-    --no-async-chunk \
+    --stage-configs-path "${STAGE_CONFIG_OFF}" \
    --host 0.0.0.0 --port "${PORT_OFF}" \
    --trust-remote-code --enforce-eager --omni \
    > "${RESULT_DIR}/server_off_${TIMESTAMP}.log" 2>&1 &
@@ -1,119 +0,0 @@
-# VoxCPM Benchmark
-
-This directory contains both:
-
- online serving benchmark through the OpenAI-compatible `/v1/audio/speech` API
- offline benchmark for `Omni` / `AsyncOmni`
- full offline smoke-matrix orchestration
-
-Both benchmark paths report:
-
- TTFP: time to first PCM packet
- E2E latency
- RTF: real-time factor (`e2e / audio_duration`)
-
-## Offline Benchmark
-
-Single offline benchmark run:
-
-```bash
-python benchmarks/voxcpm/vllm_omni/bench_tts_offline.py \
-    --model /path/to/voxcpm-model \
-    --stage-configs-path vllm_omni/model_executor/stage_configs/voxcpm.yaml \
-    --text "This is a split-stage VoxCPM synthesis example running on vLLM Omni." \
-    --warmup-runs 1 \
-    --output-dir benchmarks/voxcpm/results/offline_single
-```
-
-Streaming offline benchmark:
-
-```bash
-python benchmarks/voxcpm/vllm_omni/bench_tts_offline.py \
-    --model /path/to/voxcpm-model \
-    --stage-configs-path vllm_omni/model_executor/stage_configs/voxcpm_async_chunk.yaml \
-    --text "This is a split-stage VoxCPM streaming example running on vLLM Omni." \
-    --warmup-runs 1 \
-    --output-dir benchmarks/voxcpm/results/offline_streaming
-```
-
-Full fixed offline matrix, equivalent to the old `examples/offline_inference/voxcpm/test.py`:
-
-```bash
-python benchmarks/voxcpm/vllm_omni/run_offline_matrix.py \
-    --model /path/to/voxcpm-model \
-    --ref-audio /path/to/reference.wav \
-    --ref-text "The exact transcript spoken in reference.wav." \
-    --output-root benchmarks/voxcpm/results/offline_matrix
-```
-
-The full matrix covers both routes:
-
- streaming: `voxcpm_async_chunk.yaml`
- sync: `voxcpm.yaml`
-
-And these six scenarios under each route:
-
- warmup + single TTS
- warmup + single voice cloning
- warmup + batch TTS
- warmup + batch voice cloning
- cold single TTS
- cold single voice cloning
-
-`bench_tts_offline.py` itself no longer writes `summary.json` / `results.json`; it prints TTFP / RTF inline and saves generated WAV files only. The matrix runner keeps only per-case `run.log`.
-
-## Start the Server
-
-Async-chunk:
-
-```bash
-vllm serve /path/to/voxcpm-model \
-    --stage-configs-path vllm_omni/model_executor/stage_configs/voxcpm_async_chunk.yaml \
-    --trust-remote-code \
-    --enforce-eager \
-    --omni \
-    --port 8091
-```
-
-Non-streaming:
-
-```bash
-vllm serve /path/to/voxcpm-model \
-    --stage-configs-path vllm_omni/model_executor/stage_configs/voxcpm.yaml \
-    --trust-remote-code \
-    --enforce-eager \
-    --omni \
-    --port 8091
-```
-
-## Run the Benchmark
-
-```bash
-python benchmarks/voxcpm/vllm_omni/bench_tts_serve.py \
-    --host 127.0.0.1 \
-    --port 8091 \
-    --num-prompts 20 \
-    --max-concurrency 1 \
-    --result-dir /tmp/voxcpm_bench
-```
-
-Voice cloning benchmark:
-
-```bash
-python benchmarks/voxcpm/vllm_omni/bench_tts_serve.py \
-    --host 127.0.0.1 \
-    --port 8091 \
-    --num-prompts 10 \
-    --max-concurrency 1 \
-    --ref-audio https://example.com/reference.wav \
-    --ref-text "The exact transcript spoken in the reference audio." \
-    --result-dir /tmp/voxcpm_clone_bench
-```
-
-## Notes
-
- The benchmark uses `stream=true` and `response_format=pcm` so TTFP is measured from the first audio packet.
- `RTF < 1.0` means the server generates audio faster than real time.
- For `voxcpm_async_chunk.yaml`, keep concurrency at `1`. This matches native VoxCPM streaming more closely.
- Do not benchmark concurrent online streaming on `voxcpm_async_chunk.yaml`; use `voxcpm.yaml` for multi-request throughput runs.
- For the offline matrix mode, `--ref-audio` and `--ref-text` are required because clone cases are part of the fixed coverage set.
@@ -1,890 +0,0 @@
-"""Offline VoxCPM benchmark for vLLM Omni.
-
-Supports both:
- sync one-shot (Omni.generate)
- streaming (AsyncOmni.generate with async_chunk config)
- text-only synthesis
- voice cloning
- text/clone batch inputs from txt or jsonl
-"""
-
-from __future__ import annotations
-
-import asyncio
-import json
-import logging
-import os
-import tempfile
-import time
-import uuid
-from dataclasses import dataclass
-from pathlib import Path
-from typing import Any
-
-import torch
-from vllm.utils.argparse_utils import FlexibleArgumentParser
-
-from vllm_omni import AsyncOmni, Omni
-
-REPO_ROOT = Path(__file__).resolve().parents[3]
-DEFAULT_STAGE_ASYNC = REPO_ROOT / "vllm_omni" / "model_executor" / "stage_configs" / "voxcpm_async_chunk.yaml"
-DEFAULT_STAGE_SYNC = REPO_ROOT / "vllm_omni" / "model_executor" / "stage_configs" / "voxcpm.yaml"
-
-logger = logging.getLogger(__name__)
-
-
-@dataclass(frozen=True, slots=True)
-class PromptSpec:
-    text: str
-    label: str
-    ref_audio: str | None = None
-    ref_text: str | None = None
-
-
-def _require_soundfile():
-    try:
-        import soundfile as sf  # type: ignore
-    except ModuleNotFoundError as exc:
-        raise RuntimeError(
-            "soundfile is required to write VoxCPM benchmark WAV outputs. Install it with: pip install soundfile"
-        ) from exc
-    return sf
-
-
-def _build_prompt(
-    args,
-    *,
-    text: str,
-    ref_audio: str | None = None,
-    ref_text: str | None = None,
-    global_request_id: str | None = None,
-) -> dict[str, Any]:
-    additional_information: dict[str, list[Any]] = {
-        "text": [text],
-        "cfg_value": [args.cfg_value],
-        "inference_timesteps": [args.inference_timesteps],
-        "min_len": [args.min_len],
-        "max_new_tokens": [args.max_new_tokens],
-    }
-    if args.streaming_prefix_len is not None:
-        additional_information["streaming_prefix_len"] = [args.streaming_prefix_len]
-
-    if ref_audio:
-        additional_information["ref_audio"] = [ref_audio]
-    if ref_text:
-        additional_information["ref_text"] = [ref_text]
-    if global_request_id is not None:
-        additional_information["global_request_id"] = [global_request_id]
-
-    return {
-        "prompt_token_ids": [1],
-        "additional_information": additional_information,
-    }
-
-
-def _extract_audio_tensor(mm: dict[str, Any]) -> torch.Tensor:
-    audio = mm.get("audio", mm.get("model_outputs"))
-    if audio is None:
-        raise ValueError("No audio output found in multimodal output.")
-    if isinstance(audio, list):
-        parts = [torch.as_tensor(a).float().cpu().reshape(-1) for a in audio]
-        audio = torch.cat(parts, dim=-1) if parts else torch.zeros(0)
-    if not isinstance(audio, torch.Tensor):
-        audio = torch.as_tensor(audio)
-    return audio.float().cpu().reshape(-1)
-
-
-def _extract_sample_rate(mm: dict[str, Any]) -> int:
-    sr_raw = mm.get("sr", 24000)
-    if isinstance(sr_raw, list) and sr_raw:
-        sr_raw = sr_raw[-1]
-    if hasattr(sr_raw, "item"):
-        return int(sr_raw.item())
-    return int(sr_raw)
-
-
-def _emit_offline_metrics(
-    *,
-    request_id: str,
-    elapsed_s: float,
-    first_audio_elapsed: float | None,
-    audio_duration_s: float,
-) -> None:
-    metrics = {
-        "request_id": request_id,
-        "ttfp_ms": round(first_audio_elapsed * 1000.0, 3) if first_audio_elapsed is not None else None,
-        "audio_duration_s": round(audio_duration_s, 6),
-        "rtf": round(elapsed_s / audio_duration_s, 6) if audio_duration_s > 0 else None,
-    }
-    print(f"[OfflineMetrics] {metrics}")
-
-
-def _write_audio_tensor(output_path: Path, audio_tensor: Any, sample_rate: int) -> None:
-    sf = _require_soundfile()
-    if isinstance(audio_tensor, torch.Tensor):
-        audio_np = audio_tensor.float().cpu().clamp(-1.0, 1.0).numpy()
-    else:
-        audio_np = torch.as_tensor(audio_tensor).float().cpu().clamp(-1.0, 1.0).numpy()
-    sf.write(
-        output_path,
-        audio_np,
-        sample_rate,
-        format="WAV",
-        subtype="PCM_16",
-    )
-
-
-def _save_wav(mm: dict[str, Any], output_dir: Path, request_id: str) -> Path:
-    output_dir.mkdir(parents=True, exist_ok=True)
-    output_path = output_dir / f"output_{request_id}.wav"
-    _write_audio_tensor(output_path, _extract_audio_tensor(mm), _extract_sample_rate(mm))
-    return output_path
-
-
-def _iter_request_multimodal_outputs(request_output: Any):
-    outputs = getattr(request_output, "outputs", None)
-    if outputs:
-        for output in outputs:
-            mm = getattr(output, "multimodal_output", None)
-            if isinstance(mm, dict):
-                yield mm
-
-    mm = getattr(request_output, "multimodal_output", None)
-    if isinstance(mm, dict):
-        yield mm
-
-
-def _read_non_empty_lines(path: str) -> list[str]:
-    with open(path, encoding="utf-8") as f:
-        return [line.strip() for line in f if line.strip()]
-
-
-def _load_prompt_specs(args) -> list[PromptSpec]:
-    specs: list[PromptSpec] = []
-
-    if args.txt_prompts is not None:
-        texts = _read_non_empty_lines(args.txt_prompts)
-        if not texts:
-            raise ValueError(f"No prompts found in {args.txt_prompts}")
-        for idx, text in enumerate(texts, start=1):
-            specs.append(
-                PromptSpec(
-                    text=text,
-                    label=f"item{idx:03d}",
-                    ref_audio=args.ref_audio,
-                    ref_text=args.ref_text,
-                )
-            )
-        return specs
-
-    if args.jsonl_prompts is not None:
-        with open(args.jsonl_prompts, encoding="utf-8") as f:
-            for line_no, raw_line in enumerate(f, start=1):
-                line = raw_line.strip()
-                if not line:
-                    continue
-                try:
-                    item = json.loads(line)
-                except json.JSONDecodeError as exc:
-                    raise ValueError(f"{args.jsonl_prompts}:{line_no} is not valid JSON: {exc}") from exc
-                if not isinstance(item, dict):
-                    raise ValueError(f"{args.jsonl_prompts}:{line_no} must be a JSON object")
-
-                text = item.get("text")
-                if not isinstance(text, str) or not text.strip():
-                    raise ValueError(f"{args.jsonl_prompts}:{line_no} requires non-empty string field 'text'")
-
-                ref_audio = item.get("ref_audio", args.ref_audio)
-                ref_text = item.get("ref_text", args.ref_text)
-                if (ref_audio is None) != (ref_text is None):
-                    raise ValueError(
-                        f"{args.jsonl_prompts}:{line_no} must provide both 'ref_audio' and 'ref_text' together"
-                    )
-
-                specs.append(
-                    PromptSpec(
-                        text=text.strip(),
-                        label=f"item{len(specs) + 1:03d}",
-                        ref_audio=ref_audio,
-                        ref_text=ref_text,
-                    )
-                )
-
-        if not specs:
-            raise ValueError(f"No prompts found in {args.jsonl_prompts}")
-        return specs
-
-    specs.append(
-        PromptSpec(
-            text=args.text,
-            label="item001",
-            ref_audio=args.ref_audio,
-            ref_text=args.ref_text,
-        )
-    )
-    return specs
-
-
-def _build_prompt_for_spec(args, spec: PromptSpec, *, global_request_id: str | None = None) -> dict[str, Any]:
-    return _build_prompt(
-        args,
-        text=spec.text,
-        ref_audio=spec.ref_audio,
-        ref_text=spec.ref_text,
-        global_request_id=global_request_id,
-    )
-
-
-def _count_voice_clone_prompts(prompt_specs: list[PromptSpec]) -> int:
-    return sum(1 for spec in prompt_specs if spec.ref_audio is not None)
-
-
-def _get_warmup_specs(prompt_specs: list[PromptSpec]) -> list[PromptSpec]:
-    return prompt_specs[:1]
-
-
-def _extract_stream_finished(stage_output: Any) -> bool:
-    request_output = getattr(stage_output, "request_output", None)
-    request_finished = getattr(request_output, "finished", None)
-    if request_finished is not None:
-        return bool(request_finished)
-    return bool(getattr(stage_output, "finished", False))
-
-
-def _build_profiled_stage_config(
-    stage_configs_path: str,
-    profiler_dir: str,
-) -> str:
-    stage_config_path = Path(stage_configs_path)
-    yaml_text = stage_config_path.read_text(encoding="utf-8")
-    injected_lines: list[str] = []
-    injected_count = 0
-
-    for line in yaml_text.splitlines():
-        injected_lines.append(line)
-        if line.strip() != "engine_args:":
-            continue
-        indent = line[: len(line) - len(line.lstrip())]
-        child_indent = indent + "  "
-        grandchild_indent = child_indent + "  "
-        injected_lines.extend(
-            [
-                f"{child_indent}profiler_config:",
-                f'{grandchild_indent}profiler: "torch"',
-                f'{grandchild_indent}torch_profiler_dir: "{profiler_dir}"',
-                f"{grandchild_indent}torch_profiler_with_stack: true",
-            ]
-        )
-        injected_count += 1
-
-    if injected_count == 0:
-        raise ValueError(f"No engine_args block found in stage config: {stage_configs_path}")
-
-    tmp = tempfile.NamedTemporaryFile(
-        mode="w",
-        encoding="utf-8",
-        delete=False,
-        suffix=".yaml",
-        prefix=f"{stage_config_path.stem}_profile_",
-    )
-    tmp.write("\n".join(injected_lines) + "\n")
-    tmp.close()
-    return tmp.name
-
-
-def parse_args():
-    parser = FlexibleArgumentParser(
-        description="Offline split-stage VoxCPM inference with vLLM Omni (auto sync/streaming by stage config)"
-    )
-    parser.add_argument(
-        "--model",
-        type=str,
-        default=os.environ.get("VOXCPM_MODEL"),
-        help="Local VoxCPM model directory. Defaults to $VOXCPM_MODEL.",
-    )
-    parser.add_argument(
-        "--text",
-        type=str,
-        default="This is a split-stage VoxCPM synthesis example running on vLLM Omni.",
-        help="Text to synthesize. Ignored when --txt-prompts or --jsonl-prompts is used.",
-    )
-    parser.add_argument(
-        "--txt-prompts",
-        type=str,
-        default=None,
-        help="Path to a .txt file with one synthesis text per line.",
-    )
-    parser.add_argument(
-        "--jsonl-prompts",
-        type=str,
-        default=None,
-        help=(
-            "Path to a .jsonl file. Each line must contain at least {'text': ...}; "
-            "clone rows can also set ref_audio/ref_text, and ref_text must be the "
-            "real transcript of ref_audio."
-        ),
-    )
-    parser.add_argument(
-        "--ref-audio",
-        type=str,
-        default=None,
-        help=(
-            "Optional reference audio path for voice cloning. With --txt-prompts, "
-            "the same reference is applied to every line."
-        ),
-    )
-    parser.add_argument(
-        "--ref-text",
-        type=str,
-        default=None,
-        help=(
-            "Real transcript of the reference audio. Placeholder text or mismatched "
-            "text will usually produce noisy/electronic clone audio."
-        ),
-    )
-    parser.add_argument(
-        "--stage-configs-path",
-        type=str,
-        default=str(DEFAULT_STAGE_SYNC),
-        help="Stage config YAML path. Routing is selected only from this path.",
-    )
-    parser.add_argument(
-        "--cfg-value",
-        type=float,
-        default=2.0,
-        help="Classifier-free guidance value for VoxCPM.",
-    )
-    parser.add_argument(
-        "--inference-timesteps",
-        type=int,
-        default=10,
-        help="Number of inference timesteps.",
-    )
-    parser.add_argument(
-        "--min-len",
-        type=int,
-        default=2,
-        help="Minimum generated token length.",
-    )
-    parser.add_argument(
-        "--max-new-tokens",
-        type=int,
-        default=4096,
-        help="Maximum generated token length.",
-    )
-    parser.add_argument(
-        "--streaming-prefix-len",
-        type=int,
-        default=None,
-        help="VoxCPM streaming window (optional, streaming mode only).",
-    )
-    parser.add_argument(
-        "--output-dir",
-        type=str,
-        default=None,
-        help="Directory for output WAV files.",
-    )
-    parser.add_argument(
-        "--stage-init-timeout",
-        type=int,
-        default=600,
-        help="Stage initialization timeout in seconds.",
-    )
-    parser.add_argument(
-        "--log-stats",
-        dest="log_stats",
-        action="store_true",
-        help="Enable vLLM Omni stats logging.",
-    )
-    parser.add_argument(
-        "--no-log-stats",
-        dest="log_stats",
-        action="store_false",
-        help="Disable vLLM Omni stats logging.",
-    )
-    parser.set_defaults(log_stats=True)
-    parser.add_argument(
-        "--num-runs",
-        type=int,
-        default=1,
-        help="Number of full inference runs (same prompt each time). Default 1.",
-    )
-    parser.add_argument(
-        "--warmup-runs",
-        type=int,
-        default=0,
-        help=(
-            "Optional number of warmup passes before measured runs. Warmup uses only "
-            "the first prompt and does not save outputs."
-        ),
-    )
-    parser.add_argument(
-        "--enable-profiler",
-        action="store_true",
-        help=(
-            "Enable torch profiler for the configured stages. A temporary profiled "
-            "stage config is generated automatically."
-        ),
-    )
-    parser.add_argument(
-        "--profiler-dir",
-        type=str,
-        default=None,
-        help="Directory for profiler traces. Defaults to <output-dir>/profiler when profiling is enabled.",
-    )
-    parser.add_argument(
-        "--profiler-stages",
-        type=int,
-        nargs="*",
-        default=None,
-        help="Optional stage ids to profile. Defaults to all stages that have profiler_config.",
-    )
-    parser.add_argument(
-        "--profiler-wait-seconds",
-        type=float,
-        default=30.0,
-        help="Seconds to wait after stop_profile for trace files to flush.",
-    )
-    args = parser.parse_args()
-
-    if not args.model:
-        parser.error("--model is required unless $VOXCPM_MODEL is set")
-    if args.txt_prompts is not None and args.jsonl_prompts is not None:
-        parser.error("--txt-prompts and --jsonl-prompts are mutually exclusive")
-    if (args.ref_audio is None) != (args.ref_text is None):
-        parser.error("--ref-audio and --ref-text must be provided together")
-    if args.num_runs < 1:
-        parser.error("--num-runs must be >= 1")
-    if args.warmup_runs < 0:
-        parser.error("--warmup-runs must be >= 0")
-    if args.output_dir is None:
-        args.output_dir = (
-            "output_audio_streaming" if _is_streaming_stage_config(args.stage_configs_path) else "output_audio"
-        )
-    if args.enable_profiler and args.profiler_dir is None:
-        args.profiler_dir = str(Path(args.output_dir) / "profiler")
-    try:
-        args.prompt_specs = _load_prompt_specs(args)
-    except ValueError as exc:
-        parser.error(str(exc))
-
-    return args
-
-
-def _is_streaming_stage_config(stage_configs_path: str) -> bool:
-    cfg_name = Path(stage_configs_path).name.lower()
-    # Keep routing purely config-path based:
-    # - voxcpm.yaml => sync
-    # - voxcpm_async_chunk.yaml => streaming
-    return "async_chunk" in cfg_name
-
-
-async def _collect_streaming_audio(
-    omni: AsyncOmni,
-    args: Any,
-    spec: PromptSpec,
-    request_id: str,
-    *,
-    phase_label: str,
-    prompt_index: int,
-    prompt_count: int,
-    print_prompt: bool = False,
-) -> tuple[torch.Tensor, int, float, float | None]:
-    prompt = _build_prompt_for_spec(args, spec, global_request_id=request_id)
-    delta_chunks: list[torch.Tensor] = []
-    sample_rate = 24000
-    chunk_i = 0
-    prev_total_samples = 0
-    t_start = time.perf_counter()
-    first_audio_elapsed: float | None = None
-
-    if print_prompt:
-        print(f"---prompt---:{prompt}")
-
-    async for stage_output in omni.generate(prompt, request_id=request_id):
-        mm = getattr(stage_output, "multimodal_output", None)
-        if not isinstance(mm, dict):
-            ro = getattr(stage_output, "request_output", None)
-            if ro is None:
-                continue
-            mm = getattr(ro, "multimodal_output", None)
-            if not isinstance(mm, dict) and getattr(ro, "outputs", None):
-                seq = ro.outputs[0]
-                mm = getattr(seq, "multimodal_output", None)
-        if not isinstance(mm, dict):
-            continue
-        sample_rate = _extract_sample_rate(mm)
-        try:
-            w = _extract_audio_tensor(mm)
-            n = int(w.numel())
-            if n == 0:
-                continue
-            finished = _extract_stream_finished(stage_output)
-            if n > prev_total_samples:
-                delta = w.reshape(-1)[prev_total_samples:]
-                prev_total_samples = n
-            elif finished and n == prev_total_samples:
-                delta = w.reshape(-1)[:0]
-            else:
-                delta = w.reshape(-1)
-                prev_total_samples += int(delta.numel())
-            if int(delta.numel()) > 0:
-                delta_chunks.append(delta)
-            if first_audio_elapsed is None and int(delta.numel()) > 0:
-                first_audio_elapsed = time.perf_counter() - t_start
-            logger.info(
-                "%s prompt=%d/%d chunk=%d delta_samples=%d buf_len=%d finished=%s",
-                phase_label,
-                prompt_index + 1,
-                prompt_count,
-                chunk_i,
-                int(delta.numel()),
-                n,
-                finished,
-            )
-            chunk_i += 1
-        except ValueError:
-            if not _extract_stream_finished(stage_output):
-                logger.debug("skip non-audio partial output chunk=%d", chunk_i)
-
-    if not delta_chunks:
-        raise RuntimeError("No audio chunks received; check stage config and logs.")
-
-    audio_cat = torch.cat([c.reshape(-1) for c in delta_chunks], dim=0)
-    elapsed = time.perf_counter() - t_start
-    return audio_cat, sample_rate, elapsed, first_audio_elapsed
-
-
-async def _abort_streaming_residual_work(
-    omni: AsyncOmni,
-    request_id: str,
-    *,
-    settle_seconds: float = 0.1,
-) -> None:
-    """Stop any late stage-0 work once the final audio has been collected."""
-    await omni.engine.abort_async([request_id])
-    if settle_seconds > 0:
-        await asyncio.sleep(settle_seconds)
-
-
-async def _run_streaming_single(
-    omni: AsyncOmni,
-    args: Any,
-    spec: PromptSpec,
-    output_dir: Path,
-    request_id: str,
-    *,
-    run_index: int,
-    num_runs: int,
-    prompt_index: int,
-    prompt_count: int,
-) -> Path:
-    audio_cat, sample_rate, elapsed, first_audio_elapsed = await _collect_streaming_audio(
-        omni,
-        args,
-        spec,
-        request_id,
-        phase_label=f"run={run_index + 1}/{num_runs}",
-        prompt_index=prompt_index,
-        prompt_count=prompt_count,
-        print_prompt=(run_index == 0 and prompt_index == 0),
-    )
-    await _abort_streaming_residual_work(omni, request_id)
-    output_path = output_dir / f"output_run{run_index + 1}_{spec.label}.wav"
-    _write_audio_tensor(output_path, audio_cat, sample_rate)
-    audio_duration_s = float(audio_cat.numel()) / float(sample_rate) if sample_rate > 0 else 0.0
-    ttfp_text = f", ttfp={first_audio_elapsed:.2f}s" if first_audio_elapsed is not None else ""
-    rtf_text = f", rtf={elapsed / audio_duration_s:.3f}" if audio_duration_s > 0 else ""
-    print(
-        f"Saved (streaming) run {run_index + 1}/{num_runs}, "
-        f"prompt {prompt_index + 1}/{prompt_count}: {output_path} ({elapsed:.2f}s{ttfp_text}{rtf_text})"
-    )
-    _emit_offline_metrics(
-        request_id=request_id,
-        elapsed_s=elapsed,
-        first_audio_elapsed=first_audio_elapsed,
-        audio_duration_s=audio_duration_s,
-    )
-    return output_path
-
-
-async def _run_streaming_warmup(args, omni: AsyncOmni) -> None:
-    if args.warmup_runs == 0:
-        return
-
-    warmup_specs = _get_warmup_specs(args.prompt_specs)
-    print(
-        f"Warmup: {args.warmup_runs} run(s) using the first prompt "
-        f"({len(warmup_specs)} prompt(s)); outputs will be discarded."
-    )
-    for warmup_index in range(args.warmup_runs):
-        t_warmup = time.perf_counter()
-        tasks = []
-        request_ids: list[str] = []
-        for prompt_index, spec in enumerate(warmup_specs):
-            request_id = f"warmup_stream_{warmup_index + 1}_{spec.label}_{uuid.uuid4().hex[:8]}"
-            request_ids.append(request_id)
-            tasks.append(
-                _collect_streaming_audio(
-                    omni,
-                    args,
-                    spec,
-                    request_id,
-                    phase_label=f"warmup={warmup_index + 1}/{args.warmup_runs}",
-                    prompt_index=prompt_index,
-                    prompt_count=len(warmup_specs),
-                )
-            )
-        results = await asyncio.gather(*tasks)
-        for request_id in request_ids:
-            await _abort_streaming_residual_work(omni, request_id)
-        total_samples = sum(int(audio.numel()) for audio, _, _, _ in results)
-        warmup_ttfps = [ttfp for _, _, _, ttfp in results if ttfp is not None]
-        ttfp_text = f", ttfp={min(warmup_ttfps):.2f}s" if warmup_ttfps else ""
-        print(
-            f"Warmup (streaming) {warmup_index + 1}/{args.warmup_runs} finished: "
-            f"{len(results)} prompt(s), {total_samples} sample(s) "
-            f"({time.perf_counter() - t_warmup:.2f}s{ttfp_text})"
-        )
-
-
-async def _run_streaming(args) -> list[Path]:
-    output_dir = Path(args.output_dir)
-    output_dir.mkdir(parents=True, exist_ok=True)
-
-    omni = AsyncOmni(
-        model=args.model,
-        stage_configs_path=args.stage_configs_path,
-        log_stats=args.log_stats,
-        stage_init_timeout=args.stage_init_timeout,
-    )
-
-    await _run_streaming_warmup(args, omni)
-    profiler_started = False
-    if args.enable_profiler:
-        profile_prefix = f"voxcpm_streaming_{int(time.time())}"
-        stages_text = args.profiler_stages if args.profiler_stages is not None else "all-configured"
-        print(f"Starting profiler (streaming): stages={stages_text}, dir={args.profiler_dir}")
-        await omni.start_profile(profile_prefix=profile_prefix, stages=args.profiler_stages)
-        profiler_started = True
-    t_total = time.perf_counter()
-    total_elapsed = 0.0
-    paths: list[Path] = []
-    prompt_specs: list[PromptSpec] = args.prompt_specs
-    try:
-        for run in range(args.num_runs):
-            for prompt_index, spec in enumerate(prompt_specs):
-                request_id = f"stream_{run + 1}_{spec.label}_{uuid.uuid4().hex[:8]}"
-                paths.append(
-                    await _run_streaming_single(
-                        omni,
-                        args,
-                        spec,
-                        output_dir,
-                        request_id,
-                        run_index=run,
-                        num_runs=args.num_runs,
-                        prompt_index=prompt_index,
-                        prompt_count=len(prompt_specs),
-                    )
-                )
-        total_elapsed = time.perf_counter() - t_total
-    finally:
-        if profiler_started:
-            print("Stopping profiler (streaming)...")
-            await omni.stop_profile(stages=args.profiler_stages)
-            if args.profiler_wait_seconds > 0:
-                print(f"Waiting {args.profiler_wait_seconds:.1f}s for profiler traces to flush...")
-                await asyncio.sleep(args.profiler_wait_seconds)
-
-    print(
-        f"All streaming runs finished: {args.num_runs} run(s), "
-        f"{len(prompt_specs)} prompt(s), {len(paths)} file(s) in {total_elapsed:.2f}s total"
-    )
-    return paths
-
-
-def _run_sync(args) -> list[Path]:
-    output_dir = Path(args.output_dir)
-
-    omni = Omni(
-        model=args.model,
-        stage_configs_path=args.stage_configs_path,
-        log_stats=args.log_stats,
-        stage_init_timeout=args.stage_init_timeout,
-    )
-
-    def _run_sync_single(
-        spec: PromptSpec,
-        *,
-        request_prefix: str,
-        save_outputs: bool,
-        run_index: int | None = None,
-    ) -> tuple[list[Path], int, float | None, float, float, str]:
-        global_request_id = f"{request_prefix}_{spec.label}"
-        prompt = _build_prompt_for_spec(args, spec, global_request_id=global_request_id)
-        if save_outputs and run_index == 0 and spec.label == "item001":
-            print(f"---prompt---:{prompt}")
-
-        saved_paths: list[Path] = []
-        output_count = 0
-        first_audio_elapsed: float | None = None
-        total_audio_duration_s = 0.0
-        metrics_request_id = global_request_id
-        t_start = time.perf_counter()
-        for stage_outputs in omni.generate(prompt):
-            request_output = stage_outputs.request_output
-            if request_output is None:
-                continue
-            request_output_id = getattr(request_output, "request_id", None)
-            if isinstance(request_output_id, str) and request_output_id:
-                metrics_request_id = request_output_id
-            for j, mm in enumerate(_iter_request_multimodal_outputs(request_output)):
-                output_count += 1
-                if first_audio_elapsed is None:
-                    try:
-                        audio_tensor = _extract_audio_tensor(mm)
-                        if int(audio_tensor.numel()) > 0:
-                            first_audio_elapsed = time.perf_counter() - t_start
-                        total_audio_duration_s += float(audio_tensor.numel()) / float(_extract_sample_rate(mm))
-                    except ValueError:
-                        pass
-                else:
-                    try:
-                        audio_tensor = _extract_audio_tensor(mm)
-                        total_audio_duration_s += float(audio_tensor.numel()) / float(_extract_sample_rate(mm))
-                    except ValueError:
-                        pass
-                if not save_outputs:
-                    continue
-                save_stem = f"run{run_index + 1}_{spec.label}" if j == 0 else f"run{run_index + 1}_{spec.label}_{j}"
-                saved_paths.append(_save_wav(mm, output_dir, save_stem))
-
-        if output_count == 0:
-            raise RuntimeError("No output from Omni.generate")
-        elapsed_s = time.perf_counter() - t_start
-        return saved_paths, output_count, first_audio_elapsed, elapsed_s, total_audio_duration_s, metrics_request_id
-
-    if args.warmup_runs:
-        warmup_specs = _get_warmup_specs(args.prompt_specs)
-        print(
-            f"Warmup: {args.warmup_runs} run(s) using the first prompt "
-            f"({len(warmup_specs)} prompt(s)); outputs will be discarded."
-        )
-        for warmup_index in range(args.warmup_runs):
-            t_warmup = time.perf_counter()
-            _, output_count, first_audio_elapsed, elapsed_s, audio_duration_s, _ = _run_sync_single(
-                warmup_specs[0],
-                request_prefix=f"warmup_sync{warmup_index + 1}",
-                save_outputs=False,
-            )
-            ttfp_text = f", ttfp={first_audio_elapsed:.2f}s" if first_audio_elapsed is not None else ""
-            rtf_text = f", rtf={elapsed_s / audio_duration_s:.3f}" if audio_duration_s > 0 else ""
-            print(
-                f"Warmup (sync) {warmup_index + 1}/{args.warmup_runs} finished: "
-                f"{output_count} output(s) ({time.perf_counter() - t_warmup:.2f}s{ttfp_text}{rtf_text})"
-            )
-
-    profiler_started = False
-    if args.enable_profiler:
-        profile_prefix = f"voxcpm_sync_{int(time.time())}"
-        stages_text = args.profiler_stages if args.profiler_stages is not None else "all-configured"
-        print(f"Starting profiler (sync): stages={stages_text}, dir={args.profiler_dir}")
-        omni.start_profile(profile_prefix=profile_prefix, stages=args.profiler_stages)
-        profiler_started = True
-
-    t_total = time.perf_counter()
-    total_elapsed = 0.0
-    saved_paths: list[Path] = []
-    prompt_specs: list[PromptSpec] = args.prompt_specs
-    try:
-        for run in range(args.num_runs):
-            t_run = time.perf_counter()
-            run_paths: list[Path] = []
-            for prompt_index, spec in enumerate(prompt_specs):
-                prompt_paths, _, first_audio_elapsed, elapsed_s, audio_duration_s, metrics_request_id = (
-                    _run_sync_single(
-                        spec,
-                        request_prefix=f"sync_run{run + 1}_{prompt_index + 1:03d}",
-                        save_outputs=True,
-                        run_index=run,
-                    )
-                )
-                run_paths.extend(prompt_paths)
-                ttfp_text = f", ttfp={first_audio_elapsed:.2f}s" if first_audio_elapsed is not None else ""
-                rtf_text = f", rtf={elapsed_s / audio_duration_s:.3f}" if audio_duration_s > 0 else ""
-                print(
-                    f"Saved (sync) run {run + 1}/{args.num_runs}, "
-                    f"prompt {prompt_index + 1}/{len(prompt_specs)}: {len(prompt_paths)} file(s){ttfp_text}{rtf_text}"
-                )
-                _emit_offline_metrics(
-                    request_id=metrics_request_id,
-                    elapsed_s=elapsed_s,
-                    first_audio_elapsed=first_audio_elapsed,
-                    audio_duration_s=audio_duration_s,
-                )
-
-            saved_paths.extend(run_paths)
-            print(
-                f"Run {run + 1}/{args.num_runs} finished: {len(run_paths)} file(s) ({time.perf_counter() - t_run:.2f}s)"
-            )
-            for path in run_paths:
-                print(f"  {path}")
-
-        total_elapsed = time.perf_counter() - t_total
-    finally:
-        if profiler_started:
-            print("Stopping profiler (sync)...")
-            omni.stop_profile(stages=args.profiler_stages)
-            if args.profiler_wait_seconds > 0:
-                print(f"Waiting {args.profiler_wait_seconds:.1f}s for profiler traces to flush...")
-                time.sleep(args.profiler_wait_seconds)
-
-    print(
-        f"All sync runs finished: {args.num_runs} run(s), "
-        f"{len(prompt_specs)} prompt(s), {len(saved_paths)} file(s) in {total_elapsed:.2f}s total"
-    )
-    return saved_paths
-
-
-def main(args) -> int:
-    logging.basicConfig(level=logging.INFO)
-    profiled_stage_config_path: str | None = None
-    original_stage_config_path = args.stage_configs_path
-    if args.enable_profiler:
-        Path(args.profiler_dir).mkdir(parents=True, exist_ok=True)
-        profiled_stage_config_path = _build_profiled_stage_config(
-            args.stage_configs_path,
-            str(Path(args.profiler_dir).resolve()),
-        )
-        args.stage_configs_path = profiled_stage_config_path
-
-    is_streaming = _is_streaming_stage_config(args.stage_configs_path)
-    voice_clone_count = _count_voice_clone_prompts(args.prompt_specs)
-    print(f"Model: {args.model}")
-    print(f"Stage config: {original_stage_config_path}")
-    print(f"Route: {'streaming' if is_streaming else 'sync'} (from stage-configs-path)")
-    print(f"Prompt count: {len(args.prompt_specs)}")
-    print("Batch mode: sequential (aligned with native VoxCPM)")
-    print(f"Warmup runs: {args.warmup_runs}")
-    print(f"Voice cloning prompts: {voice_clone_count}/{len(args.prompt_specs)}")
-    if args.enable_profiler:
-        print(f"Profiler: enabled (dir={args.profiler_dir}, stages={args.profiler_stages or 'all-configured'})")
-        print(f"Profiled stage config: {args.stage_configs_path}")
-    if voice_clone_count:
-        print("Voice cloning note: --ref-text/ref_text must match the spoken content of the reference audio.")
-    print(f"Num runs: {args.num_runs}")
-    try:
-        if is_streaming:
-            asyncio.run(_run_streaming(args))
-        else:
-            _run_sync(args)
-    finally:
-        if profiled_stage_config_path is not None and os.path.exists(profiled_stage_config_path):
-            os.unlink(profiled_stage_config_path)
-    return 0
-
-
-if __name__ == "__main__":
-    os.environ["VLLM_WORKER_MULTIPROC_METHOD"] = "spawn"
-    raise SystemExit(main(parse_args()))
@@ -1,283 +0,0 @@
-"""Benchmark VoxCPM via /v1/audio/speech.
-
-Reports TTFP (time to first packet), E2E latency, and RTF (real-time factor).
-"""
-
-from __future__ import annotations
-
-import argparse
-import asyncio
-import json
-import time
-from dataclasses import asdict, dataclass, field
-from datetime import datetime
-from pathlib import Path
-
-import aiohttp
-import numpy as np
-from tqdm.asyncio import tqdm
-
-DEFAULT_MODEL = "OpenBMB/VoxCPM1.5"
-DEFAULT_SAMPLE_RATE = 24000
-PROMPTS = [
-    "Hello, welcome to the VoxCPM speech benchmark.",
-    "This is a short benchmark prompt for online text-to-speech generation.",
-    "The quick brown fox jumps over the lazy dog near the riverbank.",
-    "Please remember to bring your identification documents tomorrow morning.",
-    "Learning a new language takes patience, practice, and curiosity.",
-    "This benchmark reports TTFP and RTF for the VoxCPM online serving path.",
-]
-
-
-@dataclass
-class RequestResult:
-    success: bool = False
-    ttfp: float = 0.0
-    e2e: float = 0.0
-    audio_bytes: int = 0
-    audio_duration: float = 0.0
-    rtf: float = 0.0
-    prompt: str = ""
-    error: str = ""
-
-
-@dataclass
-class BenchmarkResult:
-    concurrency: int = 0
-    num_prompts: int = 0
-    completed: int = 0
-    failed: int = 0
-    duration_s: float = 0.0
-    mean_ttfp_ms: float = 0.0
-    median_ttfp_ms: float = 0.0
-    p95_ttfp_ms: float = 0.0
-    mean_e2e_ms: float = 0.0
-    median_e2e_ms: float = 0.0
-    p95_e2e_ms: float = 0.0
-    mean_rtf: float = 0.0
-    median_rtf: float = 0.0
-    p95_rtf: float = 0.0
-    total_audio_duration_s: float = 0.0
-    request_throughput: float = 0.0
-    per_request: list[dict[str, float | str]] = field(default_factory=list)
-
-
-def pcm_bytes_to_duration(num_bytes: int, sample_rate: int = DEFAULT_SAMPLE_RATE, sample_width: int = 2) -> float:
-    num_samples = num_bytes / sample_width
-    return num_samples / sample_rate
-
-
-async def send_tts_request(
-    session: aiohttp.ClientSession,
-    api_url: str,
-    *,
-    model: str,
-    prompt: str,
-    ref_audio: str | None,
-    ref_text: str | None,
-    pbar: tqdm | None = None,
-) -> RequestResult:
-    payload: dict[str, object] = {
-        "model": model,
-        "input": prompt,
-        "stream": True,
-        "response_format": "pcm",
-    }
-    if ref_audio is not None:
-        payload["ref_audio"] = ref_audio
-    if ref_text is not None:
-        payload["ref_text"] = ref_text
-
-    result = RequestResult(prompt=prompt)
-    started_at = time.perf_counter()
-
-    try:
-        async with session.post(api_url, json=payload) as response:
-            if response.status != 200:
-                result.error = f"HTTP {response.status}: {await response.text()}"
-                return result
-
-            first_chunk = True
-            total_bytes = 0
-            async for chunk in response.content.iter_any():
-                if not chunk:
-                    continue
-                if first_chunk:
-                    result.ttfp = time.perf_counter() - started_at
-                    first_chunk = False
-                total_bytes += len(chunk)
-
-            result.e2e = time.perf_counter() - started_at
-            result.audio_bytes = total_bytes
-            result.audio_duration = pcm_bytes_to_duration(total_bytes)
-            if result.audio_duration > 0:
-                result.rtf = result.e2e / result.audio_duration
-            result.success = True
-    except Exception as e:
-        result.error = str(e)
-        result.e2e = time.perf_counter() - started_at
-
-    if pbar is not None:
-        pbar.update(1)
-    return result
-
-
-async def run_benchmark(
-    *,
-    host: str,
-    port: int,
-    model: str,
-    num_prompts: int,
-    max_concurrency: int,
-    num_warmups: int,
-    ref_audio: str | None,
-    ref_text: str | None,
-) -> BenchmarkResult:
-    api_url = f"http://{host}:{port}/v1/audio/speech"
-    connector = aiohttp.TCPConnector(limit=max_concurrency, limit_per_host=max_concurrency, keepalive_timeout=60)
-    timeout = aiohttp.ClientTimeout(total=600)
-
-    async with aiohttp.ClientSession(connector=connector, timeout=timeout) as session:
-        if num_warmups > 0:
-            print(f"  Warming up with {num_warmups} requests...")
-            warmup_tasks = [
-                send_tts_request(
-                    session,
-                    api_url,
-                    model=model,
-                    prompt=PROMPTS[i % len(PROMPTS)],
-                    ref_audio=ref_audio,
-                    ref_text=ref_text,
-                )
-                for i in range(num_warmups)
-            ]
-            await asyncio.gather(*warmup_tasks)
-            print("  Warmup done.")
-
-        request_prompts = [PROMPTS[i % len(PROMPTS)] for i in range(num_prompts)]
-        semaphore = asyncio.Semaphore(max_concurrency)
-        pbar = tqdm(total=num_prompts, desc=f"  concurrency={max_concurrency}")
-
-        async def limited_request(prompt: str) -> RequestResult:
-            async with semaphore:
-                return await send_tts_request(
-                    session,
-                    api_url,
-                    model=model,
-                    prompt=prompt,
-                    ref_audio=ref_audio,
-                    ref_text=ref_text,
-                    pbar=pbar,
-                )
-
-        started_at = time.perf_counter()
-        results = await asyncio.gather(*[asyncio.create_task(limited_request(prompt)) for prompt in request_prompts])
-        duration = time.perf_counter() - started_at
-        pbar.close()
-
-    succeeded = [result for result in results if result.success]
-    bench = BenchmarkResult(
-        concurrency=max_concurrency,
-        num_prompts=num_prompts,
-        completed=len(succeeded),
-        failed=len(results) - len(succeeded),
-        duration_s=duration,
-    )
-
-    if not succeeded:
-        return bench
-
-    ttfps = np.array([result.ttfp * 1000 for result in succeeded], dtype=np.float64)
-    e2es = np.array([result.e2e * 1000 for result in succeeded], dtype=np.float64)
-    rtfs = np.array([result.rtf for result in succeeded], dtype=np.float64)
-    audio_durations = np.array([result.audio_duration for result in succeeded], dtype=np.float64)
-
-    bench.mean_ttfp_ms = float(np.mean(ttfps))
-    bench.median_ttfp_ms = float(np.median(ttfps))
-    bench.p95_ttfp_ms = float(np.percentile(ttfps, 95))
-    bench.mean_e2e_ms = float(np.mean(e2es))
-    bench.median_e2e_ms = float(np.median(e2es))
-    bench.p95_e2e_ms = float(np.percentile(e2es, 95))
-    bench.mean_rtf = float(np.mean(rtfs))
-    bench.median_rtf = float(np.median(rtfs))
-    bench.p95_rtf = float(np.percentile(rtfs, 95))
-    bench.total_audio_duration_s = float(np.sum(audio_durations))
-    bench.request_throughput = len(succeeded) / duration if duration > 0 else 0.0
-    bench.per_request = [
-        {
-            "prompt": result.prompt,
-            "ttfp_ms": result.ttfp * 1000,
-            "e2e_ms": result.e2e * 1000,
-            "rtf": result.rtf,
-            "audio_duration_s": result.audio_duration,
-        }
-        for result in succeeded
-    ]
-
-    return bench
-
-
-def print_summary(result: BenchmarkResult) -> None:
-    width = 54
-    print("")
-    print("=" * width)
-    print(f"{'VoxCPM Serving Benchmark':^{width}}")
-    print("=" * width)
-    print(f"concurrency         : {result.concurrency}")
-    print(f"requests            : {result.completed}/{result.num_prompts} succeeded")
-    print(f"wall time (s)       : {result.duration_s:.3f}")
-    print(f"mean TTFP (ms)      : {result.mean_ttfp_ms:.2f}")
-    print(f"p95 TTFP (ms)       : {result.p95_ttfp_ms:.2f}")
-    print(f"mean E2E (ms)       : {result.mean_e2e_ms:.2f}")
-    print(f"p95 E2E (ms)        : {result.p95_e2e_ms:.2f}")
-    print(f"mean RTF            : {result.mean_rtf:.3f}")
-    print(f"p95 RTF             : {result.p95_rtf:.3f}")
-    print(f"request throughput  : {result.request_throughput:.2f} req/s")
-    print("=" * width)
-
-
-async def main_async(args) -> None:
-    result_dir = Path(args.result_dir)
-    result_dir.mkdir(parents=True, exist_ok=True)
-
-    all_results: list[BenchmarkResult] = []
-    for concurrency in args.max_concurrency:
-        result = await run_benchmark(
-            host=args.host,
-            port=args.port,
-            model=args.model,
-            num_prompts=args.num_prompts,
-            max_concurrency=concurrency,
-            num_warmups=args.num_warmups,
-            ref_audio=args.ref_audio,
-            ref_text=args.ref_text,
-        )
-        print_summary(result)
-        all_results.append(result)
-
-    payload = {
-        "model": args.model,
-        "created_at": datetime.utcnow().isoformat() + "Z",
-        "results": [asdict(result) for result in all_results],
-    }
-    result_path = result_dir / "bench_tts_serve.json"
-    result_path.write_text(json.dumps(payload, indent=2), encoding="utf-8")
-    print(f"Saved results to: {result_path}")
-
-
-def parse_args():
-    parser = argparse.ArgumentParser(description="Benchmark VoxCPM via /v1/audio/speech")
-    parser.add_argument("--host", default="127.0.0.1", help="Server host")
-    parser.add_argument("--port", type=int, default=8091, help="Server port")
-    parser.add_argument("--model", default=DEFAULT_MODEL, help="Model name or path")
-    parser.add_argument("--num-prompts", type=int, default=20, help="Number of prompts to send")
-    parser.add_argument("--max-concurrency", type=int, nargs="+", default=[1], help="Concurrency levels to benchmark")
-    parser.add_argument("--num-warmups", type=int, default=3, help="Warmup request count")
-    parser.add_argument("--ref-audio", default=None, help="Reference audio URL or data URL for voice cloning")
-    parser.add_argument("--ref-text", default=None, help="Reference audio transcript for voice cloning")
-    parser.add_argument("--result-dir", default="results", help="Directory to save benchmark JSON")
-    return parser.parse_args()
-
-
-if __name__ == "__main__":
-    asyncio.run(main_async(parse_args()))
@@ -1,303 +0,0 @@
-"""Run the full offline VoxCPM smoke matrix.
-
-This script keeps the old `test.py` coverage, but delegates each case to
-`bench_tts_offline.py` so the benchmark runner itself stays focused on a
-single execution path.
-"""
-
-from __future__ import annotations
-
-import shlex
-import subprocess
-import sys
-import time
-from dataclasses import dataclass
-from pathlib import Path
-
-from vllm.utils.argparse_utils import FlexibleArgumentParser
-
-REPO_ROOT = Path(__file__).resolve().parents[3]
-BENCH_SCRIPT = Path(__file__).with_name("bench_tts_offline.py")
-DEFAULT_STAGE_ASYNC = REPO_ROOT / "vllm_omni" / "model_executor" / "stage_configs" / "voxcpm_async_chunk.yaml"
-DEFAULT_STAGE_SYNC = REPO_ROOT / "vllm_omni" / "model_executor" / "stage_configs" / "voxcpm.yaml"
-DEFAULT_OUTPUT_ROOT = BENCH_SCRIPT.parents[1] / "results" / "offline_matrix"
-
-SINGLE_TTS_TEXT = "This is a single text-to-speech smoke test for VoxCPM on vLLM Omni."
-SINGLE_CLONE_TEXT = "This sentence is synthesized with the cloned voice for validation."
-BATCH_TTS_TEXTS = [
-    "The first batch text-to-speech sample validates sequential batch execution.",
-    "The second batch text-to-speech sample checks another prompt in the same file.",
-    "The third batch text-to-speech sample completes the sequential batch path.",
-]
-BATCH_CLONE_TEXTS = [
-    "The first cloned sample validates sequential batch voice cloning.",
-    "The second cloned sample checks the same reference voice on another prompt.",
-    "The third cloned sample finishes the shared-reference clone batch path.",
-]
-
-
-@dataclass(frozen=True, slots=True)
-class ModeSpec:
-    name: str
-    stage_config: Path
-
-
-@dataclass(frozen=True, slots=True)
-class CaseSpec:
-    name: str
-    warmup_runs: int
-    prompt_kind: str
-    voice_clone: bool
-
-
-@dataclass(frozen=True, slots=True)
-class CaseResult:
-    mode: str
-    case: str
-    returncode: int
-    elapsed_s: float
-    output_dir: Path
-    log_path: Path
-
-    @property
-    def ok(self) -> bool:
-        return self.returncode == 0
-
-
-MODE_SPECS = [
-    ModeSpec(name="streaming", stage_config=DEFAULT_STAGE_ASYNC),
-    ModeSpec(name="sync", stage_config=DEFAULT_STAGE_SYNC),
-]
-
-CASE_SPECS = [
-    CaseSpec(name="warmup_single_tts", warmup_runs=1, prompt_kind="single", voice_clone=False),
-    CaseSpec(name="warmup_single_clone", warmup_runs=1, prompt_kind="single", voice_clone=True),
-    CaseSpec(name="warmup_batch_tts", warmup_runs=1, prompt_kind="batch", voice_clone=False),
-    CaseSpec(name="warmup_batch_clone", warmup_runs=1, prompt_kind="batch", voice_clone=True),
-    CaseSpec(name="cold_single_tts", warmup_runs=0, prompt_kind="single", voice_clone=False),
-    CaseSpec(name="cold_single_clone", warmup_runs=0, prompt_kind="single", voice_clone=True),
-]
-
-
-def _write_lines(path: Path, lines: list[str]) -> None:
-    path.parent.mkdir(parents=True, exist_ok=True)
-    path.write_text("\n".join(lines) + "\n", encoding="utf-8")
-
-
-def _prepare_batch_inputs(output_root: Path) -> tuple[Path, Path]:
-    input_dir = output_root / "inputs"
-    batch_tts_path = input_dir / "batch_tts_prompts.txt"
-    batch_clone_path = input_dir / "batch_clone_prompts.txt"
-    _write_lines(batch_tts_path, BATCH_TTS_TEXTS)
-    _write_lines(batch_clone_path, BATCH_CLONE_TEXTS)
-    return batch_tts_path, batch_clone_path
-
-
-def _base_command(args, mode: ModeSpec, output_dir: Path) -> list[str]:
-    cmd = [
-        args.python,
-        str(BENCH_SCRIPT),
-        "--model",
-        args.model,
-        "--stage-configs-path",
-        str(mode.stage_config),
-        "--output-dir",
-        str(output_dir),
-        "--num-runs",
-        str(args.num_runs),
-        "--stage-init-timeout",
-        str(args.stage_init_timeout),
-    ]
-    cmd.append("--log-stats" if args.log_stats else "--no-log-stats")
-    cmd.extend(["--cfg-value", str(args.cfg_value)])
-    cmd.extend(["--inference-timesteps", str(args.inference_timesteps)])
-    cmd.extend(["--min-len", str(args.min_len)])
-    cmd.extend(["--max-new-tokens", str(args.max_new_tokens)])
-    if args.streaming_prefix_len is not None:
-        cmd.extend(["--streaming-prefix-len", str(args.streaming_prefix_len)])
-    if args.enable_profiler:
-        profiler_dir = Path(args.profiler_dir) if args.profiler_dir is not None else (output_dir / "profiler")
-        cmd.append("--enable-profiler")
-        cmd.extend(["--profiler-dir", str(profiler_dir)])
-        cmd.extend(["--profiler-wait-seconds", str(args.profiler_wait_seconds)])
-        if args.profiler_stages is not None:
-            cmd.append("--profiler-stages")
-            cmd.extend(str(stage_id) for stage_id in args.profiler_stages)
-    return cmd
-
-
-def _build_case_command(
-    args,
-    mode: ModeSpec,
-    case: CaseSpec,
-    *,
-    batch_tts_path: Path,
-    batch_clone_path: Path,
-    output_dir: Path,
-) -> list[str]:
-    cmd = _base_command(args, mode, output_dir)
-    cmd.extend(["--warmup-runs", str(case.warmup_runs)])
-    if case.prompt_kind == "single":
-        cmd.extend(["--text", SINGLE_CLONE_TEXT if case.voice_clone else SINGLE_TTS_TEXT])
-    else:
-        cmd.extend(["--txt-prompts", str(batch_clone_path if case.voice_clone else batch_tts_path)])
-    if case.voice_clone:
-        cmd.extend(["--ref-audio", args.ref_audio, "--ref-text", args.ref_text])
-    return cmd
-
-
-def _run_case(
-    args,
-    mode: ModeSpec,
-    case: CaseSpec,
-    *,
-    batch_tts_path: Path,
-    batch_clone_path: Path,
-    output_root: Path,
-) -> CaseResult:
-    case_output_dir = output_root / mode.name / case.name
-    case_output_dir.mkdir(parents=True, exist_ok=True)
-    case_log_path = case_output_dir / "run.log"
-    cmd = _build_case_command(
-        args,
-        mode,
-        case,
-        batch_tts_path=batch_tts_path,
-        batch_clone_path=batch_clone_path,
-        output_dir=case_output_dir,
-    )
-
-    print()
-    print("=" * 80)
-    print(f"[{mode.name}] {case.name}")
-    print(f"Output directory: {case_output_dir}")
-    print(shlex.join(cmd))
-
-    start = time.perf_counter()
-    with case_log_path.open("w", encoding="utf-8") as log_fp:
-        process = subprocess.Popen(
-            cmd,
-            stdout=subprocess.PIPE,
-            stderr=subprocess.STDOUT,
-            text=True,
-            bufsize=1,
-        )
-        assert process.stdout is not None
-        for line in process.stdout:
-            print(line, end="")
-            log_fp.write(line)
-        process.wait()
-
-    elapsed_s = time.perf_counter() - start
-    status = "PASS" if (process.returncode or 0) == 0 else f"FAIL({process.returncode})"
-    print(f"[{mode.name}] {case.name} -> {status} ({elapsed_s:.2f}s)")
-    return CaseResult(
-        mode=mode.name,
-        case=case.name,
-        returncode=int(process.returncode or 0),
-        elapsed_s=elapsed_s,
-        output_dir=case_output_dir,
-        log_path=case_log_path,
-    )
-
-
-def parse_args():
-    parser = FlexibleArgumentParser(description="Run the full offline VoxCPM smoke matrix.")
-    parser.add_argument("--model", type=str, required=True, help="Local VoxCPM model directory.")
-    parser.add_argument("--ref-audio", type=str, required=True, help="Reference audio path for clone cases.")
-    parser.add_argument("--ref-text", type=str, required=True, help="Exact transcript spoken in --ref-audio.")
-    parser.add_argument("--output-root", type=str, default=str(DEFAULT_OUTPUT_ROOT), help="Root directory for outputs.")
-    parser.add_argument("--python", type=str, default=sys.executable, help="Python executable used to launch cases.")
-    parser.add_argument("--stage-init-timeout", type=int, default=600, help="Stage initialization timeout in seconds.")
-    parser.add_argument("--log-stats", dest="log_stats", action="store_true", help="Enable vLLM Omni stats logging.")
-    parser.add_argument(
-        "--no-log-stats",
-        dest="log_stats",
-        action="store_false",
-        help="Disable vLLM Omni stats logging.",
-    )
-    parser.set_defaults(log_stats=True)
-    parser.add_argument("--num-runs", type=int, default=1, help="Number of measured runs per case.")
-    parser.add_argument("--cfg-value", type=float, default=2.0, help="Classifier-free guidance value for VoxCPM.")
-    parser.add_argument("--inference-timesteps", type=int, default=10, help="Number of inference timesteps.")
-    parser.add_argument("--min-len", type=int, default=2, help="Minimum generated token length.")
-    parser.add_argument("--max-new-tokens", type=int, default=4096, help="Maximum generated token length.")
-    parser.add_argument(
-        "--streaming-prefix-len",
-        type=int,
-        default=None,
-        help="Optional VoxCPM streaming window passed to streaming cases.",
-    )
-    parser.add_argument("--enable-profiler", action="store_true", help="Enable torch profiler for each case.")
-    parser.add_argument(
-        "--profiler-dir",
-        type=str,
-        default=None,
-        help="Profiler output root. Defaults to <case-output-dir>/profiler.",
-    )
-    parser.add_argument(
-        "--profiler-stages",
-        type=int,
-        nargs="*",
-        default=None,
-        help="Optional stage ids to profile. Defaults to all configured stages.",
-    )
-    parser.add_argument(
-        "--profiler-wait-seconds",
-        type=float,
-        default=30.0,
-        help="Seconds to wait after stopping profiler for traces to flush.",
-    )
-    args = parser.parse_args()
-    if args.num_runs < 1:
-        parser.error("--num-runs must be >= 1")
-    return args
-
-
-def main(args) -> int:
-    output_root = Path(args.output_root)
-    output_root.mkdir(parents=True, exist_ok=True)
-    batch_tts_path, batch_clone_path = _prepare_batch_inputs(output_root)
-
-    print(f"Model: {args.model}")
-    print(f"Reference audio: {args.ref_audio}")
-    print(f"Reference text: {args.ref_text}")
-    print(f"Python: {args.python}")
-    print(f"Output root: {output_root}")
-    print(f"Cases: {len(MODE_SPECS) * len(CASE_SPECS)}")
-
-    results: list[CaseResult] = []
-    for mode in MODE_SPECS:
-        for case in CASE_SPECS:
-            results.append(
-                _run_case(
-                    args,
-                    mode,
-                    case,
-                    batch_tts_path=batch_tts_path,
-                    batch_clone_path=batch_clone_path,
-                    output_root=output_root,
-                )
-            )
-
-    failed = [result for result in results if not result.ok]
-    print()
-    print("=" * 80)
-    print("Summary:")
-    for result in results:
-        status = "PASS" if result.ok else f"FAIL({result.returncode})"
-        print(f"- [{result.mode}] {result.case}: {status} ({result.elapsed_s:.2f}s)")
-        print(f"  output_dir={result.output_dir}")
-        print(f"  log={result.log_path}")
-
-    print(f"Passed: {len(results) - len(failed)}/{len(results)}")
-    if failed:
-        print("Failed cases:")
-        for result in failed:
-            print(f"- [{result.mode}] {result.case}: see {result.log_path}")
-        return 1
-    return 0
-
-
-if __name__ == "__main__":
-    raise SystemExit(main(parse_args()))
@@ -7,7 +7,7 @@ COPY . .

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

@@ -7,7 +7,7 @@ WORKDIR ${COMMON_WORKDIR}

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

@@ -18,10 +18,8 @@ ARG COMMON_WORKDIR=/app
 WORKDIR ${COMMON_WORKDIR}

 # Step 1: Setup - Install system dependencies
-# Need to include ffmpeg because vllm rocm upstream docker image
-# does not include it.
 RUN apt-get update && \
-    apt-get install -y espeak-ng ffmpeg git jq && \
+    apt-get install -y espeak-ng ffmpeg git sox libsox-fmt-all jq && \
    apt-get clean && \
    rm -rf /var/lib/apt/lists/*

@@ -41,24 +39,6 @@ RUN if [ "${USE_NIGHTLY_BUILD}" = "1" ]; then \
 # Step 3: Copy vllm-omni code and install without uv
 RUN mkdir -p ${COMMON_WORKDIR}/vllm-omni
 COPY . ${COMMON_WORKDIR}/vllm-omni
-
-# This is a workaround to ensure pytest exits with the correct status code in CI tests.
-RUN printf '%s\n' \
-    'import os' \
-    '' \
-    '_exit_code = 1' \
-    '' \
-    'def pytest_sessionfinish(session, exitstatus):' \
-    '    global _exit_code' \
-    '    _exit_code = int(exitstatus)' \
-    '' \
-    'def pytest_unconfigure(config):' \
-    '    import sys' \
-    '    sys.stdout.flush()' \
-    '    sys.stderr.flush()' \
-    '    os._exit(_exit_code)' \
-    > ${COMMON_WORKDIR}/vllm-omni/conftest.py
-
 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 ln -sf /usr/bin/python3 /usr/bin/python
@@ -15,7 +15,9 @@ RUN apt clean && apt-get update -y && \
    apt-get install -y --no-install-recommends --fix-missing \
    curl \
    espeak-ng \
+    ffmpeg \
    git \
+    libsndfile1 \
    libsm6 \
    libxext6 \
    libgl1 \
@@ -64,7 +64,6 @@ nav:
        - FP8: user_guide/diffusion/quantization/fp8.md
        - Int8: user_guide/diffusion/quantization/int8.md
        - GGUF: user_guide/diffusion/quantization/gguf.md
-      - Frame Interpolation: user_guide/diffusion/frame_interpolation.md
      - Parallelism:
        - Overview: user_guide/diffusion/parallelism/overview.md
        - CFG Parallel: user_guide/diffusion/parallelism/cfg_parallel.md
@@ -81,6 +80,7 @@ nav:
 - Developer Guide:
  - General:
    - contributing/README.md
+    - pr_reviewer.md
    - glob: contributing/*
      flatten_single_child_sections: true
  - Model Implementation:
@@ -97,7 +97,6 @@ nav:
      - design/feature/disaggregated_inference.md
      - design/feature/ray_based_execution.md
      - design/feature/omni_connectors/
-      - design/feature/prefix_caching.md
      - design/feature/cfg_parallel.md
      - design/feature/expert_parallel.md
      - design/feature/sequence_parallel.md
@@ -106,7 +105,7 @@ nav:
      - design/feature/hsdp.md
      - design/feature/cache_dit.md
      - design/feature/teacache.md
-      - design/feature/async_chunk.md
+      - design/feature/async_chunk_design.md
      - design/feature/vae_parallel.md
      - design/feature/diffusion_step_execution.md
    - Module Design:
@@ -5,7 +5,7 @@
 Main entry points for vLLM-Omni inference and serving.

 - [vllm_omni.entrypoints.async_omni.AsyncOmni][]
- [vllm_omni.engine.cfg_companion_tracker.CfgCompanionTracker][]
+- [vllm_omni.entrypoints.cfg_companion_tracker.CfgCompanionTracker][]
 - [vllm_omni.entrypoints.cli.benchmark.base.OmniBenchmarkSubcommandBase][]
 - [vllm_omni.entrypoints.cli.benchmark.main.OmniBenchmarkSubcommand][]
 - [vllm_omni.entrypoints.cli.benchmark.serve.OmniBenchmarkServingSubcommand][]
@@ -6,7 +6,7 @@ For options within a vLLM Engine. Please refer to [vLLM Configuration](https://d

 Currently, the main options are maintained by stage configs for each model.

-For a specific example, see the [Qwen2.5-Omni deploy config](gh-file:vllm_omni/deploy/qwen2_5_omni.yaml). The matching frozen pipeline topology lives at [vllm_omni/model_executor/models/qwen2_5_omni/pipeline.py](gh-file:vllm_omni/model_executor/models/qwen2_5_omni/pipeline.py).
+For specific example, please refer to [Qwen2.5-omni stage config](stage_configs/qwen2_5_omni.yaml)

 For introduction, please check [Introduction for stage config](./stage_configs.md)

@@ -11,7 +11,7 @@ deployment-specific values usually change per environment:
 - connector backend and connector ports
 - connector IPs or bootstrap addresses

-Start from the [default Qwen3-Omni stage config](gh-file:vllm_omni/deploy/qwen3_omni_moe.yaml)
+Start from the [default Qwen3-Omni stage config](gh-file:vllm_omni/model_executor/stage_configs/qwen3_omni_moe.yaml)
 and copy it to your own file, for example `qwen3_omni_pd.yaml`. Then apply the
 changes below.

@@ -145,13 +145,19 @@ Compared with the default Qwen3-Omni config:
 ```yaml
 runtime:
  enabled: true
+  defaults:
+    window_size: -1
+    max_inflight: 1
  edges:
    - from: 0
      to: 1
+      window_size: -1
    - from: 1
      to: 2
+      window_size: -1
    - from: 2
      to: 3
+      window_size: -1
 ```

 ## 4. Launch with your custom config
@@ -3,147 +3,7 @@
 In vLLM-Omni, the target model is separated into multiple stages, which are processed by different LLMEngines, DiffusionEngines or other types of engines. Depending on different types of stages, such as Autoregressive (AR) stage or Diffusion transformer (DiT) stage, each can choose corresponding schedulers, model workers to load with the Engines in a plug-in fashion.

 !!! note
-    Default deploy config YAMLs (for example, `vllm_omni/deploy/qwen2_5_omni.yaml`, `vllm_omni/deploy/qwen3_omni_moe.yaml`, and `vllm_omni/deploy/qwen3_tts.yaml`) are bundled and loaded automatically when neither `--stage-configs-path` nor `--deploy-config` is provided — the model registry resolves the right pipeline + deploy YAML by `model_type`. The bundled defaults have been verified on 1xH100 for Qwen2.5-Omni and 2xH100 for Qwen3-Omni. Models that have not yet migrated to the new schema continue to use the legacy `vllm_omni/model_executor/stage_configs/<model>.yaml` files via `--stage-configs-path`.
-
-## New deploy schema reference
-
-The new deploy schema lives under `vllm_omni/deploy/` and is paired with a frozen `PipelineConfig` registered by the model's `pipeline.py`. Each deploy YAML has these top-level fields:
-
-| Field | Type | Required | Default | Description |
-|-------|------|----------|---------|-------------|
-| `base_config` | str (path) | optional | — | Overlay parent (relative or absolute). `stages:` / `platforms:` deep-merged by stage_id; other scalars overlay-wins. Intended for user-authored overlays; prod yamls stay flat. |
-| `async_chunk` | bool | optional | `true` | Enable chunked streaming between stages. Pin to `false` if the pipeline runs end-to-end. |
-| `connectors` | dict | optional | `null` | Named connector specs (`{name, extra}`). Referenced by each stage's `input_connectors` / `output_connectors`. See [Connector schema](#connector-schema). |
-| `edges` | list | optional | `null` | Explicit edge list for the KV transfer graph. Auto-derived from stage inputs if omitted. |
-| `stages` | list | required | — | Per-stage engine args + wiring (see [Stage fields](#stage-fields)). |
-| `platforms` | dict | optional | `null` | Keyed by `npu` / `rocm` / `xpu`, each contains a `stages:` list with per-platform overrides applied on top of the CUDA defaults. |
-| `pipeline` | str | optional | `null` | Override the auto-detected pipeline registry key (used for structural variants like `qwen2_5_omni_thinker_only`). |
-| `trust_remote_code` | bool | optional | `true` | **Pipeline-wide.** Trust HF remote code on model load; applies to every stage. |
-| `distributed_executor_backend` | str | optional | `"mp"` | **Pipeline-wide.** Executor backend (`"mp"` or `"ray"`). |
-| `dtype` | str \| null | optional | `null` | **Pipeline-wide.** Model dtype for every stage. |
-| `quantization` | str \| null | optional | `null` | **Pipeline-wide.** Quantization method for every stage. |
-| `enable_prefix_caching` | bool | optional | `false` | **Pipeline-wide.** Prefix cache toggle applied to every stage. |
-| `enable_chunked_prefill` | bool \| null | optional | `null` | **Pipeline-wide.** Chunked prefill toggle applied to every stage. |
-| `data_parallel_size` | int | optional | `1` | **Pipeline-wide.** DP degree for every stage. |
-| `pipeline_parallel_size` | int | optional | `1` | **Pipeline-wide.** PP degree for every stage. |
-
-### Stage fields
-
-Each entry under `stages:` accepts any `StageDeployConfig` field directly (no nested `engine_args:`). Only fields whose value legitimately varies across stages live here; pipeline-wide settings (trust_remote_code, distributed_executor_backend, dtype, quantization, prefix/chunked prefill, DP/PP sizes) are declared at the top level and applied to every stage. Unknown keys fall through to `engine_extras:` and are forwarded to the engine.
-
-| Field | Type | Required | Default | Description |
-|-------|------|----------|---------|-------------|
-| `stage_id` | int | required | — | Stage identity; matched against `PipelineConfig.stages[*].stage_id`. |
-| `max_num_seqs` | int | optional | `64` | Max concurrent sequences per stage. |
-| `gpu_memory_utilization` | float | optional | `0.9` | Per-stage memory budget. |
-| `tensor_parallel_size` | int | optional | `1` | TP degree for this stage. |
-| `enforce_eager` | bool | optional | `false` | Disable CUDA graphs. |
-| `max_num_batched_tokens` | int | optional | `32768` | Prefill budget. |
-| `max_model_len` | int \| null | optional | `null` | Per-stage context length (auto-sets `VLLM_ALLOW_LONG_MAX_MODEL_LEN=1` when larger than HF default). |
-| `async_scheduling` | bool \| null | optional | `null` | Per-stage async scheduling toggle. |
-| `devices` | str | optional | `"0"` | `CUDA_VISIBLE_DEVICES`-style device list. |
-| `output_connectors` | dict \| null | optional | `null` | Keyed by `to_stage_<n>`; values are names registered under top-level `connectors:`. |
-| `input_connectors` | dict \| null | optional | `null` | Keyed by `from_stage_<n>`; values are names registered under top-level `connectors:`. |
-| `default_sampling_params` | dict \| null | optional | `null` | Baseline sampling params. Deep-merged with pipeline `sampling_constraints` (pipeline wins). |
-| `engine_extras` | dict | optional | `{}` | Catch-all for keys not listed above; deep-merged across overlays. Also carries per-stage overrides of pipeline-wide settings (e.g. stage-specific `dtype`). |
-
-### Connector schema
-
-Each entry under top-level `connectors:` follows this shape:
-
-```yaml
-connectors:
-  <connector_name>:
-    name: <ConnectorClassName>     # required — class registered in vllm_omni.distributed
-    extra:                         # optional — forwarded to the connector's __init__
-      <key>: <value>
-      ...
-```
-
-| Connector class | Use case | `extra` keys |
-|-----------------|----------|--------------|
-| `SharedMemoryConnector` | Same-host KV transfer between stages (default for bundled YAMLs). | `shm_threshold_bytes` (int, default `65536`). |
-| `MooncakeStoreConnector` | Cross-host KV transfer over TCP. Required for multi-node deployments. | `host`, `metadata_server`, `master`, `segment` (int bytes), `localbuf` (int bytes), `proto` (`"tcp"` / `"rdma"`). |
-
-A stage references a connector by name in its `input_connectors` / `output_connectors`:
-
-```yaml
-connectors:
-  shm:
-    name: SharedMemoryConnector
-
-stages:
-  - stage_id: 0
-    output_connectors: {to_stage_1: shm}
-  - stage_id: 1
-    input_connectors:  {from_stage_0: shm}
-```
-
-### CLI flags introduced in this refactor
-
-| Flag | Description |
-|------|-------------|
-| `--deploy-config PATH` | Load a new-schema deploy YAML. Takes precedence over `--stage-configs-path`. **Optional** — when omitted, the bundled `vllm_omni/deploy/<model_type>.yaml` is auto-loaded by the model registry. |
-| `--stage-overrides JSON` | Per-stage JSON overrides, e.g. `'{"0":{"gpu_memory_utilization":0.5}}'`. Per-stage values always win over global flags. |
-| `--async-chunk` / `--no-async-chunk` | Flip the deploy YAML's `async_chunk:` bool. Unset (default) leaves the YAML value in force. |
-| `--stage-configs-path` | **Deprecated.** Accepts legacy `stage_args` yamls and (auto-detected) new deploy yamls; emits a deprecation warning. Migrate to `--deploy-config`. To be removed in a follow-up PR. |
-
-### Precedence
-
-From highest to lowest:
-
-1. Per-stage flags (`--stage-overrides` JSON, `--stage-<id>-<key>` if registered)
-2. Explicit global CLI flags (`--gpu-memory-utilization 0.85`, etc.)
-3. Platform section (`platforms.npu.stages`, etc.) on top of the base `stages:`
-4. Overlay YAML (via `base_config:`) on top of the base YAML
-5. Parser defaults
-
-### Worked override example
-
-Starting from the bundled `vllm_omni/deploy/qwen3_omni_moe.yaml`:
-
-```yaml
-# vllm_omni/deploy/qwen3_omni_moe.yaml (excerpt)
-async_chunk: true
-stages:
-  - stage_id: 0
-    gpu_memory_utilization: 0.9
-    max_num_seqs: 32
-  - stage_id: 1
-    gpu_memory_utilization: 0.7
-    max_num_seqs: 16
-```
-
-A user-authored overlay that inherits the base and overrides only stage 1:
-
-```yaml
-# my_overrides.yaml
-base_config: /path/to/vllm_omni/deploy/qwen3_omni_moe.yaml
-stages:
-  - stage_id: 1
-    gpu_memory_utilization: 0.5     # smaller GPU
-```
-
-Launched with both an explicit global flag and a per-stage override:
-
-```bash
-vllm serve Qwen/Qwen3-Omni-30B-A3B-Instruct --omni --port 8091 \
-    --deploy-config my_overrides.yaml \
-    --max-model-len 16384 \
-    --stage-overrides '{"0": {"max_num_seqs": 8}}'
-```
-
-Effective config per stage after the merge:
-
-| Stage | Field | Final value | Source |
-|-------|-------|-------------|--------|
-| 0 | `gpu_memory_utilization` | `0.9` | base YAML (overlay didn't touch stage 0) |
-| 0 | `max_num_seqs` | `8` | per-stage CLI (`--stage-overrides`) — wins over base `32` |
-| 0 | `max_model_len` | `16384` | global CLI |
-| 1 | `gpu_memory_utilization` | `0.5` | overlay YAML — wins over base `0.7` |
-| 1 | `max_num_seqs` | `16` | base YAML (overlay didn't touch this field) |
-| 1 | `max_model_len` | `16384` | global CLI |
-| 2 | (all defaults) | — | base YAML (no overrides apply) |
+    Default stage config YAMLs (for example, `vllm_omni/model_executor/stage_configs/qwen2_5_omni.yaml` and `vllm_omni/model_executor/stage_configs/qwen3_omni_moe.yaml`) are bundled and loaded automatically when `stage_configs_path` is not provided. They have been verified to work on 1xH100 for Qwen2.5-Omni and 2xH100 for Qwen3-Omni.

 Therefore, as a core part of vLLM-Omni, the stage configs for a model have several main functions:

@@ -175,7 +35,7 @@ stage_args:
  - stage_id: 0 # mark the unique id for each stage
    runtime: # The disaggregated configuration
      process: true  # Run this stage in a separate process
-      devices: "0" # Logical device index for this stage (mapped through CUDA_VISIBLE_DEVICES / ASCEND_RT_VISIBLE_DEVICES if set)
+      devices: "0" # Visible devices for this stage (CUDA_VISIBLE_DEVICES/torch.cuda.set_device)
    engine_args: # Engine arguments for a certain engine
      model_stage: thinker
      max_num_seqs: 1
@@ -254,12 +114,16 @@ stage_args:
 # Top-level runtime config (concise): default windows and stage edges
 runtime:
  enabled: true
-
+  defaults:
+    window_size: -1             # Simplified: trigger downstream only after full upstream completion
+    max_inflight: 1             # Simplified: process serially within each stage
  edges:
    - from: 0                   # thinker → talker: trigger only after receiving full input (-1)
      to: 1
+      window_size: -1
    - from: 1                   # talker → code2wav: trigger only after receiving full input (-1)
      to: 2
+      window_size: -1

 ```

@@ -291,9 +155,7 @@ Default: `true`

 #### `runtime.devices`

-Logical device indices for this stage, specified as a string. Values are **logical indices** (`0`, `1`, `2`, ...) — not physical GPU IDs — and are mapped through the platform's visibility env var (`CUDA_VISIBLE_DEVICES` on CUDA, `ASCEND_RT_VISIBLE_DEVICES` on NPU) before being applied via `torch.cuda.set_device()` (or the equivalent).
-
-Example: if `CUDA_VISIBLE_DEVICES=0,2,4` is set in the environment, then `devices: "0"` selects physical GPU 0 (the first visible), `devices: "1"` selects physical GPU 2, and `devices: "0,1"` makes physical GPUs 0 and 2 available to the stage. If no visibility env var is set, logical and physical IDs coincide.
+Visible devices for this stage, specified as a string. This controls which GPU devices are available to the stage process, similar to setting `CUDA_VISIBLE_DEVICES` or using `torch.cuda.set_device()`. For example, `"0"` uses GPU 0, `"1"` uses GPU 1, and `"0,1"` makes both GPUs 0 and 1 visible.

 Default: `"0"`

@@ -0,0 +1,94 @@
+# stage config for running qwen2.5-omni with AsyncOmniEngine + Orchestrator runtime.
+stage_args:
+  - stage_id: 0
+    runtime:
+      process: true            # Run this stage in a separate process
+      devices: "0"            # Visible devices for this stage (CUDA_VISIBLE_DEVICES/torch.cuda.set_device)
+    engine_args:
+      model_stage: thinker
+      max_num_seqs: 1
+      model_arch: Qwen2_5OmniForConditionalGeneration
+      worker_type: ar
+      scheduler_cls: vllm_omni.core.sched.omni_ar_scheduler.OmniARScheduler
+      gpu_memory_utilization: 0.8
+      enforce_eager: true  # Now we only support eager mode
+      trust_remote_code: true
+      engine_output_type: latent
+      enable_prefix_caching: false
+    is_comprehension: true
+    final_output: true
+    final_output_type: text
+    default_sampling_params:
+      temperature: 0.0
+      top_p: 1.0
+      top_k: -1
+      max_tokens: 2048
+      seed: 42
+      detokenize: True
+      repetition_penalty: 1.1
+  - stage_id: 1
+    runtime:
+      process: true
+      devices: "1"
+    engine_args:
+      model_stage: talker
+      max_num_seqs: 1
+      model_arch: Qwen2_5OmniForConditionalGeneration
+      worker_type: ar
+      scheduler_cls: vllm_omni.core.sched.omni_ar_scheduler.OmniARScheduler
+      gpu_memory_utilization: 0.8
+      enforce_eager: true
+      trust_remote_code: true
+      enable_prefix_caching: false
+      engine_output_type: latent
+    engine_input_source: [0]
+    custom_process_input_func: vllm_omni.model_executor.stage_input_processors.qwen2_5_omni.thinker2talker
+    default_sampling_params:
+      temperature: 0.9
+      top_p: 0.8
+      top_k: 40
+      max_tokens: 2048
+      seed: 42
+      detokenize: True
+      repetition_penalty: 1.05
+      stop_token_ids: [8294]
+  - stage_id: 2
+    runtime:
+      process: true
+      devices: "0"            # Example: use a different GPU than the previous stage; use "0" if single GPU
+    engine_args:
+      model_stage: code2wav
+      max_num_seqs: 1
+      model_arch: Qwen2_5OmniForConditionalGeneration
+      worker_type: generation
+      scheduler_cls: vllm_omni.core.sched.omni_generation_scheduler.OmniGenerationScheduler
+      gpu_memory_utilization: 0.15
+      enforce_eager: true
+      trust_remote_code: true
+      enable_prefix_caching: false
+      engine_output_type: audio
+    engine_input_source: [1]
+    final_output: true
+    final_output_type: audio
+    default_sampling_params:
+      temperature: 0.0
+      top_p: 1.0
+      top_k: -1
+      max_tokens: 2048
+      seed: 42
+      detokenize: True
+      repetition_penalty: 1.1
+
+# Top-level runtime config (concise): default windows and stage edges
+runtime:
+  enabled: true
+  defaults:
+    window_size: -1             # Simplified: trigger downstream only after full upstream completion
+    max_inflight: 1             # Simplified: process serially within each stage
+  edges:
+    - from: 0                   # thinker → talker: trigger only after receiving full input (-1)
+      to: 1
+      window_size: -1
+    - from: 1                   # talker → code2wav: trigger only after receiving full input (-1)
+      to: 2
+      window_size: -1
@@ -86,8 +86,7 @@ Through five levels (L1-L5) and common (Common) specifications, the system clari
        /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/dfx/perf/tests/test_qwen_omni.json (Omni), test_tts.json (TTS),<br>
-        and /tests/dfx/perf/tests/test_{diffusion_model}_vllm_omni.json (Diffusion)<br>
+        /tests/dfx/perf/tests/test.json<br>
        <strong>Doc Test:</strong><br>
        tests/example/online_serving/test_{model_name}.py<br>
        tests/example/offline_inference/test_{model_name}.py
@@ -231,7 +230,8 @@ vllm_omni/                                    tests/
                                               │   ├── test_qwen3_omni_expansion.py
                                               │   ├── test_mimo_audio.py
                                               │   ├── test_image_gen_edit.py
-                                               │   └── test_images_generations_lora.py
+                                               │   ├── test_images_generations_lora.py
+                                               │   └── stage_configs/
                                               └── offline_inference/                  ✅
                                                   ├── test_qwen2_5_omni.py
                                                   ├── test_qwen3_omni.py
@@ -242,17 +242,16 @@ vllm_omni/                                    tests/
                                                   ├── test_zimage_tensor_parallel.py
                                                   ├── test_cache_dit.py
                                                   ├── test_teacache.py
-                                                   ├── test_stable_audio_expansion.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/                  (legacy schema, still
-                                                       ├── bagel_*.yaml                 present for unmigrated
-                                                       └── npu/, rocm/, etc.            models)
-
-# Migrated models (qwen3_omni_moe, qwen2_5_omni, qwen3_tts) live under
-# vllm_omni/deploy/ instead — see docs/configuration/stage_configs.md.
+                                                   └── stage_configs/
+                                                       ├── qwen2_5_omni_ci.yaml
+                                                       ├── qwen3_omni_ci.yaml
+                                                       ├── bagel_*.yaml
+                                                       └── npu/, rocm/, etc.
 ```


@@ -531,13 +530,13 @@ L4 level testing is a comprehensive quality audit before a version release. It e
 ### 3.2 Testing Content and Scope

 -   ***Full Functionality Testing***: Executes all test cases defined in `test_{model_name}_expansion.py`, covering all implemented features, positive flows, boundary conditions, and exception handling.
-   ***Performance Testing***: Uses `tests/dfx/perf/tests/test_qwen_omni.json`, `tests/dfx/perf/tests/test_tts.json`, and diffusion configs in the form `tests/dfx/perf/tests/test_*_vllm_omni.json` (passed to `run_benchmark.py` via `--test-config-file`) to drive performance testing tools for stress, load, and endurance tests, collecting metrics like throughput, response time, and resource utilization.
+-   ***Performance Testing***: Uses the `tests/dfx/perf/tests/test.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/dfx/perf/tests/test_qwen_omni.json`, `tests/dfx/perf/tests/test_tts.json`, and diffusion configs `tests/dfx/perf/tests/test_*_vllm_omni.json` (e.g. `test_qwen_image_vllm_omni.json`)
+-   ***Performance Test Configuration***: `tests/dfx/perf/tests/test.json`
 -   ***Documentation Example Tests***:
 -   -   `tests/example/online_serving/test_{model_name}.py`
    -   `tests/example/offline_inference/test_{model_name}.py`
@@ -40,7 +40,7 @@ Currently all the features are available in online serving mode. Hence, only nee
 - Test marks: always add `advanced_model` and `diffusion`. Add GPU-related marks if needed. Ref: [Markers for Tests](https://docs.vllm.ai/projects/vllm-omni/en/latest/contributing/ci/tests_markers/).
 - To maximize code reuse, you may refer to
    - `tests/conftest.py` for `omni_server` (running server in subprocess) and `openai_client` fixtures (sending requests and validating output), `generate_synthetic_image` and `assert_XXX_valid` helper.
-    - `tests/helpers/mark.py` for `@hardware_test(...)` and `hardware_marks`.
+    - `tests/utils.py` for `@hardware_test(...)` and `hardware_marks`.
    - [Parametrizing tests (pytest doc)](https://docs.pytest.org/en/stable/example/parametrize.html) to reuse test function implementation for different cases.
 - Doc: add a concise docstring for each test function.
 - Reference L4 test implementation: [tests/e2e/online_serving/test_qwen_image_edit_expansion.py](https://github.com/vllm-project/vllm-omni/blob/main/tests/e2e/online_serving/test_qwen_image_edit_expansion.py).
@@ -1,4 +1,4 @@
-When you want to add L4-level ***performance test*** cases, you can refer to the following format for case addition in `tests/dfx/perf/tests/test_qwen_omni.json`, `tests/dfx/perf/tests/test_tts.json`, or diffusion configs such as `tests/dfx/perf/tests/test_*_vllm_omni.json` (selected via `pytest ... run_benchmark.py --test-config-file <path>`):
+When you want to add L4-level ***performance test*** cases, you can refer to the following format for case addition in tests/dfx/perf/tests/test.json:

 ```JSON
 {
@@ -45,6 +45,7 @@ Our test scripts use the pytest framework. First, please use `git clone https://
 === "L3 level & L4 level"

    ```bash
+    cd tests
    pytest -s -v -m "advanced_model" --run-level=advanced_model
    ```
    If you only want to run L3 test case, you can use:
@@ -59,9 +60,9 @@ Our test scripts use the pytest framework. First, please use `git clone https://
    ```bash
    pytest -s -v -m "core_model and distributed_cuda and L4"  --run-level=core_model
    ```
-    Note: To run performance tests (defaults to ``test_qwen_omni.json``; use ``--test-config-file tests/dfx/perf/tests/test_tts.json`` for TTS):
+    Note: To run performance tests, use:
    ```bash
-    pytest -s -v tests/dfx/perf/scripts/run_benchmark.py
+    pytest -s -v perf/scripts/run_benchmark.py
    ```

    The latest L3 test commands for various test suites can be found in the [pipeline](https://github.com/vllm-project/vllm-omni/blob/main/.buildkite/test-merge.yml).
@@ -38,7 +38,7 @@ Defined in `pyproject.toml`:
 ### Example usage for markers

 ```python
-from tests.helpers.mark import hardware_test
+from tests.utils import hardware_test

@pytest.mark.core_model
@pytest.mark.omni
@@ -53,7 +53,7 @@ def test_video_to_audio()

 ### Decorator: `@hardware_test`

-This decorator is intended to make hardware-aware, cross-platform test authoring easier and more robust for CI/CD environments. The `hardware_test` decorator in `vllm-omni/tests/helpers/mark.py` performs the following actions:
+This decorator is intended to make hardware-aware, cross-platform test authoring easier and more robust for CI/CD environments. The `hardware_test` decorator in `vllm-omni/tests/utils.py` performs the following actions:

 1. **Applies platform and resource markers**  
   Adds the appropriate pytest markers for each specified hardware platform (e.g., `cuda`, `rocm`, `xpu`, `npu`) and resource type (e.g., `L4`, `H100`, `MI325`, `B60`, `A2`, `A3`).
@@ -105,7 +105,7 @@ This decorator is intended to make hardware-aware, cross-platform test authoring
 `hardware_marks` returns a list of pytest mark objects with the same signature as `@hardware_test`. Use it when you need more flexibility, such as attaching hardware marks to individual `pytest.param` entries rather than an entire test function.

 ```python
-from tests.helpers.mark import hardware_marks
+from tests.utils import hardware_marks

 MULTI_CARD_MARKS = hardware_marks(
    res={"cuda": "H100", "rocm": "MI325", "npu": "A2"}, num_cards=2
@@ -133,9 +133,9 @@ If you want to add support for a new platform (e.g., "tpu" for a new accelerator
       "distributed_tpu: Tests that require multiple TPU devices",
   ]
   ```
-2. **Implement a marker construction function for your platform** in `vllm-omni/tests/helpers/mark.py`:
+2. **Implement a marker construction function for your platform** in `vllm-omni/tests/utils.py`:
   ```python
-   # In vllm-omni/tests/helpers/mark.py
+   # In vllm-omni/tests/utils.py

   def tpu_marks(*, res: str, num_cards: int):
       test_platform = pytest.mark.tpu
@@ -175,4 +175,4 @@ If you want to add support for a new platform (e.g., "tpu" for a new accelerator
 - Plug into `hardware_marks`  
 - You're done: tests using `@hardware_test` or `hardware_marks` with your platform now automatically get the correct markers, distribution, and isolation!

-See code in `vllm-omni/tests/helpers/mark.py` for existing examples (`cuda_marks`, `rocm_marks`, `npu_marks`).
+See code in `vllm-omni/tests/utils.py` for existing examples (`cuda_marks`, `rocm_marks`, `npu_marks`).
@@ -135,7 +135,8 @@ vllm_omni/                                    tests/
                                               │   ├── test_qwen3_omni_expansion.py
                                               │   ├── test_mimo_audio.py
                                               │   ├── test_image_gen_edit.py
-                                               │   └── test_images_generations_lora.py
+                                               │   ├── test_images_generations_lora.py
+                                               │   └── stage_configs/
                                               └── offline_inference/                  ✅
                                                   ├── test_qwen2_5_omni.py
                                                   ├── test_qwen3_omni.py
@@ -146,18 +147,17 @@ vllm_omni/                                    tests/
                                                   ├── test_zimage_tensor_parallel.py
                                                   ├── test_cache_dit.py
                                                   ├── test_teacache.py
-                                                   ├── test_stable_audio_expansion.py
+                                                   ├── test_stable_audio_model.py
                                                   ├── test_diffusion_cpu_offload.py
                                                   ├── test_diffusion_layerwise_offload.py
                                                   ├── test_diffusion_lora.py
                                                   ├── test_sequence_parallel.py
                                                   ├── test_qwen_image_edit_expansion.py
-                                                   └── stage_configs/                  (legacy schema, still present
-                                                       ├── bagel_*.yaml                 for unmigrated models)
+                                                   └── stage_configs/
+                                                       ├── qwen2_5_omni_ci.yaml
+                                                       ├── qwen3_omni_ci.yaml
+                                                       ├── bagel_*.yaml
                                                       └── npu/, rocm/, etc.
-
-# Migrated models (qwen3_omni_moe, qwen2_5_omni, qwen3_tts) live under
-# vllm_omni/deploy/ instead — see docs/configuration/stage_configs.md.
 examples/                                     tests
 │                                             └── examples
 ├── online_serving/                     →         ├── online_serving/
@@ -221,13 +221,14 @@ from pathlib import Path
 import openai
 import pytest

-from tests.helpers.media import (
-    convert_audio_bytes_to_text,
+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 tests.helpers.runtime import OmniServer, dummy_messages_from_mix_data
-from tests.helpers.stage_config import get_deploy_config_path, modify_stage_config
 from vllm_omni.platforms import current_omni_platform

 # Edit: model name and stage config path
@@ -235,7 +236,7 @@ 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 get_deploy_config_path("ci/qwen3_omni_moe.yaml")
+    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():
@@ -404,7 +405,7 @@ def test_mix_to_text_audio_001(client: openai.OpenAI, omni_server, request) -> N
    # 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 = convert_audio_bytes_to_text(audio_data)
+    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())
@@ -427,7 +428,7 @@ from pathlib import Path
 import pytest
 from vllm.assets.video import VideoAsset

-from tests.helpers.mark import hardware_test
+from tests.utils import hardware_test
 from ..multi_stages.conftest import OmniRunner

 # Optional: set process start method for workers
@@ -313,7 +313,7 @@ The registry uses lazy loading, so the model class is imported only when needed.

 ## Stage Configuration

-Create a YAML configuration file in `vllm_omni/deploy/`. For a complete example, see the [Qwen3-Omni configuration file](gh-file:vllm_omni/deploy/qwen3_omni_moe.yaml).
+Create a YAML configuration file in `vllm_omni/model_executor/stage_configs/`. For a complete example, see the [Qwen3-Omni configuration file](gh-file:vllm_omni/model_executor/stage_configs/qwen3_omni_moe.yaml).

 ### Key Configuration Fields

@@ -408,17 +408,18 @@ Understanding the data structures is crucial for implementing stage transitions:

 **Input to your function:**
 - `stage_list[source_stage_id].engine_outputs`: List of `EngineCoreOutput` objects
-   -  Each contains `outputs`: List of `RequestOutput` objects
-    - Each `RequestOutput` has:
-   -  - `token_ids`: Generated token IDs
-       - `multimodal_output`: Dict with keys like `"code_predictor_codes"`, etc.These are the hidden states or intermediate outputs from the model's forward pass
-       - `prompt_token_ids`: Original prompt token IDs
+  - Each contains `outputs`: List of `RequestOutput` objects
+  - Each `RequestOutput` has:
+    - `token_ids`: Generated token IDs
+    - `multimodal_output`: Dict with keys like `"code_predictor_codes"`, etc.
+      - These are the hidden states or intermediate outputs from the model's forward pass
+    - `prompt_token_ids`: Original prompt token IDs

 **Output from your function:**
 - Must return `list[OmniTokensPrompt]` where each `OmniTokensPrompt` contains:
-   - `prompt_token_ids`: List[int] - Token IDs for the next stage
-    - `additional_information`: Dict[str, Any] - Optional metadata (e.g., embeddings, hidden states)
-    - `multi_modal_data`: Optional multimodal data if needed
+  - `prompt_token_ids`: List[int] - Token IDs for the next stage
+  - `additional_information`: Dict[str, Any] - Optional metadata (e.g., embeddings, hidden states)
+  - `multi_modal_data`: Optional multimodal data if needed

 ### How Model Outputs Are Stored

@@ -613,7 +614,7 @@ For a complete reference implementation, see:
 - **Thinker**: `vllm_omni/model_executor/models/qwen3_omni/qwen3_omni_moe_thinker.py`
 - **Talker**: `vllm_omni/model_executor/models/qwen3_omni/qwen3_omni_moe_talker.py`
 - **Code2Wav**: `vllm_omni/model_executor/models/qwen3_omni/qwen3_omni_code2wav.py`
- **Stage config**: `vllm_omni/deploy/qwen3_omni_moe.yaml`
+- **Stage config**: `vllm_omni/model_executor/stage_configs/qwen3_omni_moe.yaml`
 - **Input processors**: `vllm_omni/model_executor/stage_input_processors/qwen3_omni.py`
 - **Registry**: `vllm_omni/model_executor/models/registry.py`
 - **Testing**: `vllm_omni/tests/e2e/offline_inference/test_qwen3_omni.py`
@@ -28,7 +28,7 @@ and can be placed on different devices. Qwen3-TTS has two stages:

 Each stage is a separate model class configured independently via YAML. The two stages
 are connected by the `async_chunk` framework, which enables inter-stage streaming for
-low first-packet latency (see [Async Chunk Design](../../design/feature/async_chunk.md)).
+low first-packet latency (see [Async Chunk Design](../../design/feature/async_chunk_design.md)).

 ### Without async_chunk (batch mode)

@@ -120,18 +120,8 @@ vllm_omni/model_executor/stage_configs/
 | `models/qwen3_tts/qwen3_tts.py` | Unified model class |
 | `models/qwen3_tts/qwen3_tts_code_predictor_vllm.py` | Stage 0 - optimized AR |
 | `models/qwen3_tts/qwen3_tts_code2wav.py` | Stage 1 - decoder |
-| `deploy/qwen3_tts.yaml` (new schema) | Deploy config (async_chunk enabled) — paired with `models/qwen3_tts/pipeline.py` for the frozen topology |
-
-> **Chunked vs end-to-end modes**: `qwen3_tts` registers a single
-> pipeline whose stage 1 declares alternate processor functions — an
-> `async_chunk_process_next_stage_input_func` (per-chunk streaming, used
-> when `deploy.async_chunk=True`) and a `sync_process_input_func`
-> (batch-end, used when `deploy.async_chunk=False`). The loader selects
-> one at merge time based on the bool, so `--no-async-chunk` alone
-> switches modes — no variant yaml or variant pipeline registration is
-> needed. Pipelines that only make sense in one mode (e.g.
-> `qwen3_omni_moe` is always chunked) can keep using the unconditional
-> `custom_process_*` fields.
+| `stage_configs/qwen3_tts.yaml` | Stage config (async_chunk enabled) |
+| `stage_configs/qwen3_tts_batch.yaml` | Batch mode config |
 | `stage_input_processors/qwen3_tts.py` | Stage transition processors |

 ## Step-by-Step Implementation
@@ -584,12 +574,11 @@ Adding a TTS model to vLLM-Omni involves:
 | `models/qwen3_tts/qwen3_tts.py` | Unified model class |
 | `models/qwen3_tts/qwen3_tts_code_predictor_vllm.py` | AR stage with vLLM fused ops |
 | `models/qwen3_tts/qwen3_tts_code2wav.py` | Decoder stage with `chunked_decode_streaming()` |
-| `models/qwen3_tts/pipeline.py` | Frozen pipeline topology (registered at import time) |
-| `deploy/qwen3_tts.yaml` | Deploy config (user-editable, async_chunk + SharedMemoryConnector) |
+| `stage_configs/qwen3_tts.yaml` | Stage configuration |
 | `stage_input_processors/qwen3_tts.py` | Stage transition processors |

 For more information, see:

 - [Architecture Overview](../../design/architecture_overview.md)
- [Async Chunk Design](../../design/feature/async_chunk.md)
+- [Async Chunk Design](../../design/feature/async_chunk_design.md)
 - [Stage Configuration Guide](../../configuration/stage_configs.md)
@@ -1,214 +1,216 @@
 # Profiling vLLM-Omni

-> **Warning:** Profiling is for development and debugging only. It adds significant overhead and should not be enabled in production.
+> **Warning:** Profiling incurs significant overhead. Use only for development and debugging, never in production.

-vLLM-Omni supports two profiler backends through `profiler_config`:
+vLLM-Omni uses the PyTorch Profiler to analyze performance across both **multi-stage omni-modality models** and **diffusion models**.

- `torch`: detailed CPU/CUDA traces written to `torch_profiler_dir`
- `cuda`: low-overhead CUDA range control for NVIDIA Nsight Systems (`nsys`)
+### 1. Configure Profiling in the Stage YAML

-## 1. Configure Profiling
-
-Use the same `profiler_config` shape everywhere:
-
-```yaml
-profiler_config:
-  profiler: torch
-  torch_profiler_dir: ./perf
-```
-
-Supported fields:
-
-| Field | Description |
-|---|---|
-| `profiler` | Profiler backend. Supported values: `torch`, `cuda`. |
-| `torch_profiler_dir` | Output directory for torch traces. Required when `profiler: torch`. |
-| `delay_iterations` | Number of worker iterations to skip before profiling starts. |
-| `max_iterations` | Maximum number of worker iterations to capture before auto-stop. |
-| `warmup_iterations` | Torch-profiler warmup iterations. |
-| `active_iterations` | Torch-profiler active iterations. |
-| `wait_iterations` | Torch-profiler wait iterations before warmup. |
-
-For multi-stage omni pipelines, put `profiler_config` under the target stage's `engine_args`.
+Enable profiling by adding `profiler_config` under `engine_args` for the stage(s) you want to profile in your stage config YAML:

 ```yaml
 stage_args:
  - stage_id: 0
    stage_type: llm
    engine_args:
+      # ... other engine args ...
      profiler_config:
        profiler: torch
        torch_profiler_dir: ./perf
 ```

-For single-stage diffusion usage, pass `profiler_config` directly to `Omni(...)` or `vllm serve`.
+| Field | Description |
+|---|---|
+| `profiler` | Profiler backend to use. Currently supports `torch`. |
+| `torch_profiler_dir` | Directory where trace files are saved. Created automatically if it doesn't exist. |

-## 2. Profiling Omni Pipelines
+> **Tip:** Only enable `profiler_config` on stages you actually need to profile. Stages without it will not start a profiler, keeping overhead minimal.

-It is usually best to profile only the stages you need.
+### 2. Profiling Omni-Modality Models
+
+**Selective Stage Profiling**
+
+It is highly recommended to profile specific stages to prevent producing overly large trace files:

 ```python
-# Profile all stages.
-omni.start_profile()
+# Profile all stages
+omni_llm.start_profile()

-# Profile selected stages only.
-omni.start_profile(stages=[0, 2])
-...
-omni.stop_profile(stages=[0, 2])
+# Only profile Stage 1
+omni_llm.start_profile(stages=[1])
+
+# Stage 0 (Thinker) and Stage 2 (Audio Decoder) for qwen omni
+omni_llm.start_profile(stages=[0, 2])
 ```

-Always stop the same stage set that you started. If only some stages have `profiler_config`, pass an explicit `stages=[...]` list instead of relying on the default "all stages" behavior.
+> **Important:** Always pass the same `stages` list to both `start_profile()` and `stop_profile()`. If you omit `stages` from `stop_profile()`, it defaults to stopping all stages — including ones that were never started — which will produce errors.

-Examples:
-
-1. [Qwen2.5-Omni end2end](https://github.com/vllm-project/vllm-omni/blob/main/examples/offline_inference/qwen2_5_omni/end2end.py)
-2. [Qwen3-Omni end2end](https://github.com/vllm-project/vllm-omni/blob/main/examples/offline_inference/qwen3_omni/end2end.py)
-
-## 3. Profiling Single-Stage Diffusion
-
-Single-stage diffusion models use the same `start_profile()` / `stop_profile()` controls, but you must provide `profiler_config` explicitly.
-
-### PyTorch profiler
+**Python Usage**: Wrap your generation logic with `start_profile()` and `stop_profile()`.

 ```python
-from vllm_omni import Omni
+profiler_stages = [0]  # Only profile the stages you need

-omni = Omni(
-    model="Wan-AI/Wan2.2-I2V-A14B-Diffusers",
-    profiler_config={
-        "profiler": "torch",
-        "torch_profiler_dir": "./perf",
-    },
-)
+# 1. Start profiling
+omni.start_profile(stages=profiler_stages)

-omni.start_profile()
-...
-omni.stop_profile()
+# Initialize generator
+omni_generator = omni.generate(prompts, sampling_params_list, py_generator=args.py_generator)
+
+total_requests = len(prompts)
+processed_count = 0
+
+# Main Processing Loop
+for stage_outputs in omni_generator:
+
+    # ... [Output processing logic for text/audio would go here] ...
+
+    # Update count to track when to stop profiling
+    processed_count += len(stage_outputs.request_output)
+
+    # 2. Check if all requests are done to stop the profiler safely
+    if profiler_enabled and processed_count >= total_requests:
+        print(f"[Info] Processed {processed_count}/{total_requests}. Stopping profiler inside active loop...")
+
+        # Stop the profiler while workers are still active
+        # Pass the same stages list used in start_profile()
+        omni_llm.stop_profile(stages=profiler_stages)
+
+        # Wait for traces to flush to disk
+        print("[Info] Waiting 30s for workers to write trace files to disk...")
+        time.sleep(30)
+        print("[Info] Trace export wait time finished.")
+
+omni_llm.close()
 ```

-### Nsight Systems (`nsys`)
-
-For Nsight Systems, use `profiler: cuda` and wrap the process with `nsys profile`.

+**CLI Usage** (using `end2end.py`):
 ```bash
-nsys profile \
-  --trace-fork-before-exec=true \
-  --cuda-graph-trace=node \
-  --capture-range=cudaProfilerApi \
-  --capture-range-end=repeat \
-  -o diffusion_trace \
-  python image_to_video.py ...
+# Profile only Stage 0 (Thinker)
+python end2end.py --output-wav output_audio \
+    --query-type text --enable-profiler --profiler-stages 0
+
+# Profile Stage 0 and Stage 2
+python end2end.py --output-wav output_audio \
+    --query-type text --enable-profiler --profiler-stages 0 2
+
+# Profile all stages (omit --profiler-stages)
+python end2end.py --output-wav output_audio \
+    --query-type text --enable-profiler
 ```

-The Python process being profiled must create the diffusion engine with:
+**Examples**:

-```python
-profiler_config={"profiler": "cuda"}
+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)
+
+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)
+
+### 3. Profiling diffusion models
+
+Diffusion profiling is End-to-End, capturing encoding, denoising loops, and decoding. Standalone diffusion scripts use `--profiler-dir` to enable profiling.
+
+**CLI Usage:**
+```bash
+python image_to_video.py \
+    --model Wan-AI/Wan2.2-I2V-A14B-Diffusers \
+    --image qwen-bear.png \
+    --prompt "A cat playing with yarn, smooth motion" \
+    --profiler-dir \
+    \
+    # Minimize Spatial Dimensions (Optional but helpful):
+    #    Drastically reduces memory usage so the profiler doesn't
+    #    crash due to overhead, though for accurate performance
+    #    tuning you often want target resolutions.
+    --height 48 \
+    --width 64 \
+    \
+    # Minimize Temporal Dimension (Frames):
+    #    Video models process 3D tensors (Time, Height, Width).
+    #    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 \
+    \
+    # Minimize Iteration Loop (Steps):
+    #    This is the most critical setting for profiling.
+    #    Diffusion models run the same loop X times.
+    #    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 \
+    \
+    --guidance-scale 5.0 \
+    --guidance-scale-high 6.0 \
+    --boundary-ratio 0.875 \
+    --flow-shift 12.0 \
+    --fps 16 \
+    --output i2v_output.mp4
 ```

-Then call `start_profile()` before the requests you want to capture and `stop_profile()` after them. The diffusion worker processes open and close the CUDA capture range themselves, so `nsys` sees the actual GPU work instead of only the parent process.
+> **Note:** For diffusion stages within a multi-stage omni pipeline, use `profiler_config` in the stage YAML instead (see Section 1).

-Examples:
+**Examples**:

-1. [Image edit example](https://github.com/vllm-project/vllm-omni/blob/main/examples/offline_inference/image_to_image/image_edit.py)
-2. [Image to video example](https://github.com/vllm-project/vllm-omni/tree/main/examples/offline_inference/image_to_video)
+1. **Qwen image edit**:  [https://github.com/vllm-project/vllm-omni/blob/main/examples/offline_inference/image_to_image/image_edit.py](https://github.com/vllm-project/vllm-omni/blob/main/examples/offline_inference/image_to_image/image_edit.py)

-## 4. Profiling Online Serving
+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)

-When any stage has `profiler_config.profiler` set, the server exposes:
+### 4. Profiling Online Serving

- `POST /start_profile`
- `POST /stop_profile`
-
-### Start the server
-
-Multi-stage omni serving:
+When `profiler_config` is set in the stage YAML, the server automatically exposes `/start_profile` and `/stop_profile` HTTP endpoints.

+**1. Start the server** with a stage YAML that has `profiler_config` enabled:
 ```bash
 vllm serve Qwen/Qwen2.5-Omni-7B \
-  --omni \
-  --port 8091
-```
-
-(The default deploy config at `vllm_omni/deploy/qwen2_5_omni.yaml` is loaded automatically. Pass `--deploy-config /path/to/custom.yaml` to override.)
-
-Single-stage diffusion serving with torch profiler:
-
-```bash
-vllm serve Wan-AI/Wan2.2-I2V-A14B-Diffusers \
-  --omni \
-  --port 8091 \
-  --profiler-config '{
-    "profiler": "torch",
-    "torch_profiler_dir": "/tmp/vllm_profile_wan22_i2v",
-    "torch_profiler_with_stack": true,
-    "torch_profiler_with_flops": false,
-    "torch_profiler_use_gzip": true,
-    "torch_profiler_dump_cuda_time_total": true,
-    "torch_profiler_record_shapes": true,
-    "torch_profiler_with_memory": true,
-    "delay_iterations": 0,
-    "max_iterations": 0
-  }'
-```
-
-Useful optional fields:
-
- `torch_profiler_with_stack`: export `by_stack` operator views and stack text files.
- `torch_profiler_record_shapes`: export `by_shape` operator views.
- `torch_profiler_with_memory`: dump `memory_snapshot_rank*.pickle` when the backend supports memory history.
- `torch_profiler_use_gzip`: write the trace as `trace_rank*.json.gz`.
-
-Single-stage diffusion serving with Nsight Systems:
-
-```bash
-nsys profile \
-  --trace-fork-before-exec=true \
-  --cuda-graph-trace=node \
-  --capture-range=cudaProfilerApi \
-  --capture-range-end=repeat \
-  -o serving_trace \
-  vllm serve Wan-AI/Wan2.2-I2V-A14B-Diffusers \
    --omni \
-    --port 8091 \
-    --profiler-config '{"profiler": "cuda"}'
+    --stage-configs-path qwen2_5_omni.yaml \
+    --port 8091
 ```

-### Control capture
+Or for one stage diffusion models:

 ```bash
-# Start profiling on all profiled stages.
+vllm serve Wan-AI/Wan2.2-I2V-A14B-Diffusers --omni --port 8091 --profiler-config '{"profiler": "torch", "torch_profiler_dir": "./vllm_profile"}'
+```
+
+**2. Start profiling** by sending a POST request:
+```bash
+# Profile all stages that have profiler_config set
 curl -X POST http://localhost:8091/start_profile

-# Start profiling on selected stages.
+# Profile specific stages only
 curl -X POST http://localhost:8091/start_profile \
-  -H "Content-Type: application/json" \
-  -d '{"stages": [0]}'
-
-# Stop profiling.
-curl -X POST http://localhost:8091/stop_profile
+    -H "Content-Type: application/json" \
+    -d '{"stages": [0]}'
 ```

-For mixed-stage pipelines, use explicit `stages` and pass the same stage list to both endpoints.
+**3. Send your inference requests** as normal while the profiler is running.

-## 5. Analyze Results
+**4. Stop profiling** and collect traces:
+```bash
+# Stop all stages
+curl -X POST http://localhost:8091/stop_profile

-Torch profiler output:
+# Stop specific stages (must match the stages you started)
+curl -X POST http://localhost:8091/stop_profile \
+    -H "Content-Type: application/json" \
+    -d '{"stages": [0]}'
+```

- Chrome/Perfetto trace: `trace_rank*.json` or `trace_rank*.json.gz`
- Excel workbook: `ops_rank*.xlsx` with `summary`, and optional `by_shape` / `by_stack` sheets
- Stack exports: `stacks_cpu_rank*.txt` and `stacks_cuda_rank*.txt` when stack capture is enabled
- Memory snapshot: `memory_snapshot_rank*.pickle` when memory capture is enabled and supported by the backend
- Optional aggregated CUDA-time tables under the same session directory
+Trace files are written to the `torch_profiler_dir` specified in your stage YAML.

-CUDA profiler / Nsight Systems output:
+> **Important:** Always stop the same stages you started. Stopping a stage that was never started will produce errors.

- `.nsys-rep` report files written by `nsys -o ...`
+### 5. Analyzing Traces

-Recommended viewers:
+Output files are saved to the `torch_profiler_dir` specified in your stage YAML config.

- [Perfetto](https://ui.perfetto.dev/) for torch traces
- `nsys stats <report>.nsys-rep` for CLI summaries
- Nsight Systems GUI for CUDA kernel timelines
+**Output**
+**Chrome Trace** (`.json.gz`): Visual timeline of kernels and stages. Open in Perfetto UI.

-vLLM-Omni reuses the vLLM profiling infrastructure where possible. For the upstream reference, see the [vLLM profiling guide](https://docs.vllm.ai/en/stable/contributing/profiling/).
+**Viewing Tools:**
+
+- [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/)
@@ -1,4 +1,4 @@
-# Async Chunk
+# Async Chunk Design

 ## Table of Contents

@@ -19,7 +19,7 @@ The `async_chunk` feature enables asynchronous, chunked processing of data acros

 For qwen3-omni:
 - **Thinker → Talker**: Per decode step (typically chunk_size=1)
- **Talker → Code2Wav**: Accumulated to `codec_chunk_frames` (default=25) before sending. During the initial phase, a dynamic initial chunk size (IC) is automatically selected based on server load to reduce TTFP. Use the per-request `initial_codec_chunk_frames` API field to override.
+- **Talker → Code2Wav**: Accumulated to `codec_chunk_frames` (default=25) before sending. During the initial phase, a dynamic initial chunk size (IC) is automatically selected based on server load to reduce TTFA. Use the per-request `initial_codec_chunk_frames` API field to override.
 - **Code2Wav**: Streaming decode with code2wav chunk_size

 With `async_chunk`:
@@ -75,85 +75,26 @@ Enabling **async_chunk** (False→True) sharply reduces time-to-first-audio (TTF
 </p>

 ## Architecture
+### Data Flow

-### Async Chunk Pipeline Overview
+#### 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>

-The following diagram illustrates the **Async Chunk Architecture** for multi-stage models (e.g., Qwen3-Omni with Thinker → Talker → Code2Wav), showing how data flows through the 4-stage pipeline with parallel processing and dual-stream output:
+#### 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="Async Chunk Pipeline Architecture" src="https://raw.githubusercontent.com/vllm-project/vllm-omni/refs/heads/main/docs/source/architecture/qwen3-omni-async-chunk.png" width=100%>
+    <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>

-**Diagram Legend:**
-
-| Step | Stage Type | Description |
-|------|-----------|------------|
-| `prefill` | Initialization | Context processing, KV cache initialization |
-| `decode` | Autoregressive | Token-by-token generation in AR stages |
-| `codes` | Audio Encoding | RVQ codec codes from Talker stage |
-| `output` | Final Output | Text chunks or audio waveforms |
-
-### Data Flow
-
-#### Stage 0: Thinker (Multimodal Understanding + Text Generation)
- **Prefill**: Processes multimodal input (text/image/audio/video), initializes KV cache
- **Decode Loop**: Generates text tokens autoregressively
- **Chunk Triggers**: Each decode step (typically `chunk_size=1`) can trigger downstream processing
- **Dual Output**:
-  - **Text Stream**: `text_0`, `text_1`, `text_2`... `text_n` streamed to output
-  - **Hidden States**: Passed to Talker stage for audio synthesis
-
-#### Stage 1: Talker (Text → RVQ Audio Codes)
- **Prefill**: Receives hidden states from Thinker as semantic condition
- **Decode Loop**: Generates RVQ codec codes autoregressively
- **Accumulation**: Codes accumulate to `codec_chunk_frames` (default=25) before forwarding
- **Dynamic IC**: Initial chunk size auto-selected based on server load to optimize TTFP
- **Output**: `codes` blocks (chunk 0, 1, ... n) sent to Code2Wav
-
-#### Stage 2: Code2Wav (Vocoder Decoder)
- **Non-Autoregressive**: Processes RVQ codes in parallel batches
- **Streaming Decode**: Converts codes to audio waveforms chunk-by-chunk
- **Batching**: Supports batched inference for multiple concurrent requests
- **Output**: Audio segments `audio_0`, `audio_1`, ... `audio_n`
-
-#### Stage 3: Output (Dual Stream)
- **Text Streaming**: `text_0` → `text_1` → `text_2` → ... (user sees response in real-time)
- **Audio Streaming**: `audio_0` → `audio_1` → ... (user hears audio progressively)
-
-### Execution Timeline
-
-```
-Timeline: Parallel vs Sequential
-
-Sequential (async_chunk=false):
-[Thinker: ████████████████████]  (2.0s)
-                            [Talker: ████████████████████]  (3.0s)
-                                                        [Code2Wav: ████]  (1.0s)
-Total: 6.0s, TTFP: 6.0s
-
-Async Chunk (async_chunk=true):
-[Thinker: ████░░░░████░░░░████]  (2.0s, streaming)
-     [Talker: ░░████░░░░████░░]  (3.0s, parallel)
-         [Code2Wav: ░░░░████░░]  (1.0s, batched)
-Total: ~3.5s, TTFP: ~0.5s
-
-█ = Active computation  ░ = Waiting/idle
-```
-
-#### Sequential Flow (for comparison)
-<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="Sequential Data Flow" 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>
-
-In sequential mode, each stage must wait for the previous stage to complete entirely before starting.
-
-### Async Chunk System Architecture
+### 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">
@@ -25,9 +25,7 @@ In standard Classifier-Free Guidance, each diffusion step requires two forward p
 1. **Positive/Conditional**: Guided by the text prompt
 2. **Negative/Unconditional**: Typically using empty or negative prompt

-Some models require 3 or more CFG branches (see [N-Branch CFG](#n-branch-cfg-3-branches)).
-
-CFG-Parallel eliminates this bottleneck by distributing the forward passes across different GPU ranks, allowing them to execute simultaneously rather than sequentially.
+CFG-Parallel eliminates this bottleneck by distributing the two forward passes across different GPU ranks, allowing them to execute simultaneously rather than sequentially.

 ### Architecture

@@ -35,11 +33,9 @@ vLLM-omni provides `CFGParallelMixin` that encapsulates all CFG parallel logic.

 | 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 2-branch CFG | Detects parallel mode, distributes computation, gathers results |
-| [`predict_noise_with_multi_branch_cfg()`](https://docs.vllm.ai/projects/vllm-omni/en/latest/api/vllm_omni/diffusion/distributed/cfg_parallel/) | Predict noise with N-branch CFG | Round-robin dispatches N branches across M GPUs |
+| [`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 | All ranks step locally (no broadcast needed) |
-| [`combine_cfg_noise()`](https://docs.vllm.ai/projects/vllm-omni/en/latest/api/vllm_omni/diffusion/distributed/cfg_parallel/) | Combine 2-branch predictions | Applies CFG formula with optional normalization |
-| [`combine_multi_branch_cfg_noise()`](https://docs.vllm.ai/projects/vllm-omni/en/latest/api/vllm_omni/diffusion/distributed/cfg_parallel/) | Combine N-branch predictions | Override for custom multi-branch combine logic |
+| [`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 |

@@ -61,22 +57,6 @@ vLLM-omni provides `CFGParallelMixin` that encapsulates all CFG parallel logic.

 - All ranks compute the scheduler step locally — no broadcast needed because `predict_noise_maybe_with_cfg` already ensures all ranks have identical noise predictions after `all_gather` + local combine.

-### N-Branch CFG (3+ branches)
-
-Some models require more than 2 CFG branches. For example, Bagel and OmniGen2 use 3 branches, DreamID Omni uses 4 branches.
-
-`predict_noise_with_multi_branch_cfg()` handles these by automatically dispatching N branches across M GPUs using round-robin (rule: branch `i` → rank `i % M`):
-
-| Branches (N) | GPUs (M) | Dispatch |
-|:---:|:---:|:---|
-| 3 | 2 | `[[0, 2], [1]]` |
-| 3 | 3 | `[[0], [1], [2]]` |
-| 4 | 2 | `[[0, 2], [1, 3]]` |
-| 4 | 3 | `[[0, 3], [1], [2]]` |
-| 4 | 4 | `[[0], [1], [2], [3]]` |
-
-When a rank handles multiple branches, it runs them sequentially. After `all_gather`, all ranks execute `combine_multi_branch_cfg_noise()` locally, producing identical results.
-
 ---

 ## Step-by-Step Implementation
@@ -118,7 +98,6 @@ class YourModelPipeline(nn.Module, CFGParallelMixin):
 - `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
- For models with 3+ CFG branches, see [Multi-Branch CFG](#multi-branch-cfg-3-branches) in the Customization section

 ### Step 2: Call `diffuse`

@@ -192,42 +171,20 @@ class LongCatImagePipeline(nn.Module, CFGParallelMixin):
 ```


-### Multi-Branch CFG (3+ branches)
+### Override `combine_cfg_noise()` for Multi-Output Models

-For models with 3 or more CFG branches, use `predict_noise_with_multi_branch_cfg()` instead of `predict_noise_maybe_with_cfg()`, and override `combine_multi_branch_cfg_noise()` for custom combine logic. This interface also works for standard 2-branch CFG — just pass 2 branches in `branches_kwargs`.
-
-**Example (3-branch with dual guidance scale):**
+When `predict_noise()` returns a tuple (e.g., video + audio), the default `combine_cfg_noise()` applies CFG to every element. Override it to apply different logic per element — for example, CFG on video but positive-only on audio:

 ```python
-class YourMultiBranchPipeline(nn.Module, CFGParallelMixin):
-    def combine_multi_branch_cfg_noise(self, predictions, true_cfg_scale, cfg_normalize=False):
-        text_scale = true_cfg_scale["text"]
-        image_scale = true_cfg_scale["image"]
-        pos, ref, uncond = predictions
-        return uncond + image_scale * (ref - uncond) + text_scale * (pos - ref)
-
-    def diffuse(self, ...):
-        for i, t in enumerate(timesteps):
-            positive_kwargs = {...}   # conditional prompt
-            ref_neg_kwargs = {...}    # negative prompt + reference
-            uncond_kwargs = {...}     # unconditional
-
-            noise_pred = self.predict_noise_with_multi_branch_cfg(
-                do_true_cfg=do_true_cfg,
-                true_cfg_scale={"text": text_guidance_scale, "image": image_guidance_scale},
-                branches_kwargs=[positive_kwargs, ref_neg_kwargs, uncond_kwargs],
-            )
-            latents = self.scheduler_step_maybe_with_cfg(noise_pred, t, latents, do_true_cfg)
-
-        return latents
+class MyVideoAudioPipeline(nn.Module, CFGParallelMixin):
+    def combine_cfg_noise(self, positive_noise_pred, negative_noise_pred, scale, normalize):
+        (video_pos, audio_pos) = positive_noise_pred
+        (video_neg, audio_neg) = negative_noise_pred
+        video_combined = super().combine_cfg_noise(video_pos, video_neg, scale, normalize)
+        return (video_combined, audio_pos)  # audio: positive only, no CFG
 ```

-### Override Combine Functions
-
-There are two combine functions for different scenarios:
-
- **`combine_cfg_noise()`** — Used by `predict_noise_maybe_with_cfg()`. Override when `predict_noise()` returns a tuple (e.g., video + audio) and you need per-element CFG logic.
- **`combine_multi_branch_cfg_noise()`** — Used by `predict_noise_with_multi_branch_cfg()`. Override to implement custom multi-branch combine formulas (see [Multi-Branch CFG](#multi-branch-cfg-3-branches) above).
+This also requires `predict_noise()` to return a tuple (see [Override predict_noise](#override-predict_noise-for-custom-transformer-calls) above).

 ### Implement a Composite Scheduler for Multi-Output Models

@@ -346,5 +303,4 @@ 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. ✅ **(Optional) Multi-branch** - For 3+ branch models, use `predict_noise_with_multi_branch_cfg()` and override `combine_multi_branch_cfg_noise()`
-4. ✅ **Test** - Verify with `--cfg-parallel-size 2` (or 3/4 for multi-branch) and compare performance
+3. ✅ **Test** - Verify with `--cfg-parallel-size 2` and compare performance
@@ -207,9 +207,9 @@ Complete examples in the codebase:

 | Model | Path | Pattern | Notes |
 |-------|------|---------|-------|
-| **HunyuanImage3.0** | `vllm_omni/diffusion/models/hunyuan_image3/hunyuan_image3_transformer.py` | Standard EP | Full implementation with validation |
+| **HunyuanImage3.0** | `vllm_omni/diffusion/models/hunyuan_image_3/hunyuan_image_3_transformer.py` | Standard EP | Full implementation with validation |
 | **EP Tests** | `vllm-omni/tests/e2e/offline_inference/test_expert_parallel.py` | E2E testing | EP correctness and performance |
-| **Constraint Tests** | `vllm-omni/tests/diffusion/models/hunyuan_image3/test_hunyuan_fused_moe.py` | Unit testing | Validation logic |
+| **Constraint Tests** | `vllm-omni/tests/diffusion/models/hunyuan_image_3/test_hunyuan_fused_moe.py` | Unit testing | Validation logic |

 ---
 ## Summary
@@ -1,164 +0,0 @@
-# Automatic Prefix Caching in Omni Models
-
-
---
-
-## Table of Contents
-
- [Overview](#overview)
- [High-Level Approach](#high-level-approach)
- [Example](#example)
- [What About Multimodal Inputs?](#what-about-multimodal-inputs)
-
---
-
-### Overview
-
-Prefix caching in the context of kv-cache management is a useful optimization for avoiding redundant computations. The main idea is that we store portions of the kv-cache from processed requests, so that we can reuse them if incoming requests have the same prefix as previous requests.
-
-vLLM manages the kv-cache as blocks, which represent a span of tokens of a fixed length. Blocks are hashable by the content that they contain, which typically means the tokens within the span, but also could be influenced by other factors, e.g., LoRA and multimodal data.
-
-vLLM implements automatic prefix caching for managing its kv-cache, which is best understood by reading the design document [here](https://docs.vllm.ai/en/latest/design/prefix_caching/). vLLM-Omni builds on top of the prefix caching mechanism in a noninvasive way to allow caching between stages in Omni pipelines. This typically means for a given stage we aim to support caching for the following:
-
- The last hidden states produced by the stage
- Model / stage specific multimodal data
-
-!!! note "Note 1"
-    This document describes vLLM-Omni's mechanism for caching tensor outputs that are meant to be passed between stages, when requests have common prefixes, similar to the way in which vLLM has prefix caching for the kv-cache. This works in conjunction with vLLM's multimodal encoder caching, but is distinct. See the final section for a concrete example for how they tie together in practice.
-
-### High-Level Approach
-!!! note "Note 2"
-    Prior to reading this section, it's recommended to take a look at the design documents in vLLM for [Automatic Prefix Caching](https://docs.vllm.ai/en/latest/features/automatic_prefix_caching/), which will make some of the concepts more clear.
-
-The main focus of vLLM-Omni's approach to prefix caching stage outputs is to build on vLLM's prefix caching in the least invasive way possible while minimizing impact for cache misses, and consuming a minimal amount of GPU memory. To understand the implementation, there are a few important things to note:
-
- Between stages, device tensors are generally moved to CPU; this is important since we're just caching the outputs of stages, so it is okay to keep the entire cache on the CPU.
-
- For a tensor to be considered cacheable, the first dimension (currently) needs to be the same as the token count, as it allows us to reuse block/slot mappings for our externally maintained tensor caches. This allows us to dynamically discover the tensors to be marked as cacheable outputs in each Omni model without having to explicitly specify cacheable output field names in every model.
-
-With this in mind, consider the set of blocks in a 2D layout, where the row represents the index of blocks being considered, and the columns represent the slots corresponding to tokens within each block. Since we know the `num_blocks` and `block_size` from our kv cache config, if we want to cache a tensor with feature size `D`, we can preallocate a CPU tensor of size `(num_blocks, block_size, D)`, and use the same block index and slot mapping to retrieve the corresponding feature vector.
-
-
-### Example
-!!! note "Note 3"
-    Prefix caching in vLLM-Omni currently is only supported on AutoRegressive stages with one kv-cache group. It can be enabled/disabled per-stage via the `enable_prefix_caching` parameter in the model's stage config.
-
-The way in which vLLM-Omni ties into vLLM's prefix caching is best understood by example. Say that we have the following:
-
- `num_blocks=8`
- `block_size=4`
- `hidden_size=2`
- A stage specific multimodal output tensor named `mm_feature` with feature dimension `16`
-
-The prefix cache flow is then outlined below.
-
-1. When the model is initialized, we can determine the `hidden_size` from the `ModelConfig`, and allocate a cache of size `(num_blocks, block_size, hidden_size)`.
-
-2. Say we process the request `The quick brown fox was tired and slept beneath the shady tree`, which is 12 tokens and evenly divides into 3 blocks as shown below.
-
-```
-         [  The quick brown fox  ] [  was tired and slept ] [beneath the shady tree ]
-Block 1: |<--- block tokens ---->|
-Block 2: |<------- prefix ------>| |<--- block tokens --->|
-Block 3: |<------------------ prefix -------------------->| |<--- block tokens ---->|
-```
-
-When the request processes, we inspect the multimodal outputs and identify the `mm_feature` tensor, which will be of shape `(seq_len, feature_dim)`, i.e., `(12, 16)` in this example. We note that the first axis is dependent on the `seq_len` and add a new cache_tensor of shape `(num_blocks, block_size, feature_dim)` to our multimodal cache for tensors.
-
-
-3. If we lay out the cache as a 2D tensor of shape (`num_blocks`, `block_size`), we'll have something like the following:
-
-```
-0: [  The quick brown fox  ]
-1: [  was tired and slept  ]
-2: [beneath the shady tree ]
-3: [EMPTY]
-...
-7: [EMPTY]
-```
-
-Or, if we flatten it down to 1D,
-```
-0: The
-1: quick
-2: brown
-3: fox
-...
-11: tree
-12: [EMPTY]
-...
-```
-
-which we can think of as row indices into the hidden states tensor if we view it as the 2D shape `(num_blocks x block_size, feature_dim)`. That is, the analogous flattened (from 3D -> 2D) mapping of the cache for hidden states becomes the following.
-```
-0: <hidden states vector of len 2 corresponding to 'The'>
-1: <hidden states vector of len 2 corresponding to 'quick'>
-2: <hidden states vector of len 2 corresponding to 'brown'>
-3: <hidden states vector of len 2 corresponding to 'fox'>
-...
-11: <hidden states vector of len 2 corresponding to 'tree'>
-12: [EMPTY]
-...
-```
-
-Similarly, for the multimodal outputs cache, the flattened coordinates are the same, but the `mm_feature` maps to vectors of length `16` instead of the hidden size of `2`. Note that in practice, we may have multiple  multimodal output tensors per forward pass, which may have different names and different feature dimensions.
-
-
-4. Now, say that we receive a new request `The quick brown fox jumped over the dog`.
-
-```
-         [  The quick brown fox  ] [  jumped over the dog ]
-Block 1: |<--- block tokens ---->|
-Block 2: |<------- prefix ------>| |<--- block tokens --->|
-```
-
-Here, we will have a cache hit for `Block 1` which will be detected by vLLM based on the hash of the first block when it's handling the prefix caching on the kv-cache. As a result, when we get the output from the scheduler, we will see that `num_computed_tokens=4` (corresponding to the cached first block), and we only need to process the remaining 4 new tokens in the new prefill.
-
-Since we have the block indices / slot mappings from the kv cache manager, we can simply mirror the mappings and leverage the same indices for the cached hidden states and multimodal outputs. This allows us to look up the correct tensors from our externally maintained 3D caches.
-
-```
-0: [  The quick brown fox  ] < already in the cache
-1: [  was tired and slept  ]
-2: [beneath the shady tree ]
-3: [ jumped over the dog  ] < added on the second request
-4: [EMPTY]
-...
-7: [EMPTY]
-...
-```
-
-Finally, to pass the full hidden states and multimodal outputs to the next stage, we simply concatenate the cached contents with the corresponding new tensors computed from the current forward call.
-
-
-### What About Multimodal Inputs?
-It's also useful to consider the case about how Omni prefix caching is handled when we have multimodal inputs that don't cleanly end on block boundaries, as well as how this works with multimodal encoder caching in vLLM. For example:
-
-```
-         [   Im0  Im1  Im2  Im3  ] [ Im4  Im5 foo <empty> ]
-Block 1: |<--- block tokens ---->|
-Block 2: |<------- prefix ------>| |<--- block tokens --->|
-```
-
-In this case, only `Block 1` will have outputs stored in the prefix tensor cache, because vLLM does not store partial blocks. This may appear to be a problem at first glance, because the multimodal input is fragmented across a new block that wasn't cached.
-
-In reality, this isn't a big problem for correctness, because vLLM also maintains an encoder cache for multimodal inputs. In other words, after the first pass, we'll have the following:
-
- The Block 1 hash, which is used for prefix caching
- The hash describing the image data starting at position 0 and with length 6
- In vLLM's encoder cache, a mapping from the image hash above to the encoder output
-
-
-To understand what happens, say we get the following input as a second request:
-```
-         [   Im0  Im1  Im2  Im3  ] [  Im4  Im5 bar  baz  ]
-Block 1: |<--- block tokens ---->|
-Block 2: |<------- prefix ------>| |<--- block tokens --->|
-```
-
-First, the scheduler will check for a prefix cache hit, which we will see on `Block 1`. As a result, we will have 4 tokens marked as precomputed, and only see the remaining 4 tokens in the following prefill.
-
-Because we have multimodal data in a scheduled span that isn't fully precomputed, we still need to call the visual encoder. However, since we have the image hash and encoder cache, we will retrieve the encoder outputs for `Im4` and `Im5` as we create the multimodal embeddings.
-
-When we pass our multimodal tensors to the language model component in the same stage, we'll then expect the same outputs, because the prefix caching behaviors in vLLM-Omni / vLLM match, so the LLM will use vLLM's KV cache manager's prefix caching to correctly handle the attention information for `Block 1` while calculating the outputs for `Block 2`, giving us the correct results for processing `Block 2` with the context of `Block 1`.
-
-Finally, we look up the output hidden states/multimodal tensors corresponding to the prefix cache hit `Block 1` and concatenate it with the forward pass result to get the final result, which is expected to be identical to the full hidden states when prefix caching is disabled.
@@ -326,41 +326,9 @@ for prompt in tqdm(prompts, desc="Collecting data"):

 # Estimate coefficients
 coeffs = estimator.estimate(poly_order=4)
-print(f"Estimated coefficients: {coeffs}")
+print(f"Estimated coefficients: {coeffs.tolist()}")
 ```

-Note: some models may require the vLLM context and config to be initialized to initialize vLLM modules. To this end, you may need a workaround like the following to be able to run coefficient estimation.
-```python
-from vllm_omni.diffusion.forward_context import set_forward_context
-from vllm_omni.diffusion.distributed.parallel_state import (
-    init_distributed_environment,
-    initialize_model_parallel,
-)
-from vllm.config import VllmConfig
-...
-
-if __name__ == "__main__":
-    os.environ["MASTER_ADDR"] = "localhost"
-    os.environ["MASTER_PORT"] = "8192"
-    os.environ["LOCAL_RANK"] = "0"
-    os.environ["RANK"] = "0"
-    os.environ["WORLD_SIZE"] = "1"
-
-    vllm_config = VllmConfig()
-    init_distributed_environment()
-    initialize_model_parallel()
-
-    # NOTE: you may have to pass an initialized OmniDiffusionConfig as a kwarg
-    # here to make current sp checks happy; if this is the case, just create one
-    # .from_kwargs() with the model name to get around this check for now,
-    # since your estimator subclass should handle the actual model configuration.
-    #
-    # This will be cleaned up in the future
-    with set_forward_context(vllm_config):
-        <create the estimator + run estimation here>
-```
-
-
 **Data Statistics Guide:**

 | Metric | Good Range | Warning Signs |
@@ -1,539 +0,0 @@
-# Speech Generation on vLLM-Omni: Performance Optimizations for Qwen3-Omni and Qwen3-TTS
-
-## Summary
-
-vLLM-Omni supports end-to-end serving for speech-generating models, including both **Qwen3-Omni** (multimodal understanding + speech) and **Qwen3-TTS** (text-to-speech). Despite their different architectures, both models share the same multi-stage pipeline design and benefit from the same set of stacked optimizations:
-
-1. **Batching** improves GPU utilization stage by stage and increases overall throughput.
-2. **CUDA Graph** reduces CPU launch overhead and decode-time jitter on stable shapes.
-3. **Async Chunk and Streaming Output** overlap compute and communication across stages and emit audio incrementally, improving both TTFP and E2E.
-
-### Model architectures
-
-**Qwen3-Omni** is a native multimodal model that understands text, audio, image, and video inputs, and generates both text and speech outputs. Its pipeline has three stages:
-
- **Thinker**: multimodal understanding and text generation
- **Talker (+ Talker-MTP / code predictor path)**: converts semantic/text representations into codec tokens
- **Code2Wav**: decodes codec tokens into waveform audio
-
-**Qwen3-TTS** is a lightweight, high-quality text-to-speech model. Its pipeline has two stages:
-
- **Talker (AR decoder)**: auto-regressively generates codec tokens from text input
- **Code2Wav (vocoder)**: decodes codec tokens into waveform audio
-
-The optimizations described in this post apply to both models. We present results for each side by side.
-
-### vLLM-Omni vs HF Transformers
-
-Compared with **HF Transformers** (offline, single request), vLLM-Omni with the full optimization stack delivers dramatically lower latency and higher efficiency for both models.
-
-**Qwen3-Omni** (A100):
-
-<table><tr>
-<td><img src="figures/omni/E2EL_s_vllm_omni_vs_transformers.png" alt="Qwen3-Omni E2EL: vLLM vs HF" width="100%"/></td>
-<td><img src="figures/omni/TTFP_s_vllm_omni_vs_transformers.png" alt="Qwen3-Omni TTFP: vLLM vs HF" width="100%"/></td>
-<td><img src="figures/omni/RTF_vllm_omni_vs_transformers.png" alt="Qwen3-Omni RTF: vLLM vs HF" width="100%"/></td>
-</tr></table>
-
-| Metric | vLLM-Omni | HF Transformers | Improvement |
-| --- | --- | --- | --- |
-| E2E latency (s) | 23.78 | 336.10 | ~93% reduction |
-| TTFP (s) | 0.934 | 336.10 | ~99.7% reduction |
-| RTF | 0.32 | 3.776 | ~91% reduction (~12× faster) |
-
- **E2E latency**: 23.78 s vs 336.10 s - **~93%** reduction
- **TTFP**: 0.934 s vs 336.10 s - **~99.7%** reduction
- **RTF**: 0.32 vs 3.776 - **~91%** reduction (~12x faster)
-
-**Qwen3-TTS** (H200, concurrency 1):
-
-<table><tr>
-<td><img src="figures/tts/Mean_E2EL_(ms)_vllm_omni_vs_transformers.png" alt="Qwen3-TTS E2EL: vLLM vs HF" width="100%"/></td>
-<td><img src="figures/tts/Mean_AUDIO_TTFP_(ms)_vllm_omni_vs_transformers.png" alt="Qwen3-TTS TTFP: vLLM vs HF" width="100%"/></td>
-<td><img src="figures/tts/Mean_AUDIO_RTF_vllm_omni_vs_transformers.png" alt="Qwen3-TTS RTF: vLLM vs HF" width="100%"/></td>
-</tr></table>
-
-| Metric | vLLM-Omni | HF Transformers | Improvement |
-| --- | --- | --- | --- |
-| E2E latency (ms) | 941 | 15,513 | ~94% reduction |
-| TTFP (ms) | 64 | 15,513 | ~99.6% reduction (242× faster) |
-| RTF | 0.16 | 2.64 | ~94% reduction (~16.5× faster) |
-
- **E2E latency**: 941 ms vs 15,513 ms - **~94%** reduction
- **TTFP**: 64 ms vs 15,513 ms - **~99.6%** reduction (242x faster)
- **RTF**: 0.16 vs 2.64 - **~94%** reduction (~16.5x faster)
-
-### Stacked optimization summary
-
-Each optimization stacks on the previous one. The summary plots below show the cumulative effect at each step, with one line per concurrency level (1, 4, 10).
-
-**Qwen3-Omni** (A100):
-
-<table><tr>
-<td><img src="figures/omni/Summary_E2EL_ms_vs_features.png" alt="Qwen3-Omni E2EL: stacked optimization" width="100%"/></td>
-<td><img src="figures/omni/Summary_TTFP_ms_vs_features.png" alt="Qwen3-Omni TTFP: stacked optimization" width="100%"/></td>
-<td><img src="figures/omni/Summary_RTF_vs_features.png" alt="Qwen3-Omni RTF: stacked optimization" width="100%"/></td>
-</tr></table>
-
- **E2EL reduction**: ~74% at concurrency 10 (410,054 ms -> 104,901 ms); ~90% at concurrency 1 (426,529 ms -> 41,216 ms)
- **TTFP reduction**: ~96% at concurrency 10 (409,705 ms -> 16,482 ms); ~99.7% at concurrency 1 (426,078 ms -> 1,164 ms)
- **RTF reduction**: ~74% at concurrency 10 (2.83 -> 0.74); ~90% at concurrency 1 (2.08 -> 0.21)
-
-**Qwen3-TTS** (H200):
-
-<table><tr>
-<td><img src="figures/tts/Summary_mean_e2e_ms_vs_features.png" alt="Qwen3-TTS E2EL: stacked optimization" width="100%"/></td>
-<td><img src="figures/tts/Summary_mean_ttfp_ms_vs_features.png" alt="Qwen3-TTS TTFP: stacked optimization" width="100%"/></td>
-<td><img src="figures/tts/Summary_mean_rtf_vs_features.png" alt="Qwen3-TTS RTF: stacked optimization" width="100%"/></td>
-</tr></table>
-
- **E2EL reduction**: ~85% at concurrency 10 (12,141 ms -> 1,767 ms); ~29% at concurrency 1 (1,323 ms -> 941 ms)
- **TTFP reduction**: ~96.5% at concurrency 10 (12,141 ms -> 425 ms); ~95% at concurrency 1 (1,323 ms -> 64 ms)
- **RTF reduction**: ~86% at concurrency 10 (2.19 -> 0.31); ~30% at concurrency 1 (0.23 -> 0.16)
-
-**Benchmark environment:**
-
-| | Qwen3-Omni                  | Qwen3-TTS |
-| --- |-----------------------------| --- |
-| **GPU** | A100                        | H200 |
-| **Model** | Qwen3-Omni-30B-A3B-Instruct | Qwen3-TTS-12Hz-1.7B-CustomVoice |
-| **vLLM** | v0.17.0                     | v0.18.0 |
-| **vllm-omni** | commit 199f7832             | v0.18.0rc2 |
-| **CUDA** | 12.9                        | 12.8 |
-
-This post walks through each optimization in the same order they are typically enabled in practice, then ends with deployment playbooks for both models.
-
---
-
-## Pipeline Batching
-
-### How stage-wise batching works
-
-For both Qwen3-Omni and Qwen3-TTS, batching is a pipeline-level optimization:
-
- Requests are grouped per stage using `runtime.max_batch_size`
- Each stage executes batch inference with its own scheduler/worker
- Stage outputs are routed to downstream stages with per-request mapping preserved
-
-**Batching strategy by stage:** The understanding and decode stages (Thinker for Omni, Talker for both) use **continuous batching**: requests can join and leave the batch over time. Code2Wav uses **static batching**: once a batch is formed, the stage runs the whole batch before starting the next. This matches the decode pattern of Code2Wav and keeps implementation simple while still improving throughput.
-
-### Batching results (Baseline vs. Batch)
-
-Batching alone greatly reduces E2EL and RTF across all concurrencies. The biggest gains appear at high concurrency where requests share GPU resources.
-
-**Qwen3-Omni** (A100):
-
-<table><tr>
-<td><img src="figures/omni/Mean_E2EL_ms_Baseline_vs_Batch.png" alt="Qwen3-Omni E2EL: Baseline vs Batch" width="100%"/></td>
-<td><img src="figures/omni/Mean_AUDIO_TTFP_ms_Baseline_vs_Batch.png" alt="Qwen3-Omni TTFP: Baseline vs Batch" width="100%"/></td>
-<td><img src="figures/omni/Mean_AUDIO_RTF_Baseline_vs_Batch.png" alt="Qwen3-Omni RTF: Baseline vs Batch" width="100%"/></td>
-</tr></table>
-
-| Metric | Concurrency | Baseline | + Batch | Improvement |
-| --- | --- | --- | --- | --- |
-| E2EL (ms) | 1 | 426,529 | 307,719 | 1.4× |
-| E2EL (ms) | 4 | 407,213 | 376,934 | 1.1× |
-| E2EL (ms) | 10 | 410,054 | 234,844 | 1.7× |
-| TTFP (ms) | 1 | 426,078 | 307,262 | 1.4× |
-| TTFP (ms) | 4 | 406,843 | 376,466 | 1.1× |
-| TTFP (ms) | 10 | 409,705 | 234,557 | 1.7× |
-| RTF | 1 | 2.08 | 1.51 | 1.4× |
-| RTF | 4 | 2.55 | 1.83 | 1.4× |
-| RTF | 10 | 2.83 | 2.28 | 1.2× |
-
-At concurrency 10, E2EL drops from ~410 s to ~235 s; at concurrency 1, from ~427 s to ~308 s.
-
-**Qwen3-TTS** (H200):
-
-<table><tr>
-<td><img src="figures/tts/Mean_mean_e2e_ms_baseline_vs_batch.png" alt="Qwen3-TTS E2EL: Baseline vs Batch" width="100%"/></td>
-<td><img src="figures/tts/Mean_mean_ttfp_ms_baseline_vs_batch.png" alt="Qwen3-TTS TTFP: Baseline vs Batch" width="100%"/></td>
-<td><img src="figures/tts/Mean_mean_rtf_baseline_vs_batch.png" alt="Qwen3-TTS RTF: Baseline vs Batch" width="100%"/></td>
-</tr></table>
-
-| Metric | Concurrency | Baseline | + Batch | Improvement |
-| --- | --- | --- | --- | --- |
-| E2EL (ms) | 1 | 1,323 | 1,339 | 1.0× |
-| E2EL (ms) | 4 | 5,171 | 1,471 | 3.5× |
-| E2EL (ms) | 10 | 12,141 | 1,705 | 7.1× |
-| RTF | 1 | 0.230 | 0.234 | 1.0× |
-| RTF | 4 | 0.908 | 0.255 | 3.6× |
-| RTF | 10 | 2.186 | 0.292 | 7.5× |
-| Throughput (audio-s/wall-s) | 10 | 3.99 | 33.53 | 8.4× |
-
-At concurrency 10, batching alone brings Qwen3-TTS RTF from 2.19 (slower than realtime) down to 0.29 (faster than realtime), and throughput from 4.0 to 33.5 audio-sec/wall-sec.
-
---
-
-## CUDA Graph on the Critical Decode Path
-
-### Why CUDA Graph helps here
-
-In decode-heavy serving, repeatedly launching many small kernels from CPU can become a visible overhead. CUDA Graph reduces this overhead by capturing and replaying stable execution graphs.
-
-In stage configs, this is represented by `enforce_eager: false` for stages where graph capture is desired (Thinker/Talker), while Code2Wav keeps eager mode depending on stage behavior.
-
-### CUDA Graph results on top of batching
-
-**Qwen3-Omni** (A100):
-
-<table><tr>
-<td><img src="figures/omni/Mean_E2EL_ms_Batch_vs_Batch_CUDA_Graph.png" alt="Qwen3-Omni E2EL: Batch vs CUDA Graph" width="100%"/></td>
-<td><img src="figures/omni/Mean_AUDIO_TTFP_ms_Batch_vs_Batch_CUDA_Graph.png" alt="Qwen3-Omni TTFP: Batch vs CUDA Graph" width="100%"/></td>
-<td><img src="figures/omni/Mean_AUDIO_RTF_Batch_vs_Batch_CUDA_Graph.png" alt="Qwen3-Omni RTF: Batch vs CUDA Graph" width="100%"/></td>
-</tr></table>
-
-| Metric | Concurrency | Batch | + CUDA Graph | Improvement |
-| --- | --- | --- | --- | --- |
-| E2EL (ms) | 1 | 307,719 | 61,613 | 5.0× |
-| E2EL (ms) | 4 | 376,934 | 79,019 | 4.8× |
-| E2EL (ms) | 10 | 234,844 | 126,867 | 1.9× |
-| TTFP (ms) | 1 | 307,262 | 61,257 | 5.0× |
-| TTFP (ms) | 4 | 376,466 | 78,634 | 4.8× |
-| TTFP (ms) | 10 | 234,557 | 126,534 | 1.9× |
-| RTF | 1 | 1.51 | 0.32 | 4.7× |
-| RTF | 4 | 1.83 | 0.43 | 4.3× |
-| RTF | 10 | 2.28 | 0.90 | 2.5× |
-
-For the larger Qwen3-Omni model (30B-A3B), CUDA Graph provides a significant improvement. At concurrency 1, E2EL drops from ~308 s to ~62 s; at concurrency 10, from ~235 s to ~127 s.
-
-**Qwen3-TTS** (H200):
-
-<table><tr>
-<td><img src="figures/tts/Mean_mean_e2e_ms_batch_vs_cuda_graph.png" alt="TTS E2EL: Batch vs +CG" width="100%"/></td>
-<td><img src="figures/tts/Mean_mean_ttfp_ms_batch_vs_cuda_graph.png" alt="TTS TTFP: Batch vs +CG" width="100%"/></td>
-<td><img src="figures/tts/Mean_mean_rtf_batch_vs_cuda_graph.png" alt="TTS RTF: Batch vs +CG" width="100%"/></td>
-</tr></table>
-
-| Metric | Concurrency | Batch | + CUDA Graph | Improvement |
-| --- | --- | --- | --- | --- |
-| E2EL (ms) | 1 | 1,339 | 733 | 1.8× |
-| E2EL (ms) | 4 | 1,471 | 987 | 1.5× |
-| E2EL (ms) | 10 | 1,705 | 1,197 | 1.4× |
-| RTF | 1 | 0.234 | 0.124 | 1.9× |
-| RTF | 10 | 0.292 | 0.203 | 1.4× |
-| Throughput (audio-s/wall-s) | 10 | 33.53 | 47.15 | 1.4× |
-
-At concurrency 1, CUDA Graph reduces E2EL from 1,339 ms to 733 ms and RTF from 0.234 to 0.124 - nearly a 2x improvement. The benefit is consistent across all concurrency levels.
-
---
-
-## Async Chunk and Streaming Output: Earlier Audio and Cross-Stage Overlap
-
-### Why this step matters for first-packet latency
-
-Two mechanisms work together to improve user-visible latency:
-
- **Streaming output**: audio streaming emits audio chunks as soon as they are decoded (lower **TTFP**). Without streaming, the client waits for larger buffers or end-of-sequence.
- **Async chunk** is the main enabler for *earlier* audio: instead of handing off whole-request results between stages, each stage forwards **chunks** so the next stage can start as soon as the first chunk is ready. For Omni: Thinker -> Talker forwards hidden-state chunks; for both: Talker -> Code2Wav forwards codec chunks; Code2Wav decodes and emits packets incrementally. This **overlaps compute and communication** across stages and directly reduces time-to-first-audio-packet (TTFP) and end-to-end latency (E2EL).
-
-So in practice: streaming output defines *how* bytes are sent to the client; async chunk defines *when* the pipeline can produce the first bytes.
-
-**Dependency between the two:** Async chunk and audio streaming output are mutually dependent. Without async chunk, **audio streaming output cannot truly take effect**. Without audio streaming output, async chunk's **TTFP advantage is not fully realized**: the client would still wait for larger buffers or end-of-sequence instead of hearing the first packet as soon as it is ready. We therefore recommend enabling **both** on top of batching + CUDA Graph; the benchmarks in this post use both.
-
-### Results: Batch + CUDA Graph vs. Batch + CUDA Graph + Async Chunk + Streaming Output
-
-**Qwen3-Omni** (A100):
-
-<table><tr>
-<td><img src="figures/omni/Mean_E2EL_ms_Batch_CUDA_Graph_vs_Async_Chunk.png" alt="Qwen3-Omni E2EL: CG vs Async Chunk" width="100%"/></td>
-<td><img src="figures/omni/Mean_AUDIO_TTFP_ms_Batch_CUDA_Graph_vs_Async_Chunk.png" alt="Qwen3-Omni TTFP: CG vs Async Chunk" width="100%"/></td>
-<td><img src="figures/omni/Mean_AUDIO_RTF_Batch_CUDA_Graph_vs_Async_Chunk.png" alt="Qwen3-Omni RTF: CG vs Async Chunk" width="100%"/></td>
-</tr></table>
-
-| Metric | Concurrency | Batch + CG | + Async Chunk | Improvement |
-| --- | --- | --- | --- | --- |
-| E2EL (ms) | 1 | 61,613 | 41,216 | 1.5× |
-| E2EL (ms) | 4 | 79,019 | 67,584 | 1.2× |
-| E2EL (ms) | 10 | 126,867 | 104,901 | 1.2× |
-| TTFP (ms) | 1 | 61,257 | 1,164 | 53× |
-| TTFP (ms) | 4 | 78,634 | 3,152 | 24.9× |
-| TTFP (ms) | 10 | 126,534 | 16,482 | 7.7× |
-| RTF | 1 | 0.32 | 0.21 | 1.5× |
-| RTF | 4 | 0.43 | 0.34 | 1.3× |
-| RTF | 10 | 0.90 | 0.74 | 1.2× |
-
-Enabling both brings TTFP down sharply (concurrency 1: 61,257 ms -> 1,164 ms, **~98% reduction**; concurrency 4: 78,634 ms -> 3,152 ms, **~96% reduction**). E2EL and RTF also improve at every concurrency.
-
-**Qwen3-TTS** (H200):
-
-<table><tr>
-<td><img src="figures/tts/Mean_mean_e2e_ms_cuda_graph_vs_async_chunk.png" alt="Qwen3-TTS E2EL: CG vs Async Chunk" width="100%"/></td>
-<td><img src="figures/tts/Mean_mean_ttfp_ms_cuda_graph_vs_async_chunk.png" alt="Qwen3-TTS TTFP: CG vs Async Chunk" width="100%"/></td>
-<td><img src="figures/tts/Mean_mean_rtf_cuda_graph_vs_async_chunk.png" alt="Qwen3-TTS RTF: CG vs Async Chunk" width="100%"/></td>
-</tr></table>
-
-| Metric | Concurrency | Batch + CG | + Async Chunk | Improvement |
-| --- | --- | --- | --- | --- |
-| TTFP (ms) | 1 | 733 | **64** | **11.5×** |
-| TTFP (ms) | 4 | 987 | **119** | **8.3×** |
-| TTFP (ms) | 10 | 1,197 | **425** | **2.8×** |
-| E2EL (ms) | 1 | 733 | 941 | 0.8× |
-| E2EL (ms) | 10 | 1,197 | 1,767 | 0.7× |
-| RTF | 1 | 0.124 | 0.160 | 0.8× |
-| RTF | 10 | 0.203 | 0.314 | 0.6× |
-
-The TTFP improvement is the headline result for both models. For Qwen3-TTS at concurrency 1, users hear the first audio in **64 ms** instead of 733 ms - an **11.5x reduction**. For Qwen3-Omni at concurrency 1, TTFP drops from 61 s to 1.2 s - a **53x reduction**.
-
-### Why E2EL and RTF are higher with async chunk (TTS)
-
-The table above shows that enabling async chunk + streaming *increases* E2EL and RTF for TTS compared to CUDA Graph alone. This is expected - the two configurations optimize for fundamentally different metrics:
-
- **CUDA Graph (no async chunk)** generates the entire audio end-to-end before returning. No chunking overhead, so total compute is minimized.
- **Async Chunk + Streaming** splits the pipeline into incremental chunks, adding overhead from chunked transport, context overlap in Code2Wav (`codec_left_context_frames=25`), and smaller effective batch sizes per chunk.
-
-**The tradeoff is intentional.** Async chunk trades ~30% higher total compute for **11x faster time-to-first-audio**. For interactive applications (voice assistants, chatbots), TTFP determines perceived responsiveness. For offline batch processing, CUDA Graph without async chunk is the better choice.
-
---
-
-## TTS-Specific: Code Predictor Re-prefill + `torch.compile`
-
-Qwen3-TTS has a **code predictor** - a small 5-layer transformer that generates residual codebook tokens (groups 1 through Q-1) autoregressively. Each AR step operates on very short sequences (2 to ~16 tokens).
-
-The naive approach uses a KV cache for this small transformer, similar to the main Talker. But the KV cache machinery (block tables, slot mappings, paged attention) introduces significant overhead relative to the tiny model. Two optimizations replace that:
-
-### Re-prefill (stateless forward, no KV cache)
-
-Instead of maintaining a KV cache across steps, the code predictor **re-feeds the full growing sequence** at each AR step using `F.scaled_dot_product_attention`. With sequences of at most ~16 tokens through 5 layers, the O(T^2) attention cost is negligible - and removing the KV cache machinery (block table management, `set_forward_context`, slot mapping) saves far more time than it costs.
-
-### `torch.compile` on the code predictor forward
-
-The 5-layer transformer forward pass launches ~60 small CUDA kernels per step. `torch.compile(mode="default", dynamic=True)` fuses these into fewer kernels via Inductor:
-
-```python
-self._compiled_model_fwd = torch.compile(
-    self.model.forward,
-    mode="default",    # no Inductor CUDA graphs, avoids conflict with vLLM's CUDAGraphWrapper
-    dynamic=True,      # sequence length grows each step (2, 3, ..., num_groups+1)
-)
-```
-
-`mode="default"` is used instead of `mode="reduce-overhead"` to avoid conflicts with vLLM's own CUDA graph capture on the main Talker model. `dynamic=True` handles the growing sequence length without recompilation.
-
-These optimizations are always-on in the current codebase - all Qwen3-TTS benchmark results in this post include them.
-
---
-
-## TTS-Specific: Dynamic Initial Chunk for Faster First Audio
-
-In the async chunk pipeline, the standard `codec_chunk_frames` is 25 (each chunk = ~2 seconds of audio at 12 Hz). Waiting for 25 frames before forwarding the first chunk to Code2Wav adds unnecessary TTFP. The **initial codec chunk** optimization sends a smaller first chunk so Code2Wav can start decoding earlier.
-
-**Dynamic initial chunk sizing (default behavior):**
-
-Rather than using a fixed initial chunk size, vLLM-Omni dynamically selects it based on current server load. The initial chunk size is chosen from power-of-2 steps [2, 4, 8, 16] based on load factor (`active_requests / max_batch_size`):
-
-| Server load | Initial chunk frames | Rationale |
-| --- | --- | --- |
-| Low (e.g. 1/10 active) | **2** (~167 ms of audio) | Minimize TTFP when there's headroom |
-| Medium (e.g. 5/10 active) | **4-8** | Balance TTFP vs decode efficiency |
-| High (e.g. 10/10 active) | **16** | Larger first chunk to amortize decode cost |
-
-After the initial chunk, all subsequent chunks use the standard `codec_chunk_frames` (25) size.
-
-**How it works in the pipeline:**
-
-1. Talker generates codec tokens auto-regressively
-2. The stage input processor checks current load and picks an initial chunk size (e.g. **2 frames** at low load)
-3. After that many frames, the first chunk is forwarded to Code2Wav
-4. Code2Wav decodes this small chunk and emits the first audio packet
-5. Subsequent chunks use the standard 25-frame size for efficient batch decoding
-
-**Per-request override:** Clients can also set a fixed initial chunk size via the API:
-
-```json
-{"initial_codec_chunk_frames": 2}
-```
-
-This overrides the dynamic calculation for that request.
-
-**Config (server-side):**
-
-```yaml
-runtime:
-  connectors:
-    connector_of_shared_memory:
-      name: SharedMemoryConnector
-      extra:
-        codec_streaming: true
-        codec_chunk_frames: 25              # standard chunk size (~2s of audio)
-        codec_left_context_frames: 25
-        # initial chunk is computed dynamically by default
-        # set initial_codec_chunk_frames: 2 to force a fixed value
-```
-
-The 64 ms TTFP result reported above for Qwen3-TTS at concurrency 1 uses the dynamic initial chunk, which picks `initial_codec_chunk_frames=2` at low load. At higher concurrency the dynamic sizing increases the initial chunk to maintain decode efficiency.
-
---
-
-## Live Demo: Streaming TTS over WebSocket
-
-vLLM-Omni supports real-time streaming audio output for Qwen3-TTS over WebSocket ([PR #1719](https://github.com/vllm-project/vllm-omni/pull/1719)). With `stream_audio: true`, the server sends chunked PCM audio frames as they are generated, so clients can start playback before full sentence synthesis completes.
-
-The WebSocket protocol uses `audio.start` / binary PCM chunks / `audio.done` framing per sentence:
-
-```json
-// Client sends:
-{"type":"session.config","voice":"Vivian","response_format":"pcm","stream_audio":true}
-{"type":"input.text","text":"Hello world. This is a streaming demo."}
-{"type":"input.done"}
-
-// Server streams back per sentence:
-{"type":"audio.start","sentence_index":0,"sentence_text":"Hello world.","format":"pcm","sample_rate":24000}
-<binary PCM chunk 1>
-<binary PCM chunk 2>
-...
-{"type":"audio.done","sentence_index":0,"total_bytes":96000,"error":false}
-{"type":"audio.start","sentence_index":1,"sentence_text":"This is a streaming demo.","format":"pcm","sample_rate":24000}
-<binary PCM chunk 1>
-...
-{"type":"audio.done","sentence_index":1,"total_bytes":72000,"error":false}
-{"type":"session.done","total_sentences":2}
-```
-
-<iframe width="560" height="315" src="https://www.youtube.com/embed/O3IVniwwKNA" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>
-
---
-
-## Deployment Playbook
-
-### Qwen3-Omni
-
-#### 1) Serve with the default 3-stage config
-
-```bash
-vllm serve Qwen/Qwen3-Omni-30B-A3B-Instruct \
-  --omni \
-  --port 8091
-```
-
-Notes:
-
- `runtime.max_batch_size` controls stage-level batching.
- Thinker/Talker commonly use `enforce_eager: false` for CUDA Graph paths.
- Code2Wav often remains eager (`enforce_eager: true`) depending on runtime behavior.
-
-#### 2) Enable async chunk
-
-```bash
-vllm serve Qwen/Qwen3-Omni-30B-A3B-Instruct \
-  --omni \
-  --port 8091 \
-  --stage-configs-path vllm_omni/model_executor/stage_configs/qwen3_omni_moe_async_chunk.yaml
-```
-
-#### 3) Key config knobs
-
-```yaml
-async_chunk: true
-stage_args:
-  - stage_id: 0  # thinker
-    runtime:
-      max_batch_size: 64
-    engine_args:
-      enforce_eager: false
-      max_num_batched_tokens: 32768
-      custom_process_next_stage_input_func: >-
-        vllm_omni.model_executor.stage_input_processors.qwen3_omni.thinker2talker_async_chunk
-
-  - stage_id: 1  # talker
-    runtime:
-      max_batch_size: 64
-    engine_args:
-      enforce_eager: false
-      max_num_batched_tokens: 32768
-      custom_process_next_stage_input_func: >-
-        vllm_omni.model_executor.stage_input_processors.qwen3_omni.talker2code2wav_async_chunk
-
-  - stage_id: 2  # code2wav
-    runtime:
-      max_batch_size: 64
-    engine_args:
-      enforce_eager: true
-      max_num_batched_tokens: 51200
-```
-
-#### Reproduce Qwen3-Omni benchmarks
-
-```bash
-vllm bench serve \
-  --dataset-name random \
-  --port ${PORT} \
-  --model ${MODEL_PATH} \
-  --endpoint /v1/chat/completions \
-  --backend openai-chat-omni \
-  --max-concurrency ${MAX_CONCURRENCY} \
-  --num-prompts ${NUM_PROMPTS} \
-  --random-input-len 2500 \
-  --ignore-eos \
-  --percentile-metrics ttft,tpot,itl,e2el,audio_ttfp,audio_rtf \
-  --random-output-len 900 \
-  --extra_body '{"modalities": ["text","audio"]}'
-```
-
-### Qwen3-TTS
-
-#### 1) Serve with async chunk (recommended)
-
-```bash
-vllm-omni serve Qwen/Qwen3-TTS-12Hz-1.7B-CustomVoice \
-  --omni \
-  --port 8000
-```
-
-The default config (`qwen3_tts.yaml`) enables the full optimization stack:
-
- Batching with `max_batch_size: 10` on the Talker stage
- CUDA Graph on the Talker (`enforce_eager: false`)
- Async chunk with streaming transport
-
-#### 2) Serve without async chunk (for comparison)
-
-```bash
-vllm-omni serve Qwen/Qwen3-TTS-12Hz-1.7B-CustomVoice \
-  --omni \
-  --port 8000 \
-  --stage-configs-path vllm_omni/model_executor/stage_configs/qwen3_tts_no_async_chunk.yaml
-```
-
-#### 3) Key config knobs
-
-```yaml
-async_chunk: true
-stage_args:
-  - stage_id: 0  # Talker (AR decoder)
-    runtime:
-      max_batch_size: 10
-    engine_args:
-      enforce_eager: false
-      max_num_batched_tokens: 512
-      custom_process_next_stage_input_func: >-
-        vllm_omni.model_executor.stage_input_processors.qwen3_tts.talker2code2wav_async_chunk
-
-  - stage_id: 1  # Code2Wav (vocoder)
-    runtime:
-      max_batch_size: 1
-    engine_args:
-      enforce_eager: true
-      max_num_batched_tokens: 8192
-
-runtime:
-  connectors:
-    connector_of_shared_memory:
-      name: SharedMemoryConnector
-      extra:
-        codec_streaming: true
-        codec_chunk_frames: 25
-        codec_left_context_frames: 25
-```
-
-#### Reproduce Qwen3-TTS benchmarks
-
-```bash
-GPU_DEVICE=0 \
-MODEL=Qwen/Qwen3-TTS-12Hz-1.7B-CustomVoice \
-NUM_PROMPTS=50 \
-CONCURRENCY="1 4 10" \
-bash benchmarks/qwen3-tts/vllm_omni/run_stacked_benchmark.sh
-```
-
-This cycles through four configs (Baseline -> + Batch -> + CUDA Graph -> + Async Chunk + Streaming), benchmarks each at the specified concurrency levels, and generates all comparison figures automatically.
@@ -1,243 +1,39 @@
-# Sleep Mode & ACK Protocol
+# Sleep Mode

-vLLM-Omni’s **Sleep Mode** allows you to temporarily release most GPU memory used by a model—such as model weights and key-value (KV) caches—**without stopping the server or unloading the Docker container**.
+vLLM-Omni’s **Sleep Mode** allows you to temporarily release most GPU memory used by a model—such as model weights and key-value (KV) caches (for autoregressive models)—**without stopping the server or unloading the Docker container**.

-This feature is inherited from [vLLM’s Sleep Mode](https://blog.vllm.ai/2025/10/26/sleep-mode.html) and extended with the **Omni ACK Protocol** to support multi-stage pipelines and heterogeneous hardware backends (NVIDIA, AMD, Intel, Huawei). It is especially useful in **RLHF**, **dynamic model switching**, or **cost-saving scenarios**.
+This feature is inherited from [vLLM’s Sleep Mode](https://blog.vllm.ai/2025/10/26/sleep-mode.html), which provides zero-reload model switching for multi-model serving.  
+
+It is especially useful in **RLHF**, **training**, or **cost-saving scenarios**, where GPU resources must be freed between inference workloads.

 ---

-## 1. Feature Documentation
+## Omni Model

-### Overview
-Omni Sleep Mode provides a mechanism to "sleep" specific model stages. When a stage enters sleep, its physical VRAM is reclaimed by the system, while the process state is preserved for rapid "wake-up" without full re-initialization.
+Omni model inherit the feature from vLLM' Sleep Mode

-### Sleep Levels
-We support two levels of hibernation to balance recovery speed and memory efficiency:
+This means:

-| Level | Name | Mechanism | Recovery Speed | Memory Freed |
-| :--- | :--- | :--- | :--- | :--- |
-| **Level 1** | **Weight Offloading** | Offloads weights to Host CPU RAM. | **Fast** (DMA) | Substantial |
-| **Level 2** | **Full De-mapping** | Physically releases memory pages via VRAM scavenging. | **Moderate** | **Maximum** (up to 95%+) |
+- Support both Level 1 and Level 2 sleep, allow to release and reset both model weights and KV Cache

-### Supported Platforms
+## Diffusion Model Extension

-Omni Sleep Mode is optimized for high-performance computing backends:
+We added Sleep Mode support for **diffusion models**, which previously lacked this functionality.  
+In diffusion pipelines, this currently only offloads **model weight memory**, as these models typically do not use KV caches.

-* **NVIDIA**: Supported via Virtual Memory Management (VMM).
-* **AMD (ROCm)**: Fully supported with physical page de-mapping.
-* **Intel XPU**: Supported via Level Zero memory management.
-* **Huawei NPU**: Supported via Ascend memory scavenging.
+This means:

-### Hardware Requirements
-* **Memory Considerations**: System RAM must be sufficient to hold offloaded weights during sleep.
-* **TP Support**: Tensor Parallel groups synchronize sleep/wake transitions across all workers.
+- Diffusion models can now enter Level 1 sleep.
+- Pipeline states (e.g., noise schedulers, buffers) remain intact after waking.
+- Useful for releasing VRAM between image generation or training cycles.

 ---

+## Enable sleep mode
+To enable sleep mode, set the `enable_sleep_mode` in `engine_args` to `True`

-## 2. Usage Examples
-
-### Python API Example
-You can programmatically control the lifecycle of stages using the `AsyncOmni` engine.

+Example:
 ```python
-
-import asyncio
-from vllm_omni.entrypoints.async_omni import AsyncOmni
-
-async def run_sleep_demo():
-    # 1. initialization
-    engine = AsyncOmni(
-        model="ByteDance-Seed/BAGEL-7B-MoT",
-        enable_sleep_mode=True
-    )
-
-    # 2. sleep mode level2
-    acks = await engine.sleep(stage_ids=[0], level=2)
-    print(f"Freed {acks[0].freed_bytes / 1024**3:.2f} GiB on Stage 0")
-
-    # 3. wake up
-    await engine.wake_up(stage_ids=[0])
-
-if __name__ == "__main__":
-    asyncio.run(run_sleep_demo())
-
+omni = Omni(model=...,enable_sleep_mode=True)
 ```
-
-### server command Example
-Start the server with sleep mode enabled:
-
-The first method
-
-```
-
-vllm serve ByteDance-Seed/BAGEL-7B-MoT \
--omni \
--enable-sleep-mode \
--trust-remote-code \
--gpu-memory-utilization 0.7
-
-```
-
-The second method
-
-```
-
-python3 -m vllm_omni.entrypoints.openai.api_server \
-    --model ByteDance-Seed/BAGEL-7B-MoT \
-    --omni \
-    --enable-sleep-mode \
-    --trust-remote-code \
--gpu-memory-utilization 0.7
-
-```
-
-
-
-
-### Test Scenarios & Commands
-
-#### Scenario 1: LLM Engine Sleep
-
-Objective: Verify VRAM reclamation for Stage 0 (Thinker).
-
-Trigger sleep (Level 1 or Level 2) via client:
-
-```
-
-curl -X POST http://localhost:8000/v1/omni/sleep \
-     -H "Content-Type: application/json" \
-     -d '{"stage_ids": [0], "level": 2}'
-
-```
-
-Tip: Open a new terminal and run rocm-smi or nvidia-smi or to observe the immediate drop in VRAM usage.
-
-
-
-#### Scenario 2: Diffusion Sleep
-Objective: Verify VRAM reclamation for Stage 1 (Diffusion).
-
-Trigger sleep (Level 1 or Level 2) via client:
-
-```
-
-curl -X POST http://localhost:8000/v1/omni/sleep \
-     -H "Content-Type: application/json" \
-     -d '{"stage_ids": [1], "level": 2}'
-
-```
-
-
-
-#### Scenario 3: Multi-Stage Coordinated Stress Test
-Objective: Test concurrent sleep and rapid wake-up across multiple stages.
-
-Concurrent Sleep (Stage 0 & 1):
-
-```
-
-curl -X POST http://localhost:8000/v1/omni/sleep \
-     -H "Content-Type: application/json" \
-     -d '{"stage_ids": [0, 1], "level": 2}'
-
-```
-
-
-Rapid Wake-up:
-
-```
-
-curl -X POST http://localhost:8000/v1/omni/wakeup \
-     -H "Content-Type: application/json" \
-     -d '{"stage_ids": [0, 1]}'
-
-```
-
-
-#### Scenario 4: Full Lifecycle Memory Audit & Functional Integrity
-Objective: Audit the complete flow from Sleep to Wake-up followed by an Inference validation.
-
-Check Initial State: Observe baseline VRAM usage.
-
-Trigger Deep Sleep (Level 2):
-
-```
-
-curl -X POST http://localhost:8000/v1/omni/sleep \
-     -H "Content-Type: application/json" \
-     -d '{"stage_ids": [0], "level": 2}'
-
-```
-
-Wake-up Model:
-
-```
-
-curl -X POST http://localhost:8000/v1/omni/wakeup \
-     -H "Content-Type: application/json" \
-     -d '{"stage_ids": [0]}'
-
-```
-
-Verify Functional Integrity (Inference):
-Ensure the model still generates valid output after reloading weights.
-
-```
-
-curl -X POST http://localhost:8000/v1/images/generations \
-     -H "Content-Type: application/json" \
-     -d '{
-       "prompt": "A huge swimming pool, with many people swimming.",
-       "model": "ByteDance-Seed/BAGEL-7B-MoT",
-       "response_format": "b64_json",
-       "extra_body": {"sampling_params": {"num_inference_steps": 4, "seed": 42}}
-     }' > post.json
-
-```
-
-
-
-
-## 3. API Reference
-
-
-### Methods
-
-| Method | Arguments | Return Type | Description |
-| :--- | :--- | :--- | :--- |
-| **sleep** | `stage_ids: List[int], level: int` | `List[OmniACK]` | Triggers hibernation for specified stages. |
-| **wake_up** | `stage_ids: List[int]` | `List[OmniACK]` | Reloads weights and re-maps memory. |
-
-
-
-### OmniACK Dataclass Fields
-
-| Field | Type | Description |
-| :--- | :--- | :--- |
-| **task_id** | `str` | Unique identifier for the operation. |
-| **status** | `str` | `SUCCESS` or `ERROR`. |
-| **stage_id** | `int` | The ID of the stage that responded. |
-| **rank** | `int` | The rank ID within the Tensor Parallel group. |
-| **freed_bytes** | `int` | Actual amount of physical VRAM reclaimed. |
-| **metadata** | `dict` | Additional platform-specific metrics. |
-
-Metadata Field Analysis
-The metadata field is a dynamic dictionary containing hardware-specific telemetry and audit data, primarily used for verifying memory reclamation on various backends (e.g., AMD ROCm, NVIDIA CUDA).
-
-```
-"metadata": {
-    "source": "Platform_AMD_Instinct_MI300X",
-    "total_freed_gib": "78.57",
-    "rank_residual_gib": "2.07"
-}
-```
-
-#### Core Utility:
-**VRAM Reclamation Audit (total_freed_gib)**: Converts raw freed_bytes into human-readable GiB. It serves as the primary metric to verify that Level 2 sleep has successfully purged model weights from VRAM.
-
-**Residual & Fragmentation Monitoring (rank_residual_gib)**: Reports the remaining VRAM footprint after memory de-mapping. A low residual value (e.g., 2.07 GiB) confirms a successful "clean" state, ensuring the device is ready for high-memory co-located tasks like training or diffusion pipelines.
-
-**Backend Traceability (source)**: Identifies the underlying hardware driver or audit source. This is critical for debugging synchronization issues in multi-stage, distributed environments.
-
-**Performance Analytics (Roadmap)**: Future updates will include latency_ms (context-switch overhead) and cuda_graph_recalled (graph engine status) to optimize performance in high-frequency sleep/wake scenarios.
@@ -26,7 +26,7 @@ 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
+uv pip install onnxruntime-rocm sox
 ```

 # --8<-- [end:pre-built-wheels]
@@ -47,7 +47,6 @@ th {
 | `Flux2KleinPipeline` | FLUX.2-klein | `black-forest-labs/FLUX.2-klein-4B`, `black-forest-labs/FLUX.2-klein-9B` | ✅︎ | ✅︎ | ✅︎ | ✅︎ |
 | `FluxKontextPipeline` | FLUX.1-Kontext-dev | `black-forest-labs/FLUX.1-Kontext-dev` | ✅︎ | ✅︎ | | |
 | `FluxPipeline` | FLUX.1-dev | `black-forest-labs/FLUX.1-dev` | ✅︎ | ✅︎ | | ✅︎ |
-| `FluxPipeline` | FLUX.1-schnell | `black-forest-labs/FLUX.1-schnell` | ✅︎ | ✅︎ | | ✅︎ |
 | `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` | ✅︎ | ✅︎ | ✅︎ | ✅︎ |
@@ -0,0 +1,255 @@
+# 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.
@@ -104,8 +104,6 @@ Content-Type: multipart/form-data
 | `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 |
-| `reference_image` | string or array | null | Reference image for inpainting |
-| `mask_image` | string or array | null | Mask for inpainting (white areas will be inpainted) |

 ### Response Format

@@ -15,7 +15,7 @@ Each server instance runs a single model (specified at startup via `vllm serve <
 ```bash
 # Qwen3-TTS: CustomVoice model (predefined speakers)
 vllm serve Qwen/Qwen3-TTS-12Hz-1.7B-CustomVoice \
-    --deploy-config vllm_omni/deploy/qwen3_tts.yaml \
+    --stage-configs-path vllm_omni/model_executor/stage_configs/qwen3_tts.yaml \
    --omni \
    --port 8091 \
    --trust-remote-code \
@@ -300,7 +300,7 @@ curl -X POST http://localhost:8091/v1/audio/speech \
 ```bash
 # Start server with VoiceDesign model first
 vllm serve Qwen/Qwen3-TTS-12Hz-1.7B-VoiceDesign \
-    --deploy-config vllm_omni/deploy/qwen3_tts.yaml \
+    --stage-configs-path vllm_omni/model_executor/stage_configs/qwen3_tts.yaml \
    --omni \
    --port 8091 \
    --trust-remote-code \
@@ -322,7 +322,7 @@ curl -X POST http://localhost:8091/v1/audio/speech \
 ```bash
 # Start server with Base model first
 vllm serve Qwen/Qwen3-TTS-12Hz-1.7B-Base \
-    --deploy-config vllm_omni/deploy/qwen3_tts.yaml \
+    --stage-configs-path vllm_omni/model_executor/stage_configs/qwen3_tts.yaml \
    --omni \
    --port 8091 \
    --trust-remote-code \
@@ -517,16 +517,15 @@ for result in response.json()["results"]:

 All items are fanned out to `generate()` concurrently. The engine's stage worker automatically batches them up to the configured `max_batch_size` and queues the rest — no client-side throttling needed.

-For best throughput, set both stages' `max_num_seqs` to ≥4 via `--stage-overrides`:
+For best throughput, use a batch-optimized stage config with `max_batch_size > 1`:

 ```bash
 vllm serve Qwen/Qwen3-TTS-12Hz-1.7B-CustomVoice \
-    --omni --port 8091 --trust-remote-code --enforce-eager \
-    --stage-overrides '{"0":{"max_num_seqs":4,"gpu_memory_utilization":0.2},
-                        "1":{"max_num_seqs":4,"gpu_memory_utilization":0.2}}'
+    --stage-configs-path vllm_omni/model_executor/stage_configs/qwen3_tts_batch.yaml \
+    --omni --port 8091 --trust-remote-code --enforce-eager
 ```

-The bundled `qwen3_tts.yaml` uses `max_num_seqs: 1` (single request) on both stages. Bumping to 4 yields roughly 4× throughput on the talker and lets stage 1 batch chunks across in-flight requests.
+The default `qwen3_tts.yaml` uses `max_batch_size: 1` (single request). The `qwen3_tts_batch.yaml` config sets `max_batch_size: 4` for ~4x throughput.

 ## Supported Models

@@ -618,7 +617,7 @@ Enable debug logging:

 ```bash
 vllm serve Qwen/Qwen3-TTS-12Hz-1.7B-CustomVoice \
-    --deploy-config vllm_omni/deploy/qwen3_tts.yaml \
+    --stage-configs-path vllm_omni/model_executor/stage_configs/qwen3_tts.yaml \
    --omni \
    --port 8091 \
    --trust-remote-code \
Author	SHA1	Message	Date
hsliuustc0106	5390783232	delete review-PR Signed-off-by: hsliuustc0106 <liuhongsheng4@huawei.com>	2026-04-13 13:47:41 +08:00
hsliuustc0106	fe7f5ee7bb	Simplify review-pr workflow Signed-off-by: hsliuustc0106 <liuhongsheng4@huawei.com>	2026-04-13 09:04:40 +08:00
hsliuustc0106	83a153bf70	Fix pre-commit issues in review-pr skill Signed-off-by: hsliuustc0106 <liuhongsheng4@huawei.com>	2026-04-13 09:04:40 +08:00
hsliuustc0106	901a8db21f	update skills Signed-off-by: hsliuustc0106 <liuhongsheng4@huawei.com>	2026-04-13 08:38:21 +08:00
hsliuustc0106	2d5e427f88	add skills Signed-off-by: hsliuustc0106 <liuhongsheng4@huawei.com>	2026-04-13 08:27:48 +08:00