[Bugfix][Core] Do not advance the segment watermark past an unmaintained request counter (#6834)

Signed-off-by: Inesh Reddy Chappidi <ineshreddy249@gmail.com>
Co-authored-by: Gao Han <hgaoaf@connect.ust.hk>

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

@@ -72,7 +72,22 @@ steps:
         - export VLLM_LOGGING_LEVEL=DEBUG
         - export VLLM_WORKER_MULTIPROC_METHOD=spawn
         - pytest -s -v tests/diffusion/offloader/test_diffusion_cpu_offload.py
-        - pytest -s -v tests/diffusion/offloader/test_diffusion_layerwise_offload.py
+        - >-
+          pytest -s -v tests/diffusion/offloader/test_diffusion_layerwise_offload.py
+          --deselect='tests/diffusion/offloader/test_diffusion_layerwise_offload.py::test_layerwise_offload_diffusion_model[stabilityai/stable-audio-open-1.0]'
+
+    # TODO: Diagnose unstable Stable Audio layerwise-offload memory savings on ROCm.
+    - label: "Diffusion Stable Audio Layerwise Offload Test (quarantined)"
+      agent_pool: mi300_1
+      depends_on: amd-build
+      mirror_hardwares: [amdproduction]
+      grade: NonBlocking
+      commands:
+        - export VLLM_LOGGING_LEVEL=DEBUG
+        - export VLLM_WORKER_MULTIPROC_METHOD=spawn
+        - >-
+          pytest -s -v
+          'tests/diffusion/offloader/test_diffusion_layerwise_offload.py::test_layerwise_offload_diffusion_model[stabilityai/stable-audio-open-1.0]'
 
     - label: "Diffusion Sequence Parallelism Test (Need 4 GPUs) · Shard 1/2"
       agent_pool: mi300_4
@@ -175,7 +190,7 @@ steps:
       grade: Blocking
       commands:
         - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-        - pytest -s -v tests/e2e/offline_inference/test_qwen3_omni.py tests/e2e/online_serving/test_qwen3_omni.py -m 'advanced_model and cuda' --run-level 'advanced_model'
+        - pytest -s -v tests/e2e/offline_inference/test_qwen3_omni.py tests/e2e/online_serving/test_qwen3_omni.py tests/e2e/offline_inference/test_qwen3_omni_colocate_async.py -m 'advanced_model and cuda' --run-level 'advanced_model'
 
     - label: "Qwen3-TTS CustomVoice E2E Test · Shard %N/%t"
       agent_pool: mi300_1

.buildkite/cuda/test-merge.yml

@@ -85,6 +85,9 @@ steps:
           - vllm_omni/model_executor/models/common/alias_free_activation.py
           - vllm_omni/model_executor/stage_input_processors/qwen3_tts.py
           - vllm_omni/deploy/qwen3_tts.yaml
+          - vllm_omni/entrypoints/openai/serving_speech.py
+          - vllm_omni/entrypoints/openai/tts_adapters/
+          - vllm_omni/engine/stage_pool.py
         commands:
           - |
             timeout 40m bash -c "
@@ -104,6 +107,9 @@ steps:
           - vllm_omni/model_executor/models/common/alias_free_activation.py
           - vllm_omni/model_executor/stage_input_processors/qwen3_tts.py
           - vllm_omni/deploy/qwen3_tts.yaml
+          - vllm_omni/entrypoints/openai/serving_speech.py
+          - vllm_omni/entrypoints/openai/tts_adapters/
+          - vllm_omni/engine/stage_pool.py
           - .buildkite/common/scripts/run_cov_split.sh
           - pyproject.toml
         # 40 -> 60: the coverage split runs the model twice.
@@ -122,6 +128,7 @@ steps:
       - label: "Omni · Qwen3-Omni Test"
         source_file_dependencies:
           - tests/e2e/offline_inference/test_qwen3_omni.py
+          - tests/e2e/offline_inference/test_qwen3_omni_colocate_async.py
           - tests/e2e/online_serving/test_qwen3_omni.py
           - tests/helpers/runtime.py
           - vllm_omni/entrypoints/openai/serving_chat.py
@@ -132,9 +139,10 @@ steps:
           - vllm_omni/model_executor/models/qwen3_tts/cuda_graph_decoder_wrapper.py
           - vllm_omni/model_executor/stage_input_processors/qwen3_omni.py
           - vllm_omni/deploy/qwen3_omni_moe.yaml
+          - vllm_omni/deploy/qwen3_omni_moe_thinking.yaml
         timeout_in_minutes: 50
         commands:
-          - pytest -s -v tests/e2e/offline_inference/test_qwen3_omni.py tests/e2e/online_serving/test_qwen3_omni.py -m 'advanced_model and cuda' --run-level 'advanced_model'
+          - pytest -s -v tests/e2e/offline_inference/test_qwen3_omni.py tests/e2e/online_serving/test_qwen3_omni.py tests/e2e/offline_inference/test_qwen3_omni_colocate_async.py -m 'advanced_model and cuda' --run-level 'advanced_model'
         mirror_hardwares: h100_2
 
       - label: "Omni · MiniCPM-o 4.5 Test"

.buildkite/cuda/test-nightly.yml

@@ -12,16 +12,22 @@ steps:
     if: build.env("NIGHTLY") == "1" || build.pull_request.labels includes "nightly-test" || build.pull_request.labels includes "omni-test"
     steps:
 
-      - label: ":full_moon: Omni · Function Test with H100"
-        timeout_in_minutes: 120
+      - label: ":full_moon: Omni · Function Test with H100 · Single-GPU"
+        timeout_in_minutes: 90
         commands:
-          - pytest -sv tests/e2e --run-level "full_model" -m "full_model and H100 and omni" --ignore=tests/e2e/accuracy --ignore=tests/e2e/online_serving/test_qwen3_omni_multi_replicas.py --ignore=tests/e2e/online_serving/test_minicpmo_4_5_duplex_expansion.py
+          - pytest -sv tests/e2e --run-level "full_model" -m "full_model and H100 and omni and cards_1" --ignore=tests/e2e/accuracy --ignore=tests/e2e/online_serving/test_minicpmo_4_5_duplex_expansion.py
+        mirror_hardwares: h100_1
+
+      - label: ":full_moon: Omni · Function Test with H100 · 2-GPU"
+        timeout_in_minutes: 90
+        commands:
+          - pytest -sv tests/e2e --run-level "full_model" -m "full_model and H100 and omni and cards_2" --ignore=tests/e2e/accuracy --ignore=tests/e2e/online_serving/test_qwen3_omni_multi_replicas.py
         mirror_hardwares: h100_2
 
       - label: ":full_moon: Omni · MiniCPM-o 4.5 Duplex Test"
         timeout_in_minutes: 50
         commands:
-          - pytest -s -v tests/e2e/online_serving/test_minicpmo_4_5_duplex_expansion.py -m "full_model and cuda and H100 and omni" --run-level "full_model"
+          - pytest -s -v tests/e2e/online_serving/test_minicpmo_4_5_duplex_expansion.py -m "full_model and cuda and H100 and omni and cards_1" --run-level "full_model"
         mirror_hardwares: h100_1
 
       - label: ":full_moon: Omni · Doc Test with L4"
@@ -31,10 +37,10 @@ steps:
           - pytest -s -v tests/examples/ -m "full_model and omni and L4" --run-level "full_model"
         mirror_hardwares: l4_4
 
-      - label: ":full_moon: Omni · Doc Test with H100"
+      - label: ":full_moon: Omni · Doc Test with H100 · 2-GPU"
         timeout_in_minutes: 90
         commands:
-          - pytest -s -v tests/examples/ -m "full_model and omni and H100" --run-level "full_model"
+          - pytest -s -v tests/examples/ -m "full_model and omni and H100 and cards_2" --run-level "full_model"
         mirror_hardwares: h100_2
 
       - label: ":full_moon: Omni · Accuracy Test"
@@ -136,15 +142,29 @@ steps:
           - pytest -s -v tests/e2e/ -m "full_model and L4 and tts" --run-level "full_model" --ignore=tests/e2e/accuracy
         mirror_hardwares: l4_1
 
-      - label: ":full_moon: TTS · Perf Test"
-        key: nightly-tts-performance
+      - label: ":full_moon: TTS · Perf Test · Single-GPU"
+        key: nightly-tts-performance-single
         timeout_in_minutes: 180
         commands:
           - export BENCHMARK_DIR=tests/dfx/perf/results
           - export VLLM_ALLOW_LONG_MAX_MODEL_LEN="1"
           - |
             set +e
-            pytest -s -v tests/dfx/perf/scripts/run_benchmark.py --test-config-file tests/dfx/perf/tests/test_tts.json
+            pytest -s -v tests/dfx/perf/scripts/run_benchmark.py --test-config-file tests/dfx/perf/tests/test_tts.json -m "H100 and cards_1"
+            EXIT=$$?
+            buildkite-agent artifact upload "tests/dfx/perf/results/*.json"
+            exit $$EXIT
+        mirror_hardwares: h100_1
+
+      - label: ":full_moon: TTS · Perf Test · 2-GPU"
+        key: nightly-tts-performance-2gpu
+        timeout_in_minutes: 180
+        commands:
+          - export BENCHMARK_DIR=tests/dfx/perf/results
+          - export VLLM_ALLOW_LONG_MAX_MODEL_LEN="1"
+          - |
+            set +e
+            pytest -s -v tests/dfx/perf/scripts/run_benchmark.py --test-config-file tests/dfx/perf/tests/test_tts.json -m "H100 and cards_2"
             EXIT=$$?
             buildkite-agent artifact upload "tests/dfx/perf/results/*.json"
             exit $$EXIT
@@ -179,7 +199,6 @@ steps:
       build.pull_request.labels includes "nightly-test" ||
       build.pull_request.labels includes "diffusion-x2iat-test"
     steps:
-      # Use explicit file shards so single-card cases do not reserve 2+ GPUs.
 
       - label: ":full_moon: Diffusion X2I(&A&T) · Function Test with H100 · Single-GPU"
         timeout_in_minutes: 120
@@ -187,24 +206,24 @@ steps:
           - >-
             pytest -sv
             tests/e2e/online_serving/test_qwen_image_expansion.py
-            -m "full_model and diffusion and H100 and not distributed_cuda"
+            -m "full_model and diffusion and H100 and cards_1"
             --run-level "full_model"
         mirror_hardwares: h100_1
 
-      - label: ":full_moon: Diffusion X2I(&A&T) · Function Test with H100 · Multi-GPU Qwen-Image"
+      - label: ":full_moon: Diffusion X2I(&A&T) · Function Test with H100 · 2-GPU"
         timeout_in_minutes: 120
         commands:
           - >-
             pytest -sv
             tests/e2e/online_serving/test_qwen_image_expansion.py
-            -m "full_model and diffusion and H100 and distributed_cuda"
+            -m "full_model and diffusion and H100 and cards_2"
             --run-level "full_model"
         mirror_hardwares: h100_2
 
-      - label: ":full_moon: Diffusion X2I(&A&T) · Function Test with L4"
+      - label: ":full_moon: Diffusion X2I(&A&T) · Function Test with L4 · Multi-GPU"
         timeout_in_minutes: 120
         commands:
-          - pytest -sv tests/e2e/ -k "not test_wan and not hunyuan" -m "full_model and diffusion and L4" --run-level "full_model" --ignore=tests/e2e/accuracy
+          - pytest -sv tests/e2e/ -k "not test_wan and not hunyuan" -m "full_model and diffusion and L4 and not cards_1" --run-level "full_model" --ignore=tests/e2e/accuracy
         mirror_hardwares: l4_4
 
       - label: ":full_moon: Diffusion X2I(&A&T) · Doc Test"
@@ -235,8 +254,8 @@ steps:
           - pytest -s -v tests/e2e/accuracy/test_hidream_o1_image.py -m full_model --run-level full_model
         mirror_hardwares: h100_2
 
-      - label: ":full_moon: Diffusion X2I(&A&T) · Perf Test · Qwen-Image"
-        key: nightly-diffusion-x2iat-performance-qwen-image
+      - label: ":full_moon: Diffusion X2I(&A&T) · Perf Test · Qwen-Image · Single-GPU"
+        key: nightly-diffusion-x2iat-performance-qwen-image-single
         timeout_in_minutes: 180
         commands:
           - export DIFFUSION_BENCHMARK_DIR=tests/dfx/perf/results
@@ -244,7 +263,23 @@ steps:
           - export CACHE_DIT_VERSION=1.5.0
           - |
             set +e
-            pytest -s -v tests/dfx/perf/scripts/run_diffusion_benchmark.py --test-config-file tests/dfx/perf/tests/test_qwen_image_vllm_omni.json
+            pytest -s -v tests/dfx/perf/scripts/run_diffusion_benchmark.py --test-config-file tests/dfx/perf/tests/test_qwen_image_vllm_omni.json -m "H100 and cards_1"
+            EXIT=$$?
+            buildkite-agent artifact upload "tests/dfx/perf/results/diffusion_result_*.json"
+            buildkite-agent artifact upload "tests/dfx/perf/results/logs/*.log"
+            exit $$EXIT
+        mirror_hardwares: h100_1
+
+      - label: ":full_moon: Diffusion X2I(&A&T) · Perf Test · Qwen-Image · 4-GPU"
+        key: nightly-diffusion-x2iat-performance-qwen-image-4gpu
+        timeout_in_minutes: 180
+        commands:
+          - export DIFFUSION_BENCHMARK_DIR=tests/dfx/perf/results
+          - export DIFFUSION_ATTENTION_BACKEND=FLASH_ATTN
+          - export CACHE_DIT_VERSION=1.5.0
+          - |
+            set +e
+            pytest -s -v tests/dfx/perf/scripts/run_diffusion_benchmark.py --test-config-file tests/dfx/perf/tests/test_qwen_image_vllm_omni.json -m "H100 and cards_4"
             EXIT=$$?
             buildkite-agent artifact upload "tests/dfx/perf/results/diffusion_result_*.json"
             buildkite-agent artifact upload "tests/dfx/perf/results/logs/*.log"
@@ -260,16 +295,28 @@ steps:
       build.pull_request.labels includes "diffusion-x2v-test"
     steps:
 
-      - label: ":full_moon: Diffusion X2V · Wan2.2 T2V Function Test"
+      - label: ":full_moon: Diffusion X2V · Wan2.2 T2V Function Test · Single-GPU"
         timeout_in_minutes: 90
         commands:
-          - pytest -s -v tests/e2e/online_serving/test_wan22_expansion.py -k "t2v" -m "full_model and cuda" --run-level "full_model"
+          - pytest -s -v tests/e2e/online_serving/test_wan22_expansion.py -k "t2v" -m "full_model and cuda and H100 and cards_1" --run-level "full_model"
+        mirror_hardwares: h100_1
+
+      - label: ":full_moon: Diffusion X2V · Wan2.2 T2V Function Test · 2-GPU"
+        timeout_in_minutes: 90
+        commands:
+          - pytest -s -v tests/e2e/online_serving/test_wan22_expansion.py -k "t2v" -m "full_model and cuda and H100 and cards_2" --run-level "full_model"
         mirror_hardwares: h100_2
 
-      - label: ":full_moon: Diffusion X2V · Wan2.2 I2V/TI2V Function Test"
+      - label: ":full_moon: Diffusion X2V · Wan2.2 I2V/TI2V Function Test · Single-GPU"
         timeout_in_minutes: 90
         commands:
-          - pytest -s -v tests/e2e/online_serving/test_wan22_expansion.py -k "i2v" -m "full_model and cuda" --run-level "full_model"
+          - pytest -s -v tests/e2e/online_serving/test_wan22_expansion.py -k "i2v" -m "full_model and cuda and H100 and cards_1" --run-level "full_model"
+        mirror_hardwares: h100_1
+
+      - label: ":full_moon: Diffusion X2V · Wan2.2 I2V/TI2V Function Test · 2-GPU"
+        timeout_in_minutes: 90
+        commands:
+          - pytest -s -v tests/e2e/online_serving/test_wan22_expansion.py -k "i2v" -m "full_model and cuda and H100 and cards_2" --run-level "full_model"
         mirror_hardwares: h100_2
 
       - label: ":full_moon: Diffusion X2V · HunyuanVideo-1.5 Function Test"
@@ -310,17 +357,32 @@ steps:
           - pytest -s -v tests/e2e/accuracy/hunyuanvideo15_i2v/test_hunyuanvideo15_i2v_video_similarity.py -m full_model --run-level full_model
         mirror_hardwares: h100_2
 
-      - label: ":full_moon: Diffusion X2V · Perf Test"
-        key: nightly-diffusion-x2v-performance
+      - label: ":full_moon: Diffusion X2V · Perf Test · Single-GPU"
+        key: nightly-diffusion-x2v-performance-single
         timeout_in_minutes: 180
         commands:
           - export DIFFUSION_BENCHMARK_DIR=tests/dfx/perf/results
           - export DIFFUSION_ATTENTION_BACKEND=FLASH_ATTN
           - |
             set +e
-            pytest -s -v tests/dfx/perf/scripts/run_diffusion_benchmark.py --test-config-file tests/dfx/perf/tests/test_wan22_i2v_vllm_omni.json
+            pytest -s -v tests/dfx/perf/scripts/run_diffusion_benchmark.py --test-config-file tests/dfx/perf/tests/test_wan22_i2v_vllm_omni.json -m "H100 and cards_1"
+            EXIT=$$?
+            buildkite-agent artifact upload "tests/dfx/perf/results/diffusion_result_*.json"
+            buildkite-agent artifact upload "tests/dfx/perf/results/logs/*.log"
+            exit $$EXIT
+        mirror_hardwares: h100_1
+
+      - label: ":full_moon: Diffusion X2V · Perf Test · 2-GPU"
+        key: nightly-diffusion-x2v-performance-2gpu
+        timeout_in_minutes: 180
+        commands:
+          - export DIFFUSION_BENCHMARK_DIR=tests/dfx/perf/results
+          - export DIFFUSION_ATTENTION_BACKEND=FLASH_ATTN
+          - |
+            set +e
+            pytest -s -v tests/dfx/perf/scripts/run_diffusion_benchmark.py --test-config-file tests/dfx/perf/tests/test_wan22_i2v_vllm_omni.json -m "H100 and cards_2"
             EXIT1=$$?
-            pytest -s -v tests/dfx/perf/scripts/run_diffusion_benchmark.py --test-config-file tests/dfx/perf/tests/test_cosmos3_vllm_omni.json
+            pytest -s -v tests/dfx/perf/scripts/run_diffusion_benchmark.py --test-config-file tests/dfx/perf/tests/test_cosmos3_vllm_omni.json -m "H100 and cards_2"
             EXIT2=$$?
             buildkite-agent artifact upload "tests/dfx/perf/results/diffusion_result_*.json"
             buildkite-agent artifact upload "tests/dfx/perf/results/logs/*.log"
@@ -371,10 +433,16 @@ steps:
           - pytest -sv tests/diffusion/distributed/ -m 'full_model and cuda and L4' --run-level "full_model"
         mirror_hardwares: l4_4
 
-      - label: ":full_moon: Diffusion · Distributed Test with H100"
+      - label: ":full_moon: Diffusion · Distributed Test with H100 · 2-GPU"
         timeout_in_minutes: 60
         commands:
-          - pytest -sv tests/diffusion/distributed/ -m 'full_model and cuda and H100' --run-level "full_model"
+          - pytest -sv tests/diffusion/distributed/ -m 'full_model and cuda and H100 and cards_2' --run-level "full_model"
+        mirror_hardwares: h100_2
+
+      - label: ":full_moon: Diffusion · Distributed Test with H100 · 4-GPU"
+        timeout_in_minutes: 60
+        commands:
+          - pytest -sv tests/diffusion/distributed/ -m 'full_model and cuda and H100 and cards_4' --run-level "full_model"
         mirror_hardwares: h100_4
 
       - label: ":full_moon: Diffusion · Offloader Test"
@@ -409,19 +477,25 @@ steps:
     depends_on:
       - nightly-omni-performance-no-async-chunk
       - nightly-omni-performance-async-chunk
-      - nightly-tts-performance
-      - nightly-diffusion-x2iat-performance-qwen-image
-      - nightly-diffusion-x2v-performance
+      - nightly-tts-performance-single
+      - nightly-tts-performance-2gpu
+      - nightly-diffusion-x2iat-performance-qwen-image-single
+      - nightly-diffusion-x2iat-performance-qwen-image-4gpu
+      - nightly-diffusion-x2v-performance-single
+      - nightly-diffusion-x2v-performance-2gpu
     if: build.env("NIGHTLY") == "1" && build.env("EMAIL_DISTRIBUTION") == "1"
     commands:
       - pip install openpyxl
       - export DEFAULT_INPUT_DIR=tests/dfx/perf/results
       - export DEFAULT_OUTPUT_DIR=tests/dfx/perf/results
-      - buildkite-agent artifact download "tests/dfx/perf/results/*.json" . --step nightly-tts-performance
+      - buildkite-agent artifact download "tests/dfx/perf/results/*.json" . --step nightly-tts-performance-single
+      - buildkite-agent artifact download "tests/dfx/perf/results/*.json" . --step nightly-tts-performance-2gpu
       - buildkite-agent artifact download "tests/dfx/perf/results/*.json" . --step nightly-omni-performance-no-async-chunk
       - buildkite-agent artifact download "tests/dfx/perf/results/*.json" . --step nightly-omni-performance-async-chunk
-      - buildkite-agent artifact download "tests/dfx/perf/results/*.json" . --step nightly-diffusion-x2iat-performance-qwen-image
-      - buildkite-agent artifact download "tests/dfx/perf/results/*.json" . --step nightly-diffusion-x2v-performance
+      - buildkite-agent artifact download "tests/dfx/perf/results/*.json" . --step nightly-diffusion-x2iat-performance-qwen-image-single
+      - buildkite-agent artifact download "tests/dfx/perf/results/*.json" . --step nightly-diffusion-x2iat-performance-qwen-image-4gpu
+      - buildkite-agent artifact download "tests/dfx/perf/results/*.json" . --step nightly-diffusion-x2v-performance-single
+      - buildkite-agent artifact download "tests/dfx/perf/results/*.json" . --step nightly-diffusion-x2v-performance-2gpu
       - python tools/nightly/generate_nightly_perf_excel.py
       - python tools/nightly/generate_nightly_perf_html.py
       - python tools/nightly/send_nightly_email.py --report-file "tests/dfx/perf/results/*.xlsx, tests/dfx/perf/results/*.html"

.buildkite/cuda/test-ready.yml

@@ -252,6 +252,9 @@ steps:
           - vllm_omni/model_executor/models/common/alias_free_activation.py
           - vllm_omni/model_executor/stage_input_processors/qwen3_tts.py
           - vllm_omni/deploy/qwen3_tts.yaml
+          - vllm_omni/entrypoints/openai/serving_speech.py
+          - vllm_omni/entrypoints/openai/tts_adapters/
+          - vllm_omni/engine/stage_pool.py
         commands:
           - |
             timeout 20m bash -c "
@@ -260,25 +263,6 @@ steps:
             "
         mirror_hardwares: l4_1
 
-      - label: "TTS · Qwen3-TTS Base Test"
-        source_file_dependencies:
-          - tests/e2e/online_serving/test_qwen3_tts_base.py
-          - vllm_omni/model_executor/models/qwen3_tts/
-          - vllm_omni/model_executor/models/common/qwen3_code_predictor.py
-          - vllm_omni/model_executor/models/common/snake_activation.py
-          - vllm_omni/model_executor/models/common/alias_free_activation.py
-          - vllm_omni/model_executor/stage_input_processors/qwen3_tts.py
-          - vllm_omni/deploy/qwen3_tts.yaml
-        commands:
-          - |
-            timeout 20m bash -c "
-              export VLLM_WORKER_MULTIPROC_METHOD=spawn
-              export VLLM_ALLOW_LONG_MAX_MODEL_LEN=1
-              export VLLM_OMNI_USE_V2_RUNNER=1
-              pytest -s -v tests/e2e/online_serving/test_qwen3_tts_base.py -m 'core_model' --run-level 'core_model'
-            "
-        mirror_hardwares: l4_1
-
       - label: "Diffusion · Wan22 Test"
         source_file_dependencies:
           - tests/e2e/online_serving/test_wan22_t2v.py

.buildkite/cuda/test-weekly.yml

@@ -44,10 +44,16 @@ steps:
           - pytest -s -v tests/dfx/reliability/test_reliability_hunyuan_image.py -m "slow"
         mirror_hardwares: h100_4
 
-      - label: "Reliability Test · Invalid parameters · H100"
-        timeout_in_minutes: 120
+      - label: "Reliability Test · Invalid parameters · H100 · Single-GPU"
+        timeout_in_minutes: 90
         commands:
-          - pytest -s -v tests/dfx/reliability/invalid_param_test/ -m "slow and H100"
+          - pytest -s -v tests/dfx/reliability/invalid_param_test/ -m "slow and H100 and cards_1"
+        mirror_hardwares: h100_1
+
+      - label: "Reliability Test · Invalid parameters · H100 · 2-GPU"
+        timeout_in_minutes: 90
+        commands:
+          - pytest -s -v tests/dfx/reliability/invalid_param_test/ -m "slow and H100 and cards_2"
         mirror_hardwares: h100_2
 
       - label: "Reliability Test · Invalid parameters · L4"
@@ -105,27 +111,37 @@ steps:
     if: build.env("NON_CRITICAL") == "1"
     steps:
 
-      - label: "Omni · L4"
+      # Qwen2.5 expansion (CUDA 4-card / online 2-card) stays on this l4_4 job.
+      - label: "Omni · L4 · Multi-GPU"
         timeout_in_minutes: 90
         commands:
           - export VLLM_ALLOW_LONG_MAX_MODEL_LEN="1"
-          - pytest -s -v tests/e2e/*/test_qwen2_5_omni_expansion.py --run-level "core_model"
+          - pytest -s -v tests/e2e/*/test_qwen2_5_omni_expansion.py -m "slow and L4 and omni and not cards_1" --run-level "core_model"
           - >-
             pytest -s -v tests/e2e/
             --ignore=tests/e2e/online_serving/test_qwen2_5_omni_expansion.py
             --ignore=tests/e2e/offline_inference/test_qwen2_5_omni_expansion.py
-            -m "slow and L4 and omni"
+            -m "slow and L4 and omni and not cards_1"
             --run-level "full_model"
         mirror_hardwares: l4_4
 
-      - label: "Omni · H100"
+      - label: "Omni · H100 · Single-GPU"
         timeout_in_minutes: 90
         commands:
           - >-
             pytest -sv
-            tests/e2e/ -m "slow and H100 and omni"
+            tests/e2e/ -m "slow and H100 and omni and cards_1"
             --run-level "full_model"
-        mirror_hardwares: h100_2
+        mirror_hardwares: h100_1
+
+      - label: "Omni · H100 · 4-GPU"
+        timeout_in_minutes: 90
+        commands:
+          - >-
+            pytest -sv
+            tests/e2e/ -m "slow and H100 and omni and cards_4"
+            --run-level "full_model"
+        mirror_hardwares: h100_4
 
       - label: "TTS · L4"
         timeout_in_minutes: 120
@@ -169,12 +185,21 @@ steps:
             --run-level "full_model"
         mirror_hardwares: l4_1
 
-      - label: "Diffusion · L4"
+      - label: "Diffusion · L4 · Single-GPU"
         timeout_in_minutes: 60
         commands:
           - >-
             pytest -sv
-            tests/e2e/ -m "slow and diffusion and L4"
+            tests/e2e/ -m "slow and diffusion and L4 and cards_1"
+            --run-level "full_model"
+        mirror_hardwares: l4_1
+
+      - label: "Diffusion · L4 · Multi-GPU"
+        timeout_in_minutes: 60
+        commands:
+          - >-
+            pytest -sv
+            tests/e2e/ -m "slow and diffusion and L4 and not cards_1"
             --run-level "full_model"
         mirror_hardwares: l4_4
 
@@ -183,15 +208,24 @@ steps:
         commands:
           - >-
             pytest -sv
-            tests/e2e/ -m "slow and diffusion and H100 and not distributed_cuda"
+            tests/e2e/ -m "slow and diffusion and H100 and cards_1"
             --run-level "full_model"
         mirror_hardwares: h100_1
 
-      - label: "Diffusion · H100 · Multi-GPU"
+      - label: "Diffusion · H100 · 2-GPU"
         timeout_in_minutes: 90
         commands:
           - >-
             pytest -sv
-            tests/e2e/ -m "slow and diffusion and H100 and distributed_cuda"
+            tests/e2e/ -m "slow and diffusion and H100 and cards_2"
+            --run-level "full_model"
+        mirror_hardwares: h100_2
+
+      - label: "Diffusion · H100 · 4-GPU"
+        timeout_in_minutes: 90
+        commands:
+          - >-
+            pytest -sv
+            tests/e2e/ -m "slow and diffusion and H100 and cards_4"
             --run-level "full_model"
         mirror_hardwares: h100_4

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

@@ -10,6 +10,18 @@ steps:
         -m 'core_model and npu and A3 and omni'
         --run-level 'core_model'
 
+  - label: "Diffusion NPU layer tests"
+    key: ready-diffusion-npu-layer-tests
+    depends_on: upload-ready-pipeline
+    mirror_hardwares: a3_npu_2
+    commands:
+      - >-
+        pytest -s -v
+        tests/diffusion/layers/test_activation.py
+        tests/diffusion/layers/test_fused_qk_norm_rope_npu.py
+        -m 'core_model and npu and A3'
+        --run-level 'core_model'
+
   - group: ":card_index_dividers: Omni Model Test"
     key: ready-omni-group
     depends_on: upload-ready-pipeline

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

@@ -338,7 +338,7 @@ Classify the model's **CI priority** first:
 
 **L2 & L3 online — same file, dual marks on the baseline smoke:** The **first / simplest** case in `test_{slug}.py` (default deploy, minimal steps, single prompt) should carry **both** `@pytest.mark.core_model` and `@pytest.mark.advanced_model` on the **same** function so L2 (`test-ready.yml`) and L3 (`test-merge.yml`) share one smoke test. Heavier deploy variants or API paths in the same file use **`advanced_model` only**. When L3 moves to nightly, migrate those heavier cases into `test_{slug}_expansion.py` with `full_model` and remove the dedicated `test-merge.yml` job (see `test_longcat_image_expansion.py`, `test_qwen_image_expansion.py`).
 
-**L4 design (high priority):** Combine multiple supported features (Cache-DiT, TP, USP, CFG, HSDP, CPU offload, quantization) into **few parametrized** `OmniServerParams` rows so each feature appears in at least one case without exploding GPU jobs. Shard single-GPU vs multi-GPU cases across the nightly X2I/X2V function steps (1-GPU vs `distributed_cuda`).
+**L4 design (high priority):** Combine multiple supported features (Cache-DiT, TP, USP, CFG, HSDP, CPU offload, quantization) into **few parametrized** `OmniServerParams` rows so each feature appears in at least one case without exploding GPU jobs. Shard single-GPU vs multi-GPU cases across the nightly X2I/X2V function steps (`cards_1` vs `not cards_1`).
 
 **L4 design (medium / low):** One or two parametrized rows covering the best quality/perf trade-off; skip perf JSON unless the model is high priority.
 

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

@@ -34,7 +34,7 @@ but the changes are correct — stage the modified files and re-commit.
 | `check-forbidden-imports` | Stdlib `re`/`base64`, pickle, Hugging Face Hub API, or direct Triton/TileLang | `import regex as re` and `pybase64`; use `vllm.transformers_utils.repo_utils`. Do **not** add the file to `allowed_files` without review |
 | `check-torch-cuda-call` | New `torch.cuda.*` call site | Use `current_omni_platform` / `OmniPlatform`; do not grow `ALLOWED_FILES` without review |
 | `check-tts-adapter-migration` | New `self._tts_model_type` branch in `serving_speech.py` | Put per-model logic in `tts_adapters/`. Lower `MAX_MODEL_TYPE_BRANCHES` when removing branches; do not raise it without review |
-| `check-test-ci-coverage` | New `tests/**/test_*.py` missing level or hardware mark | Add `core_model`/`advanced_model`/… plus `cpu`/`cuda`/`hardware_test(` |
+| `check-mark` | New `tests/**/test_*.py` missing level/hardware mark, or direct `pytest.mark.H100` | Add `core_model`/`advanced_model`/… plus `cpu`/`cuda`/`hardware_test(`; SKU marks only via helpers |
 | `mypy-3.10` | Type error on changed `vllm_omni/` files | Fix the types; model trees are excluded. Extra versions: `pre-commit run --hook-stage manual mypy-3.12` |
 | `markdownlint-cli2` | Docs/README markdown lint | Accept auto-fix under `docs/` / `recipes/` / root README |
 | `check-buildkite` | Invalid `.buildkite/*.yml` | Fix the YAML; do not grow `SKIP_FILES` without review |

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

@@ -52,8 +52,8 @@ Always attach markers deliberately:
   - `tts` — speech synthesis / TTS-only models (`/v1/audio/speech`, voice clone, etc.)
   - `diffusion` — generative diffusion models (image / audio / text / video from noise)
 - **Cross-cutting area** (when relevant): `parallel`, `cache`, `example`, `benchmark`
-- **Hardware**: `cpu`, `gpu`, `cuda`, `rocm`, `npu`, `L4`, `H100`, `distributed_cuda`, …
-- Optional: `slow`, distributed markers when multi-card is required
+- **Hardware**: `cpu`, `gpu`, `cuda`, `rocm`, `npu`, …; SKU (`H100`, `L4`, …) and `cards_{n}` via `@hardware_test(...)` / `hardware_marks(...)` only — never `pytest.mark.H100` (`check-mark` rejects it)
+- Optional: `slow`. Multi-card: `num_cards=N` on the helper (filter with `cards_N` / `not cards_1`)
 
 **Baseline smoke (L2 + L3):** The simplest e2e case per model — default deploy, minimal request — should usually carry **both** `@pytest.mark.core_model` and `@pytest.mark.advanced_model` on the **same** test function so `test-ready.yml` and `test-merge.yml` share one test. `send_*_request` picks validation depth from `--run-level`. References: `test_voxcpm2_tts.py::test_text_to_audio_001`, `test_qwen3_tts_customvoice.py::test_text_to_audio_001`. Heavier scenarios use **`advanced_model` only**; L4 expansion uses **`full_model`**.
 
@@ -274,7 +274,7 @@ def test_text_to_video_001(omni_server, online_client) -> None:
 
 **3. Cross-cutting rules**
 
-- New `.py` files need the Omni SPDX header (`Copyright contributors to the vLLM-Omni project`, not `vLLM project`). `tests/` may keep stdlib `re`/`base64`; pickle is still banned unless the file is already on the pickle allowlist. Every collected `tests/**/test_*.py` needs a CI **level** mark and a **hardware** mark/helper (`check-test-ci-coverage` runs locally; GHA skips it). Do not grow `allowed_files` to land a test. Policy: [docs/contributing/README.md](../../../docs/contributing/README.md#linting).
+- New `.py` files need the Omni SPDX header (`Copyright contributors to the vLLM-Omni project`, not `vLLM project`). `tests/` may keep stdlib `re`/`base64`; pickle is still banned unless the file is already on the pickle allowlist. Every collected `tests/**/test_*.py` needs a CI **level** mark and a **hardware** platform mark/helper (`check-mark` runs locally; GHA skips it). Do not write `pytest.mark.H100` / `pytest.mark.L4`; use the helpers so `cards_{n}` is attached. Do not grow `allowed_files` to land a test. Policy: [docs/contributing/README.md](../../../docs/contributing/README.md#linting).
 - Reuse existing fixtures for the chosen scenario; do not mix “online client” assumptions into offline `OmniRunner` tests without a clear reason.
 - Avoid external network dependency in assertions unless the scenario is explicitly “online serving” or doc examples that require a model hub (then align with CI secrets/cache).
 - Keep **one test function = one intent** (one modality combo, one endpoint contract, or one acceleration combo).

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

@@ -79,7 +79,7 @@ E2E (L2+) should not rely on mocks unless documenting a rare exception; prefer r
    - `full_model` — L4 nightly (`test-nightly.yml`); some expansion tests still carry both `advanced_model` and `full_model` during migration
 2. **Model type** (pick one): `omni`, `tts`, or `diffusion`
 3. **Cross-cutting** when relevant: `parallel`, `cache`, `example`, `benchmark`
-4. **Hardware**: `cpu`, `cuda`, `rocm`, `npu`, `L4`, `H100`, `distributed_cuda`, …
+4. **Hardware**: `cpu`, `cuda`, `rocm`, `npu`, `L4`, `H100`, `cards_1`, …
 5. Multi-card: `@hardware_test(...)` in `tests/helpers/mark.py`
 
 ## Command Templates
@@ -244,7 +244,7 @@ pytest -s -v dfx/reliability/invalid_param_test/test_invalid_image_generation.py
 
 ```bash
 cd tests
-pytest -s -v -m "core_model and distributed_cuda and L4" --run-level=core_model
+pytest -s -v -m "core_model and L4 and not cards_1" --run-level=core_model
 ```
 
 ### Concrete e2e paths (common in-tree)

.github/workflows/pre-commit.yml

@@ -27,4 +27,4 @@ jobs:
         # Don't run historical-debt hooks against the whole tree in CI yet.
         # They still run locally on changed files.
         env:
-          SKIP: check-test-ci-coverage,markdownlint-cli2,shellcheck,check-spdx-header,mypy-3.10
+          SKIP: check-mark,markdownlint-cli2,shellcheck,check-spdx-header,mypy-3.10

.pre-commit-config.yaml

@@ -105,8 +105,8 @@ repos:
       <<: *mypy_common
       stages: [manual]
 
-    - id: check-test-ci-coverage
-      name: Ensure test files have pytestmarks
+    - id: check-mark
+      name: Ensure test files have CI marks (no direct SKU)
       entry: python tools/pre_commit/check_test_marks.py
       language: python
       types: [python]

docker/Dockerfile.npu

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

docker/Dockerfile.npu.a3

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

docker/Dockerfile.npu.ci

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

docker/Dockerfile.npu.ci.a3

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

docs/contributing/README.md

@@ -49,7 +49,7 @@ vLLM-Omni's `pre-commit` hooks will now run automatically every time you commit.
     `--all-files` run stays green while historical debt is cleaned up. The
     current `SKIP` list in
     [`.github/workflows/pre-commit.yml`](https://github.com/vllm-project/vllm-omni/blob/main/.github/workflows/pre-commit.yml)
-    is `check-test-ci-coverage`, `markdownlint-cli2`, `shellcheck`,
+    is `check-mark`, `markdownlint-cli2`, `shellcheck`,
     `check-spdx-header`, and `mypy-3.10`. Those hooks still run on **your
     commit** for changed files. A passing GitHub pre-commit check does not mean
     they passed locally. Hooks **not** on that list (forbidden imports,
@@ -66,7 +66,7 @@ already there). `check-pickle-imports` is gone: pickle is now one rule inside
 | `markdownlint-cli2` | Markdown in `docs/`, `recipes/`, `README.md`, `CONTRIBUTING.md` (not `.claude/` / `.cursor/` / `CLAUDE.md`) | skipped |
 | `mypy-3.10` | Type-check changed `vllm_omni/` files (model trees excluded). `tests/` uses `--follow-imports skip` | skipped |
 | `mypy-3.11` / `3.12` / `3.13` | Same checker, extra Python versions | not installed; `pre-commit run --hook-stage manual mypy-3.12` |
-| `check-test-ci-coverage` | Every `tests/**/test_*.py` has a CI level mark and a hardware mark/helper | skipped |
+| `check-mark` | Every `tests/**/test_*.py` has a CI level mark and a hardware platform mark or helper; no direct `pytest.mark.H100` / SKU | skipped |
 | `check-tts-adapter-migration` | `self._tts_model_type` branches in `serving_speech.py` must not increase | runs |
 | `shellcheck` | `*.sh` quoting / undefined vars | skipped |
 | `check-spdx-header` | Omni SPDX header on `.py` / `.pyi` / `.sh` / `.rs` / `.proto` | skipped |
@@ -144,11 +144,13 @@ pre-commit run --hook-stage manual mypy-3.12
 
 #### Test CI marks
 
-`check-test-ci-coverage` requires each collected test module under `tests/` to
+`check-mark` requires each collected test module under `tests/` to
 have at least one CI **level** mark (`core_model`, `advanced_model`,
-`full_model`, `local_model`, or `slow`) and a **hardware** mark (`cpu`, `cuda`,
-`H100`, …) or helper (`hardware_test(` / `hardware_marks(`). GitHub Actions
-skips this hook; local commit does not. See the
+`full_model`, `local_model`, or `slow`) and a **hardware** platform mark
+(`cpu`, `cuda`, …) or helper (`hardware_test(` / `hardware_marks(`). SKU marks
+(`H100`, `L4`, …) must come from those helpers so `cards_{n}` is attached;
+direct `pytest.mark.H100` is rejected. GitHub Actions skips this hook; local
+commit does not. See the
 [test writing guide](./ci/test_writing_guide.md).
 
 #### TTS adapter ratchet

docs/contributing/ci/ci_settings.md

@@ -402,7 +402,7 @@ loads the model once per mode.
 Before splitting, confirm each half actually collects at least one test under the job's forwarded filters — pytest exits with code 5 ("no tests collected") if a half is empty, which the script reports as a failure, red-ing a job that used to pass as one combined invocation.
 
 Also check the tests' `num_cards` against the job's `mirror_hardwares` preset.
-`cuda_marks` turns `num_cards > 1` into `skipif(device_count() < num_cards)`, so a
+`_cuda_marks` turns `num_cards > 1` into `skipif(device_count() < num_cards)`, so a
 test asking for more GPUs than the preset provides is silently skipped and its
 coverage artifact comes back empty. Splitting such a job produces a file that
 looks fine and measures nothing — "Diffusion · Z Image Test" is the current

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

@@ -81,8 +81,8 @@ Supported form:
 
 | Form | Example | Effect |
 | ---- | ------- | ------ |
-| Array (single platform) | `"mark": [{"hardware_marks": {"res": {"cuda": "H100"}, "num_cards": 2}}, "full_model", "diffusion"]` | Hardware marks from `hardware_marks(...)`; `num_cards` > 1 adds `distributed_*` (+ CUDA `skipif_cuda` when applicable). String entries become extra pytest marks. |
-| Array (multi-platform) | `"mark": [{"hardware_marks": {"res": {"cuda": "H100", "rocm": "MI325", "npu": "A2"}, "num_cards": {"cuda": 2, "rocm": 2, "npu": 1}}}, "full_model", "omni"]` | Same as above, but declares **multiple platforms** in one case. Filter examples: `-m "full_model and H100 and cuda"`, `-m "full_model and MI325 and rocm"`. |
+| Array (single platform) | `"mark": [{"hardware_marks": {"res": {"cuda": "H100"}, "num_cards": 2}}, "full_model", "diffusion"]` | Hardware marks from `hardware_marks(...)`; `num_cards` adds `cards_{n}` (including `cards_1` when omitted). String entries become extra pytest marks. |
+| Array (multi-platform) | `"mark": [{"hardware_marks": {"res": {"cuda": "H100", "rocm": "MI325"}, "num_cards": 2}}, "full_model", "omni"]` | Same as above, but declares **multiple platforms** on one item. All platforms must share the same `num_cards` (int or identical dict values). Different counts need `@hardware_test` or one `hardware_marks()` per `pytest.param`. Filter examples: `-m "full_model and H100 and cards_2 and cuda"`, `-m "full_model and MI325 and cards_2 and rocm"`. |
 
 Recommended for L4 perf cases:
 
@@ -157,7 +157,7 @@ pytest -s -v tests/dfx/perf/scripts/run_benchmark.py \
 ```
 
 <!-- markdownlint-disable-next-line MD051 -->
-See also [Markers for Tests](#markers-for-tests) for registered hardware markers (`H100`, `L4`, `cuda`, `distributed_cuda`, …).
+See also [Markers for Tests](#markers-for-tests) for registered hardware markers (`H100`, `L4`, `cuda`, `cards_1`, …).
 
 #### `server_params` Configuration
 

docs/contributing/ci/test_execution_guide.md

@@ -88,7 +88,7 @@ Failed jobs: 1/2
     ```bash
     cd tests
     pytest -s -v test_xxxx.py --run-level=core_model
-    pytest -s -v -m "core_model and distributed_cuda and L4" --run-level=core_model
+    pytest -s -v -m "core_model and L4 and not cards_1" --run-level=core_model
     ```
 
 === "L3 level"
@@ -119,7 +119,7 @@ Failed jobs: 1/2
 
     ```bash
     pytest -s -v test_xxxx.py --run-level=advanced_model
-    pytest -s -v -m "advanced_model and distributed_cuda and L4" --run-level=advanced_model
+    pytest -s -v -m "advanced_model and L4 and not cards_1" --run-level=advanced_model
     ```
 
 === "L4 level"
@@ -153,12 +153,12 @@ Failed jobs: 1/2
     ```bash
     cd tests
     pytest -s -v test_xxxx.py --run-level=full_model
-    pytest -s -v -m "full_model and distributed_cuda and L4" --run-level=full_model
+    pytest -s -v -m "full_model and L4 and not cards_1" --run-level=full_model
     pytest -s -v -m "full_model and (omni or tts) and H100" --run-level=full_model
     ```
     If you only want to run specific test cases on a particular platform, you can use:
     ```bash
-    pytest -s -v -m "full_model and distributed_cuda and L4"  --run-level=full_model
+    pytest -s -v -m "full_model and L4 and not cards_1"  --run-level=full_model
     ```
     Note: ``run_benchmark.py`` and ``run_diffusion_benchmark.py`` accept an optional ``--test-config-file``. If omitted, each loads every ``*.json`` under ``tests/dfx/perf/tests/`` (omni/tts vs diffusion split by ``is_diffusion_perf_config``) and pytest ``-m`` filters by each case's JSON ``mark``:
     ```bash

docs/contributing/ci/test_writing_guide.md

@@ -48,17 +48,12 @@ Defined in `pyproject.toml`:
 | `MI325`            | Tests that require MI325 GPU (AMD/ROCm) *                                                               |
 | `A2`               | Tests that require A2 NPU *                                                                             |
 | `A3`               | Tests that require A3 NPU *                                                                             |
-| `distributed_cuda` | Tests that require multi cards on CUDA platform *                                                       |
-| `distributed_rocm` | Tests that require multi cards on ROCm platform  *                                                      |
-| `distributed_npu`  | Tests that require multi cards on NPU platform  *                                                       |
-| `skipif_cuda`      | Skip if the num of CUDA cards is less than the required *                                               |
-| `skipif_rocm`      | Skip if the num of ROCm cards is less than the required *                                               |
-| `skipif_npu`       | Skip if the num of NPU cards is less than the required *                                                |
+| `cards_{n}`        | Tests that require *n* accelerator cards (`cards_1` … `cards_8`); auto-added from `num_cards` *         |
 | `slow`             | Slow tests (may skip in quick CI)                                                                       |
 | `benchmark`        | Benchmark tests (decorator on runner test functions; perf JSON uses `full_model` + type marker instead) |
 | `local_model`      | Tests requiring local / non-HF-hub model weights                                                        |
 
-\* Means those markers are auto-added by `@hardware_test` (parametrization decorator) or `hardware_marks` (only returning the list of marks for flexibility).
+\* Means those markers are auto-added by `@hardware_test` (parametrization decorator) or `hardware_marks` (only returning the list of marks for flexibility). Do **not** write `pytest.mark.H100` / `pytest.mark.L4` (or other SKU names) by hand: `check-mark` rejects that so `cards_{n}` cannot be skipped.
 
 #### Example usage for markers
 
@@ -81,25 +76,23 @@ def test_video_to_audio()
 This decorator is intended to make hardware-aware, cross-platform test authoring easier and more robust for CI/CD environments. The `hardware_test` decorator in `vllm-omni/tests/helpers/mark.py` performs the following actions:
 
 1. **Applies platform and resource markers**  
-   Adds the appropriate pytest markers for each specified hardware platform (e.g., `cuda`, `rocm`, `xpu`, `npu`) and resource type (e.g., `L4`, `H100`, `MI325`, `B60`, `A2`, `A3`).
+   Adds the appropriate pytest markers for each specified hardware platform (e.g., `cuda`, `rocm`, `xpu`, `npu`) and resource type (e.g., `L4`, `H100`, `H200`, `B200`, `MI325`, `B60`, `A2`, `A3`).
    ```python
    @pytest.mark.cuda
    @pytest.mark.L4
    ```
-2. **Handles multi-card (distributed) scenarios**  
-   For tests requiring multiple cards, it automatically adds distributed markers such as `distributed_cuda`, `distributed_rocm`, or `distributed_npu`.
-   ```python
-   @pytest.mark.distributed_cuda(num_cards=num_cards)
-   ```
+2. **Handles card-count filtering**  
+   Adds a filterable marker `cards_{n}` (including `cards_1` when `num_cards` is omitted / 1). Use this with the SKU and platform, e.g. `-m "H100 and cards_4 and cuda"` vs `-m "H100 and cards_2 and cuda"`. Any multi-card CUDA test is `-m "cuda and not cards_1"`.
+
+   When `res` lists more than one platform **and** `num_cards` differs by platform, `@hardware_test` collects **one variant per platform** so those `cards_{n}` marks do not stack. `-m "H100 and cards_4 and cuda"` then selects only the CUDA/H100 4-card variant, not a ROCm 2-card sibling. The same count on every platform stays a single item.
 3. **Supports flexible card requirements**  
    Accepts `num_cards` as either a single integer for all platforms or as a dictionary with per-platform values. If not specified, defaults to 1 card per platform.
 
 4. **Integrates resource validation**  
-   On CUDA, adds a skip marker (`skipif_cuda`) if the system does not have the required number of devices.
-   Support for `skipif_rocm` and `skipif_npu` will be implemented later.
+   On CUDA and XPU, when `num_cards > 1`, attaches pytest's built-in `skipif` if the machine has fewer devices than requested. This is not a named `skipif_*` marker and is not used for `-m` filtering.
 
 5. **Works with pytest filtering**  
-   Allows tests to be filtered and selected at runtime using standard pytest marker expressions (e.g., `-m "distributed_cuda and L4"`).
+   Allows tests to be filtered and selected at runtime using standard pytest marker expressions (e.g., `-m "H100 and cards_2"`, `-m "L4 and not cards_1"`).
 
 ##### Example usage for decorator
 
@@ -117,13 +110,14 @@ This decorator is intended to make hardware-aware, cross-platform test authoring
         num_cards=2,
     )
     ```
-- `res` must be a dict; supported resources: CUDA (L4/H100), ROCm (MI325), XPU (B60), MUSA (S5000), NPU (A2/A3)
+- `res` must be a dict. SKU values come from `pyproject.toml` markers tagged `[hardware-resource]` plus the platform key (`[cuda]`, `[rocm]`, `[xpu]`, `[npu]`, `[musa]`).
 - `num_cards` can be int (all platforms) or dict (per platform); defaults to 1 when missing
-- Distributed markers (`distributed_cuda`, `distributed_rocm`, `distributed_npu`) are auto-added for multi-card cases
 - Filtering examples:
-    - CUDA only: `pytest -m "distributed_cuda and L4"`
-    - ROCm only: `pytest -m "distributed_rocm and MI325"`
-    - NPU only: `pytest -m "distributed_npu"`
+    - CUDA H100 4-card: `pytest -m "H100 and cards_4 and cuda"`
+    - CUDA H100 2-card: `pytest -m "H100 and cards_2 and cuda"`
+    - CUDA multi-card (any count): `pytest -m "L4 and not cards_1"`
+    - ROCm multi-card: `pytest -m "MI325 and not cards_1"`
+    - NPU multi-card: `pytest -m "npu and not cards_1"`
 
 #### Function: `hardware_marks`
 
@@ -179,37 +173,30 @@ If you want to add support for a new platform (e.g., "tpu" for a new accelerator
    [tool.pytest.ini_options]
    markers = [
        # ... existing markers ...
-       "tpu: Tests that require TPU device",
-       "TPU_V3: Tests that require TPU v3 hardware",
-       "distributed_tpu: Tests that require multiple TPU devices",
+       "tpu: [hardware-platform] Tests that require TPU device",
+       # Add [gpu] next to [hardware-platform] if this platform should also get pytest.mark.gpu.
+       "TPU_V3: [hardware-resource] [tpu] Tests that require TPU v3 hardware",
+       "cards_1: [hardware-cards] Tests that require 1 accelerator card",
+       "cards_2: [hardware-cards] Tests that require 2 accelerator cards",
    ]
    ```
+   `check-mark` treats `[hardware-platform]` names (including `cpu`/`gpu`) as allowed `pytest.mark.<name>` values. `hardware_test(res=...)` still rejects `cpu`/`gpu` as `res` keys.
 2. **Implement a marker construction function for your platform** in `vllm-omni/tests/helpers/mark.py`:
    ```python
    # In vllm-omni/tests/helpers/mark.py
 
-   def tpu_marks(*, res: str, num_cards: int):
-       test_platform = pytest.mark.tpu
-       if res == "TPU_V3":
-           test_resource = pytest.mark.TPU_V3
-       else:
-           raise ValueError(
-               f"Invalid TPU resource type: {res}. Supported: TPU_V3")
-
-       if num_cards == 1:
-           return [test_platform, test_resource]
-       else:
-           test_distributed = pytest.mark.distributed_tpu(num_cards=num_cards)
-           # Optionally: add skipif_tpu when implemented
-           return [test_platform, test_resource, test_distributed]
+   def _tpu_marks(*, res: str, num_cards: int):
+       _require_sku("tpu", res)
+       return [pytest.mark.tpu, getattr(pytest.mark, res), _cards_mark(num_cards)]
    ```
-3. **Update `hardware_marks` to recognize your new platform**:
-    In the relevant place (see the `hardware_marks` implementation), add:
+3. **Register the builder** in `_marks_for_platform`:
     ```python
-    if platform == "tpu":
-        marks = tpu_marks(res=resource, num_cards=cards)
+    builders = {
+        # ... existing platforms ...
+        "tpu": _tpu_marks,
+    }
     ```
-    (`hardware_test` calls `hardware_marks` internally, so both will pick up the change.)
+    (`hardware_test` / `hardware_marks` both go through `_marks_for_platform`.)
 4. **(Recommended) Add a test using your new markers**:
    ```python
    @hardware_test(
@@ -223,11 +210,11 @@ If you want to add support for a new platform (e.g., "tpu" for a new accelerator
 **Summary**:  
 
 - Add pytest markers for your new platform/resources  
-- Implement a marker function (`xxx_marks`)  
-- Plug into `hardware_marks`  
-- You're done: tests using `@hardware_test` or `hardware_marks` with your platform now automatically get the correct markers, distribution, and isolation!
+- Implement a private marker function (`_xxx_marks`)
+- Register it in `_marks_for_platform`
+- You're done: tests using `@hardware_test` or `hardware_marks` with your platform now automatically get the correct markers and `cards_{n}` filtering.
 
-See code in `vllm-omni/tests/helpers/mark.py` for existing examples (`cuda_marks`, `rocm_marks`, `npu_marks`).
+See code in `vllm-omni/tests/helpers/mark.py` for existing examples (`_cuda_marks`, `_rocm_marks`, `_npu_marks`).
 
 ## Test case style
 

docs/contributing/model/adding_tts_model.md

@@ -438,7 +438,7 @@ selects the scheduler and worker family:
 Key topology fields belong to `StagePipelineConfig`:
 
 | Field | Description |
-|-------|-------------|
+| ------- | ------------- |
 | `model_stage` | Logical stage name (`ar_stage`, `decoder`, etc.). |
 | `execution_type` | Runtime family for this stage. |
 | `input_sources` | Upstream stage IDs that provide input. |
@@ -960,7 +960,7 @@ Common failures and fixes:
 | `check-forbidden-imports` | Stdlib `re`/`base64`, pickle, Hugging Face Hub API, or direct Triton/TileLang | `import regex as re` and `pybase64`; do not grow the allowlist without review |
 | `check-torch-cuda-call` | New `torch.cuda.*` call site | Use `current_omni_platform`; do not grow `ALLOWED_FILES` without review |
 | `check-tts-adapter-migration` | New `_tts_model_type` branch in `serving_speech.py` | Put logic in `tts_adapters/`; lower `MAX_MODEL_TYPE_BRANCHES` when removing branches |
-| `check-test-ci-coverage` | Test file missing level or hardware mark | Add `core_model`/… plus `cpu`/`cuda`/`hardware_test(` |
+| `check-mark` | Test file missing level/hardware mark, or direct `pytest.mark.H100` | Add `core_model`/… plus `cpu`/`cuda`/`hardware_test(`; SKU marks only via helpers |
 | `shellcheck` | Native `shellcheck` missing, or script warning | Install via apt/dnf/brew (or `shellcheck.exe` on Windows). See [Linting](../README.md#linting) |
 | `mypy-3.10` / `markdownlint-cli2` / `check-buildkite` | Types, docs markdown, or Buildkite YAML | See [Linting](../README.md#linting) |
 

docs/design/feature/offloader/distributed_layerwise_offload.md

@@ -26,6 +26,8 @@ Legend: ✅ supported, ⚠️ compatibility path or limited validation, ❌ unsu
 | **TP > 1** | ⚠️ Ordinary TP-aware loader only; no direct checkpoint mmap. | ⚠️ Ordinary TP-aware loader only; no direct checkpoint mmap. |
 | **HSDP** | ❌ Rejected to avoid double-sharding parameters. | ⚠️ Limited end-to-end coverage. |
 | **Per-tensor online FP8 linears** | ✅ Ordinary loader finalizes weights and scales before DLO sharding. | ✅ Ordinary loader retains complete rank-local tensors. |
+| **Online INT8 linears** | ✅ Ordinary loader finalizes the int8 weight (contiguous or transposed view) and fp32 scale before DLO sharding. | ✅ Ordinary loader retains complete rank-local tensors. |
+| **Online MXFP8 linears** | ✅ Ordinary loader finalizes the fp8 weight (contiguous) and e8m0 block scale (contiguous or transposed view) before DLO sharding. | ✅ Ordinary loader retains complete rank-local tensors. |
 | **Other online quantization methods** | ❌ Rejected until runtime packing and scale layouts are validated. | ⚠️ Allowed through the ordinary loader; validation is method-specific. |
 | **Model-level or standard layerwise CPU offload** | ❌ Disabled because DLO takes priority. | ❌ Disabled because DLO takes priority. |
 | **Resident leading layers** | ❌ Rejected. | ✅ Requires eligible resident paths in the model's `OffloadPlan`. |
@@ -438,11 +440,15 @@ requirements.
 
 Direct checkpoint mmap can back either transfer path. It is currently limited
 to proven TP1, non-HSDP, non-online-quantized layouts. Other layouts use the
-ordinary loader. Per-tensor online FP8 linears can use DLO AllGather after the
-ordinary loader finalizes their runtime weights and scales; DLO then shards and
-reconstructs those tensors with their recorded layouts. Other online methods
-must use `--dlo-no-use-allgather` or disable online quantization until their
-runtime layouts are validated.
+ordinary loader. Per-tensor online FP8, online INT8, and online MXFP8 linears
+can use DLO AllGather after the ordinary loader finalizes their runtime
+weights and scales; DLO then shards and reconstructs those tensors with their
+recorded layouts (they all keep plain 1-byte dtypes over ordinary strided
+views: online INT8 has an int8 weight plus fp32 scale, online MXFP8 has an
+fp8 weight plus e8m0 block scale, each with a contiguous or transposed-view
+finalized tensor, all covered by the same physical-order packing as online
+FP8). Other online methods must use `--dlo-no-use-allgather` or disable
+online quantization until their runtime layouts are validated.
 
 The Host Weight Runtime representation, publication, and no-AllGather consumer
 contracts are merged; see
@@ -459,8 +465,8 @@ Current source-level validation includes:
 - HSDP + DLO without AllGather acceptance at configuration level;
 - loader preflight fallback for TP, HSDP, online quantization, unknown custom
   loaders, missing keys, and shape/dtype mismatches;
-- ordinary-loader fallback for per-tensor online FP8 linears followed by DLO
-  sharding of finalized weights and scales;
+- ordinary-loader fallback for per-tensor online FP8, online INT8, and online
+  MXFP8 linears followed by DLO sharding of finalized weights and scales;
 - exact loader-to-backend plan transfer and ordinary-loader fallback;
 - ordered publication hashing, overlapped durability, unordered parallel
   checksum fallback, and node-local source-digest reuse/invalidation;

docs/design/feature/vae_parallel.md

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

docs/design/module/engine_orchestration.md

@@ -37,6 +37,7 @@ validation_paths:
   - tests/engine/test_async_omni_engine_input.py
   - tests/engine/test_async_omni_engine_outputs.py
   - tests/engine/test_async_omni_engine_abort.py
+  - tests/e2e/offline_inference/test_qwen3_omni.py
   - tests/engine/test_async_omni_engine_stage_init.py
   - tests/engine/test_orchestrator.py
   - tests/engine/test_orchestrator_error_handling.py
@@ -113,6 +114,15 @@ Numbers become append-only after normative promotion.
 
 Test routing, output ordering, queue correlation, cancellation, failure
 propagation, shutdown ordering, and representative multi-stage execution.
+AR abort of a final-stage LLM request must deliver a terminal output with
+`finish_reason="abort"` and the generated prefix so collocated training can
+resume; diffusion abort remains whole-sample retry. Aborting a parallel-sampling
+child must drop `parent_requests` once no children remain. EngineCore control
+RPCs (`sleep` / `wake_up` / `pause_scheduler` / `resume_scheduler`) must
+propagate worker exceptions rather than returning `{"supported": False}`.
+When a request has multiple final output stages, only the last abort message
+is request-terminal. Output-processor abort state is committed only after the
+physical EngineCore abort succeeds.
 Changes that cross into stage runtime or public error behavior require review
 from that contract's owners.
 

docs/design/module/entrypoints.md

@@ -43,6 +43,7 @@ depends_on:
 validation_paths:
   - tests/entrypoints/test_omni_entrypoints.py
   - tests/entrypoints/test_async_omni.py
+  - tests/entrypoints/test_async_omni_pause_sleep_routing.py
   - tests/entrypoints/test_async_omni_duplex.py
   - tests/entrypoints/test_serve.py
   - tests/entrypoints/test_stream_finish_reason.py
@@ -119,7 +120,13 @@ promotion.
 
 Test request validation, protocol conversion, model-adapter routing,
 streaming, session identity, disconnect/cancellation, and error mapping for
-each affected offline or serving entrypoint.
+each affected offline or serving entrypoint. Sleep must wait for in-flight
+`generate()` admission before EngineCore offload; `wake_up` does not resume
+admission — callers must `resume_generation()`. Sleeping tags are tracked per
+stage so `wake_up(stage_ids=[0])` does not skip a later `wake_up(stage_ids=[1])`.
+Streaming input pumps take an admission slot immediately before each EngineCore
+ADD or update, not while waiting for the next client chunk. Frontend abort
+keeps `request_states` until `generate()` consumes the terminal output.
 
 ## Promotion gate
 

docs/user_guide/diffusion/attention_backends/rainfusion.md

@@ -17,6 +17,8 @@ globally.
 | --- | --- | --- |
 | `sparsity` | finite, `[0, 1]` | Nominal dropped-key-block fraction; default `0.8`; `0` disables sparsity |
 | `start_step` | integer, `>= 0` | Number of early denoise steps kept dense |
+| `end_step` | integer, `>= 0` | Number of final denoise steps kept dense (tail fallback) |
+| `precision` | `"bf16"`, `"fp8"`, `"mix"` | Kernel precision mode; default `"bf16"`. Requires MindIE-SD with `sparse_attention(precision=...)` |
 | `skip_layers` | selector such as `"0-3,38"` | DiT blocks kept dense |
 
 ```bash
@@ -52,6 +54,12 @@ platform raises. It is incompatible with ring sequence parallelism because
 the kernel needs the complete key sequence for ranking; use Ulysses sequence
 parallelism with `ring_degree=1`.
 
+The `precision` knob requires a MindIE-SD release whose `sparse_attention`
+accepts `precision=`; older releases accept it through `**kwargs` but
+silently ignore it and run the BF16 path. `RAINFUSION_ATTN` raises at runtime
+when a non-`bf16` precision is requested without that support, so pin a
+compatible MindIE-SD release when using `precision="mix"` or `"fp8"`.
+
 ## Geometry handling
 
 RainFusion handles arbitrary video grids by rearranging video spatially and,

docs/user_guide/diffusion/lora.md

@@ -12,15 +12,17 @@ LoRA adapters are lightweight, model-specific fine-tuning weights that can be ap
 ## LoRA Adapter Format
 
 ### PEFT (Parameter-Efficient Fine-Tuning) format (default)
+
 A typical PEFT-format LoRA adapter directory structure:
 
-```
+```text
 lora_adapter/
 ├── adapter_config.json
 └── adapter_model.safetensors
 ```
 
 The `adapter_config.json` file contains metadata about the LoRA adapter, including:
+
 - `r`: LoRA rank
 - `lora_alpha`: LoRA alpha scaling factor
 - `target_modules`: List of module names to apply LoRA to
@@ -67,7 +69,7 @@ For distilled few-step LoRAs, pass `lora_backend="distill"` together with one or
 For Qwen-Image and Wan pipelines, the distill backend calls `pipeline.load_lora_weights(...)` during worker initialization.
 
 | Pipeline | Supported distilled LoRA repo | Notes |
-|----------|--------------------------------|-------|
+| ---------- | -------------------------------- | ------- |
 | `QwenImagePipeline` | `lightx2v/Qwen-Image-2512-Lightning` | Used with Qwen-Image-2512 Lightning-style few-step inference. |
 | `Wan22Pipeline` | `lightx2v/Wan2.1-Distill-Loras`, `lightx2v/Wan2.2-Distill-Loras` | Wan2.1 uses one LoRA file. For dual-transformer Wan2.2 MoE, pass high-noise then low-noise LoRA files. |
 | `Wan22I2VPipeline` | `lightx2v/Wan2.2-Distill-Loras` | For dual-transformer Wan2.2 MoE, pass high-noise then low-noise LoRA files. |
@@ -240,6 +242,37 @@ Notes:
 - Output quality and speed depend on the replacement checkpoints and sampling params you choose.
 - If you only need to fuse distilled LoRAs into a Wan2.2 checkpoint at load time (without the full LightX2V convert + assemble pipeline), you can instead pass them directly via `--lora-backend distill --lora-path <high>.safetensors <low>.safetensors`. See the [Distill backend](#distill-backend-fuse-distilled-lora-at-init) section above.
 
+## MiniMax-H3 adapter-declared schedules
+
+MiniMax-H3 supports two few-step mechanisms that must not be conflated:
+
+- **Checkpoint-pinned schedule**: a merged release writes `base_schedule` into
+  `model_index.json`. Requests must pass `num_inference_steps` as the interval
+  count (for example `4` for `[1.0, 0.7, 0.4, 0.15, 0.0]`).
+- **Runtime Turbo LoRA**: LightX2V Turbo artifacts request five sigma points and
+  enforce `flow_shift=6`, `audio_flow_shift=3`.
+- **Runtime native LoRA**: FlashGen-style artifacts declare
+  `key_format=minimax-h3-native` and embed `base_schedule` in safetensors
+  metadata. When active, the adapter schedule overrides the base checkpoint and
+  requests must use the interval-count contract (`num_inference_steps=4`).
+  Request-mode generation may omit the field and take the count from the adapter
+  schedule; step execution requires it explicitly, because the step scheduler
+  derives the total step count from the request before the adapter schedule is
+  known.
+
+Native artifacts also declare `qkv_layout=grouped`. The H3 loader reorders fused
+`qkv_proj` LoRA rows with the same `_reorder_grouped_qkv_to_qkv` helper used for
+base-weight loading, then binds the packed Q/K/V slices through the legacy PEFT
+manager without modifying `DiffusionLoRAManager`.
+
+Both runtime adapters run with distributed layerwise offload in request-mode
+generation, where the manager keeps the LoRA A/B buffers resident on the compute
+device while DLO streams the base blocks. MiniMax-H3 step execution still
+rejects DLO, so `--step-execution` cannot be combined with
+`--enable-distributed-layerwise-offload`. Model-level CPU offload and standard
+layerwise offload are rejected in every mode, because the dynamic LoRA tensors
+are neither parameters nor registered buffers and therefore do not participate
+in those weight lifecycles.
 
 ## See Also
 

examples/online_serving/minicpmo/realtime_duplex_demo.py

@@ -155,6 +155,23 @@ def _response_in_progress(events: list[dict[str, object]]) -> bool:
     )
 
 
+async def _commit_input_after_playback_checkpoint(client: RealtimeDuplexClient) -> float:
+    """Commit input only after preserving already-played assistant history.
+
+    Native duplex can finish several responses while the user is still
+    streaming.  Under the ``ack_only`` playback policy, acknowledging those
+    responses after the final input commit is intentionally rejected: it could
+    append an old assistant message after the newly committed user message.
+    Checkpoint playback first so the server commits each assistant item at its
+    original history position.  A second acknowledgement after the commit can
+    then safely advance response-local playback that drained in the meantime.
+    """
+    await client.acknowledge_playback()
+    commit_sent_at_s = time.monotonic()
+    await client.commit()
+    return commit_sent_at_s
+
+
 def _event_count_after(
     events: list[dict[str, object]],
     event_type: str,
@@ -448,8 +465,7 @@ async def run_demo(args: argparse.Namespace) -> dict[str, object]:
             chunk_period_ms=_chunk_period_ms(client.events.events),
         )
         wait_for_post_commit_decision = False
-        commit_sent_at_s = time.monotonic()
-        await client.commit()
+        commit_sent_at_s = await _commit_input_after_playback_checkpoint(client)
         wait_error: str | None = None
         committed_index: int | None = None
         post_commit_decision: str | None = None

pyproject.toml

@@ -248,11 +248,11 @@ addopts = [
     "--dist=loadgroup"
 ]
 markers = [
-    # ci/cd required
-    "core_model: L1&L2 tests (run in each PR)",
-    "advanced_model: L3 level tests (run on each merge)",
-    "full_model: L4 level tests (run nightly)",
-    "local_model: tests (run locally)",
+    # ci/cd required — tag with [ci-level] for tests.helpers.mark.get_level_markers().
+    "core_model: [ci-level] L1&L2 tests (run in each PR)",
+    "advanced_model: [ci-level] L3 level tests (run on each merge)",
+    "full_model: [ci-level] L4 level tests (run nightly)",
+    "local_model: [ci-level] tests (run locally)",
     # function module markers
     "diffusion: Diffusion model tests",
     "omni: Omni model tests",
@@ -262,38 +262,41 @@ markers = [
     "sp: Sequence parallelism tests",
     "example: Doc example code tests",
     # platform markers
-    "cpu: Tests that run on CPU",
-    "gpu: Tests that run on GPU (auto-added)",
-    "cuda: Tests that run on CUDA (auto-added)",
-    "rocm: Tests that run on AMD/ROCm (auto-added)",
-    "xpu: Tests that run on XPU (auto-added)",
-    "npu: Tests that run on NPU/Ascend (auto-added)",
-    "musa: Tests that run on MUSA/Moore Threads (auto-added)",
+    # Tag with [hardware-platform] for tests.helpers.mark.get_supported_platforms().
+    # Add [gpu] on accelerator platforms that should also get pytest.mark.gpu.
+    # cpu/gpu are pytest marks; hardware_test(res=...) rejects them as keys.
+    "cpu: [hardware-platform] Tests that run on CPU",
+    "gpu: [hardware-platform] Tests that run on GPU (auto-added)",
+    "cuda: [hardware-platform] [gpu] Tests that run on CUDA (auto-added)",
+    "rocm: [hardware-platform] [gpu] Tests that run on AMD/ROCm (auto-added)",
+    "xpu: [hardware-platform] [gpu] Tests that run on XPU (auto-added)",
+    "npu: [hardware-platform] Tests that run on NPU/Ascend (auto-added)",
+    "musa: [hardware-platform] [gpu] Tests that run on MUSA/Moore Threads (auto-added)",
     # specified computation resources marks (auto-added)
-    # Tag with [hardware-resource] for tests.helpers.mark.get_hardware_mark_list().
-    "H100: [hardware-resource] Tests that require H100 GPU",
-    "H800: [hardware-resource] Tests that require H800 GPU",
-    "H200: [hardware-resource] Tests that require H200 GPU",
-    "L4: [hardware-resource] Tests that require L4 GPU",
-    "B200: [hardware-resource] Tests that require B200 GPU",
-    "MI325: [hardware-resource] Tests that require MI325 GPU (AMD/ROCm)",
-    "B60: [hardware-resource] Tests that require Intel Arc Pro B60 XPU",
-    "S5000: [hardware-resource] Tests that require S5000 GPU (Moore Threads/MUSA)",
-    "A2: [hardware-resource] Tests that require A2 NPU",
-    "A3: [hardware-resource] Tests that require A3 NPU",
-    "distributed_cuda: Tests that require multi cards on CUDA platform",
-    "distributed_rocm: Tests that require multi cards on ROCm platform",
-    "distributed_xpu: Tests that require multi cards on XPU platform",
-    "distributed_npu: Tests that require multi cards on NPU platform",
-    "distributed_musa: Tests that require multi cards on MUSA platform",
-    "skipif_cuda: Skip if the num of CUDA cards is less than the required",
-    "skipif_rocm: Skip if the num of ROCm cards is less than the required",
-    "skipif_xpu: Skip if the num of XPU cards is less than the required",
-    "skipif_npu: Skip if the num of NPU cards is less than the required",
-    "skipif_musa: Skip if the num of MUSA cards is less than the required",
+    # [hardware-resource] + [cuda]/[rocm]/[xpu]/[npu]/[musa] for hardware_test(res=...).
+    # Do not put [gpu] on SKU lines: that would make gpu a res key.
+    "H100: [hardware-resource] [cuda] Tests that require H100 GPU",
+    "H800: [hardware-resource] [cuda] Tests that require H800 GPU",
+    "H200: [hardware-resource] [cuda] Tests that require H200 GPU",
+    "L4: [hardware-resource] [cuda] Tests that require L4 GPU",
+    "B200: [hardware-resource] [cuda] Tests that require B200 GPU",
+    "MI325: [hardware-resource] [rocm] Tests that require MI325 GPU (AMD/ROCm)",
+    "B60: [hardware-resource] [xpu] Tests that require Intel Arc Pro B60 XPU",
+    "S5000: [hardware-resource] [musa] Tests that require S5000 GPU (Moore Threads/MUSA)",
+    "A2: [hardware-resource] [npu] Tests that require A2 NPU",
+    "A3: [hardware-resource] [npu] Tests that require A3 NPU",
+    # Tag with [hardware-cards] for tests.helpers.mark.get_supported_card_counts().
+    "cards_1: [hardware-cards] Tests that require 1 accelerator card",
+    "cards_2: [hardware-cards] Tests that require 2 accelerator cards",
+    "cards_3: [hardware-cards] Tests that require 3 accelerator cards",
+    "cards_4: [hardware-cards] Tests that require 4 accelerator cards",
+    "cards_5: [hardware-cards] Tests that require 5 accelerator cards",
+    "cards_6: [hardware-cards] Tests that require 6 accelerator cards",
+    "cards_7: [hardware-cards] Tests that require 7 accelerator cards",
+    "cards_8: [hardware-cards] Tests that require 8 accelerator cards",
     # more detailed markers
     "sp: Sequence parallelism tests (multi-GPU)",
-    "slow: Slow tests (may skip in quick CI)",
+    "slow: [ci-level] Slow tests (may skip in quick CI)",
     "benchmark: Benchmark tests",
     "xdist: xdist-parallelizable tests",
 ]

recipes/MiniMaxAI/MiniMax-H3.md

@@ -69,11 +69,12 @@ reference-video preparation and MP4 output.
 
 For CUDA and ROCm deployments, non-streaming MP4 responses are encoded on the
 host CPU through PyAV/libx264 after generation. The response encoder selects the
-path automatically at runtime: inputs with a supported frame shape, common dtype,
-and RGB per-channel contiguous layout use direct planar frames; other inputs
-fall back to the legacy path before the PyAV container is opened. No CLI flag,
-model declaration, or user configuration is required. Streaming fMP4 output is
-unchanged.
+path automatically at runtime. The server-owned parallel converter accepts
+supported frame shapes and dtypes with either per-channel-contiguous or strided
+RGB planes, including interleaved arrays materialized by output transport.
+Standalone callers without a parallel converter retain the legacy fallback for
+strided planes. No CLI flag, model declaration, or user configuration is
+required. Streaming fMP4 output is unchanged.
 
 A community benchmark on 2x Xeon 8480C reported the following comparison
 between the legacy and direct planar paths
@@ -860,6 +861,129 @@ fixed HBM usage. Model-level and standard layerwise offload remain unsupported.
 The five requested sigma points produce the four denoiser evaluations expected
 by the Turbo artifact.
 
+### FlashGen native LoRA
+
+The FlashGen 4-step T2VA artifact uses the native MiniMax-H3 module layout and
+declares its distilled sigma schedule in safetensors metadata. It is published on
+[ModelScope](https://modelscope.cn/models/FlashGen/Minimax-H3-4step-lora-flashgen):
+
+```text
+FlashGen/Minimax-H3-4step-lora-flashgen/minimax_h3_t2va_flashgen_4step_v1.0_768p_bf16.safetensors
+```
+
+Download only that file:
+
+```bash
+python -m pip install modelscope
+export FLASHGEN_DIR=/path/to/minimax-h3-flashgen-lora
+export FLASHGEN_FILE=minimax_h3_t2va_flashgen_4step_v1.0_768p_bf16.safetensors
+modelscope download FlashGen/Minimax-H3-4step-lora-flashgen \
+  --local_dir "${FLASHGEN_DIR}" \
+  --include "${FLASHGEN_FILE}"
+export FLASHGEN_LORA="${FLASHGEN_DIR}/${FLASHGEN_FILE}"
+```
+
+Start from a non-offloaded or DLO FL2VA server command and add
+`--task-type fl2va --lora-backend peft --lora-path "${FLASHGEN_LORA}"`.
+Each request must use T2VA and the distilled interval-count contract:
+
+```bash
+-F 'num_inference_steps=4' \
+-F 'extra_params={"task":"t2va","duration":5.2}' \
+-F "lora={\"name\":\"h3-flashgen-v1.0\",\"path\":\"${FLASHGEN_LORA}\",\"scale\":1.0}"
+```
+
+This path rejects Ref2VA and checkpoints that already pin `base_schedule` in
+`model_index.json`. The adapter metadata carries
+`base_schedule=1.0,0.7,0.4,0.15,0.0`, so `num_inference_steps=4` means four
+denoiser evaluations, not five sigma points. Request-mode generation may omit
+the field and take the count from the adapter schedule; `--step-execution`
+requires it explicitly, because the step scheduler reads the total step count
+off the request at admission, before the adapter schedule is known.
+
+DLO is supported in request-mode generation on the same terms as the Turbo
+adapter: the request-switchable LoRA A/B buffers stay resident on the
+accelerator while DLO streams only the base blocks, so budget for that
+additional fixed HBM usage. The native artifact is rank 64 over 259 target
+modules, and its packed `qkv_proj` and `fc1` layers reuse the full-input A
+tensor per slice while B carries slice-local output rows, so the resident
+footprint exceeds the on-disk payload; measure it for your parallel layout
+rather than assuming the checkpoint size. Pure Ulysses replicates the adapter
+on every rank, while DiT tensor parallelism shards the B buffers. Model-level
+and standard layerwise offload remain unsupported, and `--step-execution`
+cannot be combined with `--enable-distributed-layerwise-offload`.
+
+To validate a deployment, post the same fixed-seed T2VA request twice with the
+adapter and twice without it, then compare the four output digests. The adapter
+is bound and deterministic when each pair matches internally and the two pairs
+differ from each other.
+
+### FastH3 adapter
+
+[FastH3](https://haoailab.com/blogs/fasth3-preview/) is FastVideo's four-step
+DMD2 student of H3-Base. It reuses H3's text encoder, VAEs, tokenizers, and
+schedulers unchanged, replacing 49 denoiser evaluations with four. It is fused
+into the checkpoint at load time rather than switched per request, because it
+carries full-rank deltas that no LoRA layer can express.
+
+The bundle publishes four variants, so download one and point `--lora-path` at
+it; the repository root is refused rather than guessed at:
+
+```bash
+export FASTH3_DIR=/path/to/fasth3
+hf download FastVideo/FastVideo-FastH3-4-step-Preview-v1-LoRA \
+  dense-datafree/adapter_model.safetensors --local-dir "${FASTH3_DIR}"
+export FASTH3_LORA="${FASTH3_DIR}/dense-datafree/adapter_model.safetensors"
+```
+
+Add `--task-type fl2va --lora-path "${FASTH3_LORA}"` to a non-offloaded server
+command. T2VA is served by the FL2VA partition, so `--task-type fl2va` is
+correct even though FastH3 preview v1 distills T2VA only. Because the adapter is
+fused, `--lora-backend` does not apply and a request carrying a `lora=` field is
+rejected rather than served without the adapter it asked for.
+
+```bash
+-F 'num_inference_steps=4' \
+-F 'extra_params={"task":"t2va","duration":4.4}'
+```
+
+Requests must ask for `num_inference_steps=4` and `task=t2va`: the release's five
+sigma points bound four denoiser evaluations, and that count is what the step
+scheduler admits a request on. The server denoises on the release's own ladder,
+keeping H3's per-modality shifts at the checkpoint values, so a request that
+overrides `flow_shift` or `audio_flow_shift` is rejected - it would sample the
+student at noise levels it was never distilled at.
+
+Only a release that identifies itself as FastH3 is fused; any other
+`fastvideo-lora-v2` adapter stays on the dynamic LoRA route. A claimed artifact
+is then held to its own metadata: one that misdeclares its tensor counts or
+leaves a transformer block unedited is refused at startup instead of serving
+mostly base H3 weights on a four-step schedule. Offload is refused for the same
+reason - `--enable-cpu-offload`, `--enable-layerwise-offload` and
+`--enable-distributed-layerwise-offload` all bypass the fusion, so they fail fast.
+
+> [!NOTE]
+> Only the **Dense / Data-Free** variant is supported today. The three VSA
+> variants carry `.set_weight` compression gates for modules that do not exist
+> in vLLM-Omni's dense H3 attention, and the release states dense attention is
+> not a supported substitute for them, so they are refused at startup rather
+> than silently run wrong. Video Sparse Attention for H3's packed
+> `[text | cond | audio | video]` sequence is follow-up work.
+
+Measured on 8x NVIDIA B300 with USP8, VAE patch-parallel 8, `TRTLLM_ATTN`, at
+1344x768, 4.4 s, seed 1101, one warmup excluded and two runs recorded:
+
+| Adapter | Steps | End-to-end | Diffusion engine |
+| --- | ---: | ---: | ---: |
+| none (base H3) | 50 | 25.8 / 26.4 s | 16.22 / 16.28 s |
+| FastH3 Dense | 4 (5 sigma points) | 11.7 / 11.8 s | 2.37 / 2.36 s |
+
+The denoising speedup is 6.9x. End-to-end is 2.2x because text encoding, VAE
+decoding and muxing are a fixed cost that dominates a clip this short; longer
+generations move the end-to-end figure toward the denoising one. Fusing the
+adapter does not measurably change startup: weight loading took 77.3 s with it
+against 85.8 s without.
+
 ## Key parameters
 
 | Parameter | Recommended value | Notes |

recipes/Qwen/Qwen3-TTS.md

@@ -18,11 +18,11 @@ examples in this repository.
 
 Qwen3-TTS supports three task types, each backed by a dedicated model checkpoint:
 
-| Task Type     | Model                                    | Description                                                  |
-| ------------- | ---------------------------------------- | ------------------------------------------------------------ |
-| `CustomVoice` | `Qwen/Qwen3-TTS-12Hz-1.7B-CustomVoice`  | Predefined speaker voices with optional style/emotion control |
-| `VoiceDesign` | `Qwen/Qwen3-TTS-12Hz-1.7B-VoiceDesign`  | Generate speech from a natural language voice description     |
-| `Base`        | `Qwen/Qwen3-TTS-12Hz-1.7B-Base`         | Voice cloning from reference audio + transcript              |
+| Task Type     | Model                                    | Description                                                   |
+| ------------- | ---------------------------------------- | ------------------------------------------------------------- |
+| `CustomVoice` | `Qwen/Qwen3-TTS-12Hz-1.7B-CustomVoice`   | Predefined speaker voices with optional style/emotion control |
+| `VoiceDesign` | `Qwen/Qwen3-TTS-12Hz-1.7B-VoiceDesign`   | Generate speech from a natural language voice description     |
+| `Base`        | `Qwen/Qwen3-TTS-12Hz-1.7B-Base`          | Voice cloning from reference audio + transcript               |
 
 Smaller 0.6B variants are also available for `CustomVoice` and `Base`.
 
@@ -166,6 +166,15 @@ python examples/offline_inference/text_to_speech/qwen3_tts/end2end.py --query-ty
 - Key flags: `--omni` is required. `--deploy-config` points to the bundled two-stage pipeline config.
 - Async chunking: Enabled by default in `qwen3_tts.yaml` for streaming-friendly first-audio latency. Raw audio streaming requires `stream=true`, `stream_format="audio"`, and `response_format="pcm"`.
 - Task/model matching: Each task type requires its matching model checkpoint. Using a CustomVoice model for a Base (voice clone) request will fail.
+- Base codec termination: Base requests without an explicit `max_new_tokens`
+  use a text-scaled safety ceiling (at least 192 codec frames and no more than
+  the configured model limit). If the Talker reaches that ceiling without
+  codec EOS, non-streaming serving discards the incomplete audio and retries
+  once with a fresh seed; an explicit `seed` or `max_new_tokens` disables the
+  retry. SSE and WebSocket clients receive structured errors and must discard
+  previously emitted audio when the error contains `"action":"discard"`;
+  raw PCM streams terminate with a connection error after any partial bytes
+  already sent.
 - Known limitations: The server serves one model variant at a time. To switch task types (e.g., CustomVoice to Base), restart the server with the corresponding model.
 
 ## Hardware Support

tests/benchmarks/benchmark_video_response_encoding.py

@@ -1,5 +1,5 @@
 # SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
 """Benchmark legacy and automatic MP4 response encoding on synthetic input."""
 
 from __future__ import annotations
@@ -11,13 +11,16 @@ import statistics
 import time
 from collections.abc import Callable
 from dataclasses import dataclass
+from functools import partial
 from pathlib import Path
+from typing import cast
 
 import numpy as np
 
 from vllm_omni.entrypoints.openai.video_api_utils import (
     _encode_video_bytes,
     _encode_video_bytes_legacy,
+    _PlanarFrameConverter,
 )
 
 
@@ -42,12 +45,19 @@ def _build_inputs(
     fps: int,
     audio_sample_rate: int,
     seed: int,
+    input_layout: str = "planar-backed",
 ) -> tuple[np.ndarray, np.ndarray]:
     rng = np.random.default_rng(seed)
     planar = rng.random((3, frames, height, width), dtype=np.float32)
     video = planar.transpose(1, 2, 3, 0)
-    if not all(video[..., channel].flags.c_contiguous for channel in range(3)):
+    if input_layout == "interleaved":
+        video = np.ascontiguousarray(video)
+        if not video.flags.c_contiguous:
+            raise RuntimeError("synthetic video is not C-contiguous")
+    elif input_layout == "planar-backed" and not all(video[..., channel].flags.c_contiguous for channel in range(3)):
         raise RuntimeError("synthetic video does not expose contiguous channel planes")
+    elif input_layout != "planar-backed":
+        raise ValueError(f"unsupported input layout: {input_layout}")
 
     sample_count = max(1, round(frames / fps * audio_sample_rate))
     timeline = np.arange(sample_count, dtype=np.float32) / audio_sample_rate
@@ -86,8 +96,8 @@ def _measure(
 
 def _summarize(records: list[dict[str, object]], label: str) -> dict[str, object]:
     selected = [record for record in records if record["label"] == label]
-    wall_values = [float(record["wall_ms"]) for record in selected]
-    cpu_values = [float(record["process_cpu_ms"]) for record in selected]
+    wall_values = [cast(float, record["wall_ms"]) for record in selected]
+    cpu_values = [cast(float, record["process_cpu_ms"]) for record in selected]
     return {
         "runs": len(selected),
         "wall_ms": {
@@ -113,6 +123,16 @@ def _parse_args() -> argparse.Namespace:
     parser.add_argument("--warmup", type=int, default=1)
     parser.add_argument("--rounds", type=_positive_int, default=3)
     parser.add_argument("--seed", type=int, default=20260817)
+    parser.add_argument(
+        "--input-layout",
+        choices=("planar-backed", "interleaved"),
+        default="planar-backed",
+    )
+    parser.add_argument(
+        "--frame-conversion-workers",
+        type=_positive_int,
+        default=8,
+    )
     parser.add_argument("--output-json", type=Path)
     args = parser.parse_args()
     if args.warmup < 0:
@@ -129,33 +149,41 @@ def main() -> None:
         fps=args.fps,
         audio_sample_rate=args.audio_sample_rate,
         seed=args.seed,
+        input_layout=args.input_layout,
     )
     baseline = BenchmarkVariant("legacy", _encode_video_bytes_legacy)
-    candidate = BenchmarkVariant("automatic", _encode_video_bytes)
+    converter = _PlanarFrameConverter(max_workers=args.frame_conversion_workers)
+    candidate = BenchmarkVariant(
+        "automatic",
+        partial(_encode_video_bytes, frame_converter=converter),
+    )
 
-    for _ in range(args.warmup):
-        for variant in (baseline, candidate):
-            _measure(
-                variant,
-                video=video,
-                audio=audio,
-                fps=args.fps,
-                audio_sample_rate=args.audio_sample_rate,
-            )
+    try:
+        for _ in range(args.warmup):
+            for variant in (baseline, candidate):
+                _measure(
+                    variant,
+                    video=video,
+                    audio=audio,
+                    fps=args.fps,
+                    audio_sample_rate=args.audio_sample_rate,
+                )
 
-    records: list[dict[str, object]] = []
-    for round_index in range(args.rounds):
-        order = (baseline, candidate) if round_index % 2 == 0 else (candidate, baseline)
-        for variant in order:
-            _, record = _measure(
-                variant,
-                video=video,
-                audio=audio,
-                fps=args.fps,
-                audio_sample_rate=args.audio_sample_rate,
-            )
-            record["round"] = round_index + 1
-            records.append(record)
+        records: list[dict[str, object]] = []
+        for round_index in range(args.rounds):
+            order = (baseline, candidate) if round_index % 2 == 0 else (candidate, baseline)
+            for variant in order:
+                _, record = _measure(
+                    variant,
+                    video=video,
+                    audio=audio,
+                    fps=args.fps,
+                    audio_sample_rate=args.audio_sample_rate,
+                )
+                record["round"] = round_index + 1
+                records.append(record)
+    finally:
+        converter.shutdown()
 
     output_hashes = {str(record["output_sha256"]) for record in records}
     if len(output_hashes) != 1:
@@ -165,8 +193,8 @@ def main() -> None:
     paired_savings_percent = []
     for round_index in range(args.rounds):
         pair = [record for record in records if record["round"] == round_index + 1]
-        baseline_ms = float(next(record["wall_ms"] for record in pair if record["label"] == baseline.label))
-        candidate_ms = float(next(record["wall_ms"] for record in pair if record["label"] == candidate.label))
+        baseline_ms = cast(float, next(record["wall_ms"] for record in pair if record["label"] == baseline.label))
+        candidate_ms = cast(float, next(record["wall_ms"] for record in pair if record["label"] == candidate.label))
         paired_savings_ms.append(baseline_ms - candidate_ms)
         paired_savings_percent.append((baseline_ms - candidate_ms) / baseline_ms * 100)
 
@@ -180,6 +208,8 @@ def main() -> None:
             "warmup": args.warmup,
             "rounds": args.rounds,
             "seed": args.seed,
+            "input_layout": args.input_layout,
+            "frame_conversion_workers": args.frame_conversion_workers,
             "video_shape": list(video.shape),
             "video_strides": list(video.strides),
             "codec_options": {"preset": "ultrafast", "threads": "0"},

tests/benchmarks/metrics/test_metrics.py

@@ -279,17 +279,79 @@ def test_unmeasured_tpot_stays_missing_after_tokenizer_fallback():
     assert metrics.request_goodput == 0.0
 
 
-def test_measured_zero_itl_is_not_treated_as_missing():
+def test_zero_itl_does_not_create_zero_tpot():
     output = _make_output(100, output_tokens=3)
     output.itl = [0.0, 0.0]
     output.duplex_session_metrics = {"mean_ttft_ms": 100.0}
 
     metrics = _calculate_test_metrics([output], {"tpot": 1.0})
 
-    assert (metrics.num_tpot_samples, metrics.num_itl_samples) == (1, 2)
-    assert metrics.mean_tpot_ms == 0.0
+    assert (metrics.num_tpot_samples, metrics.num_itl_samples) == (0, 2)
+    assert math.isnan(metrics.mean_tpot_ms)
     assert metrics.mean_itl_ms == 0.0
-    assert metrics.request_goodput == 1.0
+    assert metrics.request_goodput == 0.0
+
+
+def test_stage_output_tokens_without_client_timing_omit_tpot(capsys):
+    output = _make_output(100, output_tokens=5)
+    output.itl = []
+    output.text_latency = 0.0
+    output.ttft = 0.1
+
+    metrics, _ = calculate_metrics(
+        input_requests=[],
+        outputs=[output],
+        dur_s=1.0,
+        tokenizer=None,
+        selected_percentiles=[50.0, 99.0],
+        goodput_config_dict={},
+        task_type=TaskType.GENERATION,
+        selected_percentile_metrics=["tpot"],
+        max_concurrency=None,
+        request_rate=float("inf"),
+        benchmark_duration=1.0,
+    )
+
+    printed = capsys.readouterr().out
+    assert metrics.num_tpot_samples == 0
+    assert math.isnan(metrics.mean_tpot_ms)
+    assert "Time per Output Token" not in printed
+    assert "Mean TPOT" not in printed
+
+
+def test_consistent_client_latency_can_supply_tpot_fallback():
+    output = _make_output(100, output_tokens=10)
+    output.itl = []
+    output.ttft = 0.1
+    output.text_latency = 1.0
+
+    metrics = _calculate_test_metrics([output])
+
+    assert metrics.num_tpot_samples == 1
+    assert metrics.mean_tpot_ms == pytest.approx(100.0)
+
+
+def test_single_token_responses_do_not_report_zero_tpot(capsys):
+    output = _make_output(100, output_tokens=1)
+
+    metrics, _ = calculate_metrics(
+        input_requests=[],
+        outputs=[output],
+        dur_s=1.0,
+        tokenizer=None,
+        selected_percentiles=[50.0],
+        goodput_config_dict={},
+        task_type=TaskType.GENERATION,
+        selected_percentile_metrics=["tpot"],
+        max_concurrency=None,
+        request_rate=float("inf"),
+        benchmark_duration=1.0,
+    )
+
+    printed = capsys.readouterr().out
+    assert metrics.num_tpot_samples == 0
+    assert math.isnan(metrics.mean_tpot_ms)
+    assert "Time per Output Token" not in printed
 
 
 def test_duplex_goodput_does_not_pair_measurements_from_different_requests():

tests/benchmarks/patch/test_patch.py

@@ -1,5 +1,5 @@
 # SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
 
 """
 Unit tests for patch.py
@@ -9,22 +9,98 @@ import asyncio
 import base64
 import json
 import time
+from argparse import Namespace
 from types import SimpleNamespace
 
 import pytest
 from pytest_mock import MockerFixture
 from vllm.benchmarks.lib.endpoint_request_func import RequestFuncInput
 
+from vllm_omni.benchmarks.data_modules.seed_tts_dataset import (
+    SeedTTSSampleRequest,
+    SeedTTSTextSampleRequest,
+)
 from vllm_omni.benchmarks.patch.patch import (
     MixRequestFuncOutput,
+    _apply_stage0_token_timings,
+    _attach_seed_tts_to_request_func_input,
     async_request_openai_chat_omni_completions,
     async_request_openai_realtime_duplex,
+    should_request_stage_metrics,
 )
 from vllm_omni.experimental.fullduplex.client import RealtimeEventCollector
 
 pytestmark = [pytest.mark.core_model, pytest.mark.benchmark, pytest.mark.cpu]
 
 
+def _seed_tts_request_func_input() -> RequestFuncInput:
+    return RequestFuncInput(
+        model="test-model",
+        model_name="test-model",
+        prompt="target text",
+        api_url="http://test.com/v1/chat/completions",
+        prompt_len=2,
+        output_len=20,
+        extra_body={"modalities": ["text", "audio"]},
+    )
+
+
+def test_seed_tts_chat_request_carries_reference_audio_once() -> None:
+    reference_audio_url = "data:audio/wav;base64,AAAA"
+    sample = SeedTTSSampleRequest(
+        prompt="target text",
+        prompt_len=2,
+        expected_output_len=20,
+        multi_modal_data=None,
+        seed_tts_system_prompt="Clone the supplied voice.",
+        seed_tts_speech_extra={
+            "ref_audio": reference_audio_url,
+            "ref_text": "reference text",
+            "task_type": "Base",
+        },
+    )
+    request_func_input = _seed_tts_request_func_input()
+
+    _attach_seed_tts_to_request_func_input(sample, request_func_input)
+
+    assert request_func_input.omni_chat_messages == [
+        {
+            "role": "system",
+            "content": [{"type": "text", "text": "Clone the supplied voice."}],
+        },
+        {"role": "user", "content": [{"type": "text", "text": "target text"}]},
+    ]
+    assert request_func_input.extra_body["ref_audio"] == reference_audio_url
+    serialized_request = json.dumps(
+        {"messages": request_func_input.omni_chat_messages, **request_func_input.extra_body},
+    )
+    assert serialized_request.count(reference_audio_url) == 1
+
+
+def test_seed_tts_text_chat_messages_do_not_add_reference_audio() -> None:
+    sample = SeedTTSTextSampleRequest(
+        prompt="target text",
+        prompt_len=2,
+        expected_output_len=20,
+        multi_modal_data=None,
+        seed_tts_system_prompt="Use the configured voice.",
+        seed_tts_speech_extra=None,
+    )
+    request_func_input = _seed_tts_request_func_input()
+
+    _attach_seed_tts_to_request_func_input(sample, request_func_input)
+
+    assert getattr(request_func_input, "seed_tts_row", False) is True
+    assert request_func_input.omni_chat_messages == [
+        {
+            "role": "system",
+            "content": [{"type": "text", "text": "Use the configured voice."}],
+        },
+        {"role": "user", "content": [{"type": "text", "text": "target text"}]},
+    ]
+    assert "ref_audio" not in request_func_input.extra_body
+
+
 class MockResponse:
     """Mock aiohttp response for testing"""
 
@@ -102,7 +178,24 @@ async def test_seed_tts_realtime_duplex_exports_per_request_metrics(monkeypatch)
                 received_at_s=now + 0.03,
             )
             self.events.add(
-                {"type": "response.done", "response": {"id": response_id}},
+                {
+                    "type": "response.done",
+                    "response": {
+                        "id": response_id,
+                        "metadata": {
+                            "vllm_omni": {
+                                "stage_metrics": {
+                                    "0": {
+                                        "num_tokens_out": 3,
+                                        "vllm_ttft_ms": 20.0,
+                                        "vllm_tpot_ms": 10.0,
+                                        "vllm_itls_ms": [10.0, 10.0],
+                                    }
+                                }
+                            }
+                        },
+                    },
+                },
                 received_at_s=now + 0.04,
             )
 
@@ -143,6 +236,7 @@ async def test_seed_tts_realtime_duplex_exports_per_request_metrics(monkeypatch)
     assert client.configure_kwargs["native_duplex"] is False
     assert client.configure_kwargs["extra_body"] == {
         "ref_audio": "data:audio/wav;base64,AAAA",
+        "return_stage_metrics": True,
     }
     assert client.sent == [
         event
@@ -167,6 +261,10 @@ async def test_seed_tts_realtime_duplex_exports_per_request_metrics(monkeypatch)
     assert output.audio_ttfp > output.ttft
     assert output.audio_rtf > 0
     assert output.latency > 0
+    assert output.output_tokens == 12
+    assert output.itl == [0.01, 0.01] * 4
+    assert output.text_latency == pytest.approx(output.ttft + 0.08)
+    assert output.tpot_measured is True
     assert output.tts_turn_pcm_bytes == [b"\x00\x00" * 2400] * 4
     assert output.tts_output_pcm_bytes == b"\x00\x00" * 9600
     session_id = output.duplex_request_metrics[0]["session_id"]
@@ -198,11 +296,160 @@ async def test_seed_tts_realtime_duplex_exports_per_request_metrics(monkeypatch)
     }
 
 
+def test_stage0_token_timings_use_weighted_tpot_when_itls_are_incomplete():
+    output = MixRequestFuncOutput(ttft=0.1)
+
+    measured = _apply_stage0_token_timings(
+        output,
+        [
+            {"output_token_count": 3, "itls_ms": [], "tpot_ms": 10.0},
+            {"output_token_count": 2, "itls_ms": [], "tpot_ms": 40.0},
+        ],
+        expected_output_tokens=5,
+    )
+
+    assert measured is True
+    assert output.itl == []
+    assert output.tpot_measured is True
+    assert (output.text_latency - output.ttft) / 4 == pytest.approx(0.02)
+
+
+def test_stage0_zero_itls_fall_back_to_positive_tpot():
+    output = MixRequestFuncOutput(ttft=0.1)
+
+    measured = _apply_stage0_token_timings(
+        output,
+        [{"output_token_count": 3, "itls_ms": [0.0, 0.0], "tpot_ms": 25.0}],
+        expected_output_tokens=3,
+    )
+
+    assert measured is True
+    assert output.itl == []
+    assert output.tpot_measured is True
+    assert (output.text_latency - output.ttft) / 2 == pytest.approx(0.025)
+
+
+def test_stage0_token_count_without_timing_is_not_measured():
+    output = MixRequestFuncOutput(ttft=0.1)
+
+    measured = _apply_stage0_token_timings(
+        output,
+        [{"output_token_count": 3, "itls_ms": [], "tpot_ms": None}],
+        expected_output_tokens=3,
+    )
+
+    assert measured is False
+    assert output.itl == []
+    assert output.text_latency == output.ttft
+    assert output.tpot_measured is False
+
+
 def create_sse_chunk(data_dict):
     """Helper to create SSE formatted chunk"""
     return f"data: {json.dumps(data_dict)}\n\n".encode()
 
 
+def test_chat_text_timing_metrics_request_stage_metrics():
+    args = Namespace(
+        backend="openai-chat-omni",
+        percentile_metrics="ttft,tpot,itl,e2el",
+        print_stage=False,
+        extra_body={},
+    )
+
+    assert should_request_stage_metrics(args) is True
+
+
+@pytest.mark.asyncio
+async def test_bundled_first_text_chunk_uses_stage0_token_timings(mocker: MockerFixture):
+    """Engine timings recover TPOT when every text token arrives together."""
+    request_input = RequestFuncInput(
+        model="test-model",
+        model_name="test-model",
+        prompt="test prompt",
+        api_url="http://test.com/v1/chat/completions",
+        prompt_len=10,
+        output_len=20,
+    )
+    chunks = [
+        create_sse_chunk(
+            {
+                "choices": [{"delta": {"content": "ABCD"}}],
+                "modality": "text",
+                "usage": {"prompt_tokens": 10, "completion_tokens": 4, "total_tokens": 14},
+                "metrics": {
+                    "stage_metrics": {
+                        "0": {
+                            "num_tokens_out": 4,
+                            "vllm_itls_ms": [10.0, 11.0, 12.0],
+                            "vllm_tpot_ms": 11.0,
+                        }
+                    }
+                },
+            }
+        ),
+        b"data: [DONE]\n\n",
+    ]
+    mock_response = MockResponse(200, chunks)
+    mock_session = mocker.AsyncMock()
+    mock_session.post = mocker.MagicMock(return_value=mock_response)
+
+    output = await async_request_openai_chat_omni_completions(request_input, mock_session)
+
+    assert output.success is True
+    assert output.output_tokens == 4
+    assert output.itl == pytest.approx([0.010, 0.011, 0.012])
+    assert output.text_latency - output.ttft == pytest.approx(0.033)
+    assert output.tpot_measured is True
+
+
+@pytest.mark.asyncio
+async def test_positive_client_text_timings_take_precedence_over_stage0(mocker: MockerFixture):
+    request_input = RequestFuncInput(
+        model="test-model",
+        model_name="test-model",
+        prompt="test prompt",
+        api_url="http://test.com/v1/chat/completions",
+        prompt_len=10,
+        output_len=20,
+    )
+    chunks = [
+        create_sse_chunk(
+            {
+                "choices": [{"delta": {"content": "A"}}],
+                "modality": "text",
+                "usage": {"prompt_tokens": 10, "completion_tokens": 1, "total_tokens": 11},
+            }
+        ),
+        create_sse_chunk(
+            {
+                "choices": [{"delta": {"content": "B"}}],
+                "modality": "text",
+                "usage": {"prompt_tokens": 10, "completion_tokens": 2, "total_tokens": 12},
+                "metrics": {
+                    "stage_metrics": {
+                        "0": {
+                            "num_tokens_out": 2,
+                            "vllm_itls_ms": [1000.0],
+                            "vllm_tpot_ms": 1000.0,
+                        }
+                    }
+                },
+            }
+        ),
+        b"data: [DONE]\n\n",
+    ]
+    mock_response = MockResponse(200, chunks, delay_between_chunks=0.01)
+    mock_session = mocker.AsyncMock()
+    mock_session.post = mocker.MagicMock(return_value=mock_response)
+
+    output = await async_request_openai_chat_omni_completions(request_input, mock_session)
+
+    assert output.success is True
+    assert len(output.itl) == 1
+    assert 0.0 < output.itl[0] < 0.1
+
+
 # ============================================================================
 # output_tokens Tests
 # ============================================================================

tests/benchmarks/test_omniinteract.py

@@ -585,7 +585,7 @@ async def test_adapter_requires_and_forwards_exact_prepared_payload(tmp_path: Pa
 
 @pytest.mark.asyncio
 @pytest.mark.parametrize(
-    ("request_metrics", "expected_itl", "expected_text_latency"),
+    ("request_metrics", "expected_itl", "expected_text_latency", "expected_tpot_measured"),
     [
         (
             [
@@ -594,6 +594,7 @@ async def test_adapter_requires_and_forwards_exact_prepared_payload(tmp_path: Pa
             ],
             [0.01, 0.02, 0.03],
             0.16,
+            True,
         ),
         (
             [
@@ -602,6 +603,7 @@ async def test_adapter_requires_and_forwards_exact_prepared_payload(tmp_path: Pa
             ],
             [],
             0.18,
+            True,
         ),
         (
             [
@@ -610,6 +612,7 @@ async def test_adapter_requires_and_forwards_exact_prepared_payload(tmp_path: Pa
             ],
             [],
             0.1,
+            False,
         ),
     ],
 )
@@ -619,6 +622,7 @@ async def test_adapter_reports_exact_or_weighted_token_timing(
     request_metrics: list[dict[str, object]],
     expected_itl: list[float],
     expected_text_latency: float,
+    expected_tpot_measured: bool,
 ):
     case, options = _case(tmp_path), _session_options(tmp_path)
     request = _request(case, options)
@@ -643,7 +647,7 @@ async def test_adapter_reports_exact_or_weighted_token_timing(
     assert output.success
     assert output.itl == expected_itl
     assert output.text_latency == pytest.approx(expected_text_latency)
-    assert output.tpot_measured is True
+    assert output.tpot_measured is expected_tpot_measured
 
 
 def test_batch_finalization_publishes_only_measured_results(tmp_path: Path, monkeypatch: pytest.MonkeyPatch):

tests/benchmarks/test_seed_tts_dataset_variants.py

@@ -1,3 +1,6 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
+
 """Tests for SeedTTSTextDataset, SeedTTSTextSampleRequest, SeedTTSDesignDataset,
 and SeedTTSDesignSampleRequest.
 
@@ -234,34 +237,6 @@ def test_seed_tts_design_dataset_rejects_missing_description(seed_tts_design_roo
         assert req.seed_tts_utterance_id == "ok"
 
 
-def test_attach_sets_seed_tts_row_even_without_extra_body():
-    """seed_tts_row=True must be set for SeedTTSTextSampleRequest (no extra body)."""
-    from vllm_omni.benchmarks.data_modules.seed_tts_dataset import SeedTTSTextSampleRequest
-
-    req = SeedTTSTextSampleRequest(
-        prompt="hello world",
-        prompt_len=2,
-        expected_output_len=100,
-        multi_modal_data=None,
-        request_id="test-0",
-        seed_tts_speech_extra=None,
-        seed_tts_ref_wav_path="",
-    )
-    assert req.seed_tts_speech_extra is None
-    assert req.seed_tts_ref_wav_path == ""
-    # The fix ensures that even with speech_extra=None, the function
-    # sets seed_tts_row=True. We verify the source code has the fix.
-    import inspect
-
-    import vllm_omni.benchmarks.patch.patch as patch_mod
-
-    src = inspect.getsource(patch_mod._attach_seed_tts_to_request_func_input)
-    # seed_tts_row must be set BEFORE the 'if not ex: return' check
-    row_pos = src.index("seed_tts_row")
-    not_ex_pos = src.index("if not ex:")
-    assert row_pos < not_ex_pos, "seed_tts_row must be set before 'if not ex: return'"
-
-
 def test_seed_tts_whisper_transcribe_passes_attention_mask(monkeypatch):
     from vllm_omni.benchmarks.data_modules import seed_tts_eval
 

tests/config/test_config_factory.py

@@ -650,7 +650,7 @@ class TestPipelineConfigNew:
             model_arch="A",
             stages=(
                 StagePipelineConfig(stage_id=0, model_stage="a"),
-                StagePipelineConfig(stage_id=1, model_stage="b", input_sources=(0,)),
+                StagePipelineConfig(stage_id=1, model_stage="b", input_sources=(0,), final_output=True),
             ),
         )
         assert p.validate() == []
@@ -659,6 +659,30 @@ class TestPipelineConfigNew:
         p = PipelineConfig(model_type="t", model_arch="A")
         assert any("no stages" in e.lower() for e in p.validate())
 
+    def test_validate_no_terminal_stage(self):
+        """A pipeline with no ``final_output`` stage can never emit a result."""
+        p = PipelineConfig(
+            model_type="t",
+            model_arch="A",
+            stages=(
+                StagePipelineConfig(stage_id=0, model_stage="a"),
+                StagePipelineConfig(stage_id=1, model_stage="b", input_sources=(0,)),
+            ),
+        )
+        assert any("no terminal stage" in e.lower() for e in p.validate())
+
+    def test_validate_terminal_stage_need_not_be_last(self):
+        """The check is cycle-agnostic: any stage may carry ``final_output``."""
+        p = PipelineConfig(
+            model_type="t",
+            model_arch="A",
+            stages=(
+                StagePipelineConfig(stage_id=0, model_stage="a", final_output=True),
+                StagePipelineConfig(stage_id=1, model_stage="b", input_sources=(0,)),
+            ),
+        )
+        assert p.validate() == []
+
 
 class TestPipelineRegistration:
     def test_resolve_pipeline_prefers_deploy_pipeline_key(self, clean_pipeline_registry, tmp_path):
@@ -1159,7 +1183,7 @@ class TestDeployConfigLoading:
         stages = merge_pipeline_deploy(pipeline, deploy)
 
         assert deploy.session_mode == "duplex"
-        assert deploy.active_stream_window == 1
+        assert deploy.active_stream_window == max_sessions
         assert deploy.duplex_session.max_sessions == max_sessions
         assert [stage.session_mode for stage in stages] == ["duplex", "duplex", "duplex"]
         assert [stage.to_omegaconf().session_mode for stage in stages] == ["duplex", "duplex", "duplex"]
@@ -2399,6 +2423,20 @@ class TestPlatformOverrides:
         assert rocm.stages[0].enforce_eager is None
         assert rocm.stages[1].enforce_eager is True
 
+    def test_higgs_audio_v3_rocm_uses_triton_attention(self):
+        deploy_path = Path(get_deploy_config_path("higgs_multimodal_qwen3.yaml"))
+
+        base = load_deploy_config(deploy_path)
+        assert base.stages[0].engine_extras["attention_backend"] == "FLASHINFER"
+
+        rocm = _apply_platform_overrides(base, platform="rocm")
+        assert rocm.stages[0].engine_extras["attention_backend"] == "TRITON_ATTN"
+
+        pipeline = resolve_pipeline_config("higgs_multimodal_qwen3")
+        assert isinstance(pipeline, PipelineConfig)
+        stages = merge_pipeline_deploy(pipeline, rocm)
+        assert stages[0].yaml_engine_args["attention_backend"] == "TRITON_ATTN"
+
     def test_qwen3_omni_cuda_uses_thinker_rotary_custom_op(self):
         deploy_path = Path(get_deploy_config_path("qwen3_omni_moe.yaml"))
         pipeline = resolve_pipeline_config(

tests/config/test_omni_config.py

@@ -653,6 +653,7 @@ def test_sub_config_fields_match_structured_scopes():
         "ring_degree",
         "allgather_degree",
         "ulysses_mode",
+        "ulysses_a2a_permute",
         "cfg_parallel_size",
         "vae_patch_parallel_size",
         "vae_parallel_mode",
@@ -858,6 +859,23 @@ def test_from_pipeline_config_derives_sequence_parallel_size_from_allgather_degr
     assert stage.parallel_config.world_size == 2
 
 
+def test_from_pipeline_config_preserves_deployed_ulysses_a2a_permute() -> None:
+    pipeline = _resolve_pipeline_or_skip("dreamzero")
+    deploy = DeployConfig(
+        stages=[
+            StageDeployConfig(
+                stage_id=0,
+                ulysses_a2a_permute=True,
+            )
+        ]
+    )
+
+    stage = VllmOmniConfig.from_pipeline_config(pipeline, user_deploy_config=deploy).stage_by_id(0)
+
+    assert isinstance(stage, VllmOmniDiffusionStageConfig)
+    assert stage.parallel_config.ulysses_a2a_permute is True
+
+
 def test_diffusion_parallel_config_accepts_four_way_guidance_parallelism():
     cfg = OmniStageDiffusionParallelConfig(cfg_parallel_size=4)
 

tests/config/test_pipeline_registry.py

@@ -73,3 +73,20 @@ def test_minimax_h3_disaggregation_is_explicit_opt_in():
     pipeline = OMNI_PIPELINES["minimax_h3_disaggregated"]
     assert isinstance(pipeline, PipelineConfig)
     assert pipeline.model_type == "minimax_h3_disaggregated"
+
+
+def test_in_tree_pipelines_pass_topology_validation():
+    """Every statically registered in-tree pipeline must have a valid topology.
+
+    ``PipelineConfig.validate()`` only runs on ``register_pipeline``, which
+    in-tree pipelines never go through, so a shipped topology error would
+    otherwise stay invisible until serving hangs or misroutes.
+    Resolver entries are skipped: they need an ``hf_config`` to produce a
+    ``PipelineConfig``.
+    """
+    failures = {
+        model_type: errors
+        for model_type, pipeline in OMNI_PIPELINES.items()
+        if isinstance(pipeline, PipelineConfig) and (errors := pipeline.validate())
+    }
+    assert not failures, f"in-tree pipelines failed topology validation: {failures}"

tests/conftest.py

@@ -25,9 +25,9 @@ pytest_plugins = (
     "tests.helpers.fixtures.clean",
     "tests.helpers.fixtures.log",
     "tests.helpers.fixtures.media",
-    "tests.helpers.fixtures.run_args",
+    "tests.helpers.fixtures.pytest_collection",
+    "tests.helpers.fixtures.pytest_run_args",
     "tests.helpers.fixtures.runtime",
-    "tests.helpers.fixtures.xdist",
 )
 
 

tests/core/sched/test_omni_ar_scheduler_aborted_queue_sweep.py

@@ -0,0 +1,89 @@
+"""Tests for ``OmniARScheduler._drop_aborted_queued_requests``.
+
+``schedule()`` sweeps ``FINISHED_ABORTED`` requests out of the queues before
+handing control to upstream, because upstream ``Scheduler.schedule()`` raises
+``RuntimeError: Invalid request status: FINISHED_ABORTED`` for any request it
+admits in a finished state -- which kills the stage's engine core, not just
+the request.
+
+Regression for the sweep missing ``skipped_waiting``, the third queue
+upstream admits from. An aborted duplex session parked there was re-selected
+by ``_select_waiting_queue_for_scheduling`` on a later tick and crashed the
+stage.
+"""
+
+from __future__ import annotations
+
+import pytest
+from vllm.v1.core.sched.request_queue import SchedulingPolicy, create_request_queue
+from vllm.v1.request import RequestStatus
+
+import vllm_omni.core.sched.omni_ar_scheduler as ar_sched_mod
+from vllm_omni.core.sched.omni_ar_scheduler import OmniARScheduler
+
+pytestmark = [pytest.mark.core_model, pytest.mark.cpu]
+
+_POLICIES = [
+    pytest.param(SchedulingPolicy.FCFS, id="fcfs"),
+    pytest.param(SchedulingPolicy.PRIORITY, id="priority"),
+]
+
+
+class _StubRequest:
+    """Minimal ``Request`` stub with the surface the sweep exercises.
+
+    Carries the ``(priority, arrival_time)`` ordering ``PriorityRequestQueue``
+    relies on so the same stub works under both scheduling policies.
+    """
+
+    def __init__(self, request_id: str, status: RequestStatus, arrival_time: float = 0.0) -> None:
+        self.request_id = request_id
+        self.status = status
+        self.priority = 0
+        self.arrival_time = arrival_time
+
+    def __lt__(self, other: _StubRequest) -> bool:
+        return (self.priority, self.arrival_time) < (other.priority, other.arrival_time)
+
+
+def _make_scheduler(policy: SchedulingPolicy, *, waiting=(), skipped_waiting=(), running=()):
+    """Bare scheduler with just the surface the sweep reads."""
+    scheduler = OmniARScheduler.__new__(OmniARScheduler)
+    scheduler.waiting = create_request_queue(policy)
+    for req in waiting:
+        scheduler.waiting.add_request(req)
+    scheduler.skipped_waiting = create_request_queue(policy)
+    for req in skipped_waiting:
+        scheduler.skipped_waiting.add_request(req)
+    scheduler.running = list(running)
+    return scheduler
+
+
+@pytest.mark.parametrize("policy", _POLICIES)
+def test_schedule_sweeps_skipped_waiting_before_upstream_selection(
+    monkeypatch: pytest.MonkeyPatch,
+    policy: SchedulingPolicy,
+) -> None:
+    """The public schedule path must remove aborted skipped requests first."""
+    aborted = _StubRequest("req-aborted", RequestStatus.FINISHED_ABORTED)
+    scheduler = _make_scheduler(policy, skipped_waiting=[aborted])
+    scheduler.chunk_transfer_adapter = None
+    scheduler.input_coordinator = None
+    scheduler.num_waiting_for_streaming_input = 0
+    scheduler._process_pending_omni_inputs = lambda model_mode: None
+    scheduler._resync_streaming_input_counter = lambda: None
+    scheduler._should_defer_waiting_admission = lambda: False
+    scheduler._restore_omni_wait_queues = lambda: None
+    scheduler._postprocess_omni_schedule_output = lambda *args, **kwargs: None
+    scheduler.get_finished_requests_needing_kv_transfer = lambda: {}
+    scheduler._wrap_omni_scheduler_output = lambda output, **kwargs: output
+
+    sentinel = object()
+
+    def upstream_schedule(_self, _throttle_prefills: bool = False):
+        assert list(scheduler.skipped_waiting) == []
+        return sentinel
+
+    monkeypatch.setattr(ar_sched_mod.VLLMScheduler, "schedule", upstream_schedule)
+
+    assert OmniARScheduler.schedule(scheduler) is sentinel

tests/core/sched/test_omni_ar_scheduler_stale_drain.py

@@ -142,13 +142,16 @@ def test_exact_drain_delivers_new_segment_frame() -> None:
     session.num_computed_tokens = 6
     session.num_output_placeholders = 1
     session.num_in_flight_tokens = 1
+    session.async_tokens_to_discard = 1
 
     _replace_streaming_session(session)
     assert session.num_stale_output_tokens == 1
+    assert session.async_tokens_to_discard == 1
 
     sched = _make_drain_sched(session)
     assert _run_step(sched, session, num_scheduled=1, token=42) is False  # late frame dropped
     assert session.num_stale_output_tokens == 0
+    assert session.async_tokens_to_discard == 0
     assert _run_step(sched, session, num_scheduled=1, token=43) is True  # new segment survives
 
 

tests/core/sched/test_omni_ar_scheduler_streaming.py

@@ -18,7 +18,7 @@ from vllm.sampling_params import SamplingParams
 from vllm.v1.core.sched.output import SchedulerOutput
 from vllm.v1.outputs import ModelRunnerOutput
 from vllm.v1.request import Request, RequestStatus, StreamingUpdate
-from vllm_omni.core.sched.omni_ar_scheduler import OmniARScheduler
+from vllm_omni.core.sched.omni_ar_scheduler import OmniARAsyncScheduler, OmniARScheduler
 
 # isort: on
 
@@ -28,7 +28,9 @@ pytestmark = [pytest.mark.core_model, pytest.mark.cpu]
 def _make_scheduler(*, stage_id: int = 0, session_mode: str = "turn") -> OmniARScheduler:
     sched = OmniARScheduler.__new__(OmniARScheduler)
     sched._new_prompt_len_snapshot = {}
-    sched.vllm_config = SimpleNamespace(model_config=SimpleNamespace(stage_id=stage_id, session_mode=session_mode))
+    sched.vllm_config = SimpleNamespace(
+        model_config=SimpleNamespace(stage_id=stage_id, session_mode=session_mode),
+    )
     sched.num_waiting_for_streaming_input = 0
     sched.log_stats = False
     sched.chunk_transfer_adapter = None
@@ -185,6 +187,7 @@ def test_resumable_segment_boundary_keeps_pre_transition_send_watermark() -> Non
 
     def replace_with_next_segment(request: Request) -> bool:
         request.num_computed_tokens = 0
+        request._omni_segment_generation = 1
         request.status = RequestStatus.WAITING
         return False
 
@@ -201,6 +204,7 @@ def test_resumable_segment_boundary_keeps_pre_transition_send_watermark() -> Non
         True,
         new_token_ids=[42],
         confirmed_num_computed_tokens=26,
+        segment_generation=0,
     )
 
 
@@ -286,6 +290,140 @@ def test_queued_streaming_update_on_async_stop_fences_in_flight_once() -> None:
     assert session.num_stale_output_tokens == session.num_in_flight_tokens == 1
 
 
+def test_stale_async_frame_is_dropped_before_output_processing() -> None:
+    session = _make_request()
+    session.status = RequestStatus.RUNNING
+    session.num_in_flight_tokens = 2
+    session.num_computed_tokens = session.num_tokens + 1
+    session.num_output_placeholders = 1
+    session.async_tokens_to_discard = 1
+    session.sampling_params = SimpleNamespace(num_logprobs=1)
+    num_computed_tokens = session.num_computed_tokens
+    num_output_placeholders = session.num_output_placeholders
+
+    sched = MagicMock()
+    sched.requests = {session.request_id: session}
+    sched.perf_metrics = None
+    sched.structured_output_manager.should_advance.return_value = False
+
+    def discard_stale_output(request: Request, token_ids: list[int]) -> tuple[list[int], bool]:
+        request.async_tokens_to_discard = 0
+        return token_ids, False
+
+    sched._update_request_with_output.side_effect = discard_stale_output
+    sched._process_kv_transfer_trigger.return_value = False
+    sched.chunk_transfer_adapter = MagicMock()
+    sched.running = [session]
+    sched.waiting_for_transfer_free = set()
+    sched.transfer_triggered_requests = set()
+    sched.active_kv_transfers = set()
+    sched.pending_stop_after_extraction = set()
+    sched.connector = None
+    sched.kv_cache_manager.take_events.return_value = None
+    sched.kv_cache_manager.estimate_cached_tokens.return_value = 0
+    sched.finished_req_ids_dict = {}
+    sched.make_stats.return_value = None
+
+    scheduler_output = MagicMock(spec=SchedulerOutput)
+    scheduler_output.num_scheduled_tokens = {session.request_id: 1}
+    scheduler_output.scheduled_spec_decode_tokens = {}
+    scheduler_output.num_invalid_spec_tokens = 0
+
+    model_runner_output = MagicMock(spec=ModelRunnerOutput)
+    model_runner_output.sampled_token_ids = [[42]]
+    model_runner_output.logprobs = None
+    model_runner_output.prompt_logprobs_dict = {}
+    model_runner_output.pooler_output = None
+    model_runner_output.multimodal_outputs = None
+    model_runner_output.inter_stage_outputs = [{"hidden": object()}]
+    model_runner_output.num_nans_in_logits = None
+    model_runner_output.kv_connector_output = None
+    model_runner_output.cudagraph_stats = None
+    model_runner_output.req_id_to_index = {session.request_id: 0}
+    model_runner_output.routed_experts = None
+
+    OmniARScheduler.update_from_output(sched, scheduler_output, model_runner_output)
+
+    assert session.async_tokens_to_discard == 0
+    assert session.status == RequestStatus.RUNNING
+    assert session.num_computed_tokens == num_computed_tokens
+    assert session.num_output_placeholders == num_output_placeholders
+    sched.chunk_transfer_adapter.save_async.assert_not_called()
+
+    session.sampling_params = SamplingParams(max_tokens=8)
+    next_payload = {"hidden": object()}
+    sched._update_request_with_output.side_effect = None
+    sched._update_request_with_output.return_value = ([43], False)
+    model_runner_output.sampled_token_ids = [[43]]
+    model_runner_output.inter_stage_outputs = [next_payload]
+
+    OmniARScheduler.update_from_output(sched, scheduler_output, model_runner_output)
+
+    sched.chunk_transfer_adapter.save_async.assert_called_once_with(
+        next_payload,
+        session,
+        False,
+        new_token_ids=[43],
+        confirmed_num_computed_tokens=None,
+    )
+
+
+def test_legacy_stale_async_marker_bypasses_real_placeholder_accounting() -> None:
+    request = _make_request()
+    request.status = RequestStatus.RUNNING
+    request.num_in_flight_tokens = 1
+    request.num_output_placeholders = 0
+    request.async_tokens_to_discard = 1
+    request.num_stale_output_tokens = 0
+
+    sched = OmniARAsyncScheduler.__new__(OmniARAsyncScheduler)
+    sched.requests = {request.request_id: request}
+    sched.perf_metrics = None
+    sched.chunk_transfer_adapter = None
+    sched.connector = None
+    sched.ec_connector = None
+    sched.waiting_for_transfer_free = set()
+    sched.transfer_triggered_requests = set()
+    sched.active_kv_transfers = set()
+    sched.pending_stop_after_extraction = set()
+    sched.finished_req_ids_dict = {}
+    sched._new_prompt_len_snapshot = {}
+    sched.kv_cache_manager = MagicMock()
+    sched.kv_cache_manager.take_events.return_value = None
+    sched._remove_stopped_requests_from_queues = MagicMock()
+    sched._handle_failed_kv_load_outputs = MagicMock(return_value=[])
+    sched._cleanup_kv_tracking = MagicMock()
+    sched._aggregate_kv_connector_stats = MagicMock(return_value=None)
+    sched._publish_kv_cache_events = MagicMock()
+    sched._attach_finished_request_sets = MagicMock()
+    sched._attach_scheduler_stats = MagicMock()
+    sched._capture_omni_connector_output = MagicMock()
+
+    scheduler_output = MagicMock(spec=SchedulerOutput)
+    scheduler_output.num_scheduled_tokens = {request.request_id: 1}
+    scheduler_output.scheduled_spec_decode_tokens = {}
+    scheduler_output.num_invalid_spec_tokens = 0
+
+    model_runner_output = MagicMock(spec=ModelRunnerOutput)
+    model_runner_output.sampled_token_ids = [[42]]
+    model_runner_output.logprobs = None
+    model_runner_output.prompt_logprobs_dict = {}
+    model_runner_output.pooler_output = None
+    model_runner_output.multimodal_outputs = None
+    model_runner_output.inter_stage_outputs = None
+    model_runner_output.num_nans_in_logits = None
+    model_runner_output.kv_connector_output = None
+    model_runner_output.cudagraph_stats = None
+    model_runner_output.req_id_to_index = {request.request_id: 0}
+    model_runner_output.routed_experts = None
+
+    OmniARScheduler.update_from_output(sched, scheduler_output, model_runner_output)
+
+    assert request.async_tokens_to_discard == 0
+    assert request.num_output_placeholders == 0
+    assert request._output_token_ids == []
+
+
 def test_stage0_streaming_update_discards_outstanding_async_placeholder_token() -> None:
     sched = _make_scheduler(stage_id=0)
     session = _make_request()

tests/core/sched/test_omni_ar_scheduler_unified_decode_deferral.py

@@ -76,6 +76,9 @@ def _make_scheduler(
         else:
             hf_config.voxcpm2_runtime_config = runtime_config
     sched.vllm_config = SimpleNamespace(model_config=model_config)
+    # Upstream ``Scheduler.__init__`` always creates ``skipped_waiting``; the
+    # abort sweep at the top of ``schedule()`` reads it.
+    sched.skipped_waiting = _MockQueue()
     return sched
 
 

tests/core/sched/test_omni_sched_deferred_free_fence.py

@@ -166,3 +166,15 @@ def test_fast_path_schedule_defer_disabled_leaves_fence() -> None:
     OmniGenerationScheduler.schedule(sched)
 
     assert sched.sched_step_seq == 0
+
+
+def test_generation_schedule_sweeps_and_resyncs_around_pending_inputs() -> None:
+    sched, request = _make_fast_path_sched(defer_block_free=False)
+    calls: list[str] = []
+    sched._drop_aborted_queued_requests.side_effect = lambda: calls.append("sweep")
+    sched._process_pending_omni_inputs.side_effect = lambda model_mode: calls.append(model_mode)
+    sched._resync_streaming_input_counter.side_effect = lambda: calls.append("resync")
+
+    OmniGenerationScheduler.schedule(sched)
+
+    assert calls == ["sweep", "generation", "sweep", "resync"]

tests/core/sched/test_omni_scheduler_mixin_timeouts.py

@@ -1,5 +1,5 @@
 # SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
 """Unit coverage for the two stage-input timeout hooks.
 
 ``_process_pending_input_timeouts`` covers the full-payload transport;
@@ -253,6 +253,38 @@ def test_log_failed_chunk_sends_noop_without_adapter():
     scheduler._log_failed_chunk_sends()
 
 
+class _FakeReceiveFailureAdapter:
+    def __init__(self, failures):
+        self._failures = dict(failures)
+
+    def collect_failed_receive_request_ids(self):
+        failures, self._failures = self._failures, {}
+        return failures
+
+
+class _FakeReceiveFailureScheduler(OmniSchedulerMixin):
+    def __init__(self, requests, adapter):
+        self.requests = requests
+        self.chunk_transfer_adapter = adapter
+        self.finish_calls = []
+
+    def finish_requests(self, req_ids, status):
+        self.finish_calls.append((set(req_ids), status))
+
+
+def test_process_chunk_receive_failures_delegates_to_finish_requests():
+    request_id = "invalid-chunk"
+    adapter = _FakeReceiveFailureAdapter({request_id: "prompt exceeds limit"})
+    scheduler = _FakeReceiveFailureScheduler({request_id: SimpleNamespace(request_id=request_id)}, adapter)
+
+    scheduler._process_chunk_receive_failures()
+
+    assert len(scheduler.finish_calls) == 1
+    finished_ids, status = scheduler.finish_calls[0]
+    assert finished_ids == {request_id}
+    assert getattr(status, "name", str(status)).endswith("FINISHED_ERROR")
+
+
 # --- R1.4: VLLM_OMNI_INPUT_WAIT_TIMEOUT_S is parsed at import time, so each of
 # these reloads the module under a different environment. One knob now gates the
 # deadline on both transports, which is why a silent 0 matters more than it did

tests/core/sched/test_omni_scheduler_streaming_input_counter.py

@@ -0,0 +1,168 @@
+"""Tests for the ``num_waiting_for_streaming_input`` resync placed in
+``OmniARScheduler.finish_requests`` / ``OmniGenerationScheduler.finish_requests``
+and in ``OmniARScheduler.schedule``.
+
+Upstream keeps that counter incrementally (``+1`` when a resumable request
+parks in a waiting queue as ``WAITING_FOR_STREAMING_REQ``, ``-1`` when the
+next update arrives or the request is finished) and
+``Scheduler.get_num_unfinished_requests`` *subtracts* it from the waiting
+queues. Omni rewrites ``request.status`` outside both hooks -- the
+chunk-transfer adapter's park/restore and
+``_realign_request_status_to_queues`` -- so a counted request can leave
+``WAITING_FOR_STREAMING_REQ`` without the matching decrement and inflate the
+counter for the lifetime of the process.
+
+An inflated counter does not degrade throughput, it hangs the stage:
+``EngineCore.has_work()`` reads false while a live request sits in
+``waiting``, so the engine blocks in ``input_queue.get()`` and never calls
+``schedule()`` again. Regression for the Qwen3-Omni duplex symptom where the
+server answered the first conversation after a boot and was silent from the
+second on while still reporting healthy.
+"""
+
+from __future__ import annotations
+
+import pytest
+from vllm.v1.core.sched.request_queue import SchedulingPolicy, create_request_queue
+from vllm.v1.request import RequestStatus
+
+import vllm_omni.core.sched.omni_ar_scheduler as ar_sched_mod
+import vllm_omni.core.sched.omni_generation_scheduler as gen_sched_mod
+from vllm_omni.core.sched.omni_ar_scheduler import OmniARScheduler
+from vllm_omni.core.sched.omni_generation_scheduler import OmniGenerationScheduler
+
+pytestmark = [pytest.mark.core_model, pytest.mark.cpu]
+
+
+class _StubRequest:
+    def __init__(self, request_id: str, status: RequestStatus) -> None:
+        self.request_id = request_id
+        self.status = status
+
+    def is_finished(self) -> bool:
+        return RequestStatus.is_finished(self.status)
+
+
+_SCHEDULER_PARAMS = [
+    pytest.param(OmniARScheduler, ar_sched_mod, id="ar"),
+    pytest.param(OmniGenerationScheduler, gen_sched_mod, id="generation"),
+]
+
+
+def _make_scheduler(scheduler_cls, *, requests, running, waiting, counter, skipped=None):
+    scheduler = scheduler_cls.__new__(scheduler_cls)
+    scheduler.chunk_transfer_adapter = None
+    scheduler.input_coordinator = None
+    scheduler.requests = requests
+    scheduler.running = running
+    scheduler.waiting = waiting
+    scheduler.skipped_waiting = skipped if skipped is not None else []
+    scheduler.num_waiting_for_streaming_input = counter
+    return scheduler
+
+
+@pytest.mark.parametrize(("scheduler_cls", "scheduler_mod"), _SCHEDULER_PARAMS)
+def test_finish_requests_clears_counter_left_by_a_stomped_status(
+    monkeypatch: pytest.MonkeyPatch,
+    scheduler_cls,
+    scheduler_mod,
+) -> None:
+    """The abort case that wedged the talker.
+
+    The request was counted while parked for streaming input, then had its
+    status rewritten to ``RUNNING`` by omni's queue surgery, so upstream's
+    ``finish_requests`` took the running branch and never decremented. With
+    no parked requests left, the counter must land at 0.
+    """
+    aborted = _StubRequest("req-aborted", RequestStatus.RUNNING)
+    scheduler = _make_scheduler(
+        scheduler_cls,
+        requests={},  # upstream already dropped it
+        running=[],
+        waiting=[],
+        counter=1,
+    )
+
+    monkeypatch.setattr(
+        scheduler_mod.VLLMScheduler,
+        "finish_requests",
+        lambda self, request_ids, finished_status: [aborted],
+    )
+
+    scheduler_cls.finish_requests(scheduler, [aborted.request_id], RequestStatus.FINISHED_ABORTED)
+
+    assert scheduler.num_waiting_for_streaming_input == 0
+
+
+def test_resync_counts_parked_requests_in_skipped_waiting() -> None:
+    """``get_num_unfinished_requests`` sums ``waiting`` and ``skipped_waiting``,
+    so the resync has to look at both or it under-counts and reintroduces the
+    spin the counter exists to prevent."""
+    parked = _StubRequest("req-skipped", RequestStatus.WAITING_FOR_STREAMING_REQ)
+    scheduler = _make_scheduler(
+        OmniARScheduler,
+        requests={parked.request_id: parked},
+        running=[],
+        waiting=[],
+        counter=0,
+        skipped=[parked],
+    )
+
+    scheduler._resync_streaming_input_counter()
+
+    assert scheduler.num_waiting_for_streaming_input == 1
+
+
+def test_ar_schedule_resyncs_before_delegating_upstream(monkeypatch: pytest.MonkeyPatch) -> None:
+    """Pins placement in ``schedule``: the chunk-transfer adapter rewrites
+    status during ``process_pending_chunks``, so the resync has to run after
+    that and before upstream schedules, while ``self.waiting`` is still the
+    real queue."""
+    parked = _StubRequest("req-resumed", RequestStatus.RUNNING)
+    scheduler = OmniARScheduler.__new__(OmniARScheduler)
+    scheduler.chunk_transfer_adapter = None
+    scheduler.input_coordinator = None
+    scheduler.requests = {parked.request_id: parked}
+    scheduler.running = [parked]
+    scheduler.waiting = []
+    scheduler.skipped_waiting = []
+    scheduler.num_waiting_for_streaming_input = 1  # leaked by the status rewrite
+    scheduler.policy = "fcfs"
+
+    seen: dict[str, int] = {}
+
+    monkeypatch.setattr(
+        ar_sched_mod.OmniSchedulerMixin, "_consume_pending_connector_output", lambda self, model_mode: None
+    )
+    monkeypatch.setattr(ar_sched_mod.OmniSchedulerMixin, "_process_pending_input_timeouts", lambda self: None)
+    monkeypatch.setattr(OmniARScheduler, "_should_defer_waiting_admission", lambda self: False)
+
+    def fake_schedule(self, throttle_prefills=False):
+        seen["counter"] = self.num_waiting_for_streaming_input
+        raise _StopScheduleError
+
+    class _StopScheduleError(Exception):
+        pass
+
+    monkeypatch.setattr(ar_sched_mod.VLLMScheduler, "schedule", fake_schedule)
+
+    with pytest.raises(_StopScheduleError):
+        OmniARScheduler.schedule(scheduler)
+
+    assert seen["counter"] == 0
+
+
+def test_generation_scheduler_sweep_removes_aborted_requests_from_all_queues() -> None:
+    aborted = _StubRequest("req-generation-aborted", RequestStatus.FINISHED_ABORTED)
+    scheduler = OmniGenerationScheduler.__new__(OmniGenerationScheduler)
+    scheduler.waiting = create_request_queue(SchedulingPolicy.FCFS)
+    scheduler.waiting.add_request(aborted)
+    scheduler.skipped_waiting = create_request_queue(SchedulingPolicy.FCFS)
+    scheduler.skipped_waiting.add_request(aborted)
+    scheduler.running = [aborted]
+
+    scheduler._drop_aborted_queued_requests()
+
+    assert list(scheduler.waiting) == []
+    assert list(scheduler.skipped_waiting) == []
+    assert scheduler.running == []

tests/dfx/perf/scripts/run_benchmark.py

@@ -1,4 +1,8 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
+
 import json
+import math
 import os
 import threading
 from collections.abc import Callable
@@ -159,6 +163,18 @@ def benchmark_params(request):
 
 def assert_result(result, params, num_prompt) -> None:
     assert result["completed"] == num_prompt, "Request failures exist"
+    baseline = params.get("baseline")
+    hardware = result.get("Hardware")
+    hardware_baseline = baseline.get(hardware) if isinstance(baseline, dict) and isinstance(hardware, str) else None
+    if isinstance(hardware_baseline, dict) and "mean_tpot_ms" in hardware_baseline:
+        num_tpot_samples = result.get("num_tpot_samples")
+        mean_tpot_ms = result.get("mean_tpot_ms")
+        assert isinstance(num_tpot_samples, int) and not isinstance(num_tpot_samples, bool) and num_tpot_samples > 0, (
+            "TPOT baseline is configured, but no measurable TPOT samples were produced"
+        )
+        assert (
+            isinstance(mean_tpot_ms, int | float) and not isinstance(mean_tpot_ms, bool) and math.isfinite(mean_tpot_ms)
+        ), "TPOT baseline is configured, but mean_tpot_ms is not finite"
     expected_audio_turns = params.get("expected_duplex_audio_turns_per_session")
     if expected_audio_turns is not None:
         session_metrics = result.get("duplex_session_metrics")

tests/dfx/perf/tests/test_minicpmo_4_5_duplex_seed_tts.json

@@ -43,7 +43,7 @@
         "extra_body": {
           "save_duplex_request_metrics": true
         },
-        "percentile-metrics": "ttft,e2el,audio_ttfp,audio_rtf,audio_duration",
+        "percentile-metrics": "ttft,tpot,itl,e2el,audio_ttfp,audio_rtf,audio_duration",
         "baseline": {
           "H100": {
             "request_throughput": [

tests/dfx/perf/tests/test_runner_metadata.py

@@ -1,3 +1,6 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
+
 """Tests for DFX runner metadata field exclusion."""
 
 import json
@@ -105,7 +108,7 @@ def test_resolve_pytest_marks_hardware_dict_with_extra():
     assert "H100" in names
     assert "cuda" in names
     assert "gpu" in names
-    assert "distributed_cuda" in names
+    assert "cards_2" in names
     assert "full_model" in names
     assert "diffusion" in names
     assert "local_model" in names
@@ -512,3 +515,41 @@ def test_omni_duplex_expected_audio_turns_rejects_incomplete_session():
             {"expected_duplex_audio_turns_per_session": 4},
             1,
         )
+
+
+def test_omni_tpot_baseline_accepts_measured_finite_sample():
+    from tests.dfx.perf.scripts.run_benchmark import assert_result
+
+    assert_result(
+        {
+            "completed": 1,
+            "Hardware": "H100",
+            "num_tpot_samples": 1,
+            "mean_tpot_ms": 10.0,
+        },
+        {"baseline": {"H100": {"mean_tpot_ms": 20.0}}},
+        1,
+    )
+
+
+@pytest.mark.parametrize(
+    ("num_tpot_samples", "mean_tpot_ms", "match"),
+    [
+        (0, float("nan"), "no measurable TPOT samples"),
+        (1, float("nan"), "mean_tpot_ms is not finite"),
+    ],
+)
+def test_omni_tpot_baseline_rejects_missing_or_nonfinite_sample(num_tpot_samples, mean_tpot_ms, match):
+    from tests.dfx.perf.scripts.run_benchmark import assert_result
+
+    with pytest.raises(AssertionError, match=match):
+        assert_result(
+            {
+                "completed": 1,
+                "Hardware": "H100",
+                "num_tpot_samples": num_tpot_samples,
+                "mean_tpot_ms": mean_tpot_ms,
+            },
+            {"baseline": {"H100": {"mean_tpot_ms": 20.0}}},
+            1,
+        )

tests/dfx/reliability/invalid_param_test/test_invalid_audio_diffusion.py

@@ -70,10 +70,14 @@ _PARAMS = [
         pytest.param({"guidance_scale": -1.0}, "guidance_scale", id="guidance_scale_negative", marks=_SKIP_ISSUE_3649),
         pytest.param({"guidance_scale": 0}, "guidance_scale", id="guidance_scale_zero", marks=_SKIP_ISSUE_3649),
         pytest.param(
-            {"num_inference_steps": 0}, "num_inference_steps", id="num_inference_steps_zero", marks=_SKIP_ISSUE_3649
+            {"num_inference_steps": 0},
+            ("num_inference_steps", "greater than or equal to 1"),
+            id="num_inference_steps_zero",
         ),
         pytest.param(
-            {"num_inference_steps": -1}, ("number of steps", "non-negative"), id="num_inference_steps_negative"
+            {"num_inference_steps": -1},
+            ("num_inference_steps", "greater than or equal to 1"),
+            id="num_inference_steps_negative",
         ),
         pytest.param(
             {"num_inference_steps": 6000},

tests/dfx/reliability/invalid_param_test/test_invalid_audio_speech.py

@@ -157,7 +157,13 @@ def test_speech_missing_required_fields(omni_server: OmniServer, online_client:
                 "ref_audio": None,
                 "ref_text": None,
             },
-            ("Invalid voice", "vivian"),
+            # NOTE: This is delegating down the TTS adapter, so the message is both specific
+            # to the Qwen3TTS adapter and dependent on whether or not the model has any speakers
+            # (as opposed to having valida speakers, but getting an invalid speaker). it would
+            # be more ideal to validate that this is landing on `.validate()` on the adapter
+            # and ensuring that the returned message matches what we would get from calling the
+            # adapter validate directly.
+            ("no speakers configured",),
             id="customvoice_invalid_voice",
         ),
         pytest.param(

tests/diffusion/attention/test_attention_config.py

@@ -11,6 +11,7 @@ Tests cover:
 """
 
 from types import SimpleNamespace
+from unittest.mock import Mock
 
 import pytest
 import torch
@@ -101,7 +102,12 @@ class TestAttentionSpec:
     def test_block_sparse_defaults_applied_when_backend_selected(self):
         spec = AttentionSpec(backend="RAINFUSION_ATTN")
         assert spec.block_sparse.sparsity == 0.8
-        assert spec.backend_kwargs() == {"sparsity": 0.8, "start_step": 0}
+        assert spec.backend_kwargs() == {
+            "sparsity": 0.8,
+            "start_step": 0,
+            "end_step": 0,
+            "precision": "bf16",
+        }
 
     def test_block_sparse_skip_layers_selector_expanded(self):
         spec = AttentionSpec(
@@ -112,6 +118,8 @@ class TestAttentionSpec:
         assert spec.backend_kwargs() == {
             "sparsity": 0.9,
             "start_step": 12,
+            "end_step": 0,
+            "precision": "bf16",
             "skip_layers": [0, 1, 2, 38],
         }
 
@@ -121,12 +129,44 @@ class TestAttentionSpec:
 
     @pytest.mark.parametrize(
         "block_sparse",
-        [{"sparsity": 1.5}, {"start_step": -1}],
+        [{"sparsity": 1.5}, {"start_step": -1}, {"end_step": -1}, {"precision": "bogus"}],
     )
     def test_block_sparse_invalid_values(self, block_sparse):
         with pytest.raises(ValueError):
             AttentionSpec(backend="RAINFUSION_ATTN", block_sparse=block_sparse)
 
+    def test_block_sparse_end_step_precision_forwarded(self):
+        spec = AttentionSpec(
+            backend="RAINFUSION_ATTN",
+            block_sparse={"sparsity": 0.9, "start_step": 5, "end_step": 3, "precision": "fp8"},
+        )
+        assert spec.backend_kwargs() == {
+            "sparsity": 0.9,
+            "start_step": 5,
+            "end_step": 3,
+            "precision": "fp8",
+        }
+
+    def test_block_sparse_precision_enum_input_normalized(self):
+        """RainFusionPrecision enum members must normalize to their string value.
+
+        Regression: the str,Enum members passed the value check but str() later
+        produced "RainFusionPrecision.MIX" instead of "mix" (StrEnum fixes it).
+        """
+        from vllm_omni.diffusion.attention.backends.rainfusion_attn import RainFusionConfig
+        from vllm_omni.diffusion.data import RainFusionPrecision
+
+        spec = AttentionSpec(
+            backend="RAINFUSION_ATTN",
+            block_sparse={"sparsity": 0.8, "precision": RainFusionPrecision.MIX},
+        )
+        kwargs = spec.backend_kwargs()
+        assert kwargs["precision"] == "mix"
+        # The config path is where str(value) used to mangle enum members.
+        cfg = RainFusionConfig.from_backend_kwargs(kwargs)
+        assert cfg.precision == "mix"
+        assert type(cfg.precision) is str
+
 
 class TestAttentionConfig:
     def test_empty_config(self):
@@ -427,6 +467,120 @@ class TestCurrentDiffusionConfig:
 
 
 class TestAttentionInitUsesCurrentDiffusionConfig:
+    @pytest.mark.parametrize("mindiesd_available", [False, True])
+    def test_paged_npu_memory_profile_uses_sdpa_only_without_mindiesd(self, monkeypatch, mindiesd_available):
+        attention = Attention.__new__(Attention)
+        attention._scheduler_paged_kv = True
+        attention.paged_kv_cache_role = "primary"
+        attention.backend_pref = "FLASH_ATTN"
+        attention.attn_backend = SimpleNamespace(supports_piecewise_spans=True, get_name=lambda: "FLASH_ATTN")
+        flash_output = torch.ones(1)
+        sdpa_output = torch.zeros(1)
+        dense_flash = Mock(return_value=flash_output)
+        sdpa = Mock(return_value=sdpa_output)
+        attention.attention = SimpleNamespace(forward=dense_flash)
+        attention.sdpa_fallback = SimpleNamespace(forward=sdpa)
+        monkeypatch.setattr(layer_mod, "is_forward_context_available", lambda: True)
+        monkeypatch.setattr(
+            layer_mod,
+            "get_forward_context",
+            lambda: SimpleNamespace(in_diffusion_kv_memory_profile=True),
+        )
+        monkeypatch.setattr(layer_mod.current_omni_platform, "is_npu", lambda: True)
+        monkeypatch.setattr(
+            layer_mod.current_omni_platform,
+            "supports_diffusion_dense_flash_attention",
+            lambda: mindiesd_available,
+        )
+
+        result = attention._run_local_attention(
+            torch.zeros(1, dtype=torch.bfloat16),
+            torch.zeros(1, dtype=torch.bfloat16),
+            torch.zeros(1, dtype=torch.bfloat16),
+            AttentionMetadata(),
+        )
+
+        if mindiesd_available:
+            assert result is flash_output
+            dense_flash.assert_called_once()
+            sdpa.assert_not_called()
+        else:
+            assert result is sdpa_output
+            dense_flash.assert_not_called()
+            sdpa.assert_called_once()
+
+    def test_dense_flash_import_error_is_not_reclassified(self, monkeypatch):
+        attention = Attention.__new__(Attention)
+        attention._scheduler_paged_kv = False
+        attention.paged_kv_cache_role = "primary"
+        attention.backend_pref = "FLASH_ATTN"
+        attention.attn_backend = SimpleNamespace(supports_piecewise_spans=True, get_name=lambda: "FLASH_ATTN")
+        attention.attention = SimpleNamespace(
+            forward=Mock(side_effect=ModuleNotFoundError("No module named 'mindiesd'", name="mindiesd"))
+        )
+        attention.sdpa_fallback = SimpleNamespace(forward=Mock(side_effect=AssertionError("must not fall back")))
+        monkeypatch.setattr(layer_mod, "is_forward_context_available", lambda: False)
+
+        with pytest.raises(ModuleNotFoundError, match="mindiesd"):
+            attention._run_local_attention(
+                torch.zeros(1, dtype=torch.bfloat16),
+                torch.zeros(1, dtype=torch.bfloat16),
+                torch.zeros(1, dtype=torch.bfloat16),
+                AttentionMetadata(),
+            )
+
+    def test_paged_marker_does_not_change_dense_backend(self, monkeypatch):
+        class _FakeAttentionImpl:
+            def __init__(self, **kwargs):
+                self.kwargs = kwargs
+
+            def forward(self, query, key, value, attn_metadata=None):
+                return query
+
+        class _DenseBackend:
+            supports_paged_kv = False
+
+            @staticmethod
+            def get_name() -> str:
+                return "TORCH_SDPA"
+
+            @staticmethod
+            def get_impl_cls():
+                return _FakeAttentionImpl
+
+        calls = []
+
+        def _fake_get_attn_backend_for_role(*args, **kwargs):
+            calls.append((args, kwargs))
+            return _DenseBackend, None
+
+        monkeypatch.setattr(layer_mod, "get_attn_backend_for_role", _fake_get_attn_backend_for_role)
+        monkeypatch.setattr(layer_mod.SDPABackend, "get_impl_cls", staticmethod(lambda: _FakeAttentionImpl))
+        monkeypatch.setattr(layer_mod, "build_parallel_attention_strategy", lambda **kwargs: object())
+        monkeypatch.setattr(layer_mod, "is_forward_context_available", lambda: False)
+
+        od_config = SimpleNamespace(
+            diffusion_attention_config=AttentionConfig(),
+            diffusion_kv_mode=layer_mod.DiffusionKVCacheMode.DENSE_LEGACY,
+            parallel_config=SimpleNamespace(ring_degree=1),
+            diffusion_kv_cache_dtype=None,
+            diffusion_kv_cache_skip_step_indices=None,
+            diffusion_kv_cache_skip_layer_indices=None,
+        )
+
+        with set_current_diffusion_config(od_config):
+            attention = Attention(
+                num_heads=4,
+                head_size=64,
+                causal=False,
+                softmax_scale=1.0,
+                paged_kv_cache_role="primary",
+            )
+
+        assert len(calls) == 1
+        assert attention.attn_backend is _DenseBackend
+        assert attention.backend_pref is None
+
     def test_attention_init_uses_current_diffusion_config_without_forward_context(self, monkeypatch):
         class _FakeAttentionImpl:
             def __init__(self, **kwargs):

tests/diffusion/attention/test_flash_attn.py

@@ -353,8 +353,15 @@ def test_cross_attn_key_padding_vs_sdpa(k_len):
     print(f"Max absolute difference: {max_diff:.6f}")
     print(f"Mean absolute difference: {mean_diff:.6f}")
 
-    assert max_diff < 0.01, f"Max difference {max_diff} exceeds threshold 0.01"
-    assert mean_diff < 0.001, f"Mean difference {mean_diff} exceeds threshold 0.001"
+    # Match the platform-specific comparison used by test_fa_vs_sdpa above.
+    if current_omni_platform.is_rocm():
+        # AITER FlashAttention and PyTorch SDPA use different BF16 reduction
+        # implementations on ROCm, so individual values can differ by one BF16
+        # step even though the outputs agree within the expected relative error.
+        torch.testing.assert_close(output_fa, output_sdpa, rtol=1e-2, atol=1.5e-2)
+    else:
+        assert max_diff < 0.01, f"Max difference {max_diff} exceeds threshold 0.01"
+        assert mean_diff < 0.001, f"Mean difference {mean_diff} exceeds threshold 0.001"
 
     print("✓ Case 3 PASSED: FA and SDPA cross-attention outputs are very close!")
 

tests/diffusion/attention/test_piecewise_attn.py

@@ -1,5 +1,5 @@
 # SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
 """End-to-end test for ``piecewise_attn`` (CPU).
 
 Verify that running attention in segments (causal outside full-attn spans,
@@ -98,9 +98,12 @@ def test_paged_piecewise_runner_uses_packed_indices_and_kv_endpoints():
     query = torch.arange(12 * 2 * 4, dtype=torch.float32).reshape(12, 2, 4)
     key = query + 100
     value = query + 200
-    calls = []
+    calls: list[object] = []
 
-    def run_segment(segment_query, segment_key, segment_value, segment_batch):
+    output_buffer = torch.empty_like(query)
+
+    def run_segment(segment_query, segment_key, segment_value, segment_batch, segment_output_buffer):
+        assert segment_output_buffer is None
         calls.append(
             (
                 [segment.mode for segment in segment_batch.row_segments],
@@ -119,9 +122,13 @@ def test_paged_piecewise_runner_uses_packed_indices_and_kv_endpoints():
         plan,
         plan.segments,
         run_segment,
+        output_buffer=output_buffer,
     )
 
+    assert output is output_buffer
     torch.testing.assert_close(output, query)
+    assert all(segment.query_range is None for segment in plan.segments)
+    assert not plan.segments_cover_query_contiguously
     assert calls == [
         (["causal", "causal"], [2, 4], [0, 1, 6, 7]),
         (["full", "full"], [5, 7], [2, 3, 4, 8, 9, 10]),
@@ -129,6 +136,161 @@ def test_paged_piecewise_runner_uses_packed_indices_and_kv_endpoints():
     ]
 
 
+def test_paged_piecewise_runner_preserves_homogeneous_batch_without_scatter(monkeypatch: pytest.MonkeyPatch):
+    plan = build_paged_piecewise_plan(
+        [[(2, 5)], [(2, 5)]],
+        query_offsets=[0, 0],
+        query_lens=[6, 6],
+        seq_lens=[6, 6],
+        device=DEVICE,
+    )
+    assert plan.homogeneous_batch_shape == (2, 6)
+    query = torch.arange(12 * 2 * 4, dtype=torch.float32).reshape(12, 2, 4)
+    calls: list[torch.Tensor] = []
+
+    def fail_index_copy(*_args, **_kwargs):
+        raise AssertionError("homogeneous piecewise path must not use index_copy_")
+
+    monkeypatch.setattr(torch.Tensor, "index_copy_", fail_index_copy)
+
+    def run_segment(segment_query, segment_key, segment_value, _metadata, segment_output_buffer):
+        assert segment_key is None
+        assert segment_value is None
+        assert segment_output_buffer is None
+        calls.append(segment_query.clone())
+        return segment_query + 1
+
+    output_buffer = torch.empty_like(query)
+    output = run_paged_piecewise_plan(
+        query,
+        None,
+        None,
+        plan,
+        plan.segments,
+        run_segment,
+        output_buffer=output_buffer,
+        use_homogeneous_batch=True,
+    )
+
+    assert output is output_buffer
+    torch.testing.assert_close(output, query + 1)
+    assert [call.shape[0] for call in calls] == [4, 6, 2]
+    torch.testing.assert_close(calls[0], query[[0, 1, 6, 7]])
+    torch.testing.assert_close(calls[1], query[[2, 3, 4, 8, 9, 10]])
+    torch.testing.assert_close(calls[2], query[[5, 11]])
+
+
+def test_paged_piecewise_runner_keeps_indexed_fallback_for_heterogeneous_layout():
+    plan = build_paged_piecewise_plan(
+        [[(2, 5)], [(1, 4)]],
+        query_offsets=[0, 0],
+        query_lens=[6, 6],
+        seq_lens=[6, 6],
+        device=DEVICE,
+    )
+    assert plan.homogeneous_batch_shape is None
+    query = torch.arange(12 * 2 * 4, dtype=torch.float32).reshape(12, 2, 4)
+    output = run_paged_piecewise_plan(
+        query,
+        None,
+        None,
+        plan,
+        plan.segments,
+        lambda segment_query, *_args: segment_query,
+        use_homogeneous_batch=True,
+    )
+    torch.testing.assert_close(output, query)
+
+
+def test_paged_piecewise_runner_slices_contiguous_segments():
+    plan = build_paged_piecewise_plan(
+        [[(2, 5)]],
+        query_offsets=[0],
+        query_lens=[6],
+        seq_lens=[6],
+        device=DEVICE,
+    )
+    query = torch.arange(6 * 2 * 4, dtype=torch.float32).reshape(6, 2, 4)
+    key = query + 100
+    value = query + 200
+    expected_ranges = [(0, 2), (2, 5), (5, 6)]
+    calls: list[tuple[int, int]] = []
+
+    def run_segment(segment_query, segment_key, segment_value, _segment_batch, segment_output_buffer):
+        assert segment_output_buffer is None
+        expected_range = expected_ranges[len(calls)]
+        start, end = expected_range
+        calls.append(expected_range)
+        assert segment_query.untyped_storage().data_ptr() == query.untyped_storage().data_ptr()
+        assert segment_key.untyped_storage().data_ptr() == key.untyped_storage().data_ptr()
+        assert segment_value.untyped_storage().data_ptr() == value.untyped_storage().data_ptr()
+        torch.testing.assert_close(segment_query, query[start:end])
+        return segment_query
+
+    output = run_paged_piecewise_plan(query, key, value, plan, plan.segments, run_segment)
+
+    assert calls == expected_ranges
+    assert [segment.query_range for segment in plan.segments] == expected_ranges
+    assert plan.segments_cover_query_contiguously
+    torch.testing.assert_close(output, query)
+
+
+def test_paged_piecewise_runner_writes_contiguous_segments_into_output_buffer():
+    plan = build_paged_piecewise_plan(
+        [[(2, 5)]],
+        query_offsets=[0],
+        query_lens=[6],
+        seq_lens=[6],
+        device=DEVICE,
+    )
+    query = torch.arange(6 * 2 * 4, dtype=torch.float32).reshape(6, 2, 4)
+    output_buffer = torch.empty(6, 2, 3)
+    targets = []
+
+    def run_segment(segment_query, _key, _value, _metadata, segment_output_buffer):
+        assert segment_output_buffer is not None
+        assert segment_output_buffer.untyped_storage().data_ptr() == output_buffer.untyped_storage().data_ptr()
+        targets.append(segment_output_buffer)
+        return segment_output_buffer.copy_(segment_query[..., :3])
+
+    output = run_paged_piecewise_plan(
+        query,
+        query,
+        query,
+        plan,
+        plan.segments,
+        run_segment,
+        output_buffer=output_buffer,
+    )
+
+    assert output is output_buffer
+    assert [target.shape[0] for target in targets] == [2, 3, 1]
+    torch.testing.assert_close(output, query[..., :3])
+
+
+def test_paged_piecewise_runner_returns_single_full_segment_directly():
+    plan = build_paged_piecewise_plan(
+        [[]],
+        query_offsets=[0],
+        query_lens=[6],
+        seq_lens=[6],
+        device=DEVICE,
+    )
+    query = torch.randn(6, 2, 4)
+    runner_output = query + 1
+
+    output = run_paged_piecewise_plan(
+        query,
+        query,
+        query,
+        plan,
+        plan.segments,
+        lambda *_args: runner_output,
+    )
+
+    assert output is runner_output
+
+
 def test_paged_piecewise_runner_uses_runner_output_shape():
     plan = build_paged_piecewise_plan(
         [[(2, 5)]],

tests/diffusion/attention/test_rainfusion_plan.py

@@ -1,5 +1,5 @@
 # SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
 
 """Geometry contract RAINFUSION_ATTN enforces before handing a forward to rf_v2.
 
@@ -13,10 +13,12 @@ to the always-kept prefix segment before generating the block mask.
 import dataclasses
 import sys
 import types
+from unittest import mock
 
 import pytest
 import torch
 
+from vllm_omni.diffusion.attention.backends import rainfusion_attn
 from vllm_omni.diffusion.attention.backends.abstract import AttentionMetadata, VideoTokenLayout, VideoTokenSpan
 from vllm_omni.diffusion.attention.backends.rainfusion_attn import (
     _BLOCK_SIZE,
@@ -26,6 +28,8 @@ from vllm_omni.diffusion.attention.backends.rainfusion_attn import (
 )
 from vllm_omni.platforms import current_omni_platform
 
+pytestmark = [pytest.mark.core_model, pytest.mark.diffusion, pytest.mark.npu]
+
 PREFIX_ROWS = 710  # 14 text rows + 696 audio rows
 ALIGNED_GRID = (62, 24, 40)  # 1280x768 -> 59520 video rows, 465 blocks
 MISALIGNED_GRID = (62, 24, 42)  # 1344x768 -> 62496 video rows, 488 blocks + 32 rows
@@ -228,7 +232,9 @@ def test_irregular_video_tail_reproduces_dense_attention_with_updated_mindiesd()
     heads, head_dim = 4, 128
     q, k, v = (torch.randn(1, used, heads, head_dim, dtype=torch.bfloat16, device="npu") for _ in range(3))
 
-    impl = make_impl()
+    # Explicit bf16: the dense comparison exercises the plain sparse kernel,
+    # and older MindIE-SD releases do not accept precision= (gate would raise).
+    impl = make_impl(precision="bf16")
     impl.rainfusion = dataclasses.replace(impl.rainfusion, sparsity=0.0)
     plan = RainFusionPlan(prefix_len=prefix_len, used_len=used, latent_shape=list(grid))
     out = impl._forward_sparse_npu(q, k, v, plan)
@@ -241,3 +247,90 @@ def test_irregular_video_tail_reproduces_dense_attention_with_updated_mindiesd()
     ).transpose(1, 2)
     error = (out.float() - reference.float()).abs().mean() / reference.float().abs().mean()
     assert error < 2e-3, f"mean relative error {error:.4%} against dense attention"
+
+
+# end_step tail fallback: the last ``end_step`` denoise steps must stay dense.
+
+
+def test_end_step_tail_window_stays_dense():
+    """step_idx inside the last end_step window must resolve no sparse plan."""
+    impl = make_impl(end_step=3)
+    fc = mock.Mock()
+    fc.denoise_step_idx = 47
+    fc.total_denoise_steps = 50
+    with (
+        mock.patch.object(rainfusion_attn, "is_forward_context_available", return_value=True),
+        mock.patch.object(rainfusion_attn, "get_forward_context", return_value=fc),
+    ):
+        assert impl._resolve_plan(make_metadata(ALIGNED_GRID)) is None
+
+
+def test_end_step_before_tail_window_still_sparse():
+    """step_idx before the tail window must still resolve a sparse plan."""
+    impl = make_impl(end_step=3)
+    fc = mock.Mock()
+    fc.denoise_step_idx = 46
+    fc.total_denoise_steps = 50
+    with (
+        mock.patch.object(rainfusion_attn, "is_forward_context_available", return_value=True),
+        mock.patch.object(rainfusion_attn, "get_forward_context", return_value=fc),
+    ):
+        assert impl._resolve_plan(make_metadata(ALIGNED_GRID)) is not None
+
+
+def test_end_step_zero_never_triggers_tail_fallback():
+    """end_step=0 must not fall back even on the final denoise step."""
+    impl = make_impl(end_step=0)
+    fc = mock.Mock()
+    fc.denoise_step_idx = 49
+    fc.total_denoise_steps = 50
+    with (
+        mock.patch.object(rainfusion_attn, "is_forward_context_available", return_value=True),
+        mock.patch.object(rainfusion_attn, "get_forward_context", return_value=fc),
+    ):
+        assert impl._resolve_plan(make_metadata(ALIGNED_GRID)) is not None
+
+
+# precision capability gate: a mindiesd without sparse_attention(precision=)
+# must fail fast instead of silently running the BF16 path.
+
+
+def _fake_mindiesd_module():
+    """Install a minimal stand-in so the import inside _forward_sparse_npu succeeds."""
+    import types
+
+    fake = types.ModuleType("mindiesd")
+    fake.sparse_attention = lambda *args, **kwargs: None
+    return fake
+
+
+def test_precision_non_bf16_requires_mindiesd_support():
+    """precision != bf16 against a mindiesd lacking the kwarg must raise RuntimeError."""
+    import sys
+
+    impl = make_impl(precision="mix")
+    sys.modules["mindiesd"] = _fake_mindiesd_module()
+    try:
+        with mock.patch.object(rainfusion_attn, "_mindiesd_supports_precision", return_value=False):
+            with pytest.raises(RuntimeError, match="requires MindIE-SD"):
+                impl._forward_sparse_npu(None, None, None, None)
+    finally:
+        sys.modules.pop("mindiesd", None)
+
+
+def test_precision_non_bf16_passes_gate_when_supported():
+    """precision != bf16 with a capable mindiesd must not raise from the gate."""
+    import sys
+
+    impl = make_impl(precision="mix")
+    sys.modules["mindiesd"] = _fake_mindiesd_module()
+    try:
+        with mock.patch.object(rainfusion_attn, "_mindiesd_supports_precision", return_value=True):
+            # q/k/v shapes: [B, S, N, D]; plan geometry must match S.
+            q = torch.randn(1, 8, 4, 128)
+            plan = RainFusionPlan(prefix_len=0, used_len=8, latent_shape=[2, 2, 2])
+            # The gate must pass; the fake mindiesd returns None so no crash.
+            out = impl._forward_sparse_npu(q, q, q, plan)
+            assert out is None
+    finally:
+        sys.modules.pop("mindiesd", None)

tests/diffusion/cache/test_teacache_extractors.py

@@ -1,5 +1,5 @@
 # SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
 
 """
 Unit tests for TeaCache extractor functions.
@@ -18,6 +18,7 @@ Currently implemented:
 - TestMiniMaxH3Extractor: MiniMaxH3DiTModel extractor
 """
 
+import math
 from abc import ABC, abstractmethod
 from types import SimpleNamespace
 from unittest.mock import MagicMock, Mock, patch
@@ -650,6 +651,29 @@ class TestMiniMaxH3Extractor(BaseExtractorTest):
         torch.testing.assert_close(actual_video, expected_video)
         torch.testing.assert_close(actual_audio, expected_audio)
 
+    def test_extractor_uses_prepared_rope_table(
+        self,
+        minimax_h3_module,
+        sample_inputs,
+        monkeypatch,
+    ):
+        """TeaCache must consume the denoise-loop RoPE table without rebuilding it."""
+        rope_table = minimax_h3_module.prepare_rope_table(
+            sample_inputs["img_position_ids"],
+            seq_len=sample_inputs["x"].shape[1],
+        )
+
+        def unexpected_rope_build(*args, **kwargs):
+            raise AssertionError("TeaCache extractor rebuilt the prepared RoPE table")
+
+        monkeypatch.setattr(minimax_h3_module, "prepare_rope_table", unexpected_rope_build)
+        monkeypatch.setattr(minimax_h3_module.rope, "forward", unexpected_rope_build)
+        context = extract_minimax_h3_context(
+            minimax_h3_module,
+            **{**sample_inputs, "rope_table": rope_table},
+        )
+        context.run_transformer_blocks()
+
     def test_run_transformer_blocks_forwards_packed_layout(
         self,
         minimax_h3_module,
@@ -705,6 +729,222 @@ class TestMiniMaxH3Extractor(BaseExtractorTest):
         assert video_logits.shape[0] == sample_inputs["img_pos_info"]["position_ids"].shape[0]
         assert audio_logits.shape[0] == sample_inputs["audio_pos_info"]["position_ids"].shape[0]
 
+    @pytest.mark.sp
+    def test_teacache_strict_sp_keeps_cache_state_rank_local(
+        self,
+        minimax_h3_module,
+        sample_inputs,
+        monkeypatch,
+    ):
+        """Strict SP must not mix local TeaCache state with gathered block rows."""
+        from vllm_omni.diffusion.attention.ops import minimax_h3_modulation
+        from vllm_omni.diffusion.cache.teacache.config import TeaCacheConfig
+        from vllm_omni.diffusion.cache.teacache.hook import TeaCacheHook
+        from vllm_omni.diffusion.distributed import parallel_state
+
+        seq_len = sample_inputs["x"].shape[1]
+        local_len = seq_len // 2
+        hidden_size = minimax_h3_module.hidden_size
+        video_width = minimax_h3_module.arch.latents_dim * math.prod(minimax_h3_module.arch.patch_size)
+        audio_width = minimax_h3_module.arch.audio_latents_dim
+        captured_modulation_indices: list[torch.Tensor] = []
+
+        class _StrictSPPrepare(nn.Module):
+            def __init__(self):
+                super().__init__()
+                self.global_indices: list[torch.Tensor] = []
+
+            def forward(self, hidden, rope_table, combined_indices):
+                assert hidden.shape[0] == local_len
+                assert rope_table.shape[0] == local_len
+                self.global_indices.append(combined_indices.clone())
+                return hidden, rope_table, combined_indices.narrow(0, 0, local_len)
+
+        class _StrictSPGather(nn.Module):
+            def __init__(self):
+                super().__init__()
+                self.inputs: list[torch.Tensor] = []
+                self.outputs: list[torch.Tensor] = []
+
+            def forward(self, tensor):
+                self.inputs.append(tensor.clone())
+                if tensor.shape[-1] == hidden_size:
+                    raise AssertionError("TeaCache must not gather hidden states in strict SP")
+                assert tensor.shape == (local_len, video_width + audio_width)
+                # Model the all-gather of compact logits from every SP rank.
+                # postprocess() indexes the resulting global layout with
+                # infer_out_pos/audio_pos, including rows owned by another rank.
+                gathered = tensor.repeat(seq_len // local_len, 1)
+                assert gathered.shape == (seq_len, video_width + audio_width)
+                self.outputs.append(gathered)
+                return gathered
+
+        class _StrictSPGroup:
+            def __init__(self):
+                super().__init__()
+                self.local_decisions: list[int] = []
+                self.ops = []
+
+            def all_reduce(self, decision, op):
+                self.local_decisions.append(int(decision.item()))
+                self.ops.append(op)
+                # Simulate another rank requiring the second step to execute
+                # the collective block path even though this rank would cache.
+                if len(self.local_decisions) == 2:
+                    decision.fill_(1)
+                return decision
+
+        class _StrictSPBlock(nn.Module):
+            def __init__(self):
+                super().__init__()
+                self.block_indices: list[torch.Tensor] = []
+
+            def adaln_proj(self, t_emb):
+                del t_emb
+                zeros = torch.zeros(3, hidden_size, dtype=torch.float32)
+                return zeros, zeros
+
+            def norm1(self, hidden):
+                return hidden.clone()
+
+            def forward(self, hidden, *, combined_indices, rope_table, **kwargs):
+                del kwargs
+                assert hidden.shape[0] == local_len
+                assert rope_table.shape[0] == local_len
+                self.block_indices.append(combined_indices.clone())
+                return hidden + 1
+
+        class _StrictSPFinalLayer(nn.Module):
+            def forward(self, hidden, *, t_emb, inverse_indices):
+                del t_emb, inverse_indices
+                assert hidden.shape == (local_len, hidden_size)
+                return hidden[:, :video_width].float(), hidden[:, :audio_width].float()
+
+        local_prepare = _StrictSPPrepare()
+        gather = _StrictSPGather()
+        sp_group = _StrictSPGroup()
+        block = _StrictSPBlock()
+        minimax_h3_module.local_sp_prepare = local_prepare
+        minimax_h3_module.sp_gather = gather
+        minimax_h3_module.blocks = nn.ModuleList([block])
+        minimax_h3_module.final_layer = _StrictSPFinalLayer()
+        monkeypatch.setattr(minimax_h3_module, "_rope_local_span", lambda _seq_len: (0, local_len))
+        monkeypatch.setattr(
+            minimax_h3_module,
+            "_embed",
+            lambda **kwargs: (
+                torch.arange(local_len * hidden_size, dtype=torch.float32).reshape(local_len, hidden_size),
+                torch.zeros(3, hidden_size, dtype=torch.float32),
+            ),
+        )
+        monkeypatch.setattr(
+            minimax_h3_module,
+            "prepare_rope_table",
+            lambda *args, **kwargs: torch.zeros(local_len, 6, dtype=torch.float32),
+        )
+        monkeypatch.setattr(parallel_state, "get_sp_group", lambda: sp_group)
+
+        original_indexed_scale_shift = minimax_h3_modulation.indexed_scale_shift_
+
+        def capture_indexed_scale_shift(hidden, shift, scale, indices):
+            captured_modulation_indices.append(indices.clone())
+            return original_indexed_scale_shift(hidden, shift, scale, indices)
+
+        monkeypatch.setattr(minimax_h3_modulation, "indexed_scale_shift_", capture_indexed_scale_shift)
+
+        local_img_pos = torch.tensor([0, 1])
+        local_audio_pos = torch.tensor([2, 3])
+        token_tags = torch.full((seq_len,), -1, dtype=torch.long)
+        token_tags[local_img_pos] = 0
+        token_tags[local_audio_pos] = 2
+        inverse_indices = torch.zeros(seq_len, dtype=torch.long)
+        inputs = {
+            **sample_inputs,
+            "inverse_indices": inverse_indices,
+            "token_tags": token_tags,
+            "update_mask": torch.ones(local_img_pos.numel(), dtype=torch.bool),
+            "prompt_embeds": torch.empty(0, 6),
+            "img_pos_info": {"position_ids": local_img_pos},
+            "audio_pos_info": {"position_ids": local_audio_pos},
+            "text_pos_info": {"position_ids": torch.empty(0, dtype=torch.long)},
+            "img_pos_for_infer_output_info": {"position_ids": local_img_pos},
+            "refiner_packed_seq_params": {
+                "cu_seqlens_q": torch.tensor([0, 0], dtype=torch.int32),
+                "max_seqlen_q": 0,
+            },
+        }
+        expected_local_indices = (inverse_indices * 3 + token_tags.clamp(min=0))[:local_len]
+
+        hook = TeaCacheHook(
+            TeaCacheConfig(
+                transformer_type="MiniMaxH3DiTModel",
+                rel_l1_thresh=0.3,
+            )
+        )
+        hook.initialize_hook(minimax_h3_module)
+
+        hook.new_forward(minimax_h3_module, **inputs)
+        hook.new_forward(minimax_h3_module, **inputs)
+        video_logits, audio_logits = hook.new_forward(minimax_h3_module, **inputs)
+        state = hook.state_manager.get_state()
+
+        assert state.previous_residual is not None
+        assert state.previous_residual.shape == (local_len, hidden_size)
+        assert len(captured_modulation_indices) == 3
+        assert all(torch.equal(indices, expected_local_indices) for indices in captured_modulation_indices)
+        assert sp_group.local_decisions == [1, 0, 0]
+        assert len(sp_group.ops) == 3
+        assert all(op == torch.distributed.ReduceOp.MAX for op in sp_group.ops)
+        assert len(local_prepare.global_indices) == 2
+        assert all(indices.shape == (seq_len,) for indices in local_prepare.global_indices)
+        assert len(block.block_indices) == 2
+        assert all(torch.equal(indices, expected_local_indices) for indices in block.block_indices)
+        assert len(gather.inputs) == 3
+        assert video_logits.shape == (local_img_pos.numel(), video_width)
+        assert audio_logits.shape == (local_audio_pos.numel(), audio_width)
+
+    @pytest.mark.cpu
+    def test_teacache_non_strict_sp_gathers_hidden_once(
+        self,
+        minimax_h3_module,
+        sample_inputs,
+        monkeypatch,
+    ):
+        """The non-strict SP cache path must not gather global hidden rows twice."""
+        from vllm_omni.diffusion.cache.teacache.config import TeaCacheConfig
+        from vllm_omni.diffusion.cache.teacache.hook import TeaCacheHook
+
+        class _Prepare(nn.Module):
+            def forward(self, hidden, rope_table, combined_indices):
+                return hidden, rope_table, combined_indices
+
+        class _Gather(nn.Module):
+            def __init__(self):
+                super().__init__()
+                self.inputs: list[torch.Tensor] = []
+
+            def forward(self, tensor):
+                self.inputs.append(tensor.clone())
+                return tensor
+
+        seq_len = sample_inputs["x"].shape[1]
+        gather = _Gather()
+        minimax_h3_module.sp_prepare = _Prepare()
+        minimax_h3_module.sp_gather = gather
+        monkeypatch.setattr(minimax_h3_module, "_rope_local_span", lambda _seq_len: (0, seq_len))
+
+        hook = TeaCacheHook(
+            TeaCacheConfig(
+                transformer_type="MiniMaxH3DiTModel",
+                rel_l1_thresh=0.3,
+            )
+        )
+        hook.initialize_hook(minimax_h3_module)
+        hook.new_forward(minimax_h3_module, **sample_inputs)
+
+        assert len(gather.inputs) == 1
+        assert gather.inputs[0].shape == (seq_len, minimax_h3_module.hidden_size)
+
     def test_invalid_module_raises_error(self):
         """Test that invalid module without blocks raises ValueError."""
         invalid_module = Mock()

tests/diffusion/conftest.py

@@ -0,0 +1,15 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import pytest
+import torch
+
+
+@pytest.fixture(autouse=True)
+def restore_torch_default_dtype():
+    """Keep process-global dtype changes from leaking between diffusion tests."""
+    default_dtype = torch.get_default_dtype()
+    try:
+        yield
+    finally:
+        torch.set_default_dtype(default_dtype)

tests/diffusion/diffusion_backend/test_diffusers_backend.py

@@ -9,6 +9,7 @@ import torch
 from diffusers import DiffusionPipeline  # pyright: ignore[reportPrivateImportUsage]
 from PIL import Image
 
+import vllm_omni.diffusion.data as diffusion_data
 from tests.helpers.mark import hardware_test
 from tests.helpers.runtime import OmniServer, OmniServerParams, OnlineOmniClient, dummy_messages_from_mix_data
 from vllm_omni.diffusion.data import (
@@ -629,6 +630,46 @@ class TestPipelineArgumentsHandling:
         with pytest.raises(ValueError):
             pipeline.forward(DiffusionRequestBatch(requests=[problematic_request]))
 
+        interpolation_request = _make_request(
+            sampling_params=OmniDiffusionSamplingParams(
+                enable_frame_interpolation=True,
+            ),
+        )
+        with pytest.raises(ValueError, match="Frame interpolation is not supported with the Diffusers backend"):
+            pipeline.forward(DiffusionRequestBatch(requests=[interpolation_request]))
+
+    def test_diffusers_config_preserves_checkpoint_class_for_metadata(self, mocker):
+        class MockWanImageToVideoPipeline:
+            pass
+
+        od_config = _make_od_config(model_class_name=None)
+        mocker.patch(
+            "vllm_omni.diffusion.utils.hf_utils.get_diffusion_model_index",
+            return_value={"_class_name": "WanImageToVideoPipeline"},
+        )
+        mocker.patch.object(
+            diffusion_data,
+            "diffusers",
+            SimpleNamespace(WanImageToVideoPipeline=MockWanImageToVideoPipeline),
+        )
+        mocker.patch.object(OmniDiffusionConfig, "update_multimodal_support")
+
+        od_config.enrich_config()
+
+        assert od_config.model_class_name == "WanImageToVideoPipeline"
+        assert od_config.diffusers_pipeline_cls is MockWanImageToVideoPipeline
+
+    def test_diffusers_config_falls_back_to_adapter_without_pipeline_index(self, mocker):
+        od_config = _make_od_config(model_class_name=None)
+        mocker.patch(
+            "vllm_omni.diffusion.utils.hf_utils.get_diffusion_model_index",
+            return_value=None,
+        )
+
+        od_config.enrich_config()
+
+        assert od_config.model_class_name == "DiffusersAdapterPipeline"
+
 
 @pytest.mark.advanced_model
 @hardware_test(res={"cuda": "L4"}, num_cards=1)

tests/diffusion/diffusion_kv/test_block_tables.py

@@ -1,5 +1,5 @@
 # SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
 
 from __future__ import annotations
 
@@ -485,8 +485,16 @@ def test_valid_sequence_and_context_install_into_native_rows(monkeypatch: pytest
     assert runner.get_diffusion_kv_row("req-0", 0, "text") == 1
     sequence_binding = runner._resolve_paged_attention_row("req-0", 0, None)
     context_binding = runner._resolve_paged_attention_row("req-0", None, "text")
-    assert (sequence_binding.row_index, sequence_binding.max_seq_len) == (0, 9)
-    assert (context_binding.row_index, context_binding.max_seq_len) == (1, 5)
+    assert (sequence_binding.row_index, sequence_binding.max_seq_len, sequence_binding.block_ids) == (
+        0,
+        9,
+        ((1, 2, 3), (4, 5)),
+    )
+    assert (context_binding.row_index, context_binding.max_seq_len, context_binding.block_ids) == (
+        1,
+        5,
+        ((6, 7), (8,)),
+    )
 
 
 def test_block_table_mutations_invalidate_prepared_attention_batches(

tests/diffusion/diffusion_kv/test_config.py

@@ -1,5 +1,5 @@
 # SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
 
 import pytest
 
@@ -36,7 +36,7 @@ def test_non_mapping_omni_kv_config_is_rejected() -> None:
         OmniDiffusionConfig.from_kwargs(omni_kv_config="paged_scheduler")
 
 
-def test_paged_scheduler_rejects_dense_legacy_kv_receive() -> None:
+def test_paged_scheduler_rejects_dense_connector_kv_receive() -> None:
     with pytest.raises(ValueError, match="does not support imported AR KV"):
         OmniDiffusionConfig.from_kwargs(
             diffusion_kv_mode="paged_scheduler",

tests/diffusion/diffusion_kv/test_initialization.py

@@ -1,5 +1,5 @@
 # SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
 
 from types import SimpleNamespace
 from unittest.mock import Mock
@@ -290,6 +290,7 @@ def test_minus_one_max_model_len_is_auto_fitted_by_native_cache_sizing() -> None
         dtype=torch.bfloat16,
         non_causal=True,
     )
+    model_limit = vllm_config.model_config.max_model_len
 
     initialization.build_native_kv_cache_configs(
         vllm_config,
@@ -298,4 +299,8 @@ def test_minus_one_max_model_len_is_auto_fitted_by_native_cache_sizing() -> None
     )
 
     assert vllm_config.model_config.original_max_model_len == -1
-    assert vllm_config.model_config.max_model_len == 256
+    # Sixteen physical pages provide ``16 * block_size`` tokens.  The native
+    # backend chooses the block geometry, so keep this assertion backend
+    # independent instead of assuming the generic 16-token block size.
+    expected_max_model_len = min(model_limit, spec.block_size * 16)
+    assert vllm_config.model_config.max_model_len == expected_max_model_len

tests/diffusion/diffusion_kv/test_paged_attention_adapter.py

@@ -1,5 +1,5 @@
 # SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
 
 from __future__ import annotations
 
@@ -20,12 +20,16 @@ from vllm_omni.diffusion.attention.layer import Attention
 from vllm_omni.diffusion.diffusion_kv import paged_attention_adapter as adapter_module
 from vllm_omni.diffusion.diffusion_kv.paged_attention_adapter import (
     DiffusionPagedAttentionAdapter,
+    DiffusionPagedAttentionMetadata,
     DiffusionPagedAttentionRow,
     DiffusionPagedAttentionRowBinding,
+    DiffusionPagedAttentionRuntime,
 )
 from vllm_omni.diffusion.forward_context import (
+    get_forward_context,
     override_paged_kv_adapter,
     set_forward_context,
+    set_forward_context_denoise_step_idx,
 )
 
 pytestmark = [pytest.mark.core_model, pytest.mark.cpu, pytest.mark.diffusion]
@@ -122,8 +126,8 @@ class _FakeNativeImpl:
     def do_kv_cache_update(
         self,
         _layer,
-        key: torch.Tensor,
-        value: torch.Tensor,
+        key: torch.Tensor | None,
+        value: torch.Tensor | None,
         _kv_cache,
         slot_mapping: torch.Tensor,
     ) -> None:
@@ -155,6 +159,11 @@ def _make_adapter(
     capacity: int = 16,
 ) -> tuple[DiffusionPagedAttentionAdapter, _FakeBlockTables, _FakeLayer, list[tuple]]:
     events: list[tuple] = []
+    monkeypatch.setattr(
+        adapter_module.current_omni_platform,
+        "requires_diffusion_paged_kv_prewrite",
+        lambda: False,
+    )
     block_tables = _FakeBlockTables()
     block_tables.max_num_batched_tokens = capacity
     block_tables.num_blocks.np.fill(math.ceil(capacity / block_tables.block_sizes[0]))
@@ -301,6 +310,47 @@ def test_paged_adapter_accepts_native_gqa_shapes(monkeypatch: pytest.MonkeyPatch
     assert context.value_write.shape == (3, 8, 4)
 
 
+def test_paged_adapter_preserves_strided_kv_for_platform_writer(monkeypatch: pytest.MonkeyPatch) -> None:
+    adapter, _, _, _ = _make_adapter(monkeypatch)
+    batch = adapter.prepare_batch(
+        [DiffusionPagedAttentionRow(request_id="req-0", sequence_id=0, query_len=5, seq_len=5)]
+    )
+    packed_qkv = torch.randn(1, 5, 3 * 2 * 4)
+    query, key, value = packed_qkv.split(2 * 4, dim=-1)
+    query = query.reshape(1, 5, 2, 4)
+    key = key.reshape(1, 5, 2, 4)
+    value = value.reshape(1, 5, 2, 4)
+    assert not key.is_contiguous()
+    assert not value.is_contiguous()
+
+    with adapter.activate(batch):
+        context = adapter.prepare_layer_context("layer-0", query, key, value)
+
+    assert context.query.is_contiguous()
+    assert not context.key_write.is_contiguous()
+    assert not context.value_write.is_contiguous()
+    assert context.key_write.data_ptr() == key.data_ptr()
+    assert context.value_write.data_ptr() == value.data_ptr()
+
+
+def test_paged_adapter_packs_noncontiguous_kv_head_layout(monkeypatch: pytest.MonkeyPatch) -> None:
+    adapter, _, _, _ = _make_adapter(monkeypatch)
+    batch = adapter.prepare_batch(
+        [DiffusionPagedAttentionRow(request_id="req-0", sequence_id=0, query_len=5, seq_len=5)]
+    )
+    query = torch.randn(1, 5, 2, 4)
+    key = torch.randn(1, 5, 4, 2).transpose(-1, -2)
+    value = torch.randn(1, 5, 4, 2).transpose(-1, -2)
+    assert key.shape == (1, 5, 2, 4)
+    assert key.stride(-1) != 1
+
+    with adapter.activate(batch):
+        context = adapter.prepare_layer_context("layer-0", query, key, value)
+
+    assert context.key_write.is_contiguous()
+    assert context.value_write.is_contiguous()
+
+
 def test_omni_paged_backend_defers_backend_owned_cache_update(monkeypatch: pytest.MonkeyPatch) -> None:
     adapter, _, layer, _ = _make_adapter(monkeypatch)
     layer.attn_backend.forward_includes_kv_cache_update = True
@@ -350,10 +400,48 @@ def test_omni_piecewise_backend_defers_backend_owned_cache_update(monkeypatch: p
     assert [metadata.query_start_loc_cpu.tolist() for metadata in segment_metadata] == [[0, 1], [0, 2], [0, 1]]
     assert [metadata.positions.tolist() for metadata in segment_metadata] == [[0], [1, 2], [3]]
     assert [metadata.slot_mappings.tolist() for metadata in segment_metadata] == [[[0]], [[1, 2]], [[3]]]
+    for event in events[1:]:
+        build_kwargs = event[1]
+        assert build_kwargs["positions"].untyped_storage().data_ptr() == batch.positions.untyped_storage().data_ptr()
+        assert (
+            build_kwargs["slot_mappings"].untyped_storage().data_ptr()
+            == batch.slot_mappings.untyped_storage().data_ptr()
+        )
+
+
+def test_platform_prewrite_updates_once_then_piecewise_reads_cache(monkeypatch: pytest.MonkeyPatch) -> None:
+    adapter, _, layer, _ = _make_adapter(monkeypatch)
+    layer.attn_backend.forward_includes_kv_cache_update = True
+    monkeypatch.setattr(
+        adapter_module.current_omni_platform,
+        "requires_diffusion_paged_kv_prewrite",
+        lambda: True,
+    )
+    adapter.resolve_row = lambda *_args: DiffusionPagedAttentionRowBinding(
+        row_index=2,
+        max_seq_len=16,
+        block_ids=((3, 7, 9, 11),),
+    )
+    row = DiffusionPagedAttentionRow(request_id="req-0", sequence_id=0, query_len=4, seq_len=4)
+    batch = adapter.prepare_batch([row])
+    qkv = torch.randn(1, 4, 2, 4)
+    metadata = SimpleNamespace(full_attn_spans=[[(1, 3)]], extra={})
+
+    with adapter.activate(batch):
+        context = adapter.prepare_layer_context("layer-0", qkv, qkv, qkv, omni_attn_metadata=metadata)
+        output = _run_omni_paged_backend(context)
+
+    assert torch.equal(output, qkv)
+    assert layer.native_events == ["update", "forward", "forward", "forward"]
+    assert len(layer.updates) == 1
+    assert all(call[1] is None and call[2] is None for call in layer.calls)
 
 
 def test_omni_paged_backend_runs_hunyuan_piecewise_segments(monkeypatch: pytest.MonkeyPatch) -> None:
     adapter, _, layer, events = _make_adapter(monkeypatch)
+    # Exercise the Ascend-only homogeneous batch path. CUDA keeps its native
+    # output-buffer contract and is covered by the GPU adapter tests.
+    monkeypatch.setattr(adapter_module.current_omni_platform, "is_npu", lambda: True)
     batch = adapter.prepare_batch(
         [
             DiffusionPagedAttentionRow(
@@ -450,6 +538,48 @@ def test_omni_paged_backend_runs_hunyuan_piecewise_segments(monkeypatch: pytest.
     ]
 
 
+def test_piecewise_metadata_snapshots_reused_native_scheduler_buffer(monkeypatch: pytest.MonkeyPatch) -> None:
+    adapter, _, _, _ = _make_adapter(monkeypatch)
+    shared_scheduler_metadata = torch.zeros(1, dtype=torch.int32)
+    build_id = 0
+
+    def build_metadata(**kwargs):
+        nonlocal build_id
+        shared_scheduler_metadata.fill_(build_id)
+        metadata = SimpleNamespace(
+            build_id=build_id,
+            causal=kwargs["causal"],
+            seq_lens=kwargs["seq_lens"].clone(),
+            query_start_loc_cpu=kwargs["query_start_loc_cpu"].clone(),
+            positions=kwargs["positions"].clone(),
+            slot_mappings=kwargs["slot_mappings"].clone(),
+            scheduler_metadata=shared_scheduler_metadata,
+            prefix_scheduler_metadata=None,
+        )
+        build_id += 1
+        return {"layer-0": metadata}
+
+    monkeypatch.setattr(adapter_module.current_omni_platform, "build_diffusion_kv_attn_metadata", build_metadata)
+    batch = adapter.prepare_batch(
+        [DiffusionPagedAttentionRow(request_id="req-0", sequence_id=0, query_len=4, seq_len=4)]
+    )
+    qkv = torch.randn(1, 4, 2, 4)
+    metadata = SimpleNamespace(full_attn_spans=[[(1, 3)]], extra={})
+
+    with adapter.activate(batch):
+        context = adapter.prepare_layer_context(
+            "layer-0",
+            qkv,
+            qkv,
+            qkv,
+            omni_attn_metadata=metadata,
+        )
+
+    segment_schedules = [item.scheduler_metadata for item in context.piecewise_native_metadata]
+    assert [item.item() for item in segment_schedules] == [1, 2, 3]
+    assert len({item.data_ptr() for item in segment_schedules}) == 3
+
+
 def test_omni_paged_backend_treats_empty_full_spans_as_causal(monkeypatch: pytest.MonkeyPatch) -> None:
     adapter, _, layer, events = _make_adapter(monkeypatch)
     batch = adapter.prepare_batch(
@@ -851,6 +981,28 @@ def test_forward_rejects_batched_layout_that_does_not_match_rows(monkeypatch: py
         adapter.prepare_layer_context("layer-0", qkv, qkv, qkv)
 
 
+def test_forward_packs_and_restores_right_padded_heterogeneous_rows(monkeypatch: pytest.MonkeyPatch) -> None:
+    adapter, _, _, _ = _make_adapter(monkeypatch)
+    batch = adapter.prepare_batch(
+        [
+            DiffusionPagedAttentionRow(request_id="req-0", sequence_id=0, query_len=1, seq_len=1),
+            DiffusionPagedAttentionRow(request_id="req-1", sequence_id=1, query_len=3, seq_len=3),
+        ]
+    )
+    qkv = torch.arange(2 * 3 * 2 * 4, dtype=torch.float32).reshape(2, 3, 2, 4)
+
+    with adapter.activate(batch):
+        context = adapter.prepare_layer_context("layer-0", qkv, qkv, qkv)
+
+    assert context.output_scatter_indices.tolist() == [0, 3, 4, 5]
+    torch.testing.assert_close(context.query, qkv.reshape(-1, 2, 4)[[0, 3, 4, 5]])
+    restored = context.restore_output(context.query)
+    assert restored.shape == qkv.shape
+    torch.testing.assert_close(restored[0, 0], qkv[0, 0])
+    assert torch.count_nonzero(restored[0, 1:]) == 0
+    torch.testing.assert_close(restored[1], qkv[1])
+
+
 def test_forward_rejects_padded_full_kv(monkeypatch: pytest.MonkeyPatch) -> None:
     adapter, _, _, _ = _make_adapter(monkeypatch)
     batch = adapter.prepare_batch(
@@ -878,6 +1030,41 @@ def test_forward_rejects_padded_full_kv(monkeypatch: pytest.MonkeyPatch) -> None
         adapter.prepare_layer_context("layer-0", query, full_kv, full_kv)
 
 
+def test_runner_runtime_switches_from_prefill_to_reusable_denoise_metadata(
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    adapter, _, _, _ = _make_adapter(monkeypatch)
+    metadata = DiffusionPagedAttentionMetadata(
+        prefill_rows=(DiffusionPagedAttentionRow(request_id="req-0", sequence_id=0, query_len=6, seq_len=6),),
+        denoise_rows=(
+            DiffusionPagedAttentionRow(
+                request_id="req-0",
+                sequence_id=0,
+                query_len=2,
+                seq_len=5,
+                kv_start_pos=3,
+            ),
+        ),
+    )
+    runtime = DiffusionPagedAttentionRuntime(adapter, metadata)
+
+    with set_forward_context(paged_kv_runtime=runtime), runtime.activate():
+        set_forward_context_denoise_step_idx(0)
+        assert Attention._active_paged_kv_adapter() is adapter
+        assert adapter._active_batch.rows == metadata.prefill_rows
+
+        set_forward_context_denoise_step_idx(1)
+        assert Attention._active_paged_kv_adapter() is adapter
+        denoise_batch = adapter._active_batch
+        assert denoise_batch.rows == metadata.denoise_rows
+
+        set_forward_context_denoise_step_idx(2)
+        assert Attention._active_paged_kv_adapter() is adapter
+        assert adapter._active_batch is denoise_batch
+
+    assert adapter._active_batch is None
+
+
 def test_causal_batch_rejects_non_suffix_write(monkeypatch: pytest.MonkeyPatch) -> None:
     adapter, _, _, _ = _make_adapter(monkeypatch, non_causal=False)
     row = DiffusionPagedAttentionRow(
@@ -992,7 +1179,8 @@ def test_layer_adapter_accepts_platform_native_backend_and_uses_rank_local_heads
     monkeypatch: pytest.MonkeyPatch,
 ) -> None:
     selected_backends = []
-    impl_cls = Mock(return_value=SimpleNamespace(forward=Mock()))
+    specialized_backends = []
+    impl_cls = Mock(return_value=SimpleNamespace(forward=Mock(), do_kv_cache_update=Mock()))
     native_backend = SimpleNamespace(
         get_name=lambda: "ASCEND",
         indexes_kv_by_block_stride=lambda: True,
@@ -1003,16 +1191,28 @@ def test_layer_adapter_accepts_platform_native_backend_and_uses_rank_local_heads
     original_backend_per_kind = {"full": object()}
     config = SimpleNamespace(
         attention_config=SimpleNamespace(backend=None, backend_per_kind=original_backend_per_kind),
+        parallel_config=SimpleNamespace(prefill_context_parallel_size=2),
         model_config=SimpleNamespace(dtype=torch.float16),
         cache_config=SimpleNamespace(cache_dtype="auto"),
     )
 
     def select_backend(**_kwargs):
-        selected_backends.append((config.attention_config.backend, config.attention_config.backend_per_kind))
+        selected_backends.append(
+            (
+                config.attention_config.backend,
+                config.attention_config.backend_per_kind,
+                config.parallel_config.prefill_context_parallel_size,
+            )
+        )
         return native_backend
 
     monkeypatch.setattr(adapter_module, "get_attn_backend", select_backend)
     monkeypatch.setattr(adapter_module, "set_current_vllm_config", lambda _config: nullcontext())
+    monkeypatch.setattr(
+        adapter_module.current_omni_platform,
+        "get_diffusion_paged_kv_attn_backend",
+        lambda backend, *, ulysses_degree: specialized_backends.append((backend, ulysses_degree)) or backend,
+    )
     layer = SimpleNamespace(num_heads=8, softmax_scale=0.125)
     spec = FullAttentionSpec(
         block_size=16,
@@ -1031,9 +1231,11 @@ def test_layer_adapter_accepts_platform_native_backend_and_uses_rank_local_heads
         ulysses_degree=2,
     )
 
-    assert selected_backends == [(adapter_module.AttentionBackendEnum.FLASH_ATTN, {})]
+    assert selected_backends == [(adapter_module.AttentionBackendEnum.FLASH_ATTN, {}, 2)]
+    assert specialized_backends == [(native_backend, 2)]
     assert config.attention_config.backend is None
     assert config.attention_config.backend_per_kind is original_backend_per_kind
+    assert config.parallel_config.prefill_context_parallel_size == 2
     assert native_layer.num_heads == 4
     assert native_layer.num_kv_heads == 2
     assert native_layer.spec.num_kv_heads == 2
@@ -1094,6 +1296,51 @@ def test_omni_attention_wraps_paged_kernel_with_sp_hooks() -> None:
     assert torch.equal(output, torch.full_like(original, 6))
 
 
+def test_omni_attention_strips_paged_ulysses_padding_around_kernel() -> None:
+    events: list[tuple[str, tuple[int, ...]]] = []
+
+    class Strategy:
+        name = "ulysses"
+
+        def pre_attention(self, query, key, value, metadata):
+            return query, key, value, metadata, SimpleNamespace(joint_len=0, joint_strategy="front")
+
+        def post_attention(self, output, _ctx):
+            events.append(("post", tuple(output.shape)))
+            return output
+
+    class Adapter:
+        def prepare_layer_context(self, _layer_name, query, key, value, *, omni_attn_metadata):
+            assert omni_attn_metadata is None
+            events.append(("kernel_input", tuple(query.shape)))
+            assert query[:, -1].tolist() == [[[3.0]]]
+            assert torch.equal(query, key)
+            assert torch.equal(query, value)
+            return SimpleNamespace(query=query)
+
+    class Backend:
+        def forward_paged(self, context):
+            return context.query
+
+    layer = Attention.__new__(Attention)
+    nn.Module.__init__(layer)
+    layer.prefix = "layer-0"
+    layer.paged_kv_cache_role = "primary"
+    layer.attn_backend = SimpleNamespace(supports_paged_kv=True, get_name=lambda: "FLASH_ATTN")
+    layer.attention = Backend()
+    layer.use_ring = False
+    layer._no_parallel_strategy = object()
+    layer._get_active_parallel_strategy = lambda: Strategy()
+    tensor = torch.arange(5, dtype=torch.float32).reshape(1, 5, 1, 1)
+
+    with set_forward_context(), override_paged_kv_adapter(Adapter()):
+        get_forward_context().sp_padding_size = 1
+        output = layer._forward_impl(tensor, tensor, tensor)
+
+    assert events == [("kernel_input", (1, 4, 1, 1)), ("post", (1, 5, 1, 1))]
+    assert output.flatten().tolist() == [0.0, 1.0, 2.0, 3.0, 0.0]
+
+
 def test_omni_attention_rejects_paged_request_for_backend_without_paged_support() -> None:
     class Adapter:
         def prepare_layer_context(self, *args, **kwargs):
@@ -1145,3 +1392,15 @@ def test_omni_attention_keeps_dense_kernel_without_active_adapter() -> None:
 
     assert events == ["pre", "dense", "post"]
     assert output is qkv
+
+
+def test_omni_attention_rejects_scheduler_paged_forward_without_active_adapter() -> None:
+    layer = Attention.__new__(Attention)
+    nn.Module.__init__(layer)
+    layer._scheduler_paged_kv = True
+    layer.paged_kv_cache_role = "primary"
+    layer._get_active_parallel_strategy = lambda: object()
+    qkv = torch.zeros(1, 2, 2, 4)
+
+    with set_forward_context(), pytest.raises(RuntimeError, match="without an active Worker adapter"):
+        layer._forward_impl(qkv, qkv, qkv)

tests/diffusion/diffusion_kv/test_paged_attention_adapter_gpu.py

@@ -154,8 +154,17 @@ def test_adapter_executes_native_paged_attention_on_non_contiguous_blocks() -> N
 
     torch.manual_seed(1)
     query = torch.randn(19, _NUM_HEADS, _HEAD_SIZE, dtype=torch.float16, device=device)
-    key = torch.randn_like(query)
-    value = torch.randn_like(query)
+    packed_qkv = torch.randn(
+        19,
+        3 * _NUM_HEADS * _HEAD_SIZE,
+        dtype=torch.float16,
+        device=device,
+    )
+    _, key_flat, value_flat = packed_qkv.split(_NUM_HEADS * _HEAD_SIZE, dim=-1)
+    key = key_flat.reshape(19, _NUM_HEADS, _HEAD_SIZE)
+    value = value_flat.reshape(19, _NUM_HEADS, _HEAD_SIZE)
+    assert not key.is_contiguous()
+    assert not value.is_contiguous()
     omni_backend = FlashAttentionImpl(
         num_heads=_NUM_HEADS,
         head_size=_HEAD_SIZE,

tests/diffusion/diffusion_kv/test_spec_discovery.py

@@ -1,5 +1,5 @@
 # SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
 
 from contextlib import nullcontext
 from types import SimpleNamespace
@@ -42,9 +42,10 @@ class _RunnerKVBackend:
         self.kv_cache_config = config
 
 
-def _attention(*, enabled: bool) -> Attention:
+def _attention(*, enabled: bool, prefix: str = "image_attention") -> Attention:
     attention = Attention.__new__(Attention)
     nn.Module.__init__(attention)
+    attention.prefix = prefix
     attention.paged_kv_cache_role = "primary" if enabled else None
     attention.paged_kv_cache_dtype = torch.bfloat16
     attention.num_kv_heads = 2
@@ -82,6 +83,23 @@ def test_runner_discovers_native_spec_from_loaded_attention() -> None:
     assert spec.non_causal is True
 
 
+def test_runner_uses_attention_prefix_as_canonical_layer_name() -> None:
+    runner = _runner(_attention(enabled=True, prefix="layers.0.self_attn.image_attn.attn"))
+
+    specs = runner.get_kv_cache_spec()
+
+    assert set(specs) == {"layers.0.self_attn.image_attn.attn"}
+
+
+def test_runner_rejects_duplicate_attention_prefixes() -> None:
+    runner = _runner(_attention(enabled=True, prefix="shared"))
+    second_attention = _attention(enabled=True, prefix="shared")
+    runner.pipeline.second_image_attention = second_attention
+
+    with pytest.raises(RuntimeError, match="Duplicate canonical paged Diffusion Attention prefix 'shared'"):
+        runner.get_kv_cache_spec()
+
+
 def test_runner_rejects_paged_mode_without_cache_enabled_attention() -> None:
     runner = _runner(_attention(enabled=False))
 

tests/diffusion/diffusion_kv/test_worker_contract.py

@@ -42,7 +42,10 @@ def make_metadata(request_id: str = "req-0") -> DiffusionKVMetadata:
 
 def make_runner(mode: DiffusionKVCacheMode) -> DiffusionModelRunner:
     runner = object.__new__(DiffusionModelRunner)
-    runner.od_config = SimpleNamespace(diffusion_kv_mode=mode)
+    runner.od_config = SimpleNamespace(
+        diffusion_kv_mode=mode,
+        parallel_config=SimpleNamespace(cfg_parallel_size=1),
+    )
     return runner
 
 
@@ -85,6 +88,51 @@ def test_new_request_data_carries_scheduler_allocation_atomically() -> None:
     assert new_req.diffusion_kv_metadata is metadata
 
 
+def test_runner_builds_prefill_and_denoise_rows_from_scheduler_metadata() -> None:
+    runner = make_runner(DiffusionKVCacheMode.PAGED_SCHEDULER)
+    metadata = DiffusionKVMetadata(
+        request_id="req-0",
+        allocation_generation=1,
+        sequences=(
+            DiffusionKVSequenceMetadata(0, 4, 2, 8, ([1, 2],)),
+            DiffusionKVSequenceMetadata(1, 5, 2, 9, ([3, 4],)),
+        ),
+    )
+
+    attn_metadata = runner._build_paged_attention_metadata([metadata])
+
+    assert [
+        (row.request_id, row.sequence_id, row.kv_start_pos, row.query_len, row.seq_len)
+        for row in attn_metadata.prefill_rows
+    ] == [("req-0", 0, 0, 8, 8), ("req-0", 1, 0, 9, 9)]
+    assert [
+        (row.request_id, row.sequence_id, row.kv_start_pos, row.query_len, row.seq_len)
+        for row in attn_metadata.denoise_rows
+    ] == [("req-0", 0, 4, 2, 6), ("req-0", 1, 5, 2, 7)]
+
+
+def test_runner_selects_only_local_cfg_parallel_row(monkeypatch: pytest.MonkeyPatch) -> None:
+    runner = make_runner(DiffusionKVCacheMode.PAGED_SCHEDULER)
+    runner.od_config.parallel_config.cfg_parallel_size = 2
+    monkeypatch.setattr(
+        "vllm_omni.diffusion.worker.diffusion_model_runner.get_classifier_free_guidance_rank",
+        lambda: 1,
+    )
+    metadata = DiffusionKVMetadata(
+        request_id="req-0",
+        allocation_generation=1,
+        sequences=(
+            DiffusionKVSequenceMetadata(0, 4, 2, 8, ([1, 2],)),
+            DiffusionKVSequenceMetadata(1, 5, 2, 9, ([3, 4],)),
+        ),
+    )
+
+    attn_metadata = runner._build_paged_attention_metadata([metadata])
+
+    assert [row.sequence_id for row in attn_metadata.prefill_rows] == [1]
+    assert [row.sequence_id for row in attn_metadata.denoise_rows] == [1]
+
+
 def test_paged_request_without_metadata_fails_before_request_forward() -> None:
     runner = make_runner(DiffusionKVCacheMode.PAGED_SCHEDULER)
     runner._execute_request_list = Mock()

tests/diffusion/distributed/test_a2a_permute.py

@@ -0,0 +1,116 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+from types import SimpleNamespace
+
+import pytest
+import torch
+
+from vllm_omni.diffusion.distributed import a2a_permute
+
+pytestmark = [pytest.mark.core_model, pytest.mark.cpu]
+
+
+def test_jit_build_includes_cuda_headers_from_nvidia_wheels(tmp_path, monkeypatch) -> None:
+    cu13_include = tmp_path / "nvidia" / "cu13" / "include"
+    nccl_include = tmp_path / "nvidia" / "nccl" / "include"
+    nccl_lib = tmp_path / "nvidia" / "nccl" / "lib" / "libnccl.so.2"
+    cu13_include.mkdir(parents=True)
+    nccl_include.mkdir(parents=True)
+    nccl_lib.parent.mkdir(parents=True)
+    (cu13_include / "cusparse.h").touch()
+    (nccl_include / "nccl.h").touch()
+    nccl_lib.touch()
+
+    load_kwargs = {}
+    monkeypatch.setattr(a2a_permute.sysconfig, "get_paths", lambda: {"purelib": str(tmp_path)})
+    monkeypatch.setattr(
+        torch.utils.cpp_extension,
+        "load",
+        lambda **kwargs: load_kwargs.update(kwargs),
+    )
+    monkeypatch.setattr(a2a_permute.symm_mem, "set_backend", lambda _backend: None)
+    monkeypatch.setattr(a2a_permute, "_BUILT", False)
+
+    a2a_permute.ensure_a2a_permute_available()
+
+    assert set(load_kwargs["extra_include_paths"]) == {str(cu13_include), str(nccl_include)}
+    assert load_kwargs["extra_ldflags"] == [str(nccl_lib)]
+
+
+@dataclass
+class _FakeAllocation:
+    """Byte workspace stand-in that slices and reinterprets like ``symm_mem.empty()``."""
+
+    size: int
+    device: torch.device
+
+    def __post_init__(self) -> None:
+        self.buffer = torch.empty(self.size, dtype=torch.uint8)
+
+    def __getitem__(self, item: slice) -> torch.Tensor:
+        return self.buffer[item]
+
+
+def test_workspace_reuses_peak_capacity_across_shapes(monkeypatch) -> None:
+    allocations: list[_FakeAllocation] = []
+    all_reduce_calls: list[object] = []
+    synchronize_calls: list[torch.device] = []
+    stream_id = [1]
+    a2a_permute._SYMM_WORKSPACES.clear()
+
+    monkeypatch.setattr(a2a_permute.torch.cuda, "is_current_stream_capturing", lambda: False)
+    monkeypatch.setattr(
+        a2a_permute.torch.cuda,
+        "current_stream",
+        lambda _device: SimpleNamespace(cuda_stream=stream_id[0]),
+    )
+    monkeypatch.setattr(a2a_permute, "_resolve_process_group", lambda _name: object())
+    monkeypatch.setattr(a2a_permute.torch, "ones", lambda *_args, **_kwargs: object())
+    monkeypatch.setattr(
+        a2a_permute.dist,
+        "all_reduce",
+        lambda _tensor, group: all_reduce_calls.append(group),
+    )
+    monkeypatch.setattr(
+        a2a_permute.torch.accelerator,
+        "synchronize",
+        lambda device: synchronize_calls.append(device),
+    )
+
+    def empty(size, *, dtype, device):
+        assert dtype == torch.uint8
+        allocation = _FakeAllocation(size=size, device=torch.device(device))
+        allocations.append(allocation)
+        return allocation
+
+    monkeypatch.setattr(a2a_permute.symm_mem, "empty", empty)
+    # The handle is only retained to keep the rendezvous registration alive.
+    monkeypatch.setattr(a2a_permute.symm_mem, "rendezvous", lambda _allocation, _group: object())
+
+    device = torch.device("cuda:0")
+    first = a2a_permute._get_symm_buffer((2, 3), torch.float16, device, "group")
+    smaller = a2a_permute._get_symm_buffer((1, 4), torch.float16, device, "group")
+    larger = a2a_permute._get_symm_buffer((4, 3), torch.float16, device, "group")
+
+    assert (first.shape, first.dtype) == ((2, 3), torch.float16)
+    assert (smaller.shape, smaller.dtype) == ((1, 4), torch.float16)
+    assert (larger.shape, larger.dtype) == ((4, 3), torch.float16)
+    # A sub-capacity request reuses the workspace; only growth allocates again.
+    assert smaller.untyped_storage().data_ptr() == first.untyped_storage().data_ptr()
+    assert larger.untyped_storage().data_ptr() != first.untyped_storage().data_ptr()
+    assert [allocation.size for allocation in allocations] == [12, 24]
+    assert len(a2a_permute._SYMM_WORKSPACES) == 1
+    assert len(all_reduce_calls) == 1
+    assert synchronize_calls == [device, device]
+
+    stream_id[0] = 2
+    with pytest.raises(RuntimeError, match="single CUDA stream"):
+        a2a_permute._get_symm_buffer((1, 4), torch.float16, device, "group")
+
+    a2a_permute.clear_a2a_permute_workspaces()
+    assert not a2a_permute._SYMM_WORKSPACES
+    assert synchronize_calls == [device, device, device]

tests/diffusion/distributed/test_sp_plan_hooks.py

@@ -1,5 +1,5 @@
 # SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
 """Tests for the Sequence Parallelism (SP) framework.
 
 These tests verify the SP plan mechanism and hooks work correctly without
@@ -17,6 +17,7 @@ import pytest
 import torch
 import torch.nn as nn
 
+from tests.helpers.mark import hardware_marks
 from vllm_omni.diffusion.distributed.sp_plan import (
     SequenceParallelInput,
     SequenceParallelOutput,
@@ -419,7 +420,6 @@ class TestHookRegistration:
 # =============================================================================
 
 
-@pytest.mark.L4
 class TestModelSpPlans:
     """Test that model _sp_plan definitions are valid.
 
@@ -427,6 +427,8 @@ class TestModelSpPlans:
     May require GPU for model imports.
     """
 
+    pytestmark = hardware_marks(res={"cuda": "L4"})
+
     def test_zimage_transformer_sp_plan(self):
         """Test ZImageTransformer2DModel _sp_plan structure.
 

tests/diffusion/layers/test_activation.py

@@ -0,0 +1,32 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
+
+import pytest
+import torch
+import torch.nn.functional as F
+
+from tests.helpers.mark import hardware_test
+from vllm_omni.diffusion.layers.activation import SiluAndMul
+
+pytestmark = [pytest.mark.core_model, pytest.mark.diffusion]
+
+
+@pytest.mark.cpu
+def test_silu_and_mul_native_matches_packed_reference() -> None:
+    packed = torch.randn(257, 256, dtype=torch.float32)
+    gate, up = packed.chunk(2, dim=-1)
+
+    torch.testing.assert_close(SiluAndMul().forward_native(packed), F.silu(gate) * up)
+
+
+@hardware_test(res={"npu": "A3"}, num_cards=1)
+def test_silu_and_mul_npu_matches_packed_reference() -> None:
+    packed = torch.randn(257, 256, device="npu", dtype=torch.bfloat16)
+    gate, up = packed.chunk(2, dim=-1)
+
+    torch.testing.assert_close(
+        SiluAndMul()(packed),
+        F.silu(gate) * up,
+        atol=2e-2,
+        rtol=2e-2,
+    )

tests/diffusion/layers/test_fused_qk_norm_rope_npu.py

@@ -0,0 +1,194 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import sys
+import types
+
+import pytest
+import torch
+import torch.nn.functional as F
+
+from tests.helpers.mark import hardware_test
+
+pytestmark = [pytest.mark.core_model, pytest.mark.diffusion]
+
+_HEAD_DIM = 128
+_ROTARY_DIM = 96
+_EPS = 1e-5
+
+
+def _reference(
+    q: torch.Tensor,
+    k: torch.Tensor,
+    q_weight: torch.Tensor,
+    k_weight: torch.Tensor,
+    rope_table: torch.Tensor,
+) -> tuple[torch.Tensor, torch.Tensor]:
+    half = _ROTARY_DIM // 2
+
+    def apply(x: torch.Tensor, weight: torch.Tensor) -> torch.Tensor:
+        x = F.rms_norm(x, (_HEAD_DIM,), weight, _EPS)
+        cos = rope_table[..., :half].unsqueeze(1)
+        sin = rope_table[..., half:].unsqueeze(1)
+        return torch.cat(
+            (
+                x[..., :half] * cos - x[..., half:_ROTARY_DIM] * sin,
+                x[..., half:_ROTARY_DIM] * cos + x[..., :half] * sin,
+                x[..., _ROTARY_DIM:],
+            ),
+            dim=-1,
+        )
+
+    return apply(q, q_weight), apply(k, k_weight)
+
+
+def _inputs() -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
+    torch.manual_seed(7)
+    tokens, heads = 11, 4
+    q = torch.randn(tokens, heads, _HEAD_DIM, dtype=torch.bfloat16)
+    k = torch.randn(tokens, heads, _HEAD_DIM, dtype=torch.bfloat16)
+    q_weight = torch.randn(_HEAD_DIM, dtype=torch.bfloat16)
+    k_weight = torch.randn(_HEAD_DIM, dtype=torch.bfloat16)
+    freqs = torch.randn(tokens, _ROTARY_DIM // 2)
+    rope_table = torch.cat((torch.cos(freqs), torch.sin(freqs)), dim=-1).to(torch.bfloat16)
+    return q, k, q_weight, k_weight, rope_table
+
+
+@pytest.mark.cpu
+def test_npu_qk_norm_rope_uses_torch_npu_fused_primitives_without_mindiesd(
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    from vllm_omni.diffusion.layers import fused_qk_norm_rope as fused
+
+    calls: dict[str, int] = {"rms_norm": 0, "rotary_mul": 0}
+
+    def npu_rms_norm(x: torch.Tensor, gamma: torch.Tensor, epsilon: float):
+        calls["rms_norm"] += 1
+        return (F.rms_norm(x, (_HEAD_DIM,), gamma, epsilon),)
+
+    def npu_rotary_mul(x: torch.Tensor, cos: torch.Tensor, sin: torch.Tensor):
+        calls["rotary_mul"] += 1
+        assert x.shape[-1] == _ROTARY_DIM
+        assert cos.shape == (1, x.shape[1], 1, _ROTARY_DIM)
+        assert sin.shape == cos.shape
+        half = _ROTARY_DIM // 2
+        return torch.cat(
+            (
+                x[..., :half] * cos[..., :half] - x[..., half:] * sin[..., :half],
+                x[..., half:] * cos[..., half:] + x[..., :half] * sin[..., half:],
+            ),
+            dim=-1,
+        )
+
+    monkeypatch.setitem(
+        sys.modules,
+        "torch_npu",
+        types.SimpleNamespace(npu_rms_norm=npu_rms_norm, npu_rotary_mul=npu_rotary_mul),
+    )
+    monkeypatch.setattr(fused, "find_spec", lambda _: None)
+    q, k, q_weight, k_weight, rope_table = _inputs()
+
+    actual_q, actual_k = fused._npu_qk_norm_rope(
+        q,
+        k,
+        q_weight,
+        k_weight,
+        rope_table,
+        _EPS,
+        _ROTARY_DIM,
+    )
+    expected_q, expected_k = _reference(q, k, q_weight, k_weight, rope_table)
+
+    assert calls == {"rms_norm": 2, "rotary_mul": 2}
+    torch.testing.assert_close(actual_q, expected_q, atol=0, rtol=0)
+    torch.testing.assert_close(actual_k, expected_k, atol=0, rtol=0)
+
+
+@pytest.mark.cpu
+def test_npu_qk_norm_rope_uses_mindiesd_and_normalizes_packed_layout(
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    from vllm_omni.diffusion.layers import fused_qk_norm_rope as fused
+
+    calls: dict[str, int] = {"rms_norm": 0, "mindiesd_rope": 0}
+
+    def npu_rms_norm(x: torch.Tensor, gamma: torch.Tensor, epsilon: float):
+        calls["rms_norm"] += 1
+        return (F.rms_norm(x, (_HEAD_DIM,), gamma, epsilon),)
+
+    def rotary_position_embedding(x: torch.Tensor, cos: torch.Tensor, sin: torch.Tensor, **kwargs):
+        calls["mindiesd_rope"] += 1
+        assert x.dim() == 4
+        assert x.shape[0] == 1
+        assert x.shape[-1] == _ROTARY_DIM
+        assert cos.shape == (1, x.shape[1], 1, _ROTARY_DIM)
+        assert sin.shape == cos.shape
+        assert kwargs == {
+            "rotated_mode": "rotated_half",
+            "head_first": False,
+            "fused": True,
+        }
+        half = _ROTARY_DIM // 2
+        return torch.cat(
+            (
+                x[..., :half] * cos[..., :half] - x[..., half:] * sin[..., :half],
+                x[..., half:] * cos[..., half:] + x[..., :half] * sin[..., half:],
+            ),
+            dim=-1,
+        )
+
+    monkeypatch.setitem(sys.modules, "torch_npu", types.SimpleNamespace(npu_rms_norm=npu_rms_norm))
+    monkeypatch.setitem(
+        sys.modules,
+        "mindiesd",
+        types.SimpleNamespace(rotary_position_embedding=rotary_position_embedding),
+    )
+    monkeypatch.setattr(fused, "find_spec", lambda _: object())
+    q, k, q_weight, k_weight, rope_table = _inputs()
+
+    actual_q, actual_k = fused._npu_qk_norm_rope(
+        q,
+        k,
+        q_weight,
+        k_weight,
+        rope_table,
+        _EPS,
+        _ROTARY_DIM,
+    )
+    expected_q, expected_k = _reference(q, k, q_weight, k_weight, rope_table)
+
+    assert calls == {"rms_norm": 2, "mindiesd_rope": 2}
+    torch.testing.assert_close(actual_q, expected_q, atol=0, rtol=0)
+    torch.testing.assert_close(actual_k, expected_k, atol=0, rtol=0)
+
+
+@hardware_test(res={"npu": "A3"}, num_cards=1)
+def test_fused_qk_norm_rope_npu_matches_reference(monkeypatch: pytest.MonkeyPatch) -> None:
+    """Exercise the public Ascend dispatch with real torch_npu/MindIE operators."""
+    from vllm_omni.diffusion.layers import fused_qk_norm_rope as fused
+
+    q, k, q_weight, k_weight, rope_table = (tensor.to("npu") for tensor in _inputs())
+    calls = 0
+    original_npu_impl = fused._npu_qk_norm_rope
+
+    def capture_npu_impl(*args, **kwargs):
+        nonlocal calls
+        calls += 1
+        return original_npu_impl(*args, **kwargs)
+
+    monkeypatch.setattr(fused, "_npu_qk_norm_rope", capture_npu_impl)
+    actual_q, actual_k = fused.fused_qk_norm_rope(
+        q,
+        k,
+        q_weight,
+        k_weight,
+        rope_table,
+        _EPS,
+        head_dim=_HEAD_DIM,
+        rotary_dim=_ROTARY_DIM,
+    )
+    expected_q, expected_k = _reference(q, k, q_weight, k_weight, rope_table)
+
+    assert calls == 1
+    torch.testing.assert_close(actual_q, expected_q, atol=2e-2, rtol=2e-2)
+    torch.testing.assert_close(actual_k, expected_k, atol=2e-2, rtol=2e-2)

tests/diffusion/model_loader/test_diffusers_loader.py

@@ -273,12 +273,15 @@ def test_required_hwr_miss_fails_before_ordinary_loading_or_publication(
     assert loader.take_host_weight_plan() is None
 
 
-def _make_dlo_online_quant_config() -> OmniDiffusionConfig:
+def _make_dlo_online_quant_config(dp_size: int = 2) -> OmniDiffusionConfig:
     return OmniDiffusionConfig(
         model="",
         dtype=torch.float32,
         quantization_config="fp8",
-        parallel_config=DiffusionParallelConfig(data_parallel_size=2),
+        parallel_config=DiffusionParallelConfig(
+            data_parallel_size=dp_size,
+            sequence_parallel_size=1,
+        ),
         enable_distributed_layerwise_offload=True,
         dlo_use_allgather=True,
     )
@@ -824,6 +827,93 @@ def test_dlo_allgather_online_fp8_uses_ordinary_loader(monkeypatch):
     assert loader.take_host_weight_plan() is None
 
 
+def test_dlo_allgather_online_int8_uses_ordinary_loader(monkeypatch):
+    import vllm_omni.diffusion.model_loader.diffusers_loader as loader_mod
+    from vllm_omni.quantization.int8_config import NPUInt8OnlineLinearMethod
+
+    od_config = _make_dlo_online_quant_config()
+    loader = DiffusersPipelineLoader(LoadConfig(), od_config)
+    model = nn.Module()
+    model.transformer = nn.Linear(2, 2, bias=False)
+    model.transformer.quant_method = object.__new__(NPUInt8OnlineLinearMethod)
+    model.transformer.quant_method.uses_meta_device = True
+    calls: list[object] = []
+    allowlist_models: list[nn.Module] = []
+
+    original_allowlist_check = loader._unsupported_dlo_allgather_online_quant_methods
+
+    def check_allowlist(candidate: nn.Module) -> tuple[str, ...]:
+        allowlist_models.append(candidate)
+        return original_allowlist_check(candidate)
+
+    loader._init_from_load_format = lambda *_args, **_kwargs: model  # type: ignore[method-assign]
+    monkeypatch.setattr(loader, "_unsupported_dlo_allgather_online_quant_methods", check_allowlist)
+    loader._request_offload_after_quant = lambda _model: 1  # type: ignore[method-assign]
+    loader.load_weights = (  # type: ignore[method-assign]
+        lambda _model, *, stream_online_quant_to_cpu=False: calls.append(("load", stream_online_quant_to_cpu))
+    )
+    loader._process_weights_after_loading = lambda *_args: calls.append("process")  # type: ignore[method-assign]
+    loader._apply_skip_softmax_calibration = lambda _model: None  # type: ignore[method-assign]
+    monkeypatch.setattr(
+        loader_mod,
+        "build_checkpoint_mmap_plan",
+        lambda *_args, **_kwargs: HostWeightPlanResult(
+            None,
+            "online quantization requires the ordinary loader",
+        ),
+    )
+
+    assert loader.load_model(load_device="cpu", device=torch.device("cpu")) is model
+    assert allowlist_models == [model]
+    assert calls == [("load", True), "process"]
+    assert loader.take_host_weight_plan() is None
+
+
+def test_dlo_online_quant_group_size_one_skips_allgather_gate(monkeypatch):
+    """A DLO group of one runs no weight collective, so an otherwise
+    unvalidated online method must not be rejected by the AllGather gate."""
+
+    class UnsupportedOnlineMethod:
+        uses_meta_device = True
+
+    import vllm_omni.diffusion.model_loader.diffusers_loader as loader_mod
+
+    od_config = _make_dlo_online_quant_config(dp_size=1)
+    loader = DiffusersPipelineLoader(LoadConfig(), od_config)
+    model = nn.Module()
+    model.transformer = nn.Linear(2, 2, bias=False)
+    model.transformer.quant_method = UnsupportedOnlineMethod()
+    calls: list[object] = []
+    allowlist_models: list[nn.Module] = []
+
+    original_allowlist_check = loader._unsupported_dlo_allgather_online_quant_methods
+
+    def check_allowlist(candidate: nn.Module) -> tuple[str, ...]:
+        allowlist_models.append(candidate)
+        return original_allowlist_check(candidate)
+
+    loader._init_from_load_format = lambda *_args, **_kwargs: model  # type: ignore[method-assign]
+    monkeypatch.setattr(loader, "_unsupported_dlo_allgather_online_quant_methods", check_allowlist)
+    loader._request_offload_after_quant = lambda _model: 1  # type: ignore[method-assign]
+    loader.load_weights = (  # type: ignore[method-assign]
+        lambda _model, *, stream_online_quant_to_cpu=False: calls.append(("load", stream_online_quant_to_cpu))
+    )
+    loader._process_weights_after_loading = lambda *_args: calls.append("process")  # type: ignore[method-assign]
+    loader._apply_skip_softmax_calibration = lambda _model: None  # type: ignore[method-assign]
+    monkeypatch.setattr(
+        loader_mod,
+        "build_checkpoint_mmap_plan",
+        lambda *_args, **_kwargs: HostWeightPlanResult(
+            None,
+            "online quantization requires the ordinary loader",
+        ),
+    )
+
+    assert loader.load_model(load_device="cpu", device=torch.device("cpu")) is model
+    assert allowlist_models == []
+    assert calls == [("load", True), "process"]
+
+
 def test_dlo_allgather_rejects_unvalidated_online_quant_method(monkeypatch):
     import vllm_omni.diffusion.model_loader.diffusers_loader as loader_mod
 
@@ -847,10 +937,54 @@ def test_dlo_allgather_rejects_unvalidated_online_quant_method(monkeypatch):
         ),
     )
 
-    with pytest.raises(ValueError, match="per-tensor FP8 linears"):
+    with pytest.raises(ValueError, match="per-tensor FP8, INT8, and MXFP8 linears"):
         loader.load_model(load_device="cpu")
 
 
+def test_dlo_allgather_online_mxfp8_uses_ordinary_loader(monkeypatch):
+    mxfp8_config = pytest.importorskip("vllm_omni.quantization.mxfp8_config")
+    NPUMxfp8OnlineLinearMethod = mxfp8_config.NPUMxfp8OnlineLinearMethod
+
+    import vllm_omni.diffusion.model_loader.diffusers_loader as loader_mod
+
+    od_config = _make_dlo_online_quant_config()
+    loader = DiffusersPipelineLoader(LoadConfig(), od_config)
+    model = nn.Module()
+    model.transformer = nn.Linear(2, 2, bias=False)
+    model.transformer.quant_method = object.__new__(NPUMxfp8OnlineLinearMethod)
+    model.transformer.quant_method.uses_meta_device = True
+    calls: list[object] = []
+    allowlist_models: list[nn.Module] = []
+
+    original_allowlist_check = loader._unsupported_dlo_allgather_online_quant_methods
+
+    def check_allowlist(candidate: nn.Module) -> tuple[str, ...]:
+        allowlist_models.append(candidate)
+        return original_allowlist_check(candidate)
+
+    loader._init_from_load_format = lambda *_args, **_kwargs: model  # type: ignore[method-assign]
+    monkeypatch.setattr(loader, "_unsupported_dlo_allgather_online_quant_methods", check_allowlist)
+    loader._request_offload_after_quant = lambda _model: 1  # type: ignore[method-assign]
+    loader.load_weights = (  # type: ignore[method-assign]
+        lambda _model, *, stream_online_quant_to_cpu=False: calls.append(("load", stream_online_quant_to_cpu))
+    )
+    loader._process_weights_after_loading = lambda *_args: calls.append("process")  # type: ignore[method-assign]
+    loader._apply_skip_softmax_calibration = lambda _model: None  # type: ignore[method-assign]
+    monkeypatch.setattr(
+        loader_mod,
+        "build_checkpoint_mmap_plan",
+        lambda *_args, **_kwargs: HostWeightPlanResult(
+            None,
+            "online quantization requires the ordinary loader",
+        ),
+    )
+
+    assert loader.load_model(load_device="cpu", device=torch.device("cpu")) is model
+    assert allowlist_models == [model]
+    assert calls == [("load", True), "process"]
+    assert loader.take_host_weight_plan() is None
+
+
 def test_hsdp_processes_quantized_weights_before_sharding(mocker):
     import vllm_omni.diffusion.model_loader.diffusers_loader as loader_mod
     from vllm_omni.diffusion.offloader.module_collector import PipelineModules

tests/diffusion/models/hunyuan_image3/test_hunyuan_image3_step_execution.py

@@ -1,5 +1,5 @@
 # SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
 
 from types import SimpleNamespace
 
@@ -270,6 +270,17 @@ def test_grouped_denoise_allows_sdpa_attention_backend():
     pipeline._ensure_grouped_attention_backend_supported(2)
 
 
+def test_scheduler_paged_step_execution_is_rejected():
+    pipeline = _pipeline()
+    pipeline.od_config.diffusion_kv_mode = hy3_module.DiffusionKVCacheMode.PAGED_SCHEDULER
+    state = _state("paged-step", 0)
+
+    with pytest.raises(ValueError, match="request-level execution only"):
+        pipeline.prepare_encode(state)
+    with pytest.raises(ValueError, match="request-level execution only"):
+        pipeline.denoise_step(InputBatch.make_batch([state]))
+
+
 def test_step_scheduler_preserves_latent_dtype_for_mixed_progress_batches():
     pipeline = _pipeline()
     pipeline._pipeline = SimpleNamespace(prepare_extra_func_kwargs=lambda step, kwargs: {})

tests/diffusion/models/hunyuan_image3/test_image_kv_cache_manager.py

@@ -1,5 +1,5 @@
 # SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
 """Unit tests for ImageKVCacheManager.
 
 Covers: cache → reuse flow, AR KV injection, CFG (sequential & parallel), SP, cross-request isolation.
@@ -37,12 +37,21 @@ class MockAttention(nn.Module):
         super().__init__()
         self.paged_kv_active = False
         self.calls = []
+        self.paged_calls = []
+        self.paged_metadata = []
 
     def is_paged_kv_active(self):
         return self.paged_kv_active
 
     def forward(self, query, key, value, attn_metadata=None, **kwargs):
         self.calls.append((query, key, value))
+        from vllm_omni.diffusion.forward_context import get_forward_context, is_forward_context_available
+
+        if is_forward_context_available() and get_forward_context().paged_kv_adapter is not None:
+            self.paged_calls.append((query, key, value))
+            self.paged_metadata.append(attn_metadata)
+        if attn_metadata is not None and attn_metadata.joint_query is not None:
+            return torch.cat((attn_metadata.joint_query, query), dim=1)
         return query
 
 
@@ -105,6 +114,7 @@ def _call_mgr(
     shard_image_size=None,
     gen_timestep_scatter_index=None,
     position_ids=None,
+    full_attn_spans=None,
 ):
     query = torch.randn(bs * q_len, NUM_HEADS, HEAD_DIM)
     attn_mask = torch.zeros(bs, 1, seq_len, seq_len)
@@ -121,6 +131,7 @@ def _call_mgr(
         shard_image_size=shard_image_size,
         gen_timestep_scatter_index=gen_timestep_scatter_index,
         position_ids=position_ids,
+        full_attn_spans=full_attn_spans,
     )
 
 
@@ -132,6 +143,83 @@ def test_cache_manager_registers_attention_without_adding_dense_state() -> None:
     assert mgr.state_dict() == {}
 
 
+def test_scheduler_paged_kv_runs_piecewise_for_first_and_later_steps() -> None:
+    from vllm_omni.diffusion.forward_context import override_paged_kv_adapter, set_forward_context
+
+    mgr = _make_cache_mgr()
+    mgr.attn.paged_kv_active = True
+    bs = 2
+    prefix_lens = torch.tensor([[2], [4]], dtype=torch.long)
+    first_q_len = 12
+    first_k, first_v = _make_known_kv(bs * first_q_len, base=1.0)
+    full_attn_spans = [[(2, 12)], [(2, 12)]]
+
+    with set_forward_context(), override_paged_kv_adapter(object()):
+        _call_mgr(
+            mgr,
+            bs,
+            q_len=first_q_len,
+            seq_len=first_q_len,
+            key_flat=first_k,
+            value_flat=first_v,
+            first_step=True,
+            gen_timestep_scatter_index=prefix_lens,
+            full_attn_spans=full_attn_spans,
+        )
+
+        assert mgr.image_kv_cache_map is None
+        assert mgr.image_kv_cache_lens is None
+        assert len(mgr.attn.paged_calls) == 1
+        assert mgr.attn.paged_calls[0][0].shape == (bs, first_q_len, NUM_HEADS, HEAD_DIM)
+        assert mgr.attn.paged_calls[0][1].shape == (bs, first_q_len, NUM_KV_HEADS, HEAD_DIM)
+        assert mgr.attn.paged_calls[0][2].shape == (bs, first_q_len, NUM_KV_HEADS, HEAD_DIM)
+        assert mgr.attn.paged_metadata[0].full_attn_spans == full_attn_spans
+
+        current_q_len = IMAGE_TOKEN_LEN
+        current_k, current_v = _make_known_kv(bs * current_q_len, base=50.0)
+        _call_mgr(
+            mgr,
+            bs,
+            q_len=current_q_len,
+            seq_len=prefix_lens[-1].item() + current_q_len,
+            key_flat=current_k,
+            value_flat=current_v,
+            position_ids=torch.arange(4, 4 + current_q_len).repeat(bs, 1),
+            full_attn_spans=full_attn_spans,
+        )
+
+    assert len(mgr.attn.paged_calls) == 2
+    assert mgr.attn.paged_calls[1][0].shape == (bs, current_q_len, NUM_HEADS, HEAD_DIM)
+    assert mgr.attn.paged_calls[1][1].shape == (bs, current_q_len, NUM_KV_HEADS, HEAD_DIM)
+    assert mgr.attn.paged_calls[1][2].shape == (bs, current_q_len, NUM_KV_HEADS, HEAD_DIM)
+    assert mgr.attn.paged_metadata[1].full_attn_spans == full_attn_spans
+
+
+def test_cache_manager_rejects_dense_imported_prefix_for_paged_attention() -> None:
+    from vllm_omni.diffusion.forward_context import override_paged_kv_adapter, set_forward_context
+
+    mgr = _make_cache_mgr()
+    mgr.attn.paged_kv_active = True
+    prefix_len = 4
+    prefix_key, prefix_value = _make_known_kv(prefix_len, base=100.0)
+    mgr._injected_ar_kv = [(prefix_key, prefix_value)]
+    current_key, current_value = _make_known_kv(3, base=1.0)
+
+    with set_forward_context(), override_paged_kv_adapter(object()):
+        with pytest.raises(NotImplementedError, match="does not support imported AR KV"):
+            _call_mgr(
+                mgr,
+                bs=1,
+                q_len=3,
+                seq_len=7,
+                key_flat=current_key,
+                value_flat=current_value,
+                first_step=True,
+                gen_timestep_scatter_index=torch.tensor([[0]]),
+                full_attn_spans=[[(4, 7)]],
+            )
+
+
 @pytest.mark.parametrize(
     ("paged_kv_active", "expected_kv_heads"),
     [(False, NUM_HEADS), (True, NUM_KV_HEADS)],
@@ -155,6 +243,7 @@ def test_gqa_kv_stays_compressed_for_paged_attention(
         first_step=True,
         num_image_tokens=0,
         gen_timestep_scatter_index=_gen_timestep_index(1, 0),
+        full_attn_spans=[[(0, q_len)]] if paged_kv_active else None,
     )
 
     _, key_input, value_input = mgr.attn.calls[-1]

tests/diffusion/models/minimax_h3/test_minimax_h3_contract.py

@@ -57,7 +57,8 @@ def test_h3_prepares_resolved_cache_state_immediately_before_denoise():
         return torch.zeros(1), torch.zeros(1)
 
     pipeline.diffuse = diffuse
-    pipeline.decode = Mock(return_value=(torch.zeros(1), torch.zeros(1)))
+    decoded_audio = torch.zeros(1)
+    pipeline.decode = Mock(return_value=(torch.zeros(1, 3, 1, 1, 1), decoded_audio))
     sampling = OmniDiffusionSamplingParams(
         quality="high",
         width=1344,
@@ -86,7 +87,9 @@ def test_h3_prepares_resolved_cache_state_immediately_before_denoise():
         num_inference_steps=50,
         extra_args={"task": "t2va", "aspect_ratio": "16:9"},
     )
-    assert output.output == pipeline.decode.return_value
+    assert output.output[0].shape == (1, 1, 1, 1, 3)
+    assert output.output[0].dtype == torch.uint8
+    assert output.output[1] is decoded_audio
 
 
 def test_pipeline_import_registry_and_component_discovery():
@@ -493,18 +496,41 @@ def test_joint_postprocess_is_multiprocessing_picklable():
 
     postprocess = get_minimax_h3_post_process_func(SimpleNamespace())
     postprocess = ForkingPickler.loads(ForkingPickler.dumps(postprocess))
-    video = torch.linspace(0, 1, 2 * 3 * 2 * 4 * 5).reshape(2, 3, 2, 4, 5)
+    # decode() hands over quantised frames already laid out as (B, T, H, W, C).
+    video = torch.arange(2 * 2 * 4 * 5 * 3, dtype=torch.uint8).reshape(2, 2, 4, 5, 3)
     audio = torch.arange(12, dtype=torch.float32).reshape(1, 2, 6)
 
     result = postprocess((video, audio), output_type="np")
 
     assert isinstance(result["video"], list)
+    assert result["video"][0].dtype == np.uint8
     assert result["video"][0].shape == (2, 4, 5, 3)
+    np.testing.assert_array_equal(result["video"][0], video[0].numpy())
     np.testing.assert_array_equal(result["audio"], audio.numpy())
     assert result["audio_sample_rate"] == 32000
     assert result["fps"] == 24
 
 
+def test_joint_video_output_is_quantized_before_transfer():
+    from vllm_omni.diffusion.models.minimax_h3.pipeline_minimax_h3 import (
+        _minimax_h3_post_process,
+        _prepare_minimax_h3_video_output,
+    )
+
+    video = torch.linspace(-0.1, 1.1, 2 * 3 * 2 * 4 * 5, dtype=torch.float64).reshape(2, 3, 2, 4, 5)
+    expected = torch.round(video.float().clamp(0, 1) * 255).to(torch.uint8).permute(0, 2, 3, 4, 1)
+    prepared = _prepare_minimax_h3_video_output(video)
+
+    assert prepared.dtype == torch.uint8
+    assert prepared.is_contiguous()
+    assert prepared.shape == (2, 2, 4, 5, 3)
+    torch.testing.assert_close(prepared, expected, rtol=0, atol=0)
+
+    result = _minimax_h3_post_process((prepared, torch.zeros(1, 2, 6)))
+    assert result["video"][0].dtype == np.uint8
+    np.testing.assert_array_equal(result["video"][0], expected[0].numpy())
+
+
 def test_cfg_parallel_is_rejected_for_distilled_checkpoint():
     from vllm_omni.diffusion.models.minimax_h3 import MiniMaxH3Pipeline
 
@@ -572,7 +598,7 @@ def test_base_schedule_overrides_the_uniform_sigma_positions():
 @pytest.mark.parametrize(
     "base_schedule",
     [
-        [0.9, 0.5, 0.0],
+        [1.5, 0.5, 0.0],
         [1.0, 0.5, 0.1],
         [1.0, 0.5, 0.5, 0.0],
         [1.0],
@@ -627,7 +653,7 @@ def _distilled_pipeline(diffuse_calls, base_schedule_by_partition):
         return torch.zeros(1), torch.zeros(1)
 
     pipeline.diffuse = diffuse
-    pipeline.decode = Mock(return_value=(torch.zeros(1), torch.zeros(1)))
+    pipeline.decode = Mock(return_value=(torch.zeros(1, 3, 1, 1, 1), torch.zeros(1)))
     return pipeline
 
 

tests/diffusion/models/minimax_h3/test_minimax_h3_fasth3.py

@@ -0,0 +1,498 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
+
+from __future__ import annotations
+
+import json
+from types import SimpleNamespace
+
+import pytest
+import torch
+from safetensors.torch import save_file
+
+from vllm_omni.diffusion.models.minimax_h3.fasth3 import (
+    FASTH3_BASE_MODEL,
+    FASTH3_BASE_SCHEDULE,
+    FASTH3_DENOISE_STEPS,
+    FASTH3_FORMAT,
+    FastH3AdapterError,
+    FastH3WeightFusion,
+    resolve_fasth3_fusion,
+)
+from vllm_omni.diffusion.models.minimax_h3.minimax_h3_transformer import _reorder_grouped_qkv_to_qkv
+from vllm_omni.diffusion.models.minimax_h3.time_request import minimax_h3_time_shift_sigmas
+from vllm_omni.errors import OmniClientError
+
+pytestmark = [pytest.mark.core_model, pytest.mark.diffusion, pytest.mark.cpu]
+
+_RANK = 2
+_HIDDEN = 4
+_HEAD_DIM = 2
+_HEADS = 3
+_INNER = _HEAD_DIM * _HEADS  # attention inner size
+_FFN = 5
+
+
+def _factors(out_dim: int, in_dim: int) -> tuple[torch.Tensor, torch.Tensor]:
+    """A rank-2 pair whose product is reproducible and non-symmetric."""
+    a = torch.arange(_RANK * in_dim, dtype=torch.float32).reshape(_RANK, in_dim) / (in_dim * _RANK)
+    b = torch.arange(out_dim * _RANK, dtype=torch.float32).reshape(out_dim, _RANK) / (out_dim * _RANK)
+    return a, b
+
+
+def _write_adapter(
+    path,
+    *,
+    tensors=None,
+    drop: str | None = None,
+    blocks: int = 1,
+    metadata: dict[str, str | None] | None = None,
+) -> None:
+    """Write an artifact in the published ``fastvideo-lora-v2`` shape."""
+    payload: dict[str, torch.Tensor] = {}
+    for block in range(blocks):
+        for suffix, (out_dim, in_dim) in {
+            "attn.to_q": (_INNER, _HIDDEN),
+            "attn.to_k": (_INNER, _HIDDEN),
+            "attn.to_v": (_INNER, _HIDDEN),
+            "attn.to_out.0": (_HIDDEN, _INNER),
+            "ff.net.0.proj": (2 * _FFN, _HIDDEN),
+            "ff.net.2": (_HIDDEN, _FFN),
+        }.items():
+            a, b = _factors(out_dim, in_dim)
+            payload[f"transformer_blocks.{block}.{suffix}.lora_A.weight"] = a
+            payload[f"transformer_blocks.{block}.{suffix}.lora_B.weight"] = b
+    payload.update(tensors or {})
+    if drop is not None:
+        payload.pop(drop, None)
+    path.parent.mkdir(parents=True, exist_ok=True)
+    # The published writer records what it emitted; mirror it so the fixtures
+    # are as self-describing as the real artifact.
+    declared = {
+        "format": FASTH3_FORMAT,
+        "finetuned_model": "FastVideo/FastVideo-FastH3-Dense-4-step-v1",
+        "base_model": FASTH3_BASE_MODEL,
+        "rank": str(_RANK),
+        "low_rank_tensors": str(sum(1 for key in payload if key.endswith((".lora_A.weight", ".lora_B.weight")))),
+        "diff_tensors": str(sum(1 for key in payload if key.endswith((".diff", ".diff_b")))),
+        "set_weight_tensors": str(sum(1 for key in payload if key.endswith(".set_weight"))),
+    }
+    # safetensors metadata is string-only, so a ``None`` override drops the key.
+    declared.update(metadata or {})
+    save_file(payload, str(path), metadata={key: value for key, value in declared.items() if value is not None})
+
+
+def _claim(path, **kwargs) -> FastH3WeightFusion | None:
+    """Whatever from_path decides, including declining the artifact."""
+    kwargs.setdefault("head_dim", _HEAD_DIM)
+    kwargs.setdefault("num_blocks", 1)
+    kwargs.setdefault("num_refiner_blocks", 0)
+    return FastH3WeightFusion.from_path(path, **kwargs)
+
+
+def _load(path, **kwargs) -> FastH3WeightFusion:
+    """The same, for the tests that only make sense on a claimed adapter."""
+    fusion = _claim(path, **kwargs)
+    assert fusion is not None, f"{path} was not claimed as a FastH3 adapter"
+    return fusion
+
+
+def test_only_an_artifact_carrying_the_release_identity_is_claimed(tmp_path):
+    plain = tmp_path / "peft" / "adapter_model.safetensors"
+    plain.parent.mkdir(parents=True)
+    save_file({"transformer_blocks.0.attn.to_q.lora_A.weight": torch.ones((_RANK, _HIDDEN))}, str(plain))
+    # No fastvideo-lora-v2 metadata: this is somebody else's LoRA and has to
+    # stay on the dynamic route rather than being fused.
+    assert _claim(plain.parent) is None
+    assert _claim(tmp_path / "missing") is None
+
+    claimed = tmp_path / "fasth3" / "adapter_model.safetensors"
+    _write_adapter(claimed)
+    assert _claim(claimed.parent) is not None
+
+
+@pytest.mark.parametrize(
+    "identity",
+    [
+        # An ordinary H3 adapter out of FastVideo's own extraction tools.
+        {"finetuned_model": "someone/minimax-h3-style-lora"},
+        # Somebody else's adapter that merely names the student it imitates.
+        {"finetuned_model": "someone/fasth3-style-lora-for-h3"},
+        {"finetuned_model": ""},
+        {"finetuned_model": None},
+        # A FastH3 name over a different base model.
+        {"base_model": "Wan-AI/Wan2.2-TI2V-5B"},
+    ],
+)
+def test_a_generic_fastvideo_h3_adapter_stays_on_the_dynamic_route(identity, tmp_path):
+    """The fixture edits every block, so only the identity separates it from FastH3."""
+    path = tmp_path / "generic" / "adapter_model.safetensors"
+    _write_adapter(path, metadata=identity)
+
+    assert _claim(path.parent) is None
+
+
+def test_a_claimed_artifact_must_declare_every_tensor_count(tmp_path):
+    """Declining is for other people's adapters; a FastH3 file is held to its word."""
+    path = tmp_path / "fasth3" / "adapter_model.safetensors"
+    _write_adapter(path, metadata={"diff_tensors": None})
+
+    with pytest.raises(FastH3AdapterError, match="omits diff_tensors"):
+        _claim(path.parent)
+
+
+def test_the_published_bundle_root_is_refused_rather_than_guessed(tmp_path):
+    root = tmp_path / "FastVideo-FastH3-4-step-Preview-v1-LoRA"
+    for slug in ("dense-datafree", "vsa-datafree"):
+        _write_adapter(root / slug / "adapter_model.safetensors")
+    (root / "adapter_manifest.json").write_text(json.dumps({"schema_version": "fasth3-lora-bundle-v1"}))
+
+    with pytest.raises(FastH3AdapterError, match="point --lora-path at one variant"):
+        _load(root)
+    # One variant inside it is unambiguous.
+    assert _claim(root / "dense-datafree") is not None
+
+
+def test_a_multi_shard_non_fasth3_adapter_stays_on_the_dynamic_route(tmp_path):
+    # A plain PEFT LoRA saved as several shards must not hard-fail startup with a
+    # FastH3-specific message; it has to fall through to the dynamic LoRA route.
+    directory = tmp_path / "peft-sharded"
+    directory.mkdir()
+    for shard in ("adapter_model-00001-of-00002.safetensors", "adapter_model-00002-of-00002.safetensors"):
+        save_file(
+            {"transformer_blocks.0.attn.to_q.lora_A.weight": torch.ones((_RANK, _HIDDEN))}, str(directory / shard)
+        )
+
+    assert _claim(directory) is None
+
+
+def test_low_rank_factors_reach_the_fused_projections(tmp_path):
+    path = tmp_path / "fasth3" / "adapter_model.safetensors"
+    _write_adapter(path)
+    fusion = _load(path.parent)
+
+    qkv = torch.zeros((3 * _INNER, _HIDDEN), dtype=torch.float32)
+    fused = fusion.fuse("blocks.0.attn.qkv_proj.weight", qkv).cpu()
+    # The checkpoint stores one head group at a time as [q, k, v], so the delta
+    # has to survive the loader's own unpacking as three separate projections.
+    q, k, v = torch.split(
+        _reorder_grouped_qkv_to_qkv(fused, num_query_groups=_HEADS, heads_per_group=1, head_dim=_HEAD_DIM),
+        [_INNER, _INNER, _INNER],
+    )
+    a, b = _factors(_INNER, _HIDDEN)
+    expected = b @ a
+    for got in (q, k, v):
+        assert torch.allclose(got, expected, atol=1e-5)
+
+
+def test_the_fused_mlp_delta_is_swapped_into_gate_first_order(tmp_path):
+    path = tmp_path / "fasth3" / "adapter_model.safetensors"
+    _write_adapter(path)
+    fusion = _load(path.parent)
+
+    fused = fusion.fuse("blocks.0.mlp.fc1.weight", torch.zeros((2 * _FFN, _HIDDEN))).cpu()
+    a, b = _factors(2 * _FFN, _HIDDEN)
+    value, gate = (b @ a).chunk(2, dim=0)
+    # The diffusers export is value-first; H3's fc1 is gate-first.
+    assert torch.allclose(fused, torch.cat((gate, value), dim=0), atol=1e-5)
+
+
+def test_diff_and_diff_b_edit_weights_and_biases(tmp_path):
+    path = tmp_path / "fasth3" / "adapter_model.safetensors"
+    _write_adapter(
+        path,
+        tensors={
+            "transformer_blocks.0.norm1.diff": torch.full((_HIDDEN,), 0.25),
+            "transformer_blocks.0.adaln_proj.linear.diff_b": torch.full((_HIDDEN,), -0.5),
+            "context_embedder.diff": torch.full((_HIDDEN, _HIDDEN), 0.125),
+            "context_embedder.diff_b": torch.full((_HIDDEN,), 2.0),
+        },
+    )
+    fusion = _load(path.parent)
+
+    # A full-rank delta on an RMSNorm vector is exactly what a LoRA layer
+    # cannot express, and why this release is fused instead of switched.
+    assert torch.allclose(fusion.fuse("blocks.0.norm1.weight", torch.ones(_HIDDEN)).cpu(), torch.full((_HIDDEN,), 1.25))
+    assert torch.allclose(
+        fusion.fuse("blocks.0.adaln_proj.linear.bias", torch.ones(_HIDDEN)).cpu(), torch.full((_HIDDEN,), 0.5)
+    )
+    assert torch.allclose(
+        fusion.fuse("condition_proj.weight", torch.zeros((_HIDDEN, _HIDDEN))).cpu(),
+        torch.full((_HIDDEN, _HIDDEN), 0.125),
+    )
+    assert torch.allclose(fusion.fuse("condition_proj.bias", torch.zeros(_HIDDEN)).cpu(), torch.full((_HIDDEN,), 2.0))
+
+
+def test_a_streamed_checkpoint_keeps_the_parameters_the_adapter_never_edits(tmp_path):
+    path = tmp_path / "fasth3" / "adapter_model.safetensors"
+    _write_adapter(path, tensors={"transformer_blocks.0.norm1.diff": torch.full((_HIDDEN,), 3.0)})
+    fusion = _load(path.parent)
+
+    untouched = torch.arange(6, dtype=torch.bfloat16).reshape(2, 3)
+    streamed = dict(
+        fusion.apply([("blocks.0.norm1.weight", torch.zeros(_HIDDEN)), ("blocks.0.attn.q_norm.weight", untouched)])
+    )
+    assert torch.allclose(streamed["blocks.0.norm1.weight"].cpu(), torch.full((_HIDDEN,), 3.0))
+    assert streamed["blocks.0.attn.q_norm.weight"] is untouched
+
+    # validate_fully_applied() releases the deltas, so a second stream would
+    # fuse nothing at all; it has to fail rather than serve base H3 weights.
+    with pytest.raises(FastH3AdapterError, match="already been fused"):
+        next(fusion.apply([("blocks.0.norm1.weight", torch.zeros(_HIDDEN))]))
+
+
+def test_the_fused_result_keeps_the_parameter_dtype(tmp_path):
+    path = tmp_path / "fasth3" / "adapter_model.safetensors"
+    _write_adapter(path, tensors={"transformer_blocks.0.norm1.diff": torch.full((_HIDDEN,), 0.5)})
+    fusion = _load(path.parent)
+
+    fused = fusion.fuse("blocks.0.norm1.weight", torch.ones(_HIDDEN, dtype=torch.bfloat16))
+    assert fused.dtype == torch.bfloat16
+
+
+def test_a_vsa_variant_is_recognised_by_its_compression_gates(tmp_path):
+    dense = tmp_path / "dense" / "adapter_model.safetensors"
+    _write_adapter(dense)
+    assert _load(dense.parent).requires_vsa is False
+
+    sparse = tmp_path / "vsa" / "adapter_model.safetensors"
+    _write_adapter(sparse, tensors={"transformer_blocks.0.attn.to_gate_compress.set_weight": torch.ones((2, 2))})
+    assert _load(sparse.parent).requires_vsa is True
+
+
+def test_a_tensor_naming_no_h3_parameter_is_an_error(tmp_path):
+    path = tmp_path / "fasth3" / "adapter_model.safetensors"
+    _write_adapter(path, tensors={"transformer_blocks.0.attn.norm_q.lora_A.weight": torch.ones((_RANK, _HIDDEN))})
+    # Dropping it silently would load a model that is not the distilled student.
+    with pytest.raises(FastH3AdapterError, match="name no known"):
+        _load(path.parent)
+
+
+def test_an_unpaired_low_rank_factor_is_an_error(tmp_path):
+    path = tmp_path / "fasth3" / "adapter_model.safetensors"
+    _write_adapter(path, drop="transformer_blocks.0.attn.to_q.lora_B.weight")
+
+    with pytest.raises(FastH3AdapterError, match="unpaired factor"):
+        _load(path.parent)
+
+
+def test_a_delta_the_checkpoint_never_offered_is_reported(tmp_path):
+    path = tmp_path / "fasth3" / "adapter_model.safetensors"
+    _write_adapter(path)
+    fusion = _load(path.parent)
+
+    fusion.fuse("blocks.0.attn.qkv_proj.weight", torch.zeros((3 * _INNER, _HIDDEN)))
+    # An unapplied delta is the failure that matters: the model would load and
+    # generate, just not as the distilled student.
+    with pytest.raises(FastH3AdapterError, match="never provided"):
+        fusion.validate_fully_applied()
+
+    for name, shape in (
+        ("blocks.0.attn.out_proj.weight", (_HIDDEN, _INNER)),
+        ("blocks.0.mlp.fc1.weight", (2 * _FFN, _HIDDEN)),
+        ("blocks.0.mlp.fc2.weight", (_HIDDEN, _FFN)),
+    ):
+        fusion.fuse(name, torch.zeros(shape))
+    fusion.validate_fully_applied()
+
+
+def _pipeline_stub(fusion=None):
+    from vllm_omni.diffusion.models.minimax_h3 import MiniMaxH3Pipeline
+
+    pipeline = object.__new__(MiniMaxH3Pipeline)
+    torch.nn.Module.__init__(pipeline)
+    pipeline.partition = "combined"
+    pipeline.supported_tasks = frozenset({"t2va", "fl2va", "ref2va"})
+    pipeline._turbo_lora_adapter_ids = set()
+    pipeline._fasth3 = fusion
+    pipeline.od_config = SimpleNamespace()
+    pipeline.default_video_shift, pipeline.default_audio_shift = 12.0, 3.0
+    return pipeline
+
+
+def _sampling(steps=FASTH3_DENOISE_STEPS, **kwargs) -> SimpleNamespace:
+    fields = {"num_inference_steps": steps, "extra_args": {}, "lora_request": None}
+    return SimpleNamespace(**{**fields, **kwargs})
+
+
+def _check_request(fusion, sampling) -> None:
+    fusion.check_request(sampling, video_shift=12.0, audio_shift=3.0)
+
+
+def _fusion(tmp_path, **kwargs) -> FastH3WeightFusion:
+    path = tmp_path / "fasth3" / "adapter_model.safetensors"
+    _write_adapter(path, **kwargs)
+    return _load(path.parent)
+
+
+@pytest.mark.parametrize("task", ["fl2va", "ref2va"])
+def test_an_active_fasth3_fusion_restricts_requests_to_t2va(task, tmp_path):
+    pipeline = _pipeline_stub(_fusion(tmp_path))
+
+    with pytest.raises(OmniClientError, match="distills \\['t2va'\\] only"):
+        pipeline._resolve_task(task, {})
+    assert pipeline._resolve_task("t2va", {}) == "t2va"
+    assert _pipeline_stub()._resolve_task(task, {}) == task
+
+
+@pytest.mark.parametrize("num_inference_steps", [None, 5, 50])
+def test_fasth3_requires_one_forward_per_sigma_interval(num_inference_steps, tmp_path):
+    fusion = _fusion(tmp_path)
+
+    # The five sigma points bound four forwards, and a request states forwards:
+    # the interval contract the pinned schedules use, and the count the step
+    # scheduler admits a request on before any pipeline hook runs.
+    with pytest.raises(OmniClientError, match=f"num_inference_steps={FASTH3_DENOISE_STEPS}"):
+        _check_request(fusion, _sampling(num_inference_steps))
+    _check_request(fusion, _sampling())
+    # The pinned branch coerces the string form; this one cannot disagree.
+    _check_request(fusion, _sampling(str(FASTH3_DENOISE_STEPS)))
+
+
+def test_the_request_denoises_on_the_release_ladder(tmp_path):
+    pipeline = _pipeline_stub(_fusion(tmp_path))
+
+    positions, num_steps = pipeline._resolve_sigma_positions("t2va", _sampling())
+
+    # dmd_denoising_steps [999, 749, 500, 250] out of 1000, closed with the
+    # terminal 0.0: the pre-shift positions the student was distilled at, not
+    # the uniform ladder num_inference_steps would otherwise derive.
+    assert positions == (0.999, 0.749, 0.5, 0.25, 0.0)
+    # Five sigma points bound four transformer forwards, and forwards is the
+    # unit num_steps carries downstream (Cache-DiT, quality hints).
+    assert num_steps == FASTH3_DENOISE_STEPS == len(positions) - 1
+    # H3's own per-modality shifts still apply on top, and are what turn those
+    # positions into the noise levels the four forwards run at.
+    assert minimax_h3_time_shift_sigmas(shift_scale=12.0, base_schedule=positions) == pytest.approx(
+        [0.999917, 0.972833, 0.923077, 0.8, 0.0], abs=1e-6
+    )
+    assert minimax_h3_time_shift_sigmas(shift_scale=3.0, base_schedule=positions) == pytest.approx(
+        [0.999666, 0.899520, 0.75, 0.5, 0.0], abs=1e-6
+    )
+    # Without a fused adapter the same request keeps the undistilled ladder.
+    assert _pipeline_stub()._resolve_sigma_positions("t2va", _sampling()) == (None, FASTH3_DENOISE_STEPS)
+
+
+def test_the_contract_leaves_the_checkpoint_shifts_alone(tmp_path):
+    pipeline = _pipeline_stub(_fusion(tmp_path))
+    pipeline.partition = "fl2va"
+
+    pipeline._fasth3.check_serving_contract(
+        partition=pipeline.partition,
+        od_config=pipeline.od_config,
+        video_shift=pipeline.default_video_shift,
+        audio_shift=pipeline.default_audio_shift,
+    )
+
+    # The ladder is stated pre-shift, so H3's 12/3 shifts are what place the
+    # student on the levels it was distilled at; the contract must not touch them.
+    assert pipeline.default_video_shift == 12.0
+    assert pipeline.default_audio_shift == 3.0
+
+
+@pytest.mark.parametrize(
+    "extra",
+    [{"flow_shift": 1.0}, {"audio_flow_shift": 1.0}, {"flow_shift": "bad"}],
+)
+def test_a_request_may_not_move_the_student_off_its_rungs(extra, tmp_path):
+    fusion = _fusion(tmp_path)
+
+    with pytest.raises(OmniClientError, match="FastH3 requires"):
+        _check_request(fusion, _sampling(extra_args=extra))
+
+    _check_request(fusion, _sampling(extra_args={"flow_shift": 12.0, "audio_flow_shift": 3.0}))
+
+
+def test_a_request_may_not_carry_a_lora_on_a_fused_server(tmp_path):
+    fusion = _fusion(tmp_path)
+
+    # The dynamic LoRA manager is skipped for a fused adapter, so an adapter
+    # asked for per request would be neither applied nor reported.
+    with pytest.raises(OmniClientError, match="per-request lora is unavailable"):
+        _check_request(fusion, _sampling(lora_request=SimpleNamespace(lora_int_id=1)))
+
+
+def test_offload_is_refused_because_it_bypasses_the_fusion(tmp_path):
+    fusion = _fusion(tmp_path)
+    for flag in ("enable_cpu_offload", "enable_layerwise_offload", "enable_distributed_layerwise_offload"):
+        # A host-weight plan installs the transformer without load_weights(),
+        # so the fusion and its completeness check would both be skipped.
+        with pytest.raises(ValueError, match="cannot be combined with"):
+            fusion.check_serving_contract(
+                partition="fl2va",
+                od_config=SimpleNamespace(**{flag: True}),
+                video_shift=12.0,
+                audio_shift=3.0,
+            )
+
+
+def test_adopting_the_contract_refuses_a_vsa_variant_and_ref2va(tmp_path):
+    sparse = tmp_path / "vsa" / "adapter_model.safetensors"
+    _write_adapter(sparse, tensors={"transformer_blocks.0.attn.to_gate_compress.set_weight": torch.ones((2, 2))})
+    contract = {"od_config": SimpleNamespace(), "video_shift": 12.0, "audio_shift": 3.0}
+    with pytest.raises(ValueError, match="Video Sparse Attention variant"):
+        _load(sparse.parent).check_serving_contract(partition="fl2va", **contract)
+
+    with pytest.raises(ValueError, match="cannot serve a Ref2VA partition"):
+        _fusion(tmp_path).check_serving_contract(partition="ref2va", **contract)
+
+
+def test_a_full_rank_delta_on_a_fused_parameter_is_refused(tmp_path):
+    # Only low-rank factors are placed into H3's fused QKV and gate/up layouts,
+    # so a .diff aimed at one would otherwise be added transposed.
+    path = tmp_path / "fasth3" / "adapter_model.safetensors"
+    _write_adapter(path, tensors={"transformer_blocks.0.attn.to_q.diff": torch.ones((_INNER, _HIDDEN))})
+    with pytest.raises(FastH3AdapterError, match="fused layout"):
+        _claim(path.parent)
+
+
+def test_an_adapter_that_leaves_blocks_untouched_is_refused(tmp_path):
+    path = tmp_path / "fasth3" / "adapter_model.safetensors"
+    _write_adapter(path, blocks=2)
+
+    # Every tensor in this file is well formed and pairs up, so the fusion's
+    # own completeness check passes; only the model's depth reveals that a
+    # third of it would have been served as base H3 weights.
+    with pytest.raises(FastH3AdapterError, match="missing=\\[2\\]"):
+        _claim(path.parent, num_blocks=3)
+    with pytest.raises(FastH3AdapterError, match="unknown=\\[1\\]"):
+        _claim(path.parent, num_blocks=1)
+    assert _claim(path.parent, num_blocks=2) is not None
+
+
+def test_an_adapter_shorter_than_it_declares_is_refused(tmp_path):
+    path = tmp_path / "fasth3" / "adapter_model.safetensors"
+    _write_adapter(path, metadata={"low_rank_tensors": "724", "diff_tensors": "85"})
+
+    # The writer records what it emitted, which is the file's one statement
+    # about its own completeness.
+    with pytest.raises(FastH3AdapterError, match="declares low_rank_tensors=724 but carries 12"):
+        _claim(path.parent)
+
+
+def test_only_a_configured_adapter_path_is_read(tmp_path):
+    path = tmp_path / "fasth3" / "adapter_model.safetensors"
+    _write_adapter(path)
+    transformer = SimpleNamespace(
+        arch=SimpleNamespace(attention_head_dim=_HEAD_DIM, num_layers=1, token_refiner_num_layers=0)
+    )
+    unbuilt = object()  # a transformer whose arch must not be touched
+
+    assert resolve_fasth3_fusion(SimpleNamespace(lora_path=str(path)), transformer) is not None
+    assert resolve_fasth3_fusion(SimpleNamespace(lora_path=[str(path)]), transformer) is not None
+    # Nothing configured, or more than one artifact, stays on the dynamic route
+    # without reading anything off the model.
+    assert resolve_fasth3_fusion(SimpleNamespace(lora_path=None), unbuilt) is None
+    assert resolve_fasth3_fusion(SimpleNamespace(), unbuilt) is None
+    assert resolve_fasth3_fusion(SimpleNamespace(lora_path=[str(path), str(path)]), unbuilt) is None
+
+
+def test_a_pipeline_that_fused_its_adapter_needs_no_lora_manager(tmp_path):
+    assert _pipeline_stub(_fusion(tmp_path)).lora_is_fused is True
+    assert _pipeline_stub().lora_is_fused is False
+
+
+def test_the_ladder_is_the_one_the_release_publishes():
+    # FASTH3_DENOISE_STEPS is derived from it, so the two cannot drift apart.
+    assert FASTH3_BASE_SCHEDULE.base_schedule == (0.999, 0.749, 0.5, 0.25, 0.0)
+    assert FASTH3_BASE_SCHEDULE.num_inference_steps == FASTH3_DENOISE_STEPS == 4

tests/diffusion/models/minimax_h3/test_minimax_h3_lora.py

@@ -223,6 +223,8 @@ def test_h3_turbo_allows_distributed_layerwise_offload(monkeypatch):
     torch.nn.Module.__init__(pipeline)
     pipeline.partition = "fl2va"
     pipeline._turbo_lora_adapter_ids = set()
+    pipeline._native_lora_adapter_ids = set()
+    pipeline._lora_sigma_schedules = {}
     pipeline.od_config = SimpleNamespace(
         enable_cpu_offload=False,
         enable_layerwise_offload=False,
@@ -321,6 +323,8 @@ def test_only_an_active_recognized_turbo_adapter_restricts_ref2va(monkeypatch):
     pipeline.partition = "combined"
     pipeline.supported_tasks = frozenset({"t2va", "fl2va", "ref2va"})
     pipeline._turbo_lora_adapter_ids = set()
+    pipeline._native_lora_adapter_ids = set()
+    pipeline._lora_sigma_schedules = {}
     request = _request("generic-peft")
 
     def load():

tests/diffusion/models/minimax_h3/test_minimax_h3_native_lora.py

@@ -0,0 +1,864 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
+
+from __future__ import annotations
+
+from types import SimpleNamespace
+
+import pytest
+import torch
+from safetensors.torch import save_file
+from vllm.lora.lora_weights import PackedLoRALayerWeights
+
+from vllm_omni.diffusion.lora.manager import DiffusionLoRAManager
+from vllm_omni.diffusion.models.minimax_h3.npu import lora as lora_module
+from vllm_omni.diffusion.models.minimax_h3.npu.lora import load_minimax_h3_native_lora
+from vllm_omni.diffusion.sched.sigma_schedule import DMD2SigmaSchedule
+from vllm_omni.errors import OmniClientError
+from vllm_omni.lora.request import LoRARequest
+
+pytestmark = [pytest.mark.core_model, pytest.mark.diffusion, pytest.mark.cpu]
+
+_TINY_HIDDEN = 2
+_TINY_FFN = 2
+_TINY_TIME = 2
+_TINY_BLOCK_ADALN = 4
+_TINY_FINAL_ADALN = 3
+_TINY_RANK = 4
+_TINY_NUM_QUERY_GROUPS = 2
+_TINY_HEADS_PER_GROUP = 1
+_TINY_HEAD_DIM = 1
+_TINY_QKV_SLICE = _TINY_NUM_QUERY_GROUPS * _TINY_HEADS_PER_GROUP * _TINY_HEAD_DIM
+# Grouped qkv stores (heads_per_group + 2) * head_dim rows per query group, so
+# the attention inner size follows from the head layout and cannot be picked
+# independently: 3 * inner must equal the grouped row count.
+_TINY_INNER = _TINY_QKV_SLICE
+
+
+@pytest.fixture(autouse=True)
+def _use_tiny_native_dimensions(monkeypatch):
+    monkeypatch.setattr(lora_module, "_NATIVE_RANK", _TINY_RANK)
+    monkeypatch.setattr(lora_module, "_NATIVE_ALPHA", _TINY_RANK)
+    monkeypatch.setattr(lora_module, "_NATIVE_HIDDEN_SIZE", _TINY_HIDDEN)
+    monkeypatch.setattr(lora_module, "_NATIVE_ATTENTION_INNER_SIZE", _TINY_INNER)
+    monkeypatch.setattr(lora_module, "_NATIVE_FFN_HIDDEN_SIZE", _TINY_FFN)
+    monkeypatch.setattr(lora_module, "_NATIVE_TIME_EMBED_DIM", _TINY_TIME)
+    monkeypatch.setattr(lora_module, "_NATIVE_BLOCK_ADALN_OUT", _TINY_BLOCK_ADALN)
+    monkeypatch.setattr(lora_module, "_NATIVE_FINAL_ADALN_OUT", _TINY_FINAL_ADALN)
+    monkeypatch.setattr(lora_module, "_NATIVE_NUM_QUERY_GROUPS", _TINY_NUM_QUERY_GROUPS)
+    monkeypatch.setattr(lora_module, "_NATIVE_HEADS_PER_GROUP", _TINY_HEADS_PER_GROUP)
+    monkeypatch.setattr(lora_module, "_NATIVE_HEAD_DIM", _TINY_HEAD_DIM)
+    monkeypatch.setattr(lora_module, "_NATIVE_QKV_SLICE", _TINY_QKV_SLICE)
+    monkeypatch.setattr(
+        lora_module,
+        "_NATIVE_TARGET_DIMS",
+        {
+            "attn.qkv_proj": (_TINY_HIDDEN, 3 * _TINY_INNER),
+            "attn.out_proj": (_TINY_INNER, _TINY_HIDDEN),
+            "mlp.fc1": (_TINY_HIDDEN, 2 * _TINY_FFN),
+            "mlp.fc2": (_TINY_FFN, _TINY_HIDDEN),
+            "adaln_proj.linear": (_TINY_TIME, _TINY_BLOCK_ADALN),
+        },
+    )
+    monkeypatch.setattr(lora_module, "_NATIVE_FINAL_ADALN_DIMS", (_TINY_TIME, _TINY_FINAL_ADALN))
+    monkeypatch.setattr(
+        lora_module,
+        "_NATIVE_EXPECTED_TARGETS",
+        frozenset(
+            [
+                *(
+                    f"blocks.{block_index}.{suffix}"
+                    for block_index in range(50)
+                    for suffix in lora_module._NATIVE_TARGET_SUFFIXES
+                )
+            ]
+            + [
+                *(
+                    f"token_refiner.blocks.{block_index}.{suffix}"
+                    for block_index in range(2)
+                    for suffix in lora_module._NATIVE_TOKEN_REFINER_SUFFIXES
+                )
+            ]
+            + ["final_layer.adaln_proj.linear"]
+        ),
+    )
+
+
+def _request(path) -> LoRARequest:
+    return LoRARequest(
+        lora_name="flashgen",
+        lora_int_id=7,
+        lora_path=str(path),
+    )
+
+
+def _write_tiny_native(
+    path,
+    *,
+    omit_target: str | None = None,
+    shape_overrides: dict[str, tuple[int, int]] | None = None,
+    metadata: dict[str, str] | None = None,
+) -> None:
+    rank = _TINY_RANK
+    tensors = {}
+    overrides = shape_overrides or {}
+    for target in sorted(lora_module._NATIVE_EXPECTED_TARGETS):
+        if target == omit_target:
+            continue
+        input_dim, output_dim = lora_module._native_target_dims(target)
+        raw_target = f"transformer.{target}"
+        a_name = f"{raw_target}.lora_A.default.weight"
+        b_name = f"{raw_target}.lora_B.default.weight"
+        tensors[a_name] = torch.ones(overrides.get(a_name, (rank, input_dim)))
+        if target.endswith(".attn.qkv_proj"):
+            grouped_rows = []
+            for group in range(_TINY_NUM_QUERY_GROUPS):
+                grouped_rows.extend(
+                    [
+                        torch.full((1, rank), float(group * 10 + 1)),
+                        torch.full((1, rank), float(group * 10 + 2)),
+                        torch.full((1, rank), float(group * 10 + 3)),
+                    ]
+                )
+            tensors[b_name] = torch.cat(grouped_rows, dim=0)
+        elif target.endswith(".mlp.fc1"):
+            tensors[b_name] = torch.cat(
+                (
+                    torch.full((output_dim // 2, rank), 2.0),
+                    torch.full((output_dim // 2, rank), 1.0),
+                ),
+                dim=0,
+            )
+        else:
+            tensors[b_name] = torch.ones(overrides.get(b_name, (output_dim, rank)))
+    save_file(
+        tensors,
+        str(path),
+        metadata=metadata
+        or {
+            "format": "pt",
+            "key_format": "minimax-h3-native",
+            "qkv_layout": "grouped",
+            "lora_rank": str(rank),
+            "lora_alpha": str(rank),
+            "base_schedule": "1.0,0.7,0.4,0.15,0.0",
+            "tasks": "t2va",
+        },
+    )
+
+
+def test_h3_native_loads_and_packs_qkv_and_fc1(tmp_path):
+    path = tmp_path / "minimax_h3_t2va_flashgen_4step_v1.0_768p_bf16.safetensors"
+    _write_tiny_native(path)
+
+    loaded = load_minimax_h3_native_lora(
+        partition="fl2va",
+        lora_request=_request(path),
+        lora_path=path,
+        dtype=torch.float32,
+    )
+
+    assert loaded is not None
+    lora_model, peft_helper, schedule = loaded
+    assert peft_helper.r == _TINY_RANK
+    assert schedule.num_inference_steps == 4
+    assert len(lora_model.loras) == 259
+    assert "blocks.0.adaln_proj.linear" in lora_model.loras
+    assert "final_layer.adaln_proj.linear" in lora_model.loras
+
+    # The writer tags group g with q=g*10+1, k=g*10+2, v=g*10+3, so a correct
+    # grouped -> q/k/v permutation yields slices that are uniform per role.
+    qkv = lora_model.get_lora("blocks.0.attn.qkv_proj")
+    assert isinstance(qkv, PackedLoRALayerWeights)
+    torch.testing.assert_close(qkv.lora_b[0], torch.tensor([[1.0] * _TINY_RANK, [11.0] * _TINY_RANK]))
+    torch.testing.assert_close(qkv.lora_b[1], torch.tensor([[2.0] * _TINY_RANK, [12.0] * _TINY_RANK]))
+    torch.testing.assert_close(qkv.lora_b[2], torch.tensor([[3.0] * _TINY_RANK, [13.0] * _TINY_RANK]))
+
+    fc1 = lora_model.get_lora("blocks.0.mlp.fc1")
+    assert isinstance(fc1, PackedLoRALayerWeights)
+    torch.testing.assert_close(fc1.lora_b[0], torch.full((_TINY_FFN, _TINY_RANK), 2.0))
+    torch.testing.assert_close(fc1.lora_b[1], torch.full((_TINY_FFN, _TINY_RANK), 1.0))
+
+
+def test_h3_native_rejects_bad_metadata_and_ref2va(tmp_path):
+    path = tmp_path / "minimax_h3_t2va_flashgen_4step_v1.0_768p_bf16.safetensors"
+    _write_tiny_native(path, metadata={"key_format": "minimax-h3-native", "qkv_layout": "runtime"})
+    with pytest.raises(ValueError, match="qkv_layout='grouped'"):
+        load_minimax_h3_native_lora(
+            partition="fl2va",
+            lora_request=_request(path),
+            lora_path=path,
+            dtype=torch.float32,
+        )
+
+    # The exact-filename guard runs before the partition check, so the artifact
+    # has to keep the published name for this to reach the Ref2VA rejection.
+    valid_dir = tmp_path / "valid"
+    valid_dir.mkdir()
+    valid = valid_dir / "minimax_h3_t2va_flashgen_4step_v1.0_768p_bf16.safetensors"
+    _write_tiny_native(valid)
+    with pytest.raises(ValueError, match="supports T2VA only"):
+        load_minimax_h3_native_lora(
+            partition="ref2va",
+            lora_request=_request(valid),
+            lora_path=valid,
+            dtype=torch.float32,
+        )
+
+    renamed = tmp_path / "renamed.safetensors"
+    _write_tiny_native(renamed)
+    with pytest.raises(ValueError, match="supports only"):
+        load_minimax_h3_native_lora(
+            partition="fl2va",
+            lora_request=_request(renamed),
+            lora_path=renamed,
+            dtype=torch.float32,
+        )
+
+
+def test_h3_native_rejects_truncated_artifact(tmp_path):
+    path = tmp_path / "minimax_h3_t2va_flashgen_4step_v1.0_768p_bf16.safetensors"
+    _write_tiny_native(path, omit_target="blocks.49.mlp.fc2")
+    with pytest.raises(ValueError, match="target set does not match"):
+        load_minimax_h3_native_lora(
+            partition="fl2va",
+            lora_request=_request(path),
+            lora_path=path,
+            dtype=torch.float32,
+        )
+
+
+def test_h3_native_rejects_schedule_with_wrong_interval_count(tmp_path):
+    """A mislabeled schedule must fail closed instead of silently changing steps."""
+    path = tmp_path / "minimax_h3_t2va_flashgen_4step_v1.0_768p_bf16.safetensors"
+    _write_tiny_native(
+        path,
+        metadata={
+            "format": "pt",
+            "key_format": "minimax-h3-native",
+            "qkv_layout": "grouped",
+            "lora_rank": str(_TINY_RANK),
+            "lora_alpha": str(_TINY_RANK),
+            "base_schedule": "1.0,0.5,0.0",
+            "tasks": "t2va",
+        },
+    )
+    with pytest.raises(ValueError, match="4-interval base_schedule"):
+        load_minimax_h3_native_lora(
+            partition="fl2va",
+            lora_request=_request(path),
+            lora_path=path,
+            dtype=torch.float32,
+        )
+
+
+@pytest.mark.parametrize(
+    ("raw_schedule", "message"),
+    [
+        (None, "requires safetensors metadata base_schedule"),
+        ("", "must not be empty"),
+        (",,", "must not be empty"),
+        ("1.0,oops,0.15,0.0", "is malformed"),
+        # A capped opening rung below 1.0 is a legal DMD2 schedule, so the
+        # out-of-range cases are a first position above 1.0 and a tail that
+        # never reaches clean latents.
+        ("1.5,0.7,0.4,0.15,0.0", "is malformed"),
+        ("1.0,0.7,0.4,0.15", "is malformed"),
+    ],
+)
+def test_h3_native_rejects_malformed_schedule_metadata(tmp_path, raw_schedule, message):
+    """safetensors metadata is string-only, so the loader owns the parse contract."""
+    metadata = {
+        "format": "pt",
+        "key_format": "minimax-h3-native",
+        "qkv_layout": "grouped",
+        "lora_rank": str(_TINY_RANK),
+        "lora_alpha": str(_TINY_RANK),
+        "tasks": "t2va",
+    }
+    if raw_schedule is not None:
+        metadata["base_schedule"] = raw_schedule
+    path = tmp_path / "minimax_h3_t2va_flashgen_4step_v1.0_768p_bf16.safetensors"
+    _write_tiny_native(path, metadata=metadata)
+
+    with pytest.raises(ValueError, match=message):
+        load_minimax_h3_native_lora(
+            partition="fl2va",
+            lora_request=_request(path),
+            lora_path=path,
+            dtype=torch.float32,
+        )
+
+
+def test_h3_native_declared_file_fails_closed_on_invalid_metadata(tmp_path):
+    path = tmp_path / "minimax_h3_t2va_flashgen_4step_v1.0_768p_bf16.safetensors"
+    _write_tiny_native(path, metadata={"key_format": "other"})
+    with pytest.raises(ValueError, match="requires safetensors metadata"):
+        load_minimax_h3_native_lora(
+            partition="fl2va",
+            lora_request=_request(path),
+            lora_path=path,
+            dtype=torch.float32,
+        )
+
+
+def test_pipeline_native_schedule_and_task_validation(monkeypatch):
+    from vllm_omni.diffusion.models.minimax_h3 import MiniMaxH3Pipeline
+
+    pipeline = object.__new__(MiniMaxH3Pipeline)
+    torch.nn.Module.__init__(pipeline)
+    pipeline.partition = "fl2va"
+    pipeline.supported_tasks = frozenset({"t2va", "fl2va", "ref2va"})
+    pipeline._turbo_lora_adapter_ids = set()
+    pipeline._native_lora_adapter_ids = {7}
+    pipeline._lora_sigma_schedules = {7: DMD2SigmaSchedule.from_positions([1.0, 0.7, 0.4, 0.15, 0.0])}
+    pipeline._base_schedule_by_partition = {"fl2va": None}
+
+    sampling = SimpleNamespace(
+        lora_request=LoRARequest("flashgen", 7, "/tmp/native.safetensors"),
+        lora_scale=1.0,
+        num_inference_steps=4,
+        extra_args={},
+    )
+    assert pipeline._sigma_schedule_for_request(sampling, "t2va").num_inference_steps == 4
+    with pytest.raises(OmniClientError, match="num_inference_steps must be 4"):
+        pipeline._validate_native_sampling(
+            SimpleNamespace(lora_request=sampling.lora_request, num_inference_steps=5),
+            task="t2va",
+        )
+    with pytest.raises(OmniClientError, match="supports T2VA requests only"):
+        pipeline._resolve_task(
+            "fl2va",
+            {},
+            has_turbo_lora=False,
+            has_native_lora=True,
+        )
+
+    pipeline._base_schedule_by_partition = {"fl2va": DMD2SigmaSchedule.from_positions([1.0, 0.5, 0.0])}
+    with pytest.raises(OmniClientError, match="already pins base_schedule"):
+        pipeline._sigma_schedule_for_request(sampling, "t2va")
+
+
+def test_pipeline_native_step_count_follows_adapter_schedule():
+    """The accepted step count is the adapter's interval count, never a literal."""
+    from vllm_omni.diffusion.models.minimax_h3 import MiniMaxH3Pipeline
+
+    pipeline = object.__new__(MiniMaxH3Pipeline)
+    torch.nn.Module.__init__(pipeline)
+    pipeline.partition = "fl2va"
+    pipeline._native_lora_adapter_ids = {7}
+    pipeline._lora_sigma_schedules = {7: DMD2SigmaSchedule.from_positions([1.0, 0.5, 0.0])}
+    request = LoRARequest("flashgen", 7, "/tmp/native.safetensors")
+
+    pipeline._validate_native_sampling(
+        SimpleNamespace(lora_request=request, num_inference_steps=2),
+        task="t2va",
+    )
+    with pytest.raises(OmniClientError, match="num_inference_steps must be 2 or omitted, not 3"):
+        pipeline._validate_native_sampling(
+            SimpleNamespace(lora_request=request, num_inference_steps=3),
+            task="t2va",
+        )
+    with pytest.raises(OmniClientError, match="requires num_inference_steps=2"):
+        pipeline._validate_native_sampling(
+            SimpleNamespace(lora_request=request, num_inference_steps=4),
+            task="t2va",
+        )
+
+
+@pytest.mark.parametrize("step_execution", [False, True])
+def test_pipeline_native_omitted_step_count_follows_execution_mode(step_execution):
+    """Only request mode can resolve an omitted count from the adapter schedule.
+
+    ``StepScheduler`` reads ``num_inference_steps`` off the request at admission,
+    before this pipeline sees it, so under step execution an omitted value can
+    only disagree with the denoise loop. The advertised contract has to match.
+    """
+    from vllm_omni.diffusion.models.minimax_h3 import MiniMaxH3Pipeline
+
+    pipeline = object.__new__(MiniMaxH3Pipeline)
+    torch.nn.Module.__init__(pipeline)
+    pipeline.partition = "fl2va"
+    pipeline._native_lora_adapter_ids = {7}
+    pipeline._lora_sigma_schedules = {7: DMD2SigmaSchedule.from_positions([1.0, 0.7, 0.4, 0.15, 0.0])}
+    pipeline.od_config = SimpleNamespace(step_execution=step_execution)
+    sampling = SimpleNamespace(
+        lora_request=LoRARequest("flashgen", 7, "/tmp/native.safetensors"),
+        num_inference_steps=None,
+    )
+
+    if not step_execution:
+        # Request mode defaults to the adapter's own interval count.
+        pipeline._validate_native_sampling(sampling, task="t2va")
+        return
+
+    with pytest.raises(OmniClientError, match="explicit num_inference_steps=4 under step execution"):
+        pipeline._validate_native_sampling(sampling, task="t2va")
+    # The step-mode message must not offer omission it cannot accept.
+    with pytest.raises(OmniClientError, match=r"must be 4, not 5$"):
+        pipeline._validate_native_sampling(
+            SimpleNamespace(lora_request=sampling.lora_request, num_inference_steps=5),
+            task="t2va",
+        )
+
+
+def test_pipeline_replaces_native_classification_after_reload(monkeypatch, tmp_path):
+    from vllm_omni.diffusion.models.minimax_h3 import MiniMaxH3Pipeline
+    from vllm_omni.diffusion.models.minimax_h3 import pipeline_minimax_h3 as pipeline_module
+
+    path = tmp_path / "minimax_h3_t2va_flashgen_4step_v1.0_768p_bf16.safetensors"
+    _write_tiny_native(path)
+    request = _request(path)
+
+    pipeline = object.__new__(MiniMaxH3Pipeline)
+    torch.nn.Module.__init__(pipeline)
+    pipeline.partition = "fl2va"
+    pipeline.od_config = SimpleNamespace(
+        enable_cpu_offload=False,
+        enable_layerwise_offload=False,
+        enable_distributed_layerwise_offload=False,
+    )
+    pipeline._turbo_lora_adapter_ids = set()
+    pipeline._native_lora_adapter_ids = set()
+    pipeline._lora_sigma_schedules = {}
+
+    loaded = pipeline._load_diffusion_lora_adapter(
+        lora_request=request,
+        lora_path=path,
+        dtype=torch.float32,
+    )
+    assert loaded is not None
+    assert request.lora_int_id in pipeline._native_lora_adapter_ids
+    assert request.lora_int_id in pipeline._lora_sigma_schedules
+
+    monkeypatch.setattr(pipeline_module, "load_minimax_h3_turbo_lora", lambda **_: None)
+    monkeypatch.setattr(pipeline_module, "load_minimax_h3_native_lora", lambda **_: None)
+    assert pipeline._load_diffusion_lora_adapter(lora_request=request, lora_path=path, dtype=torch.float32) is None
+    assert request.lora_int_id not in pipeline._native_lora_adapter_ids
+    assert request.lora_int_id not in pipeline._lora_sigma_schedules
+
+
+@pytest.mark.parametrize(
+    "offload_mode",
+    [
+        "model-level CPU offload (--enable-cpu-offload)",
+        "layerwise offload (--enable-layerwise-offload)",
+    ],
+)
+def test_h3_native_rejects_offload_modes(tmp_path, offload_mode):
+    path = tmp_path / "minimax_h3_t2va_flashgen_4step_v1.0_768p_bf16.safetensors"
+    _write_tiny_native(path)
+
+    with pytest.raises(ValueError, match="does not support"):
+        load_minimax_h3_native_lora(
+            partition="fl2va",
+            lora_request=_request(path),
+            lora_path=path,
+            dtype=torch.float32,
+            unsupported_offload_mode=offload_mode,
+        )
+
+
+def test_h3_native_allows_distributed_layerwise_offload(monkeypatch):
+    """DLO keeps LoRA A/B buffers resident, so native must not fail closed."""
+    from vllm_omni.diffusion.models.minimax_h3 import MiniMaxH3Pipeline
+    from vllm_omni.diffusion.models.minimax_h3 import pipeline_minimax_h3 as pipeline_module
+
+    pipeline = object.__new__(MiniMaxH3Pipeline)
+    torch.nn.Module.__init__(pipeline)
+    pipeline.partition = "fl2va"
+    pipeline.od_config = SimpleNamespace(
+        enable_cpu_offload=False,
+        enable_layerwise_offload=False,
+        enable_distributed_layerwise_offload=True,
+    )
+    pipeline._turbo_lora_adapter_ids = set()
+    pipeline._native_lora_adapter_ids = set()
+    pipeline._lora_sigma_schedules = {}
+    captured: dict[str, object] = {}
+    schedule = DMD2SigmaSchedule.from_positions([1.0, 0.7, 0.4, 0.15, 0.0])
+
+    def load_native(**kwargs):
+        captured.update(kwargs)
+        return object(), object(), schedule
+
+    monkeypatch.setattr(pipeline_module, "load_minimax_h3_turbo_lora", lambda **_: None)
+    monkeypatch.setattr(pipeline_module, "load_minimax_h3_native_lora", load_native)
+
+    request = _request("flashgen")
+    loaded = pipeline._load_diffusion_lora_adapter(
+        lora_request=request,
+        lora_path="flashgen",
+        dtype=torch.bfloat16,
+    )
+
+    assert loaded is not None
+    assert captured["unsupported_offload_mode"] is None
+    assert request.lora_int_id in pipeline._native_lora_adapter_ids
+    assert pipeline._lora_sigma_schedules[request.lora_int_id] is schedule
+
+
+def test_h3_native_qkv_reorder_matches_base_loader_contract():
+    from vllm_omni.diffusion.models.minimax_h3.minimax_h3_transformer import _reorder_grouped_qkv_to_qkv
+
+    num_query_groups = 56
+    heads_per_group = 1
+    head_dim = 128
+    grouped = torch.arange(num_query_groups * (heads_per_group + 2) * head_dim, dtype=torch.float32)
+    grouped = grouped.reshape(num_query_groups, (heads_per_group + 2) * head_dim)
+    reordered = _reorder_grouped_qkv_to_qkv(
+        grouped.reshape(-1, 1),
+        num_query_groups=num_query_groups,
+        heads_per_group=heads_per_group,
+        head_dim=head_dim,
+    ).reshape(-1)
+    q_size = num_query_groups * heads_per_group * head_dim
+    k_size = num_query_groups * head_dim
+    q, k, v = torch.split(reordered, [q_size, k_size, k_size])
+    assert q.reshape(num_query_groups, head_dim)[0, 0] == grouped[0, 0]
+    assert k.reshape(num_query_groups, head_dim)[0, 0] == grouped[0, head_dim]
+    assert v.reshape(num_query_groups, head_dim)[0, 0] == grouped[0, 2 * head_dim]
+
+
+def test_native_target_pattern_matches_component_qualified_names():
+    """The manager matches "<component>.<module>", so the pattern needs the prefix."""
+    import regex as re
+
+    pattern = lora_module._NATIVE_TARGET_PATTERN
+    matched = [
+        target
+        for target in lora_module._NATIVE_EXPECTED_TARGETS
+        if re.search(pattern, f"transformer.{target}") is not None
+    ]
+    assert len(matched) == 259
+    assert re.search(pattern, "transformer.blocks.0.attn.q_proj") is None
+    assert re.search(pattern, "transformer.token_refiner.blocks.0.adaln_proj.linear") is None
+
+
+def test_replace_layers_binds_every_native_target(monkeypatch):
+    """Regression for bound=0/259: layer replacement must reach all 259 modules."""
+    from vllm.lora.layers import BaseLayerWithLoRA
+    from vllm.lora.peft_helper import PEFTHelper
+
+    from vllm_omni.diffusion.lora import manager as manager_module
+
+    class _StubLoRALayer(BaseLayerWithLoRA):
+        def __init__(self, base_layer):
+            super().__init__()
+            self.base_layer = base_layer
+
+    def _linear() -> torch.nn.Module:
+        return torch.nn.Linear(_TINY_HIDDEN, _TINY_HIDDEN)
+
+    def _block(*, with_adaln: bool) -> torch.nn.Module:
+        block = torch.nn.Module()
+        block.attn = torch.nn.Module()
+        block.attn.qkv_proj = _linear()
+        block.attn.out_proj = _linear()
+        block.mlp = torch.nn.Module()
+        block.mlp.fc1 = _linear()
+        block.mlp.fc2 = _linear()
+        if with_adaln:
+            block.adaln_proj = torch.nn.Module()
+            block.adaln_proj.linear = _linear()
+        return block
+
+    transformer = torch.nn.Module()
+    transformer.blocks = torch.nn.ModuleList([_block(with_adaln=True) for _ in range(50)])
+    transformer.token_refiner = torch.nn.Module()
+    transformer.token_refiner.blocks = torch.nn.ModuleList([_block(with_adaln=False) for _ in range(2)])
+    transformer.final_layer = torch.nn.Module()
+    transformer.final_layer.adaln_proj = torch.nn.Module()
+    transformer.final_layer.adaln_proj.linear = _linear()
+    pipeline = torch.nn.Module()
+    pipeline.transformer = transformer
+
+    monkeypatch.setattr(manager_module, "from_layer_diffusion", lambda *, layer, **_: _StubLoRALayer(layer))
+
+    manager = object.__new__(DiffusionLoRAManager)
+    manager.pipeline = pipeline
+    manager.dtype = torch.float32
+    manager.max_cached_adapters = 1
+    manager._lora_modules = {}
+    manager._max_lora_rank = 64
+    manager._resident_lora_device = None
+
+    peft_helper = PEFTHelper.from_dict(
+        {
+            "r": _TINY_RANK,
+            "lora_alpha": _TINY_RANK,
+            "target_modules": lora_module._NATIVE_TARGET_PATTERN,
+        }
+    )
+    manager._replace_layers_with_lora(peft_helper)
+
+    assert set(manager._lora_modules) == {f"transformer.{target}" for target in lora_module._NATIVE_EXPECTED_TARGETS}
+
+
+def test_legacy_manager_uses_native_loader(tmp_path):
+    path = tmp_path / "minimax_h3_t2va_flashgen_4step_v1.0_768p_bf16.safetensors"
+    _write_tiny_native(path)
+
+    class _Pipeline:
+        def _load_diffusion_lora_adapter(self, **kwargs):
+            from vllm_omni.diffusion.models.minimax_h3.pipeline_minimax_h3 import (
+                MiniMaxH3Pipeline,
+            )
+
+            pipeline = object.__new__(MiniMaxH3Pipeline)
+            pipeline.partition = "fl2va"
+            pipeline.od_config = SimpleNamespace(
+                enable_cpu_offload=False,
+                enable_layerwise_offload=False,
+                enable_distributed_layerwise_offload=False,
+            )
+            pipeline._turbo_lora_adapter_ids = set()
+            pipeline._native_lora_adapter_ids = set()
+            pipeline._lora_sigma_schedules = {}
+            return pipeline._load_diffusion_lora_adapter(**kwargs)
+
+    manager = object.__new__(DiffusionLoRAManager)
+    manager.pipeline = _Pipeline()
+    manager.dtype = torch.float32
+    manager._expected_lora_modules = {"qkv_proj", "fc1", "adaln_proj.linear"}
+
+    lora_model, peft_helper = manager._load_adapter(_request(path))
+    assert lora_model.id == 7
+    assert peft_helper.lora_alpha == _TINY_RANK
+    assert len(lora_model.loras) == 259
+
+
+def test_pipeline_schedule_inactive_when_scale_zero():
+    from vllm_omni.diffusion.models.minimax_h3 import MiniMaxH3Pipeline
+
+    pipeline = object.__new__(MiniMaxH3Pipeline)
+    torch.nn.Module.__init__(pipeline)
+    pipeline.partition = "fl2va"
+    pipeline._turbo_lora_adapter_ids = set()
+    pipeline._native_lora_adapter_ids = {7}
+    pipeline._lora_sigma_schedules = {7: DMD2SigmaSchedule.from_positions([1.0, 0.7, 0.4, 0.15, 0.0])}
+    pipeline._base_schedule_by_partition = {"fl2va": None}
+
+    sampling = SimpleNamespace(
+        lora_request=LoRARequest("flashgen", 7, "/tmp/native.safetensors"),
+        lora_scale=0.0,
+        num_inference_steps=4,
+    )
+    assert pipeline._sigma_schedule_for_request(sampling, "t2va") is None
+    assert not pipeline._has_active_native_lora(sampling)
+
+
+def test_pipeline_schedule_falls_back_after_eviction(monkeypatch, tmp_path):
+    from vllm_omni.diffusion.models.minimax_h3 import MiniMaxH3Pipeline
+    from vllm_omni.diffusion.models.minimax_h3 import pipeline_minimax_h3 as pipeline_module
+
+    path = tmp_path / "minimax_h3_t2va_flashgen_4step_v1.0_768p_bf16.safetensors"
+    _write_tiny_native(path)
+    request = _request(path)
+
+    pipeline = object.__new__(MiniMaxH3Pipeline)
+    torch.nn.Module.__init__(pipeline)
+    pipeline.partition = "fl2va"
+    pipeline.od_config = SimpleNamespace(
+        enable_cpu_offload=False,
+        enable_layerwise_offload=False,
+        enable_distributed_layerwise_offload=False,
+    )
+    pipeline._turbo_lora_adapter_ids = set()
+    pipeline._native_lora_adapter_ids = set()
+    pipeline._lora_sigma_schedules = {}
+    pipeline._base_schedule_by_partition = {"fl2va": None}
+
+    loaded = pipeline._load_diffusion_lora_adapter(
+        lora_request=request,
+        lora_path=path,
+        dtype=torch.float32,
+    )
+    assert loaded is not None
+    assert request.lora_int_id in pipeline._lora_sigma_schedules
+
+    monkeypatch.setattr(pipeline_module, "load_minimax_h3_turbo_lora", lambda **_: None)
+    monkeypatch.setattr(pipeline_module, "load_minimax_h3_native_lora", lambda **_: None)
+    assert pipeline._load_diffusion_lora_adapter(lora_request=request, lora_path=path, dtype=torch.float32) is None
+    assert request.lora_int_id not in pipeline._lora_sigma_schedules
+
+    sampling = SimpleNamespace(
+        lora_request=request,
+        lora_scale=1.0,
+        num_inference_steps=4,
+    )
+    assert pipeline._sigma_schedule_for_request(sampling, "t2va") is None
+
+
+def test_native_packed_qkv_slices_are_tp2_divisible():
+    assert lora_module._NATIVE_QKV_SLICE % 2 == 0
+    assert lora_module._NATIVE_FFN_HIDDEN_SIZE % 2 == 0
+
+
+def test_lora_manager_activates_native_packed_qkv(tmp_path):
+    path = tmp_path / "minimax_h3_t2va_flashgen_4step_v1.0_768p_bf16.safetensors"
+    _write_tiny_native(path)
+    loaded = load_minimax_h3_native_lora(
+        partition="fl2va",
+        lora_request=_request(path),
+        lora_path=path,
+        dtype=torch.float32,
+    )
+    assert loaded is not None
+    lora_model, _, _ = loaded
+    packed = lora_model.get_lora("blocks.0.attn.qkv_proj")
+    assert isinstance(packed, PackedLoRALayerWeights)
+    assert len(packed.lora_b) == 3
+    assert all(b.shape[0] == _TINY_QKV_SLICE for b in packed.lora_b)
+
+    class _DummyLoRALayer:
+        def __init__(self):
+            self.n_slices = 3
+            self.output_slices = (_TINY_QKV_SLICE, _TINY_QKV_SLICE, _TINY_QKV_SLICE)
+            self.set_calls: list[tuple[list, list]] = []
+            self.reset_calls = 0
+
+        def reset_lora(self, index: int):
+            self.reset_calls += 1
+
+        def set_lora(self, index: int, lora_a, lora_b):
+            self.set_calls.append((lora_a, lora_b))
+
+    layer = _DummyLoRALayer()
+    manager = object.__new__(DiffusionLoRAManager)
+    manager.pipeline = torch.nn.Module()
+    manager._lora_modules = {"blocks.0.attn.qkv_proj": layer}
+    manager._registered_adapters = {
+        lora_model.id: lora_model,
+    }
+    manager._active_adapter_id = None
+    manager._adapter_scales = {}
+    manager._activate_adapter(lora_model.id, scale=0.5)
+
+    assert manager._active_adapter_id == lora_model.id
+
+    assert len(layer.set_calls) == 1
+    lora_a_list, lora_b_list = layer.set_calls[0]
+    assert len(lora_a_list) == 3
+    assert len(lora_b_list) == 3
+    torch.testing.assert_close(lora_b_list[0], packed.lora_b[0] * 0.5)
+    torch.testing.assert_close(lora_b_list[1], packed.lora_b[1] * 0.5)
+    torch.testing.assert_close(lora_b_list[2], packed.lora_b[2] * 0.5)
+
+
+def test_lora_manager_tp2_splits_native_packed_qkv_per_rank():
+    """Each Q/K/V slice must be divisible by TP2 so rank-local layers can shard rows."""
+
+    tp_size = 2
+    slice_rows = lora_module._NATIVE_QKV_SLICE
+    assert slice_rows % tp_size == 0
+    local_rows = slice_rows // tp_size
+
+    class _TpQkvLoRALayer:
+        def __init__(self, tp_rank: int):
+            self.tp_rank = tp_rank
+            self.n_slices = 3
+            self.output_slices = (local_rows, local_rows, local_rows)
+            self.set_calls: list[tuple[list, list]] = []
+
+        def reset_lora(self, index: int):
+            return
+
+        def set_lora(self, index: int, lora_a, lora_b):
+            self.set_calls.append((lora_a, lora_b))
+
+    for tp_rank in range(tp_size):
+        layer = _TpQkvLoRALayer(tp_rank)
+        start = tp_rank * local_rows
+        end = start + local_rows
+        full_packed = PackedLoRALayerWeights(
+            module_name="blocks.0.attn.qkv_proj",
+            rank=lora_module._NATIVE_RANK,
+            lora_alphas=[64, 64, 64],
+            lora_a=[torch.ones(lora_module._NATIVE_RANK, lora_module._NATIVE_HIDDEN_SIZE)] * 3,
+            lora_b=[torch.full((slice_rows, lora_module._NATIVE_RANK), float(i + 1)) for i in range(3)],
+            scaling=[1.0, 1.0, 1.0],
+        )
+        local_b = [b[start:end].contiguous() for b in full_packed.lora_b]
+        layer.set_lora(0, full_packed.lora_a, local_b)
+        assert len(layer.set_calls) == 1
+        _, lora_b_list = layer.set_calls[0]
+        assert all(b.shape[0] == local_rows for b in lora_b_list)
+        assert lora_b_list[0][0, 0].item() == 1.0
+        assert lora_b_list[1][0, 0].item() == 2.0
+        assert lora_b_list[2][0, 0].item() == 3.0
+
+
+# 259 modules, of which 52 qkv expand to 3 slices and 52 fc1 expand to 2.
+_EXPECTED_NATIVE_SLICE_PAIRS = 415
+
+
+def _iter_native_slice_pairs(lora_model):
+    """Yield ``(module_name, lora_a, lora_b)`` per slice, flattening packed weights."""
+    for module_name, weights in lora_model.loras.items():
+        lora_a = weights.lora_a if isinstance(weights.lora_a, list) else [weights.lora_a]
+        lora_b = weights.lora_b if isinstance(weights.lora_b, list) else [weights.lora_b]
+        for a, b in zip(lora_a, lora_b, strict=True):
+            yield module_name, a, b
+
+
+@pytest.mark.parametrize("dtype", [torch.float32, torch.float16, torch.bfloat16])
+def test_native_lora_load_is_platform_agnostic(tmp_path, dtype):
+    """The artifact is trained on Ascend, but loading it is not Ascend-only.
+
+    This whole module runs in the CPU job with no ``torch_npu`` present, so a
+    passing run is itself the evidence. The assertions pin the property that
+    makes it portable: tensors land on CPU in exactly the requested dtype and
+    the loader never picks a device, leaving placement to the pipeline.
+    """
+    path = tmp_path / "minimax_h3_t2va_flashgen_4step_v1.0_768p_bf16.safetensors"
+    _write_tiny_native(path)
+
+    loaded = load_minimax_h3_native_lora(
+        partition="fl2va",
+        lora_request=_request(path),
+        lora_path=path,
+        dtype=dtype,
+    )
+
+    assert loaded is not None
+    lora_model, _, _ = loaded
+    tensors = {
+        f"{module_name}.{side}": tensor
+        for module_name, a, b in _iter_native_slice_pairs(lora_model)
+        for side, tensor in (("lora_a", a), ("lora_b", b))
+    }
+    assert sum(1 for _ in _iter_native_slice_pairs(lora_model)) == _EXPECTED_NATIVE_SLICE_PAIRS
+    assert {name: tensor.device.type for name, tensor in tensors.items() if tensor.device.type != "cpu"} == {}
+    assert {name: tensor.dtype for name, tensor in tensors.items() if tensor.dtype != dtype} == {}
+
+
+@pytest.mark.cuda
+@pytest.mark.skipif(not torch.cuda.is_available(), reason="requires a CUDA device")
+def test_native_lora_delta_matches_between_cpu_and_cuda(tmp_path):
+    """The packed adapter produces the same delta weight on CUDA as on CPU.
+
+    Covers the GPU side of the same artifact: every ``lora_b @ lora_a`` product,
+    including the reordered q/k/v slices, is recomputed on CUDA and compared
+    against the CPU result, so a GPU deployment cannot silently diverge.
+    """
+    path = tmp_path / "minimax_h3_t2va_flashgen_4step_v1.0_768p_bf16.safetensors"
+    _write_tiny_native(path)
+
+    loaded = load_minimax_h3_native_lora(
+        partition="fl2va",
+        lora_request=_request(path),
+        lora_path=path,
+        dtype=torch.float32,
+    )
+
+    assert loaded is not None
+    lora_model, _, _ = loaded
+    checked = 0
+    for module_name, a, b in _iter_native_slice_pairs(lora_model):
+        cpu_delta = b @ a
+        cuda_delta = b.cuda() @ a.cuda()
+        assert cuda_delta.device.type == "cuda"
+        torch.testing.assert_close(cuda_delta.cpu(), cpu_delta, msg=f"delta mismatch for {module_name}")
+        checked += 1
+    assert checked == _EXPECTED_NATIVE_SLICE_PAIRS

tests/diffusion/models/minimax_h3/test_minimax_h3_parallel.py

@@ -1,5 +1,5 @@
 # SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
 
 import pytest
 import torch
@@ -113,6 +113,146 @@ def test_h3_rope_table_materializes_local_rows_in_fused_kernel_layout():
     torch.testing.assert_close(actual, expected, atol=0, rtol=0)
 
 
+def test_h3_prepared_rope_table_is_rank_local_and_validated(monkeypatch):
+    from vllm_omni.diffusion.models.minimax_h3 import minimax_h3_transformer as h3
+
+    class Rope(nn.Module):
+        def forward(self, position_ids):
+            freqs_half = position_ids[0].float()
+            return torch.cat((freqs_half, freqs_half), dim=-1)
+
+    model = object.__new__(h3.MiniMaxH3DiTModel)
+    nn.Module.__init__(model)
+    model.arch = h3.MiniMaxH3DiTArchConfig(rope_inv_freq_len=1)
+    model.rope = Rope()
+    model.local_sp_prepare = nn.Identity()
+    monkeypatch.setattr(h3, "_sequence_parallel_local_span", lambda *args, **kwargs: (1, 2))
+
+    position_ids = torch.arange(12, dtype=torch.long).reshape(1, 4, 3)
+    actual = model.prepare_rope_table(position_ids, seq_len=4)
+    expected = h3._build_rope_table(model.rope(position_ids[:, 1:3]))
+
+    torch.testing.assert_close(actual, expected, atol=0, rtol=0)
+    model._validate_prepared_rope_table(actual, local_len=2, device=torch.device("cpu"))
+    with pytest.raises(ValueError, match="local_seq_len"):
+        model._validate_prepared_rope_table(actual[:1], local_len=2, device=torch.device("cpu"))
+
+
+def test_denoise_branch_prepares_rope_table_once_per_npu_run(monkeypatch: pytest.MonkeyPatch):
+    from vllm_omni.diffusion.models.minimax_h3 import denoise_loop
+    from vllm_omni.diffusion.models.minimax_h3.denoise_loop import (
+        MiniMaxH3DenoiseBranch,
+        minimax_h3_denoise_loop,
+    )
+
+    monkeypatch.setattr(denoise_loop.current_omni_platform, "is_npu", lambda: True)
+
+    packed = {
+        "seq_len": torch.tensor(3),
+        "img_pos": torch.tensor([1]),
+        "audio_pos": torch.tensor([2]),
+        "text_pos": torch.tensor([0]),
+        "update_mask": torch.tensor([True]),
+        "cu_seqlens": torch.tensor([0, 3, 3], dtype=torch.int32),
+        "img_position_ids": torch.zeros(3, 3, dtype=torch.long),
+        "latent_grid": torch.tensor([1, 1, 1]),
+        "video_row_start": torch.tensor(1),
+    }
+    branch = MiniMaxH3DenoiseBranch(
+        packed=packed,
+        text_embeddings=torch.zeros(1, 2),
+        token_tags=torch.zeros(3, dtype=torch.long),
+        device=torch.device("cpu"),
+    )
+
+    class Model:
+        def __init__(self):
+            self.rope_table = torch.zeros(3, 6, dtype=torch.bfloat16)
+            self.prepare_calls = 0
+            self.forward_calls = 0
+
+        def prepare_rope_table(self, img_position_ids, *, seq_len):
+            assert img_position_ids is branch.static_kwargs["img_position_ids"]
+            assert seq_len == 3
+            self.prepare_calls += 1
+            return self.rope_table
+
+        def __call__(self, **kwargs):
+            assert kwargs["rope_table"] is self.rope_table
+            self.forward_calls += 1
+            return torch.zeros(1, 96), torch.zeros(1, 32)
+
+    model = Model()
+    minimax_h3_denoise_loop(
+        model=model,
+        positive=branch,
+        initial_video_rows=torch.zeros(1, 96),
+        initial_audio_rows=torch.zeros(1, 32),
+        keyframe_cond_rows=None,
+        sigmas_video=[1.0, 0.5, 0.0],
+        sigmas_audio=[1.0, 0.5, 0.0],
+        device=torch.device("cpu"),
+    )
+
+    assert model.prepare_calls == 1
+    assert model.forward_calls == 2
+
+
+def test_denoise_branch_keeps_rope_construction_on_the_reference_path_off_npu(
+    monkeypatch: pytest.MonkeyPatch,
+):
+    from vllm_omni.diffusion.models.minimax_h3 import denoise_loop
+    from vllm_omni.diffusion.models.minimax_h3.denoise_loop import (
+        MiniMaxH3DenoiseBranch,
+        minimax_h3_denoise_loop,
+    )
+
+    monkeypatch.setattr(denoise_loop.current_omni_platform, "is_npu", lambda: False)
+    packed = {
+        "seq_len": torch.tensor(3),
+        "img_pos": torch.tensor([1]),
+        "audio_pos": torch.tensor([2]),
+        "text_pos": torch.tensor([0]),
+        "update_mask": torch.tensor([True]),
+        "cu_seqlens": torch.tensor([0, 3, 3], dtype=torch.int32),
+        "img_position_ids": torch.zeros(3, 3, dtype=torch.long),
+        "latent_grid": torch.tensor([1, 1, 1]),
+        "video_row_start": torch.tensor(1),
+    }
+    branch = MiniMaxH3DenoiseBranch(
+        packed=packed,
+        text_embeddings=torch.zeros(1, 2),
+        token_tags=torch.zeros(3, dtype=torch.long),
+        device=torch.device("cpu"),
+    )
+
+    class Model:
+        def __init__(self):
+            self.forward_calls = 0
+
+        def prepare_rope_table(self, *_args, **_kwargs):
+            raise AssertionError("the reference path must not pre-build a rope_table")
+
+        def __call__(self, **kwargs):
+            assert "rope_table" not in kwargs
+            self.forward_calls += 1
+            return torch.zeros(1, 96), torch.zeros(1, 32)
+
+    model = Model()
+    minimax_h3_denoise_loop(
+        model=model,
+        positive=branch,
+        initial_video_rows=torch.zeros(1, 96),
+        initial_audio_rows=torch.zeros(1, 32),
+        keyframe_cond_rows=None,
+        sigmas_video=[1.0, 0.5, 0.0],
+        sigmas_audio=[1.0, 0.5, 0.0],
+        device=torch.device("cpu"),
+    )
+
+    assert model.forward_calls == 2
+
+
 def test_strict_sp_local_span_uses_rank_owned_rows(monkeypatch):
     from vllm_omni.diffusion import forward_context
     from vllm_omni.diffusion.distributed import parallel_state

tests/diffusion/models/qwen_image/test_qwen_image_postprocess.py

@@ -1,5 +1,5 @@
 # SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
 import json
 from types import SimpleNamespace
 

tests/diffusion/offloader/test_distributed_layerwise_backend.py

@@ -245,6 +245,160 @@ class TestDistributedLayerwiseOffloadHook:
         )
         torch.testing.assert_close(rank0_block.weight_scale, expected_scale)
 
+    def test_allgather_reconstructs_online_int8_weight_and_scale(
+        self,
+        patched_offload_runtime,
+        monkeypatch,
+    ):
+        """DLO must reconstruct finalized online-INT8 tensors byte-for-byte.
+
+        One block carries both finalized online-INT8 layouts: the NPU method
+        leaves a contiguous pre-transposed (K, N) int8 weight with a 1-D fp32
+        per-channel scale, while the CUDA method leaves a transposed-view
+        weight (stride (1, K)) with a scalar fp32 scale.  The int8 and fp32
+        dtype groups must survive the flat-shard AllGather independently.
+        """
+
+        class OnlineInt8Block(nn.Module):
+            def __init__(self):
+                super().__init__()
+                logical = torch.arange(1.0, 13.0).reshape(3, 4)
+                # NPU online int8: qweight.t().contiguous() -> (K, N), 1-D scale
+                self.npu_weight = nn.Parameter(logical.t().contiguous().to(torch.int8), requires_grad=False)
+                self.npu_weight_scale = nn.Parameter(torch.tensor([2.0, 3.0, 4.0]), requires_grad=False)
+                # CUDA online int8: Parameter(qweight.t().data) -> transposed
+                # view (stride (1, K)) over the (N, K) storage, scalar scale
+                self.cuda_weight = nn.Parameter((logical + 100).to(torch.int8).t(), requires_grad=False)
+                self.cuda_weight_scale = nn.Parameter(torch.tensor([0.5]), requires_grad=False)
+
+        current_block = OnlineInt8Block()
+        rank0_block = OnlineInt8Block()
+        rank1_block = OnlineInt8Block()
+        expected = {name: tensor.detach().clone() for name, tensor in rank0_block.named_parameters()}
+        expected_strides = {name: tensor.stride() for name, tensor in rank0_block.named_parameters()}
+
+        def make_hook(block: OnlineInt8Block, rank: int) -> DistributedLayerwiseOffloadHook:
+            return DistributedLayerwiseOffloadHook(
+                next_block=block,
+                device=torch.device("cpu"),
+                dp_group=object(),
+                dp_size=2,
+                rank=rank,
+                copy_stream=DummyStream(),
+                comm_stream=DummyStream(),
+                pin_memory=False,
+            )
+
+        rank0_hook = make_hook(rank0_block, 0)
+        rank1_hook = make_hook(rank1_block, 1)
+        rank0_hook.initialize_hook(current_block)
+        rank1_hook.initialize_hook(OnlineInt8Block())
+        rank0_hook.gpu_shard_buffers = [
+            {dtype: torch.empty_like(shard) for dtype, shard in rank0_hook.cpu_shards.items()} for _ in range(2)
+        ]
+
+        def fake_allgather(output, local_shard, *, group):
+            del group
+            remote_shard = rank1_hook.cpu_shards[local_shard.dtype]
+            shard_size = local_shard.numel()
+            output[:shard_size].copy_(local_shard)
+            output[shard_size : 2 * shard_size].copy_(remote_shard)
+
+        monkeypatch.setattr(torch.distributed, "all_gather_into_tensor", fake_allgather)
+        rank0_hook.prefetch_layer(slot=0, non_blocking=False)
+
+        for name, tensor in rank0_block.named_parameters():
+            assert tensor.stride() == expected_strides[name]
+            torch.testing.assert_close(
+                tensor.float(),
+                expected[name].float(),
+                rtol=0,
+                atol=0,
+            )
+
+    def test_allgather_reconstructs_online_mxfp8_weight_and_scale(
+        self,
+        patched_offload_runtime,
+        monkeypatch,
+    ):
+        """DLO must reconstruct finalized online-MXFP8 tensors byte-for-byte.
+
+        One block carries both finalized online-MXFP8 layouts: the NPU method
+        leaves a contiguous pre-transposed (K, N) fp8 weight with a contiguous
+        (K_groups/2, N, 2) e8m0 block scale, while the vLLM-kernel method
+        (XPU) leaves an (N, K) fp8 weight whose e8m0 scale is a transposed
+        view over a contiguous (K/32, N) buffer.  The fp8 and e8m0 dtype
+        groups must survive the flat-shard AllGather independently.
+        """
+
+        class OnlineMxfp8Block(nn.Module):
+            def __init__(self):
+                super().__init__()
+                logical = torch.arange(1.0, 13.0).reshape(3, 4)
+                # Build scales from raw e8m0 bit patterns (1 byte/elem).
+                npu_scale_bits = torch.arange(1, 13, dtype=torch.uint8).reshape(2, 3, 2)
+                kernel_scale_bits = torch.arange(1, 7, dtype=torch.uint8).reshape(2, 3)
+                # NPU online mxfp8: (N, K).t().contiguous() -> (K, N) fp8
+                # weight, contiguous (K_groups/2, N, 2) e8m0 scale
+                self.npu_weight = nn.Parameter(logical.t().contiguous().to(torch.float8_e4m3fn), requires_grad=False)
+                self.npu_weight_scale = nn.Parameter(npu_scale_bits.view(torch.float8_e8m0fnu), requires_grad=False)
+                # vLLM-kernel online mxfp8 (XPU): (N, K) fp8 weight, e8m0
+                # scale kept as a transposed view (stride (1, K/32)) over the
+                # contiguous (K/32, N) storage
+                self.kernel_weight = nn.Parameter((logical + 100).to(torch.float8_e4m3fn), requires_grad=False)
+                self.kernel_weight_scale = nn.Parameter(
+                    kernel_scale_bits.view(torch.float8_e8m0fnu).t(), requires_grad=False
+                )
+
+        current_block = OnlineMxfp8Block()
+        rank0_block = OnlineMxfp8Block()
+        rank1_block = OnlineMxfp8Block()
+        expected = {name: tensor.detach().clone() for name, tensor in rank0_block.named_parameters()}
+        expected_strides = {name: tensor.stride() for name, tensor in rank0_block.named_parameters()}
+
+        def make_hook(block: OnlineMxfp8Block, rank: int) -> DistributedLayerwiseOffloadHook:
+            return DistributedLayerwiseOffloadHook(
+                next_block=block,
+                device=torch.device("cpu"),
+                dp_group=object(),
+                dp_size=2,
+                rank=rank,
+                copy_stream=DummyStream(),
+                comm_stream=DummyStream(),
+                pin_memory=False,
+            )
+
+        rank0_hook = make_hook(rank0_block, 0)
+        rank1_hook = make_hook(rank1_block, 1)
+        rank0_hook.initialize_hook(current_block)
+        rank1_hook.initialize_hook(OnlineMxfp8Block())
+        rank0_hook.gpu_shard_buffers = [
+            {dtype: torch.empty_like(shard) for dtype, shard in rank0_hook.cpu_shards.items()} for _ in range(2)
+        ]
+
+        def fake_allgather(output, local_shard, *, group):
+            del group
+            remote_shard = rank1_hook.cpu_shards[local_shard.dtype]
+            shard_size = local_shard.numel()
+            output[:shard_size].copy_(local_shard)
+            output[shard_size : 2 * shard_size].copy_(remote_shard)
+
+        monkeypatch.setattr(torch.distributed, "all_gather_into_tensor", fake_allgather)
+        rank0_hook.prefetch_layer(slot=0, non_blocking=False)
+
+        for name, tensor in rank0_block.named_parameters():
+            assert tensor.stride() == expected_strides[name]
+            # Compare raw bytes in logical order: float8/e8m0 CPU upcasts are
+            # version-sensitive, while contiguous().view(uint8) is an exact
+            # byte-level comparison for both contiguous and transposed-view
+            # parameters.
+            torch.testing.assert_close(
+                tensor.detach().contiguous().view(torch.uint8),
+                expected[name].contiguous().view(torch.uint8),
+                rtol=0,
+                atol=0,
+            )
+
     def test_dtensor_wrapper_preserved_across_prefetch_and_offload(self, dist_group, patched_offload_runtime):
         """DTensor wrapper should be preserved through prefetch/offload cycle."""
         current_block = TinyBlock(_make_values(1.0))

tests/diffusion/sched/test_dmd2_sigma_schedule.py

@@ -1,5 +1,5 @@
 # SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
 import pytest
 
 from vllm_omni.diffusion.sched import DMD2SigmaSchedule
@@ -18,7 +18,8 @@ def test_step_count_is_intervals_not_boundaries():
     [
         [],
         [1.0],
-        [0.9, 0.5, 0.0],
+        [1.5, 0.5, 0.0],
+        [0.0, 0.5, 0.0],
         [1.0, 0.5, 0.1],
         [1.0, 0.4, 0.6, 0.0],
         [1.0, 0.5, 0.5, 0.0],
@@ -31,6 +32,15 @@ def test_malformed_positions_are_rejected(positions):
         DMD2SigmaSchedule.from_positions(positions)
 
 
+def test_a_capped_first_position_is_a_schedule_not_a_malformed_one():
+    # A DMD2 student trained with max_timestep_ratio < 1 never sees pure noise,
+    # so its opening rung sits just below 1.0 (FastH3: 999/1000).
+    schedule = DMD2SigmaSchedule.from_positions([0.999, 0.749, 0.5, 0.25, 0.0])
+
+    assert schedule.base_schedule[0] == 0.999
+    assert schedule.num_inference_steps == 4
+
+
 def test_one_base_schedule_drives_several_shift_scales():
     schedule = DMD2SigmaSchedule.from_positions([1.0, 0.7, 0.4, 0.15, 0.0])
 

tests/diffusion/test_diffusion_engine.py

@@ -1,5 +1,5 @@
 # SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
 
 import asyncio
 import queue
@@ -197,6 +197,59 @@ class TestRequestBatchCapability:
 
         assert diffusion_engine_module.supports_request_batch(od_config) is True
 
+    def test_engine_rejects_multi_seq_diffusers_using_adapter_capability(
+        self,
+        monkeypatch: pytest.MonkeyPatch,
+        mocker: MockerFixture,
+    ) -> None:
+        od_config = SimpleNamespace(
+            model_class_name="QwenImagePipeline",
+            custom_pipeline_args=None,
+            diffusion_load_format="diffusers",
+            streaming_output=False,
+            max_num_seqs=2,
+        )
+        registry_load = mocker.Mock(
+            side_effect=lambda model_class_name: (
+                _SingleRequestPipeline if model_class_name == "DiffusersAdapterPipeline" else _BatchCapablePipeline
+            )
+        )
+        monkeypatch.setattr(
+            diffusion_engine_module.DiffusionModelRegistry,
+            "_try_load_model_cls",
+            registry_load,
+        )
+
+        engine = DiffusionEngine.__new__(DiffusionEngine)
+        with pytest.raises(ValueError, match="max_num_seqs=1"):
+            engine._resolve_execution_mode(od_config)
+
+        registry_load.assert_called_once_with("DiffusersAdapterPipeline")
+
+    def test_engine_prefers_batch_capable_custom_pipeline_over_diffusers_adapter(
+        self,
+        monkeypatch: pytest.MonkeyPatch,
+        mocker: MockerFixture,
+    ) -> None:
+        od_config = SimpleNamespace(
+            model_class_name="QwenImagePipeline",
+            custom_pipeline_args={"pipeline_class": _BatchCapablePipeline},
+            diffusion_load_format="diffusers",
+            streaming_output=False,
+            max_num_seqs=2,
+        )
+        registry_load = mocker.Mock(return_value=_SingleRequestPipeline)
+        monkeypatch.setattr(
+            diffusion_engine_module.DiffusionModelRegistry,
+            "_try_load_model_cls",
+            registry_load,
+        )
+
+        engine = DiffusionEngine.__new__(DiffusionEngine)
+
+        assert engine._resolve_execution_mode(od_config) is DiffusionExecutionMode.REQUEST_BATCH
+        registry_load.assert_not_called()
+
     def test_supports_request_batch_uses_custom_pipeline_class(self, monkeypatch: pytest.MonkeyPatch) -> None:
         od_config = SimpleNamespace(
             model_class_name="SinglePipeline",

tests/diffusion/test_diffusion_model_runner.py

@@ -1,5 +1,5 @@
 # SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
 
 from contextlib import contextmanager
 from types import SimpleNamespace
@@ -533,7 +533,14 @@ def test_profile_run_executes_forward_without_scheduler_kv_validation(monkeypatc
         return original_execute(*args, **kwargs)
 
     runner._execute_request_list = execute
-    monkeypatch.setattr(model_runner_module, "set_forward_context", _noop_forward_context)
+    forward_context_calls = []
+
+    @contextmanager
+    def record_forward_context(*args, **kwargs):
+        forward_context_calls.append((args, kwargs))
+        yield
+
+    monkeypatch.setattr(model_runner_module, "set_forward_context", record_forward_context)
     reset_peak_memory_stats = Mock()
     monkeypatch.setattr(
         model_runner_module.current_omni_platform,
@@ -549,6 +556,8 @@ def test_profile_run_executes_forward_without_scheduler_kv_validation(monkeypatc
 
     assert runner.pipeline.forward_calls == 1
     assert record_names == ["pipeline_memory_profile"]
+    assert len(forward_context_calls) == 1
+    assert forward_context_calls[0][1]["in_diffusion_kv_memory_profile"] is True
     runner._validate_diffusion_kv_metadata.assert_not_called()
     reset_peak_memory_stats.assert_not_called()
     synchronize.assert_called_once_with()
@@ -575,7 +584,14 @@ def test_profile_run_executes_maximum_step_batch_without_resetting_peak(monkeypa
 
     runner.pipeline.denoise_step = denoise_step
     runner._validate_diffusion_kv_metadata = Mock(side_effect=AssertionError("profile must bypass admission"))
-    monkeypatch.setattr(model_runner_module, "set_forward_context", _noop_forward_context)
+    forward_context_calls = []
+
+    @contextmanager
+    def record_forward_context(*args, **kwargs):
+        forward_context_calls.append((args, kwargs))
+        yield
+
+    monkeypatch.setattr(model_runner_module, "set_forward_context", record_forward_context)
     reset_peak_memory_stats = Mock()
     monkeypatch.setattr(
         model_runner_module.current_omni_platform,
@@ -592,6 +608,8 @@ def test_profile_run_executes_maximum_step_batch_without_resetting_peak(monkeypa
     assert observed_batch_rows == [(2, 2)]
     assert runner.state_cache == {}
     assert runner.input_batch is None
+    assert len(forward_context_calls) == 1
+    assert forward_context_calls[0][1]["in_diffusion_kv_memory_profile"] is True
     runner._validate_diffusion_kv_metadata.assert_not_called()
     reset_peak_memory_stats.assert_not_called()
     synchronize.assert_called_once_with()

tests/diffusion/test_diffusion_output_formatter.py

@@ -328,6 +328,25 @@ def test_formatter_preserves_audio_model_video_audio_and_actions(
     }
 
 
+def test_formatter_uses_native_model_identity_for_diffusers_video_frames(
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    monkeypatch.setattr(output_formatter, "supports_audio_output", lambda _: False)
+    frames = [["frame-0", "frame-1"]]
+    postprocess_output = normalize_diffusion_postprocess_output(frames)
+
+    [result] = format_diffusion_outputs(
+        request=_request("make a video"),
+        od_config=_config("WanImageToVideoPipeline"),
+        diffusion_output=DiffusionOutput(output=frames),
+        output_data=frames,
+        postprocess_output=postprocess_output,
+    )
+
+    assert result.images == frames
+    assert result.final_output_type == "video"
+
+
 def test_formatter_preserves_audio_only_postprocess_dict(
     monkeypatch: pytest.MonkeyPatch,
 ) -> None:

tests/diffusion/test_diffusion_plugin_hooks.py

@@ -1,5 +1,5 @@
 # SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
 
 """
 Unit tests for diffusion engine plugin extensibility hooks.
@@ -20,6 +20,8 @@ from vllm_omni.diffusion.registry import (
     _DIFFUSION_MODELS,
     _DIFFUSION_POST_PROCESS_FUNCS,
     _DIFFUSION_PRE_PROCESS_FUNCS,
+    get_diffusion_post_process_func,
+    get_diffusion_pre_process_func,
     register_diffusion_model,
 )
 from vllm_omni.platforms.interface import OmniPlatform, OmniPlatformEnum
@@ -112,6 +114,49 @@ class TestRegisterDiffusionModel:
         assert _DIFFUSION_POST_PROCESS_FUNCS["LegacyActionPipeline"] == "test_post_process"
 
 
+@pytest.mark.parametrize(
+    ("model_class_name", "get_process_func"),
+    [
+        ("QwenImagePipeline", get_diffusion_post_process_func),
+        ("WanImageToVideoPipeline", get_diffusion_post_process_func),
+        ("WanImageToVideoPipeline", get_diffusion_pre_process_func),
+    ],
+)
+def test_diffusers_backend_skips_native_process_hooks(model_class_name, get_process_func):
+    od_config = SimpleNamespace(
+        model_class_name=model_class_name,
+        custom_pipeline_args=None,
+        diffusion_load_format="diffusers",
+    )
+
+    with patch("vllm_omni.diffusion.registry._load_process_func") as load_process_func:
+        assert get_process_func(od_config) is None
+
+    load_process_func.assert_not_called()
+
+
+@pytest.mark.parametrize(
+    ("model_class_name", "get_process_func"),
+    [
+        ("QwenImagePipeline", get_diffusion_post_process_func),
+        ("WanImageToVideoPipeline", get_diffusion_post_process_func),
+        ("WanImageToVideoPipeline", get_diffusion_pre_process_func),
+    ],
+)
+def test_custom_pipeline_preserves_native_process_hooks(model_class_name, get_process_func):
+    process_func = Mock()
+    od_config = SimpleNamespace(
+        model_class_name=model_class_name,
+        custom_pipeline_args={"pipeline_class": "custom.Pipeline"},
+        diffusion_load_format="diffusers",
+    )
+
+    with patch("vllm_omni.diffusion.registry._load_process_func", return_value=process_func) as load_process_func:
+        assert get_process_func(od_config) is process_func
+
+    load_process_func.assert_called_once()
+
+
 class TestWorkerUsesHook:
     """Test that DiffusionWorker resolves model runner via platform hook."""
 

tests/diffusion/test_stage_diffusion_proc.py

@@ -1,5 +1,5 @@
 # SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
 
 import asyncio
 import time
@@ -7,6 +7,8 @@ from dataclasses import asdict, dataclass
 
 import pytest
 
+import vllm_omni.diffusion.stage_diffusion_proc as stage_diffusion_proc
+import vllm_omni.plugins as omni_plugins
 from vllm_omni.diffusion.stage_diffusion_proc import StageDiffusionProc
 from vllm_omni.inputs.data import OmniDiffusionSamplingParams
 from vllm_omni.outputs import OmniRequestOutput
@@ -26,6 +28,33 @@ BASE_INFER_STEPS = 10
 DELAY_BASE = 0.01
 
 
+def test_run_diffusion_proc_sets_lifecycle_before_loading_plugins(monkeypatch: pytest.MonkeyPatch) -> None:
+    events: list[str] = []
+
+    class StopProcessError(Exception):
+        pass
+
+    class TestStageDiffusionProc(StageDiffusionProc):
+        def __init__(self, model, od_config):
+            events.append("proc")
+            raise StopProcessError
+
+    monkeypatch.setattr(omni_plugins, "load_omni_general_plugins", lambda: events.append("plugins"))
+    monkeypatch.setattr(stage_diffusion_proc, "set_death_signal", lambda _: events.append("death_signal"))
+    monkeypatch.setattr(stage_diffusion_proc.signal, "signal", lambda *_: events.append("signal_handler"))
+
+    with pytest.raises(StopProcessError):
+        TestStageDiffusionProc.run_diffusion_proc(
+            model="test-model",
+            od_config=None,
+            handshake_address="test-address",
+            local_client=True,
+            headless=False,
+        )
+
+    assert events == ["death_signal", "signal_handler", "signal_handler", "plugins", "proc"]
+
+
 class MockDiffusionEngine:
     async def step_streaming(self, request):
         def simulate_step_delay(height, width, num_inference_steps) -> float:

tests/distributed/omni_connectors/test_chunk_transfer_adapter.py

@@ -57,6 +57,19 @@ def _req(req_id: str, status: RequestStatus, external_req_id: str | None = None)
     return request
 
 
+def _streaming_request(mocker: MockerFixture, num_computed_tokens: int) -> SimpleNamespace:
+    prompt_token_ids = [0] * num_computed_tokens
+    return SimpleNamespace(
+        _all_token_ids=prompt_token_ids.copy(),
+        _output_token_ids=[],
+        prompt_token_ids=prompt_token_ids,
+        num_computed_tokens=num_computed_tokens,
+        num_prompt_tokens=num_computed_tokens,
+        num_output_placeholders=0,
+        update_block_hashes=mocker.Mock(),
+    )
+
+
 def test_streaming_payload_can_replace_placeholder_prompt(mocker: MockerFixture) -> None:
     request = SimpleNamespace(
         _all_token_ids=[0, 0, 7, 8],
@@ -84,30 +97,219 @@ def test_streaming_payload_can_replace_placeholder_prompt(mocker: MockerFixture)
     request.update_block_hashes.assert_called_once_with()
 
 
-def test_streaming_payload_can_append_exact_prompt_length(mocker: MockerFixture) -> None:
+def test_turn_start_replacement_ignores_accumulated_prompt_capacity(mocker: MockerFixture) -> None:
+    request = _streaming_request(mocker, num_computed_tokens=4064)
+    payload = {
+        "ids": {"prompt": [1]},
+        "meta": {
+            "replace_streaming_prompt": True,
+            "next_stage_prompt_len": 10,
+            "next_stage_generation_tokens": 26,
+        },
+    }
+
+    replaced = construct_next_stage_streaming_input_prompt(
+        payload,
+        request,
+        max_model_len=4096,
+        previous_condition_len=10,
+        previous_condition_seq=7,
+        condition_seq=8,
+        recompute_previous_chunks=1,
+    )
+
+    assert replaced is True
+    assert request.prompt_token_ids == [0] * 10
+    assert payload["meta"]["streaming_prompt_recompute"] is False
+    assert "streaming_prompt_previous_codes" not in payload["ids"]
+
+
+@pytest.mark.parametrize("reserve", [True, 0, -1, 1.5, "26", None])
+def test_streaming_prompt_rejects_invalid_generation_reserve(
+    mocker: MockerFixture,
+    reserve: object,
+) -> None:
+    request = _streaming_request(mocker, num_computed_tokens=10)
+    payload = {
+        "ids": {"prompt": [1]},
+        "meta": {
+            "next_stage_prompt_len": 10,
+            "next_stage_generation_tokens": reserve,
+        },
+    }
+
+    with pytest.raises(ValueError, match="next_stage_generation_tokens must be a positive integer"):
+        construct_next_stage_streaming_input_prompt(payload, request, max_model_len=4096)
+
+
+def test_capacity_managed_streaming_prompt_at_limit_still_appends(mocker: MockerFixture) -> None:
+    request = _streaming_request(mocker, num_computed_tokens=4060)
+    payload = {
+        "ids": {"prompt": [1]},
+        "meta": {
+            "next_stage_prompt_len": 10,
+            "next_stage_generation_tokens": 26,
+        },
+    }
+
+    replaced = construct_next_stage_streaming_input_prompt(payload, request, max_model_len=4096)
+
+    assert replaced is False
+    assert request.num_computed_tokens == 4060
+    assert request.num_prompt_tokens == 4070
+
+
+def test_streaming_window_builds_one_chunk_recompute_recipe(mocker: MockerFixture) -> None:
+    previous_condition_len = 10
+    previous_codes = list(range(25))
     request = SimpleNamespace(
-        _all_token_ids=[0, 0, 7, 8],
-        _output_token_ids=[7, 8],
-        prompt_token_ids=[0, 0],
-        num_computed_tokens=4,
-        num_prompt_tokens=2,
+        _all_token_ids=[0] * 4039 + previous_codes + [999],
+        _output_token_ids=previous_codes + [999],
+        prompt_token_ids=[0] * 4039,
+        num_computed_tokens=4065,
+        num_prompt_tokens=4039,
+        num_output_placeholders=1,
         update_block_hashes=mocker.Mock(),
     )
     payload = {
-        "ids": {"prompt": [1, 2, 3]},
-        "meta": {"next_stage_prompt_len": 3},
+        "ids": {"prompt": [1]},
+        "meta": {
+            "next_stage_prompt_len": 10,
+            "next_stage_generation_tokens": 26,
+        },
     }
 
-    construct_next_stage_streaming_input_prompt(payload, request)
+    replaced = construct_next_stage_streaming_input_prompt(
+        payload,
+        request,
+        max_model_len=4096,
+        previous_condition_len=previous_condition_len,
+        previous_condition_seq=7,
+        condition_seq=8,
+        recompute_previous_chunks=1,
+    )
 
-    assert request.prompt_token_ids == [0, 0, 7, 8, 0, 0, 0]
-    assert request._all_token_ids == [0, 0, 7, 8, 0, 0, 0]
-    assert request._output_token_ids == []
-    assert request.num_computed_tokens == 4
-    assert request.num_prompt_tokens == 7
+    assert replaced is True
+    assert request.prompt_token_ids == [0] * 45
+    assert request.num_prompt_tokens == 45
+    assert request.num_computed_tokens == 0
+    assert payload["ids"]["streaming_prompt_previous_codes"] == previous_codes
+    assert payload["meta"] == {
+        "next_stage_prompt_len": 10,
+        "next_stage_generation_tokens": 26,
+        "streaming_prompt_recompute": True,
+        "streaming_condition_seq": 8,
+    }
     request.update_block_hashes.assert_called_once_with()
 
 
+def test_streaming_window_recomputes_each_condition_without_accumulating(mocker: MockerFixture) -> None:
+    request = SimpleNamespace(
+        _all_token_ids=[0] * 10 + [101, 102],
+        _output_token_ids=[101, 102],
+        prompt_token_ids=[0] * 10,
+        num_computed_tokens=12,
+        num_prompt_tokens=10,
+        num_output_placeholders=0,
+        update_block_hashes=mocker.Mock(),
+    )
+    second = {
+        "ids": {"prompt": [1]},
+        "meta": {
+            "next_stage_prompt_len": 12,
+            "next_stage_generation_tokens": 26,
+        },
+    }
+
+    assert construct_next_stage_streaming_input_prompt(
+        second,
+        request,
+        max_model_len=4096,
+        previous_condition_len=10,
+        previous_condition_seq=0,
+        condition_seq=1,
+        recompute_previous_chunks=1,
+    )
+    assert request.num_prompt_tokens == 24
+    assert second["ids"]["streaming_prompt_previous_codes"] == [101, 102]
+
+    request._all_token_ids.extend([201, 202, 203])
+    request._output_token_ids.extend([201, 202, 203])
+    request.num_computed_tokens = 27
+    third = {
+        "ids": {"prompt": [2]},
+        "meta": {
+            "next_stage_prompt_len": 8,
+            "next_stage_generation_tokens": 26,
+        },
+    }
+
+    assert construct_next_stage_streaming_input_prompt(
+        third,
+        request,
+        max_model_len=4096,
+        previous_condition_len=12,
+        previous_condition_seq=1,
+        condition_seq=2,
+        recompute_previous_chunks=1,
+    )
+    assert request.num_prompt_tokens == 23
+    assert third["ids"]["streaming_prompt_previous_codes"] == [201, 202, 203]
+    assert third["meta"]["streaming_condition_seq"] == 2
+
+
+def test_capacity_rollover_requires_explicit_window_contract(mocker: MockerFixture) -> None:
+    request = _streaming_request(mocker, num_computed_tokens=4064)
+    payload = {
+        "ids": {"prompt": [1]},
+        "meta": {
+            "next_stage_prompt_len": 10,
+            "next_stage_generation_tokens": 26,
+        },
+    }
+
+    with pytest.raises(ValueError, match="requires window_size=1"):
+        construct_next_stage_streaming_input_prompt(
+            payload,
+            request,
+            max_model_len=4096,
+            previous_condition_len=10,
+            previous_condition_seq=0,
+            condition_seq=1,
+        )
+
+
+@pytest.mark.parametrize("next_stage_prompt_len", [0, -1])
+def test_capacity_managed_streaming_prompt_rejects_nonpositive_length(
+    mocker: MockerFixture, next_stage_prompt_len: int
+) -> None:
+    request = _streaming_request(mocker, num_computed_tokens=0)
+    payload = {
+        "ids": {"prompt": [1]},
+        "meta": {
+            "next_stage_prompt_len": next_stage_prompt_len,
+            "next_stage_generation_tokens": 26,
+        },
+    }
+
+    with pytest.raises(ValueError, match="positive next_stage_prompt_len"):
+        construct_next_stage_streaming_input_prompt(payload, request, max_model_len=4096)
+
+
+def test_capacity_managed_streaming_prompt_rejects_fresh_overflow(mocker: MockerFixture) -> None:
+    request = _streaming_request(mocker, num_computed_tokens=0)
+    payload = {
+        "ids": {"prompt": [1]},
+        "meta": {
+            "next_stage_prompt_len": 4071,
+            "next_stage_generation_tokens": 26,
+        },
+    }
+
+    with pytest.raises(ValueError, match="exceeds max_model_len"):
+        construct_next_stage_streaming_input_prompt(payload, request, max_model_len=4096)
+
+
 @pytest.fixture
 def build_adapter(monkeypatch, mocker: MockerFixture):
     def _build(
@@ -115,6 +317,10 @@ def build_adapter(monkeypatch, mocker: MockerFixture):
         stage_id: int = 1,
         model_mode: str = "ar",
         max_num_seqs: int = 2,
+        max_model_len: int = 0,
+        tts_max_model_len: int = 0,
+        tts_attention_type: str = "full_attention",
+        flat_tts_config: bool = False,
         active_stream_window: int = 0,
         connector_extra: dict | None = None,
     ):
@@ -131,6 +337,10 @@ def build_adapter(monkeypatch, mocker: MockerFixture):
             self._cancelled_load_reqs = set()
             self._pending_save_reqs = deque()
             self._finished_save_reqs = set()
+            self._send_failures = {}
+            self._send_failure_lock = threading.Lock()
+            self._receive_failures = {}
+            self._receive_failure_lock = threading.Lock()
             self.stop_event = threading.Event()
             self._recv_cond = threading.Condition()
             self._save_cond = threading.Condition()
@@ -142,9 +352,23 @@ def build_adapter(monkeypatch, mocker: MockerFixture):
             classmethod(lambda cls, _model_config: connector),
         )
 
+        hf_config = SimpleNamespace(
+            model_type="minicpmtts",
+            max_position_embeddings=tts_max_model_len,
+            attention_type=tts_attention_type,
+        )
+        if not flat_tts_config:
+            hf_config = SimpleNamespace(
+                tts_config=SimpleNamespace(
+                    max_position_embeddings=tts_max_model_len,
+                    attention_type=tts_attention_type,
+                )
+            )
         model_config = SimpleNamespace(
             worker_type=model_mode,
             max_num_seqs=max_num_seqs,
+            max_model_len=max_model_len,
+            hf_config=hf_config,
             active_stream_window=active_stream_window,
             stage_connector_config={
                 "name": "SharedMemoryConnector",
@@ -160,6 +384,27 @@ def build_adapter(monkeypatch, mocker: MockerFixture):
     return _build
 
 
+def _dequeue_load_entry(adapter, request):
+    """Register and take the entry as the background recv loop would."""
+    adapter.load_async(request)
+    entry = adapter._registered_load_entries[request.request_id]
+    try:
+        adapter._pending_load_reqs.remove(entry)
+    except ValueError:
+        pass
+    return entry
+
+
+@pytest.mark.parametrize(
+    ("attention_type", "expected_previous_chunks"),
+    [("full_attention", 0), ("sliding_recompute", 1)],
+)
+def test_talker_attention_policy_controls_streaming_recompute(build_adapter, attention_type, expected_previous_chunks):
+    adapter, _ = build_adapter(tts_attention_type=attention_type)
+
+    assert adapter._streaming_prompt_previous_chunks == expected_previous_chunks
+
+
 @pytest.mark.parametrize(
     ("raw_cfg", "expected_name", "expected_extra"),
     [
@@ -200,15 +445,78 @@ def test_load_poll(build_adapter):
         "meta": {"finished": torch.tensor(True, dtype=torch.bool)},
     }
     connector.get.return_value = (payload, 16)
-    adapter._poll_single_request(request)
+    adapter._poll_single_request(_dequeue_load_entry(adapter, request))
 
+    assert request.additional_information is None
+    assert "req-1" in adapter._finished_load_reqs
+    request.status = RequestStatus.WAITING_FOR_CHUNK
+    adapter.process_pending_chunks(
+        DummyWaitingQueue(),
+        [request],
+        scheduler_requests={request.request_id: request},
+    )
     assert request.additional_information == payload
     assert adapter.get_req_chunk["req-1"] == 1
-    assert "req-1" in adapter._finished_load_reqs
+    assert "req-1" not in adapter._finished_load_reqs
     assert "req-1" in adapter.upstream_exhausted_requests
     assert "req-1" not in adapter._pending_load_reqs
 
 
+def test_load_async_does_not_requeue_registered_inflight_request(build_adapter):
+    adapter, connector = build_adapter(stage_id=1, model_mode="ar")
+    request = _req("req-inflight", RequestStatus.RUNNING, external_req_id="ext-inflight")
+
+    entry = _dequeue_load_entry(adapter, request)
+
+    # The recv loop owns the popped request until polling completes. A
+    # scheduler-side duplicate registration must not append a second copy.
+    adapter.load_async(request)
+    assert adapter._pending_load_reqs == deque()
+
+    connector.get.return_value = (
+        {
+            "ids": {"prompt": [1]},
+            "meta": {"finished": True},
+        },
+        1,
+    )
+    assert adapter._poll_single_request(entry) is True
+
+    # Successful polling releases the registration for the next chunk.
+    adapter.load_async(request)
+    assert [queued.request for queued in adapter._pending_load_reqs] == [request]
+
+
+def test_cleanup_then_reregister_drops_stale_queued_entry(build_adapter):
+    adapter, connector = build_adapter(stage_id=1, model_mode="ar")
+    request = _req("req-resume", RequestStatus.WAITING, external_req_id="ext-resume")
+
+    adapter.load_async(request)
+    stale_entry = adapter._pending_load_reqs[0]
+    adapter.cleanup_receiver(request.request_id)
+    adapter.load_async(request)
+    fresh_entry = adapter._pending_load_reqs[-1]
+    assert len(adapter._pending_load_reqs) == 2
+    assert adapter._pending_load_reqs.popleft() is stale_entry
+    assert adapter._pending_load_reqs.popleft() is fresh_entry
+
+    connector.get.return_value = (
+        {
+            "ids": {"prompt": [1]},
+            "meta": {"finished": True},
+        },
+        1,
+    )
+    assert adapter._poll_single_request(stale_entry) is True
+    connector.get.assert_not_called()
+    assert adapter._registered_load_entries[request.request_id] is fresh_entry
+
+    assert adapter._poll_single_request(fresh_entry) is True
+    connector.get.assert_called_once()
+    assert request.request_id not in adapter._registered_load_entries
+    assert adapter.get_req_chunk[request.request_id] == 1
+
+
 def test_load_poll_ar_requeues_explicitly_replaced_running_prompt(build_adapter):
     adapter, connector = build_adapter(stage_id=1, model_mode="ar")
     request = _req("req-replace", RequestStatus.RUNNING, external_req_id="external-replace")
@@ -234,15 +542,18 @@ def test_load_poll_ar_requeues_explicitly_replaced_running_prompt(build_adapter)
         1,
     )
 
-    assert adapter._poll_single_request(request) is True
-    assert request.num_computed_tokens == 0
-    assert request.prompt_token_ids == [0] * 10
-    assert request.request_id in adapter.replaced_streaming_prompt_ids
-
+    assert adapter._poll_single_request(_dequeue_load_entry(adapter, request)) is True
     request.status = RequestStatus.WAITING_FOR_CHUNK
     running_queue = [request]
     waiting_queue = DummyWaitingQueue()
-    adapter.process_pending_chunks(waiting_queue, running_queue)
+    adapter.process_pending_chunks(
+        waiting_queue,
+        running_queue,
+        scheduler_requests={request.request_id: request},
+    )
+    assert request.num_computed_tokens == 0
+    assert request.prompt_token_ids == [0] * 10
+    assert request.request_id in adapter.replaced_streaming_prompt_ids
     assert running_queue == []
     assert waiting_queue == [request]
     assert request.status == RequestStatus.WAITING
@@ -258,6 +569,134 @@ def test_load_poll_ar_requeues_explicitly_replaced_running_prompt(build_adapter)
     assert request.external_req_id not in adapter.requests_num_chunks_sent
 
 
+@pytest.mark.parametrize("flat_tts_config", [False, True])
+def test_load_poll_ar_recomputes_native_duplex_prompt_each_condition(build_adapter, flat_tts_config):
+    adapter, connector = build_adapter(
+        stage_id=1,
+        model_mode="ar",
+        max_model_len=8192,
+        tts_max_model_len=4096,
+        tts_attention_type="sliding_recompute",
+        flat_tts_config=flat_tts_config,
+    )
+    request = _req("req-rollover", RequestStatus.RUNNING, external_req_id="external-rollover")
+    request.resumable = True
+    previous_codes = list(range(25))
+    request.prompt_token_ids = [0] * 10
+    request._all_token_ids = [0] * 10 + previous_codes
+    request._output_token_ids = previous_codes.copy()
+    request.num_prompt_tokens = 10
+    request.num_computed_tokens = 35
+    request.num_output_placeholders = 0
+    request.update_block_hashes = Mock()
+    adapter.get_req_chunk[request.request_id] = 1
+    adapter.request_ids_mapping[request.request_id] = request.external_req_id
+    adapter._streaming_condition_lengths[request.request_id] = 10
+    adapter._streaming_condition_seqs[request.request_id] = 0
+    connector.get.return_value = (
+        {
+            "ids": {"prompt": [1]},
+            "meta": {
+                "next_stage_prompt_len": 10,
+                "next_stage_generation_tokens": 26,
+                "finished": True,
+            },
+        },
+        1,
+    )
+
+    assert adapter._poll_single_request(_dequeue_load_entry(adapter, request)) is True
+    request.status = RequestStatus.WAITING_FOR_CHUNK
+    running_queue = [request]
+    waiting_queue = DummyWaitingQueue()
+    adapter.process_pending_chunks(
+        waiting_queue,
+        running_queue,
+        scheduler_requests={request.request_id: request},
+    )
+
+    assert request.num_computed_tokens == 0
+    assert request.prompt_token_ids == [0] * 45
+    assert request.additional_information["ids"]["streaming_prompt_previous_codes"] == previous_codes
+    assert request.additional_information["meta"]["streaming_prompt_recompute"] is True
+    assert request.resumable is False
+    assert request.request_id in adapter.replaced_streaming_prompt_ids
+    assert adapter._max_model_len == 4096
+    assert running_queue == []
+    assert waiting_queue == [request]
+
+
+def test_window_condition_sequence_ignores_control_only_chunks(build_adapter) -> None:
+    adapter, connector = build_adapter(
+        stage_id=1,
+        model_mode="ar",
+        tts_attention_type="sliding_recompute",
+    )
+    request = _req("req-condition-seq", RequestStatus.RUNNING)
+
+    def receive(payload):
+        connector.get.return_value = (payload, 1)
+        assert adapter._poll_single_request(_dequeue_load_entry(adapter, request)) is True
+        adapter.requests_with_ready_chunks.add(request.request_id)
+        adapter._apply_pending_ar_prompt_updates({request.request_id: request})
+        adapter.requests_with_ready_chunks.discard(request.request_id)
+        return request.additional_information
+
+    first = receive(
+        {
+            "ids": {"prompt": [1]},
+            "meta": {"next_stage_prompt_len": 10},
+        }
+    )
+    assert first["meta"]["streaming_condition_seq"] == 0
+    assert adapter._streaming_condition_seqs[request.request_id] == 0
+
+    control = receive({"meta": {"is_segment_finished": True}})
+    assert "streaming_condition_seq" not in control["meta"]
+    assert adapter._streaming_condition_seqs[request.request_id] == 0
+
+    second = receive(
+        {
+            "ids": {"prompt": [2]},
+            "meta": {"next_stage_prompt_len": 12},
+        }
+    )
+    assert second["meta"]["streaming_condition_seq"] == 1
+    assert adapter._streaming_condition_lengths[request.request_id] == 12
+
+
+def test_load_poll_ar_reports_invalid_capacity_metadata(build_adapter):
+    adapter, connector = build_adapter(stage_id=1, model_mode="ar", max_model_len=4096)
+    request = _req("req-invalid-capacity", RequestStatus.RUNNING)
+    request.resumable = True
+    adapter.get_req_chunk[request.request_id] = 1
+    connector.get.return_value = (
+        {
+            "ids": {"prompt": [1]},
+            "meta": {
+                "next_stage_prompt_len": 4096,
+                "next_stage_generation_tokens": 26,
+                "finished": False,
+            },
+        },
+        1,
+    )
+
+    assert adapter._poll_single_request(_dequeue_load_entry(adapter, request)) is True
+    request.status = RequestStatus.WAITING_FOR_CHUNK
+    running_queue = [request]
+    adapter.process_pending_chunks(
+        DummyWaitingQueue(),
+        running_queue,
+        scheduler_requests={request.request_id: request},
+    )
+    assert adapter.collect_failed_receive_request_ids() == {
+        request.request_id: "fresh streaming prompt plus generation reserve exceeds max_model_len: "
+        "prompt=4096, reserve=26, limit=4096"
+    }
+    assert request.request_id not in adapter._finished_load_reqs
+
+
 def test_load_poll_generation_tensor_codes_use_placeholder_prompt(build_adapter):
     adapter, connector = build_adapter(stage_id=1, model_mode="generation")
     request = _req("req-tensor", RequestStatus.WAITING, external_req_id="external-tensor")
@@ -272,7 +711,7 @@ def test_load_poll_generation_tensor_codes_use_placeholder_prompt(build_adapter)
     }
     connector.get.return_value = (payload, 16)
 
-    adapter._poll_single_request(request)
+    adapter._poll_single_request(_dequeue_load_entry(adapter, request))
 
     assert request.prompt_token_ids == [0]
     assert request.num_computed_tokens == 0
@@ -302,12 +741,13 @@ def test_load_poll_generation_empty_nonterminal_chunk_keeps_polling(build_adapte
     }
     connector.get.side_effect = [(empty_payload, 16), (ready_payload, 16)]
 
-    assert adapter._poll_single_request(request) is False
+    entry = _dequeue_load_entry(adapter, request)
+    assert adapter._poll_single_request(entry) is False
     assert request.request_id not in adapter._finished_load_reqs
     assert request.request_id not in adapter.requests_with_ready_chunks
     assert adapter.get_req_chunk[request.request_id] == 1
 
-    assert adapter._poll_single_request(request) is True
+    assert adapter._poll_single_request(entry) is True
     assert request.request_id in adapter._finished_load_reqs
     assert torch.equal(request.additional_information["codes"]["audio"], ready_payload["codes"]["audio"])
     assert adapter.get_req_chunk[request.request_id] == 2
@@ -347,6 +787,7 @@ def test_segment_boundary_starts_new_send_watermark_before_background_flush(buil
     adapter, _ = build_adapter(stage_id=1)
     request = _req("req-stream", RequestStatus.WAITING, external_req_id="ext-stream")
     request.resumable = True
+    request._omni_segment_generation = 1
     request.num_computed_tokens = 0
     adapter.requests_num_chunks_sent["ext-stream"] = 26
 
@@ -355,13 +796,16 @@ def test_segment_boundary_starts_new_send_watermark_before_background_flush(buil
         request=request,
         is_segment_finished=True,
         confirmed_num_computed_tokens=26,
+        segment_generation=0,
     )
 
     assert len(adapter._pending_save_reqs) == 1
     boundary_task = adapter._pending_save_reqs.popleft()
+    assert boundary_task["segment_generation"] == 0
     assert "ext-stream" not in adapter.requests_num_chunks_sent
 
     request.num_computed_tokens = 3
+    request._omni_segment_generation = 1
     adapter.save_async(
         multimodal_output=None,
         request=request,
@@ -414,6 +858,7 @@ def test_background_send_uses_enqueued_request_snapshot(build_adapter):
     request.additional_information["meta"]["segment"] = "next"
     request.prompt_token_ids.append(3)
     request.append_output_token_ids([8])
+    request._omni_segment_generation = 1
     adapter.save_async(
         multimodal_output=None,
         request=request,
@@ -662,7 +1107,7 @@ def test_personaplex_sender_cleanup_drops_delayed_frame_state(build_adapter):
     assert second.codes is None
 
 
-def test_save_async_skips_stale_resumable_chunk_until_dedup_is_reset(build_adapter):
+def test_save_async_skips_stale_resumable_chunk_within_segment(build_adapter):
     adapter, _ = build_adapter(stage_id=1)
     request = _req("req-stream", RequestStatus.WAITING, external_req_id="ext-stream")
     request.resumable = True
@@ -681,6 +1126,51 @@ def test_save_async_skips_stale_resumable_chunk_until_dedup_is_reset(build_adapt
     assert adapter.requests_num_chunks_sent["ext-stream"] == 0
 
 
+def test_save_async_drops_late_previous_segment_after_boundary_reset(build_adapter):
+    adapter, _ = build_adapter(stage_id=1)
+    request = _req("req-stream", RequestStatus.WAITING, external_req_id="ext-stream")
+    request.resumable = True
+    request._omni_segment_generation = 0
+    adapter.requests_num_chunks_sent["ext-stream"] = 111
+
+    adapter.save_async(multimodal_output=None, request=request, is_segment_finished=True)
+
+    late = _req("req-stream-late", RequestStatus.WAITING, external_req_id="ext-stream")
+    late.resumable = True
+    late.num_computed_tokens = 111
+    late._omni_segment_generation = 0
+    adapter.save_async(multimodal_output=None, request=late, is_segment_finished=False)
+
+    next_segment = _req("req-stream-next", RequestStatus.WAITING, external_req_id="ext-stream")
+    next_segment.resumable = True
+    next_segment.num_computed_tokens = 3
+    next_segment._omni_segment_generation = 1
+    adapter.save_async(multimodal_output=None, request=next_segment, is_segment_finished=False)
+
+    assert len(adapter._pending_save_reqs) == 2
+    assert adapter.requests_num_chunks_sent["ext-stream"] == 3
+    assert adapter._pending_save_reqs[-1]["request"] is next_segment
+
+
+def test_send_single_request_preserves_queued_preboundary_chunk(build_adapter):
+    adapter, connector = build_adapter(stage_id=1)
+    adapter.custom_process_next_stage_input_func = lambda **kwargs: OmniPayloadStruct(meta=MetaStruct())
+    request = _req("req-queued-old", RequestStatus.WAITING, external_req_id="ext-queued-old")
+    request.resumable = True
+    request._omni_segment_generation = 0
+
+    adapter.save_async(multimodal_output=None, request=request, is_segment_finished=False)
+    adapter.save_async(multimodal_output=None, request=request, is_segment_finished=True)
+
+    old_task = adapter._pending_save_reqs.popleft()
+    adapter._send_single_request(old_task)
+    connector.put.assert_called_once()
+
+    boundary_task = adapter._pending_save_reqs.popleft()
+    adapter._send_single_request(boundary_task)
+    assert connector.put.call_count == 2
+
+
 def test_send_single_request_cleans_up_after_finished_payload(build_adapter, monkeypatch):
     adapter, _ = build_adapter(stage_id=1)
     request = _req("req-finished", RequestStatus.FINISHED_STOPPED, external_req_id="ext-finished")
@@ -701,6 +1191,77 @@ def test_send_single_request_cleans_up_after_finished_payload(build_adapter, mon
     assert args[1] == "ext-finished"
 
 
+def test_old_turn_terminal_send_preserves_new_turn_state_and_sender(build_adapter):
+    adapter, connector = build_adapter(
+        stage_id=1,
+        model_mode="ar",
+        max_model_len=4096,
+        tts_attention_type="sliding_recompute",
+    )
+    request = _req("req-live", RequestStatus.RUNNING, external_req_id="ext-live")
+    request.resumable = True
+    request._all_token_ids = []
+    request._output_token_ids = []
+    request.update_block_hashes = Mock()
+    adapter.custom_process_next_stage_input_func = lambda **kwargs: OmniPayloadStruct(
+        meta=MetaStruct(
+            finished=torch.tensor(True, dtype=torch.bool),
+            codec_streaming=True,
+        )
+    )
+
+    put_started = threading.Event()
+    release_put = threading.Event()
+    put_keys = []
+
+    def blocking_first_put(*_args, **kwargs):
+        put_keys.append(kwargs["put_key"])
+        if len(put_keys) == 1:
+            put_started.set()
+            assert release_put.wait(timeout=5)
+        return True, 1, {}
+
+    connector.put.side_effect = blocking_first_put
+    adapter.save_async(multimodal_output=None, request=request)
+    first_task = adapter._pending_save_reqs.popleft()
+    first_sender_token = first_task["sender_token"]
+    send_thread = threading.Thread(target=lambda: adapter._send_single_request(first_task))
+    send_thread.start()
+    assert put_started.wait(timeout=5)
+
+    entry = _dequeue_load_entry(adapter, request)
+    connector.get.return_value = (
+        {
+            "ids": {"prompt": [1]},
+            "meta": {
+                "replace_streaming_prompt": True,
+                "next_stage_prompt_len": 10,
+                "next_stage_generation_tokens": 26,
+            },
+        },
+        1,
+    )
+    assert adapter._poll_single_request(entry) is True
+    adapter.requests_with_ready_chunks.add(request.request_id)
+    adapter._apply_pending_ar_prompt_updates({request.request_id: request})
+    assert adapter._streaming_condition_seqs[request.request_id] == 0
+
+    request.num_computed_tokens = 1
+    adapter.save_async(multimodal_output=None, request=request)
+    second_task = adapter._pending_save_reqs.popleft()
+    assert second_task["sender_token"] is first_sender_token
+
+    release_put.set()
+    send_thread.join(timeout=5)
+    assert not send_thread.is_alive()
+    assert adapter._streaming_condition_seqs[request.request_id] == 0
+    assert adapter._sender_tokens[request.external_req_id] is first_sender_token
+    assert first_sender_token.cancelled is False
+
+    adapter._send_single_request(second_task)
+    assert put_keys == ["ext-live_1_0", "ext-live_1_1"]
+
+
 def test_load_poll_non_ar_merges_into_existing_additional_information(build_adapter):
     adapter, connector = build_adapter(stage_id=2, model_mode="diffusion")
     request = _req("req-non-ar", RequestStatus.WAITING, external_req_id="ext-non-ar")
@@ -720,7 +1281,7 @@ def test_load_poll_non_ar_merges_into_existing_additional_information(build_adap
     }
     connector.get.return_value = (payload, 8)
 
-    assert adapter._poll_single_request(request) is True
+    assert adapter._poll_single_request(_dequeue_load_entry(adapter, request)) is True
 
     assert request.prompt_token_ids == [7, 8]
     assert request.num_computed_tokens == 0
@@ -761,7 +1322,7 @@ def test_load_poll_generation_segment_marker_replaces_previous_chunk(build_adapt
         1,
     )
 
-    assert adapter._poll_single_request(request) is True
+    assert adapter._poll_single_request(_dequeue_load_entry(adapter, request)) is True
 
     assert request.prompt_token_ids == [7, 8]
     assert "audio" not in request.additional_information["codes"]
@@ -790,7 +1351,7 @@ def test_load_poll_generation_empty_replacement_snapshot_is_ready(build_adapter)
         1,
     )
 
-    assert adapter._poll_single_request(request) is True
+    assert adapter._poll_single_request(_dequeue_load_entry(adapter, request)) is True
 
     assert request.prompt_token_ids == [0]
     assert "codes" not in request.additional_information
@@ -816,7 +1377,7 @@ def test_load_poll_generation_without_snapshot_marker_keeps_incremental_state(bu
         1,
     )
 
-    assert adapter._poll_single_request(request) is False
+    assert adapter._poll_single_request(_dequeue_load_entry(adapter, request)) is False
 
     assert torch.equal(request.additional_information["codes"]["audio"], torch.tensor([1, 2]))
     assert request.additional_information["meta"]["cache_epoch"] == 3
@@ -840,9 +1401,17 @@ def test_load_poll_ar_request_additional_information_concats_tensors(build_adapt
     }
     connector.get.return_value = (payload, 8)
 
-    adapter._poll_single_request(request)
+    adapter._poll_single_request(_dequeue_load_entry(adapter, request))
 
-    # AR mode now forwards the latest payload directly.
+    # The recv thread records the latest payload; scheduler-side queue
+    # restoration publishes it onto the Request.
+    assert torch.equal(request.additional_information["hidden_states"]["output"], torch.tensor([[1.0]]))
+    request.status = RequestStatus.WAITING_FOR_CHUNK
+    adapter.process_pending_chunks(
+        DummyWaitingQueue(),
+        [request],
+        scheduler_requests={request.request_id: request},
+    )
     assert request.additional_information == payload
     assert request.additional_information["meta"]["finished"].item() is True
 
@@ -860,7 +1429,7 @@ def test_non_ar_poll_reinitializes_prefill_stats_for_later_chunks(build_adapter)
         },
         8,
     )
-    assert adapter._poll_single_request(request) is True
+    assert adapter._poll_single_request(_dequeue_load_entry(adapter, request)) is True
     OmniGenerationScheduler._record_prefill_stats(request)
     first_chunk_stats = request.prefill_stats
     request.prefill_stats = None
@@ -872,7 +1441,7 @@ def test_non_ar_poll_reinitializes_prefill_stats_for_later_chunks(build_adapter)
         },
         8,
     )
-    assert adapter._poll_single_request(request) is True
+    assert adapter._poll_single_request(_dequeue_load_entry(adapter, request)) is True
     assert isinstance(request.prefill_stats, PrefillStats)
     OmniGenerationScheduler._record_prefill_stats(request)
     second_chunk_stats = request.prefill_stats
@@ -971,7 +1540,7 @@ def test_non_active_waiting_request_is_held_off_scheduler(build_adapter):
     assert waiting_queue == []
     assert active.status == RequestStatus.WAITING_FOR_CHUNK
     assert non_active.status == RequestStatus.WAITING
-    assert list(adapter._pending_load_reqs) == [active]
+    assert [entry.request for entry in adapter._pending_load_reqs] == [active]
     assert list(adapter.waiting_for_chunk_waiting_requests) == [active, non_active]
 
 
@@ -1122,8 +1691,19 @@ def _populate_adapter_state(adapter, req_id="req-1", ext_id="ext-1"):
     adapter.get_req_chunk[req_id] = 3
     adapter.requests_with_ready_chunks.add(req_id)
     adapter.request_ids_mapping[req_id] = ext_id
-    adapter._pending_load_reqs.append(SimpleNamespace(request_id=req_id))
+    entry = SimpleNamespace(request_id=req_id)
+    adapter._pending_load_reqs.append(entry)
+    adapter._registered_load_entries[req_id] = entry
     adapter._finished_load_reqs.add(req_id)
+    adapter._pending_ar_prompt_updates[req_id] = (
+        SimpleNamespace(request_id=req_id),
+        {},
+        False,
+        False,
+        False,
+    )
+    adapter._streaming_condition_lengths[req_id] = 10
+    adapter._streaming_condition_seqs[req_id] = 2
 
     adapter.put_req_chunk[ext_id] = 5
     adapter.request_payload[ext_id] = {"hidden": [1, 2]}
@@ -1145,12 +1725,71 @@ def test_cleanup_clears_all_state(build_adapter):
     assert req_id not in adapter.request_ids_mapping
     assert req_id in adapter._cancelled_load_reqs
     assert req_id not in adapter._finished_load_reqs
+    assert req_id not in adapter._pending_ar_prompt_updates
+    assert req_id not in adapter._streaming_condition_lengths
+    assert req_id not in adapter._streaming_condition_seqs
+    assert req_id not in adapter._registered_load_entries
 
     assert ext_id not in adapter.put_req_chunk
     assert ext_id not in adapter.request_payload
     assert ext_id not in adapter.code_prompt_token_ids
 
 
+def test_cleanup_and_reregister_discards_inflight_old_segment_chunk(build_adapter):
+    """A late old-segment chunk cannot overwrite a resumed registration."""
+    adapter, connector = build_adapter(stage_id=1, model_mode="ar")
+    request = _req("req-late", RequestStatus.RUNNING, external_req_id="ext-late")
+    request.resumable = True
+    entry = _dequeue_load_entry(adapter, request)
+
+    get_started = threading.Event()
+    release_get = threading.Event()
+
+    def blocking_get(*_args):
+        get_started.set()
+        assert release_get.wait(timeout=5)
+        return (
+            {
+                "ids": {"prompt": [1]},
+                "meta": {
+                    "next_stage_prompt_len": 10,
+                },
+            },
+            1,
+        )
+
+    connector.get.side_effect = blocking_get
+    poll_results = []
+    poll_thread = threading.Thread(target=lambda: poll_results.append(adapter._poll_single_request(entry)))
+    poll_thread.start()
+    assert get_started.wait(timeout=5)
+
+    adapter.cleanup_receiver(request.request_id)
+    fresh_entry = _dequeue_load_entry(adapter, request)
+    release_get.set()
+    poll_thread.join(timeout=5)
+
+    assert not poll_thread.is_alive()
+    assert poll_results == [True]
+    assert request.additional_information is None
+    assert request.request_id not in adapter._pending_ar_prompt_updates
+    assert request.request_id not in adapter._streaming_condition_seqs
+    assert adapter.get_req_chunk[request.request_id] == 0
+    assert adapter._registered_load_entries[request.request_id] is fresh_entry
+
+    connector.get.side_effect = None
+    connector.get.return_value = (
+        {
+            "ids": {"prompt": [2]},
+            "meta": {"finished": True},
+        },
+        1,
+    )
+    assert adapter._poll_single_request(fresh_entry) is True
+    assert adapter.get_req_chunk[request.request_id] == 1
+    assert request.request_id not in adapter._registered_load_entries
+
+
 def test_cleanup_infers_external_id(build_adapter):
     """When external_req_id is None, cleanup should look it up from the mapping."""
     adapter, _ = build_adapter(stage_id=1)
@@ -1299,7 +1938,7 @@ def test_cleanup_after_poll_flow(build_adapter):
         "meta": {"finished": torch.tensor(True, dtype=torch.bool)},
     }
     connector.get.return_value = (payload, 8)
-    adapter._poll_single_request(request)
+    adapter._poll_single_request(_dequeue_load_entry(adapter, request))
 
     assert "req-flow" in adapter.upstream_exhausted_requests
     assert adapter.get_req_chunk["req-flow"] == 1
@@ -2320,3 +2959,34 @@ def test_abort_clears_native_codec_state_before_external_id_reuse(build_adapter)
     )
 
     assert torch.equal(replacement_payload.codes.audio, torch.full((1, 31), 202, dtype=torch.long))
+
+
+def test_save_async_boundary_holds_generation_without_request_counter(build_adapter):
+    """A boundary must not advance past a generation nothing can catch up to.
+
+    When the producing stage maintains ``_omni_segment_generation`` on the
+    request, a boundary may start the next segment's watermark at
+    ``generation + 1`` because the following frame will carry it. On paths
+    where the request has no such counter, that speculative advance makes every
+    subsequent frame look stale and starves the stream permanently
+    (vllm-project/vllm-omni#6816).
+    """
+    adapter, _ = build_adapter(stage_id=1)
+
+    request = _req("req-nogen", RequestStatus.WAITING, external_req_id="ext-nogen")
+    request.resumable = True
+    request._omni_segment_generation = None
+
+    adapter.save_async(multimodal_output=None, request=request, is_segment_finished=True)
+    assert adapter._segment_generation["ext-nogen"] == 0
+
+    follow_up = _req("req-nogen-next", RequestStatus.WAITING, external_req_id="ext-nogen")
+    follow_up.resumable = True
+    follow_up.num_computed_tokens = 3
+    follow_up._omni_segment_generation = None
+    queued_before = len(adapter._pending_save_reqs)
+
+    adapter.save_async(multimodal_output=None, request=follow_up, is_segment_finished=False)
+
+    assert len(adapter._pending_save_reqs) == queued_before + 1
+    assert adapter._pending_save_reqs[-1]["request"] is follow_up

tests/e2e/features/fullduplex/test_personaplex_elastic.py

@@ -1,5 +1,5 @@
 # SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
 """GPU e2e for PersonaPlex elastic batching (bit-exact isolation + recycle).
 
 Greedy decoding makes every cross-slot leak observable as a bit difference:
@@ -24,12 +24,17 @@ from __future__ import annotations
 import numpy as np
 import pytest
 
+from tests.helpers.mark import hardware_marks
+
 torch = pytest.importorskip("torch")
 
-# slow + H100 + omni routes this module into the weekly Buildkite E2E group
-# ("Omni · H100" in .buildkite/cuda/test-weekly.yml: -m "slow and H100 and
-# omni" --run-level full_model); the ready/merge CPU sweeps exclude it by mark.
-pytestmark = [pytest.mark.full_model, pytest.mark.omni, pytest.mark.slow, pytest.mark.H100]
+# slow + H100 + omni + cards_1 routes this module into weekly
+# "Omni · H100 · Single-GPU" (test-weekly.yml).
+pytestmark = [
+    pytest.mark.omni,
+    pytest.mark.slow,
+    *hardware_marks(res={"cuda": "H100"}),
+]
 
 if not torch.cuda.is_available():  # noqa: E402
     pytest.skip("needs CUDA", allow_module_level=True)

tests/e2e/offline_inference/test_dots_tts_expansion.py

@@ -0,0 +1,129 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""E2E tests for dots.tts native AR offline inference."""
+
+from collections.abc import Mapping
+
+import pytest
+import torch
+
+from tests.helpers.mark import hardware_test
+from tests.helpers.runtime import OmniRunner
+from tests.helpers.stage_config import get_deploy_config_path
+
+DOTS_TTS_MODEL = "rednote-hilab/dots.tts-soar"
+DEPLOY_CONFIG = get_deploy_config_path("dots_tts.yaml")
+SAMPLE_RATE = 48000
+
+# dots.tts ships a custom tokenizer carrying the added ``<|audio_gen_start|>``
+# token, so remote code must be explicitly enabled.
+_OMNI_RUNNER_PARAM = (DOTS_TTS_MODEL, DEPLOY_CONFIG, {"trust_remote_code": True})
+
+pytestmark = pytest.mark.parametrize("omni_runner", [_OMNI_RUNNER_PARAM], indirect=True)
+
+
+def _build_prompt(omni_runner: OmniRunner, text: str) -> dict:
+    """Wrap ``text`` in the prefix scaffold dots.tts prefill expects.
+
+    Unlike most TTS models here, a bare ``{"prompt": ...}`` string does not
+    work: prefill needs ``[文本]<text>[文本对应语音]<|audio_gen_start|>`` as
+    real Qwen2 token ids.
+    """
+    from transformers import AutoTokenizer
+
+    from vllm_omni.model_executor.models.dots_tts.dots_tts_prompt import (
+        build_dots_tts_prompt,
+    )
+
+    tokenizer = AutoTokenizer.from_pretrained(omni_runner.model_name, trust_remote_code=True)
+    return build_dots_tts_prompt(tokenizer=tokenizer, text=text)
+
+
+def _extract_audio(multimodal_output: dict) -> torch.Tensor:
+    """Extract the final complete audio tensor from multimodal output."""
+    assert isinstance(multimodal_output, (dict, Mapping)), f"Expected dict/Mapping, got {type(multimodal_output)}"
+
+    # The talker emits delta chunks; the output processor accumulates them
+    # under "model_outputs" ("audio" kept for backwards compat).
+    audio = multimodal_output.get("model_outputs")
+    if audio is None:
+        audio = multimodal_output.get("audio")
+    assert audio is not None, f"No audio key, got {list(multimodal_output.keys())}"
+
+    if isinstance(audio, list):
+        valid = [torch.as_tensor(x).float().cpu().reshape(-1) for x in audio if x is not None]
+        assert valid, "No valid audio tensors in output list"
+        audio = torch.cat(valid, dim=0) if len(valid) > 1 else valid[0]
+
+    assert isinstance(audio, torch.Tensor), f"Expected Tensor, got {type(audio)}"
+    return audio.float().cpu().reshape(-1)
+
+
+@pytest.mark.slow
+@pytest.mark.tts
+@hardware_test(res={"cuda": "L4"}, num_cards=1)
+def test_dots_tts_zero_shot_001(omni_runner: OmniRunner) -> None:
+    """Zero-shot synthesis produces audio of a plausible duration."""
+    prompt = _build_prompt(omni_runner, "Hello, this is a test of dots TTS running on vLLM Omni.")
+
+    outputs = omni_runner.omni.generate([prompt])
+    assert len(outputs) == 1
+
+    audio = _extract_audio(outputs[0].outputs[0].multimodal_output)
+    duration_s = audio.shape[0] / SAMPLE_RATE
+    assert 0.5 < duration_s < 30.0, f"Audio duration out of range: {duration_s:.2f}s"
+    assert torch.isfinite(audio).all(), "Audio contains NaN/Inf"
+    assert audio.abs().max() > 1e-3, "Audio is silent"
+
+
+@pytest.mark.slow
+@pytest.mark.tts
+@hardware_test(res={"cuda": "L4"}, num_cards=1)
+def test_dots_tts_prefill_decode_mixed_batch_002(omni_runner: OmniRunner) -> None:
+    """Mixed prefill+decode batches must keep per-request rows aligned.
+
+    ``compute_logits`` fills ``logits[i]`` positionally from
+    ``_results_queue``, so every request in the batch has to push exactly one
+    entry per step, prefills included. When the prefill branch pushed nothing,
+    a shorter request's stop signal could be read into another request's row.
+    Regression guard for that pairing (PR #4765 review).
+    """
+    long_text = (
+        "This is a deliberately long sentence that stays in the decode phase "
+        "for many steps, so the shorter requests behind it keep entering "
+        "prefill alongside it and reproduce the mixed batch pattern."
+    )
+    short_texts = ["Hello one.", "Hello two.", "Hello three.", "Hello four."]
+    prompts = [_build_prompt(omni_runner, long_text)] + [_build_prompt(omni_runner, t) for t in short_texts]
+
+    outputs = omni_runner.omni.generate(prompts)
+    assert len(outputs) == len(prompts)
+
+    for i, out in enumerate(outputs):
+        audio = _extract_audio(out.outputs[0].multimodal_output)
+        duration_s = audio.shape[0] / SAMPLE_RATE
+        assert 0.1 < duration_s < 30.0, f"Request {i} audio duration out of range: {duration_s:.2f}s"
+
+
+@pytest.mark.slow
+@pytest.mark.tts
+@hardware_test(res={"cuda": "L4"}, num_cards=1)
+def test_dots_tts_deterministic_flow_matching_noise_003(omni_runner: OmniRunner) -> None:
+    """The same text twice must yield the same audio.
+
+    The DiT flow-matching noise is drawn from a per-request generator seeded
+    from ``blake2b(seed:request_key:noise_step)`` rather than the global CUDA
+    RNG. Drawing from the global RNG made output non-reproducible and let
+    concurrent requests perturb each other's noise streams (PR #4765 review).
+    """
+    text = "Reading aloud every evening helps children build a lasting love of language."
+
+    first = _extract_audio(
+        omni_runner.omni.generate([_build_prompt(omni_runner, text)])[0].outputs[0].multimodal_output
+    )
+    second = _extract_audio(
+        omni_runner.omni.generate([_build_prompt(omni_runner, text)])[0].outputs[0].multimodal_output
+    )
+
+    assert first.shape == second.shape, f"Length differs across runs: {first.shape} vs {second.shape}"
+    assert torch.allclose(first, second, atol=1e-4), "Audio differs across runs; flow-matching noise is not seeded"

tests/e2e/offline_inference/test_hunyuanimage3_i2t_expansion.py

@@ -1,13 +1,13 @@
 # SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
 """Smoke test for HunyuanImage-3.0 Image-to-Text (I2T) pipeline."""
 
 from collections.abc import Generator
 
 import pytest
-import torch
 from PIL import Image
 
+from tests.helpers.mark import hardware_test
 from tests.helpers.runtime import OmniRunner
 from tests.helpers.stage_config import get_deploy_config_path
 from vllm_omni import Omni
@@ -44,7 +44,7 @@ EXPECTED_PREFIX_TOKEN_IDS: list[int] = [
 # Decoded form, kept only for human-readable assertion messages.
 EXPECTED_PREFIX_TEXT = "The image is a solid, uniform green color with no variations, objects, or details present. It"
 
-pytestmark = [pytest.mark.full_model, pytest.mark.diffusion, pytest.mark.cuda]
+pytestmark = [pytest.mark.full_model, pytest.mark.diffusion]
 
 
 @pytest.fixture(scope="module")
@@ -57,7 +57,7 @@ def omni() -> Generator[Omni, None, None]:
         yield runner.omni
 
 
-@pytest.mark.skipif(torch.accelerator.device_count() < 4, reason="Need at least 4 CUDA GPUs.")
+@hardware_test(res={"cuda": "H100"}, num_cards=4)
 def test_i2t_generates_text(omni: Omni) -> None:
     """Verify I2T output's first 20 token IDs match the HF greedy baseline."""
     # Solid-color image keeps the input self-contained and reproducible.

tests/e2e/offline_inference/test_krea2_expansion.py

@@ -91,8 +91,8 @@ def test_krea2_cache_dit(offline_client: OfflineOmniClient) -> None:
 def test_krea2_hsdp(offline_client: OfflineOmniClient) -> None:
     """Exercise Hybrid Sharded Data Parallel (HSDP) weight sharding on the DiT across 2 GPUs.
 
-    Validates the docs' HSDP-support claim for Krea 2. Scheduled on the multi-GPU (distributed_cuda)
-    shard by ``num_cards=2``; skipped automatically on boxes with fewer than 2 CUDA devices.
+    Validates the docs' HSDP-support claim for Krea 2. Scheduled on the multi-GPU
+    shard by ``num_cards=2`` (``not cards_1``); skipped automatically on boxes with fewer than 2 CUDA devices.
     """
     offline_client.send_diffusion_request({"model": MODEL, "prompt": PROMPT, "sampling_params": _sampling()})
 
@@ -111,7 +111,7 @@ def test_krea2_vae_patch_parallel(offline_client: OfflineOmniClient) -> None:
     """Exercise VAE patch-parallel (decode) across 2 GPUs.
 
     Validates the docs' VAE-patch-parallel (decode) support claim for Krea 2. Scheduled on the
-    multi-GPU (distributed_cuda) shard by ``num_cards=2``; skipped automatically on boxes with
+    multi-GPU shard by ``num_cards=2`` (``not cards_1``); skipped automatically on boxes with
     fewer than 2 CUDA devices.
     """
     offline_client.send_diffusion_request({"model": MODEL, "prompt": PROMPT, "sampling_params": _sampling()})

tests/e2e/offline_inference/test_qwen3_omni.py

@@ -3,6 +3,9 @@
 
 """
 E2E offline tests for Omni model with video input and audio output.
+
+Abort / sleep-admission lives in ``test_qwen3_omni_colocate_async.py`` so it
+does not overlap this module's OmniRunners.
 """
 
 import os

tests/e2e/offline_inference/test_qwen3_omni_colocate_async.py

@@ -0,0 +1,182 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
+
+"""Isolated AsyncOmni abort / sleep-admission regression for Qwen3-Omni.
+
+This lives in its own module so the L3 merge job can run it *after*
+``test_qwen3_omni.py`` (offline) and the online serving file. Those modules keep
+module-scoped OmniRunner / OmniServer instances until teardown; overlapping a
+CuMem sleep-mode engine with them kills StageEngineCoreProc at handshake
+(``Failed core proc(s): {}``).
+"""
+
+import asyncio
+import os
+from contextlib import ExitStack
+
+os.environ["VLLM_WORKER_MULTIPROC_METHOD"] = "spawn"
+
+import pytest
+from vllm import SamplingParams
+from vllm.inputs import TokensPrompt
+
+from tests.helpers.clean import wait_for_gpu_memory_to_clear
+from tests.helpers.mark import hardware_test
+from tests.helpers.stage_config import get_deploy_config_path, modify_stage_config
+from vllm_omni.entrypoints.async_omni import AsyncOmni
+from vllm_omni.platforms import current_omni_platform
+
+MODEL = "Qwen/Qwen3-Omni-30B-A3B-Instruct"
+
+
+def _output_token_ids(output) -> list[int]:
+    if not getattr(output, "outputs", None):
+        return []
+    completion = output.outputs[0]
+    token_ids = list(completion.token_ids or [])
+    if token_ids:
+        return token_ids
+    return list(getattr(completion, "cumulative_token_ids", None) or [])
+
+
+def _finish_reason(output) -> str | None:
+    if not getattr(output, "outputs", None):
+        return None
+    return getattr(output.outputs[0], "finish_reason", None)
+
+
+def _colocate_async_deploy() -> str:
+    """Thinker-only Instruct deploy with sleep mode on a single GPU.
+
+    Abort / sleep admission is a thinker control-plane contract. The 3-stage
+    CI yaml plus CuMem on every stage fails engine-core startup in the L3
+    merge job: Stage 1 resolves ``devices: "1"`` while only GPU 0 is visible.
+    """
+    return modify_stage_config(
+        get_deploy_config_path("qwen3_omni_moe_thinking.yaml"),
+        updates={
+            "stages": {
+                0: {
+                    "enable_sleep_mode": True,
+                    "enforce_eager": True,
+                    "trust_remote_code": True,
+                    "tensor_parallel_size": 1,
+                    "devices": "0",
+                    "gpu_memory_utilization": 0.9,
+                    "max_num_seqs": 1,
+                    "max_model_len": 8192,
+                    "max_num_batched_tokens": 8192,
+                    "enable_prefix_caching": False,
+                    "skip_mm_profiling": True,
+                },
+            },
+        },
+    )
+
+
+@pytest.mark.advanced_model
+@pytest.mark.omni
+@hardware_test(res={"cuda": "H100", "rocm": "MI325"}, num_cards=1)
+@pytest.mark.usefixtures("clean_gpu_memory_between_tests")
+@pytest.mark.asyncio
+async def test_colocate_async_abort_tokens_and_sleep_admission() -> None:
+    """**required** regression. Control-plane APIs (``abort`` / ``sleep`` /
+    ``wake_up`` / ``resume_generation``) are not in ``send_omni_request``.
+
+    On ``origin/main`` this test fails:
+    - abort: ``generate()`` never yields ``finish_reason="abort"`` with the
+      tokens produced so far, so resume has an empty prefix (or the stream
+      hangs after frontend state is dropped).
+    - sleep: ``generate()`` is admitted into EngineCore while weights/KV are
+      offloaded, so the task errors (asleep / corrupted ADD frame) instead of
+      waiting for ``resume_generation()``.
+
+    On this PR both legs pass: abort returns a resumeable prefix, and sleep
+    holds ``generate()`` until wake + resume.
+    """
+    prompt = "What color is the sky? Write a long, detailed explanation."
+    device_count = current_omni_platform.device_count()
+    if device_count > 0:
+        # Fail closed if a prior module-scoped runner is still on the GPU.
+        # ``clean_gpu_memory_between_tests`` only logs a note on timeout.
+        wait_for_gpu_memory_to_clear(
+            devices=list(range(device_count)),
+            threshold_ratio=0.15,
+            timeout_s=120,
+        )
+
+    with ExitStack() as after:
+        engine = AsyncOmni(
+            model=MODEL,
+            deploy_config=_colocate_async_deploy(),
+            enable_sleep_mode=True,
+        )
+        after.callback(engine.shutdown)
+
+        request_id = "qwen3-abort-partial"
+        outputs: list = []
+
+        async def _generate(max_tokens: int, req_id: str, gen_prompt) -> None:
+            async for output in engine.generate(
+                prompt=gen_prompt,
+                request_id=req_id,
+                sampling_params=SamplingParams(temperature=0.0, max_tokens=max_tokens),
+                output_modalities=["text"],
+            ):
+                outputs.append(output)
+
+        abort_task = asyncio.create_task(_generate(256, request_id, prompt))
+        prefix: list[int] = []
+        prompt_token_ids: list[int] = []
+        for _ in range(600):
+            if abort_task.done():
+                break
+            if outputs:
+                latest = outputs[-1]
+                prefix = _output_token_ids(latest)
+                prompt_token_ids = list(getattr(latest, "prompt_token_ids", None) or [])
+                if prefix:
+                    break
+            await asyncio.sleep(0.1)
+
+        assert prefix, "generate produced no tokens before abort"
+        assert not abort_task.done(), "generate finished before abort; raise max_tokens"
+        await engine.abort(request_id)
+        await asyncio.wait_for(abort_task, timeout=90)
+
+        final = outputs[-1]
+        assert final.finished
+        assert _finish_reason(final) == "abort"
+        abort_prefix = _output_token_ids(final)
+        assert abort_prefix, "abort dropped the generated prefix (main-branch behavior)"
+
+        resume_prompt: str | TokensPrompt = (
+            TokensPrompt(prompt_token_ids=prompt_token_ids + abort_prefix) if prompt_token_ids else prompt
+        )
+        outputs.clear()
+        async for output in engine.generate(
+            prompt=resume_prompt,
+            request_id="qwen3-abort-resume",
+            sampling_params=SamplingParams(temperature=0.0, max_tokens=8),
+            output_modalities=["text"],
+        ):
+            outputs.append(output)
+        assert outputs
+        assert any(_output_token_ids(out) for out in outputs)
+
+        # Trainer order is pause → abort → sleep. Pause here after the
+        # resume generate so EngineCore is idle before CuMem offload.
+        await engine.pause_generation(mode="abort", clear_cache=True)
+        await engine.sleep(level=1)
+        outputs.clear()
+        sleep_task = asyncio.create_task(_generate(8, "qwen3-sleep-admission", prompt))
+        await asyncio.sleep(1.0)
+        assert not sleep_task.done(), "generate() ran while EngineCore was sleeping (main-branch admission race)"
+        await engine.wake_up()
+        await asyncio.sleep(0.5)
+        assert not sleep_task.done(), "generate() resumed before resume_generation()"
+        await engine.resume_generation()
+        await asyncio.wait_for(sleep_task, timeout=180)
+        assert outputs
+        assert _finish_reason(outputs[-1]) != "abort"
+    await asyncio.sleep(5)

tests/e2e/online_serving/helpers/minicpmo_4_5_duplex.py

@@ -101,7 +101,8 @@ def duplex_camera_frames(*, seconds: int, cache_dir: Path) -> list[str]:
     from tests.helpers.media import generate_synthetic_video
 
     video = generate_synthetic_video(448, 448, 30 * seconds, cache_dir=cache_dir)
-    return _video_frames_from_file(Path(video["file_path"]))
+    frames, _ = _video_frames_from_file(Path(video["file_path"]))
+    return frames
 
 
 def realtime_url(omni_server) -> str:

tests/e2e/online_serving/run_minicpmo_realtime_duplex_soft_interrupt.py

@@ -252,6 +252,19 @@ def summarize_artifacts(
         (summary["done_indices"][0] for summary in reversed(response_summaries) if summary.get("done_indices")),
         None,
     )
+    # A soft interrupt's follow-up response can drain past the commit (residual
+    # model unit), so its response.done lands after input_audio_buffer.committed.
+    # Anchor the "listen after last done, before commit" sandwich on the last
+    # turn that closed its floor strictly before the commit, not the
+    # globally-last done (which may sit after the commit and leave the interval empty).
+    last_done_before_commit_index = next(
+        (
+            summary["done_indices"][0]
+            for summary in reversed(response_summaries)
+            if summary.get("done_indices") and commit_index is not None and summary["done_indices"][0] < commit_index
+        ),
+        None,
+    )
     listen_indices = [index for index, event in enumerate(events) if event.get("type") == "response.listen"]
     effective_min_responses = min_responses if validation_mode == "response-required" else 1
     enough_responses = len(response_summaries) >= effective_min_responses
@@ -279,9 +292,9 @@ def summarize_artifacts(
     )
     listen_after_last_done = last_done_index is not None and any(index > last_done_index for index in listen_indices)
     listen_after_response_before_commit = (
-        last_done_index is not None
+        last_done_before_commit_index is not None
         and commit_index is not None
-        and any(last_done_index < index < commit_index for index in listen_indices)
+        and any(last_done_before_commit_index < index < commit_index for index in listen_indices)
     )
     final_listen_after_commit = commit_index is not None and any(index > commit_index for index in listen_indices)
     transcript = "".join(str(summary.get("transcript") or "") for summary in response_summaries)

tests/e2e/online_serving/test_higgs_audio_v3.py

@@ -48,10 +48,6 @@ DEFAULT_SPEECH_TIMEOUT_S = 180.0
 # silence-only outputs without flagging short legitimate clips.
 _MIN_AUDIO_BYTES = 20_000
 
-_SKIP_ISSUE_6418 = pytest.mark.skip(
-    reason="https://github.com/vllm-project/vllm-omni/issues/6418",
-)
-
 # Reuse the shared TTS reference clip (clean ~5 s 24 kHz mono human speech)
 # vendored under tests/assets/qwen3_tts/. Keeps a single WAV across TTS
 # tests rather than duplicating asset bytes.
@@ -65,7 +61,7 @@ class TestHiggsAudioV3OnlineHappyPath:
 
     @pytest.mark.slow
     @pytest.mark.tts
-    @hardware_test(res={"cuda": "L4"}, num_cards=1)
+    @hardware_test(res={"cuda": "L4", "rocm": "MI325"}, num_cards=1)
     def test_plain_text_wav(self, omni_server, online_client) -> None:
         """Single non-streaming WAV request - canonical TTS happy path."""
         online_client.send_audio_speech_request(
@@ -82,10 +78,9 @@ class TestHiggsAudioV3OnlineHappyPath:
             }
         )
 
-    @_SKIP_ISSUE_6418
     @pytest.mark.slow
     @pytest.mark.tts
-    @hardware_test(res={"cuda": "L4"}, num_cards=1)
+    @hardware_test(res={"cuda": "L4", "rocm": "MI325"}, num_cards=1)
     def test_plain_text_with_max_new_tokens(self, omni_server, online_client) -> None:
         """``max_new_tokens`` is one of the few extra fields the v3 validator accepts."""
         online_client.send_audio_speech_request(
@@ -95,15 +90,14 @@ class TestHiggsAudioV3OnlineHappyPath:
                 "stream": False,
                 "response_format": "wav",
                 "timeout": DEFAULT_SPEECH_TIMEOUT_S,
-                "max_new_tokens": 32,
+                "max_new_tokens": 1024,
                 "min_audio_bytes": _MIN_AUDIO_BYTES,
             }
         )
 
-    @_SKIP_ISSUE_6418
     @pytest.mark.slow
     @pytest.mark.tts
-    @hardware_test(res={"cuda": "L4"}, num_cards=1)
+    @hardware_test(res={"cuda": "L4", "rocm": "MI325"}, num_cards=1)
     def test_concurrent_plain_text(self, omni_server, online_client) -> None:
         """Three concurrent non-streaming requests - guards per-slot audio state and Stage-0 PC under batching."""
         online_client.send_audio_speech_request(
@@ -119,7 +113,7 @@ class TestHiggsAudioV3OnlineHappyPath:
 
     @pytest.mark.slow
     @pytest.mark.tts
-    @hardware_test(res={"cuda": "L4"}, num_cards=1)
+    @hardware_test(res={"cuda": "L4", "rocm": "MI325"}, num_cards=1)
     def test_plain_text_pcm_streaming(self, omni_server, online_client) -> None:
         """Streaming PCM via talker2code2wav_async_chunk + the connector's codec_streaming path.
 
@@ -150,7 +144,7 @@ class TestHiggsAudioV3OnlineHappyPath:
 
     @pytest.mark.slow
     @pytest.mark.tts
-    @hardware_test(res={"cuda": "L4"}, num_cards=1)
+    @hardware_test(res={"cuda": "L4", "rocm": "MI325"}, num_cards=1)
     def test_concurrent_pcm_streaming(self, omni_server, online_client) -> None:
         """Three concurrent streaming requests - guards per-request frame cursors
         in ``talker2code2wav_async_chunk`` and per-slot delay-pattern state under batched AR.
@@ -185,15 +179,13 @@ class TestHiggsAudioV3OnlineInlineControlTokens:
     tokens (pause, sfx) go inline. SFX tokens must pair with their written
     onomatopoeia.
 
-    These tests check the *serving surface* - the validator accepts the
-    payload, the engine produces audio, and the WAV has non-trivial size.
-    They do not assert audio quality (out of scope for CI).
+    These tests check the serving surface and spoken text. They do not assert
+    whether the requested control changes audio quality.
     """
 
-    @_SKIP_ISSUE_6418
     @pytest.mark.slow
     @pytest.mark.tts
-    @hardware_test(res={"cuda": "L4"}, num_cards=1)
+    @hardware_test(res={"cuda": "L4", "rocm": "MI325"}, num_cards=1)
     def test_inline_emotion_and_expressive(self, omni_server, online_client) -> None:
         """Delivery tokens (emotion + expressive_high) at the start of input."""
         online_client.send_audio_speech_request(
@@ -204,13 +196,13 @@ class TestHiggsAudioV3OnlineInlineControlTokens:
                 "response_format": "wav",
                 "timeout": DEFAULT_SPEECH_TIMEOUT_S,
                 "min_audio_bytes": _MIN_AUDIO_BYTES,
+                "transcript_expected_text": "Wait, that was actually hilarious.",
             }
         )
 
-    @_SKIP_ISSUE_6418
     @pytest.mark.slow
     @pytest.mark.tts
-    @hardware_test(res={"cuda": "L4"}, num_cards=1)
+    @hardware_test(res={"cuda": "L4", "rocm": "MI325"}, num_cards=1)
     def test_inline_style_whispering(self, omni_server, online_client) -> None:
         """Style token at the start - ``whispering``."""
         online_client.send_audio_speech_request(
@@ -221,13 +213,13 @@ class TestHiggsAudioV3OnlineInlineControlTokens:
                 "response_format": "wav",
                 "timeout": DEFAULT_SPEECH_TIMEOUT_S,
                 "min_audio_bytes": _MIN_AUDIO_BYTES,
+                "transcript_expected_text": "It is just between you and me, alright?",
             }
         )
 
-    @_SKIP_ISSUE_6418
     @pytest.mark.slow
     @pytest.mark.tts
-    @hardware_test(res={"cuda": "L4"}, num_cards=1)
+    @hardware_test(res={"cuda": "L4", "rocm": "MI325"}, num_cards=1)
     def test_inline_prosody_speed_and_pitch(self, omni_server, online_client) -> None:
         """Two prosody tokens at the start - slow speed plus low pitch."""
         online_client.send_audio_speech_request(
@@ -240,13 +232,13 @@ class TestHiggsAudioV3OnlineInlineControlTokens:
                 "response_format": "wav",
                 "timeout": DEFAULT_SPEECH_TIMEOUT_S,
                 "min_audio_bytes": _MIN_AUDIO_BYTES,
+                "transcript_expected_text": "The radar shows a storm approaching from the east.",
             }
         )
 
-    @_SKIP_ISSUE_6418
     @pytest.mark.slow
     @pytest.mark.tts
-    @hardware_test(res={"cuda": "L4"}, num_cards=1)
+    @hardware_test(res={"cuda": "L4", "rocm": "MI325"}, num_cards=1)
     def test_inline_pause_mid_text(self, omni_server, online_client) -> None:
         """Positional pause token placed inline between two clauses."""
         online_client.send_audio_speech_request(
@@ -257,13 +249,13 @@ class TestHiggsAudioV3OnlineInlineControlTokens:
                 "response_format": "wav",
                 "timeout": DEFAULT_SPEECH_TIMEOUT_S,
                 "min_audio_bytes": _MIN_AUDIO_BYTES,
+                "transcript_expected_text": "Hold on a moment. Let me think about it.",
             }
         )
 
-    @_SKIP_ISSUE_6418
     @pytest.mark.slow
     @pytest.mark.tts
-    @hardware_test(res={"cuda": "L4"}, num_cards=1)
+    @hardware_test(res={"cuda": "L4", "rocm": "MI325"}, num_cards=1)
     def test_inline_sfx_with_onomatopoeia(self, omni_server, online_client) -> None:
         """SFX token paired with its written onomatopoeia (``<|sfx:laughter|>Hehe``)."""
         online_client.send_audio_speech_request(
@@ -277,6 +269,7 @@ class TestHiggsAudioV3OnlineInlineControlTokens:
                 "response_format": "wav",
                 "timeout": DEFAULT_SPEECH_TIMEOUT_S,
                 "min_audio_bytes": _MIN_AUDIO_BYTES,
+                "transcript_expected_text": "I cannot believe that just happened. I am still recovering from it.",
             }
         )
 
@@ -287,7 +280,7 @@ class TestHiggsAudioV3OnlineVoiceClone:
 
     @pytest.mark.slow
     @pytest.mark.tts
-    @hardware_test(res={"cuda": "L4"}, num_cards=1)
+    @hardware_test(res={"cuda": "L4", "rocm": "MI325"}, num_cards=1)
     def test_voice_clone_ref_audio_ref_text(self, omni_server, online_client) -> None:
         """Canonical vllm-omni voice clone via ``ref_audio`` + ``ref_text``."""
         online_client.send_audio_speech_request(
@@ -304,7 +297,7 @@ class TestHiggsAudioV3OnlineVoiceClone:
 
     @pytest.mark.slow
     @pytest.mark.tts
-    @hardware_test(res={"cuda": "L4"}, num_cards=1)
+    @hardware_test(res={"cuda": "L4", "rocm": "MI325"}, num_cards=1)
     def test_voice_clone_references_alias(self, omni_server, online_client) -> None:
         """BosonAI cookbook payload: ``references=[{audio_path, text}]``.
 

tests/e2e/online_serving/test_mimo_audio_expansion.py

@@ -1,7 +1,7 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
 """
-E2E expansion tests for MiMo-Audio online serving (nightly Omni · Function Test with H100).
+E2E expansion tests for MiMo-Audio online serving (nightly Omni · Function Test with H100 · Single-GPU).
 """
 
 import os

tests/e2e/online_serving/test_minicpmo_4_5.py

@@ -1,12 +1,20 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
+
 """E2E online tests for MiniCPM-o 4.5 multimodal input and audio/text output.
 
 Exercises async chunk streaming (``--async-chunk``) across separate Thinker,
 Talker, and Code2Wav stages.
 """
 
+import json
 import os
+import tempfile
+from copy import deepcopy
+from pathlib import Path
 
 import pytest
+from vllm.logger import DEFAULT_LOGGING_CONFIG
 
 from tests.helpers.mark import hardware_test
 from tests.helpers.media import generate_synthetic_audio, generate_synthetic_image, generate_synthetic_video
@@ -19,10 +27,36 @@ _MODEL = "openbmb/MiniCPM-o-4_5"
 _CI_DEPLOY = modify_stage_config(
     get_deploy_config_path("minicpmo_4_5.yaml"),
     updates={
-        "stages": {0: {"default_sampling_params.max_tokens": 64}, 1: {"default_sampling_params.max_tokens": 1024}}
+        "stages": {
+            0: {"default_sampling_params.max_tokens": 64},
+            1: {
+                "default_sampling_params.max_tokens": 1024,
+                # Content-consistency assertions must not depend on a random
+                # codec trajectory: different valid TTS samples can append an
+                # audible tail even when Thinker text is identical.
+                "default_sampling_params.temperature": 0.0,
+            },
+        },
     },
 )
 
+_PROMPT_LOG_TEMP_DIR = tempfile.TemporaryDirectory(prefix="minicpmo45-prompt-selection-")
+_PROMPT_SELECTION_LOG = Path(_PROMPT_LOG_TEMP_DIR.name) / "prompt-selection.log"
+_PROMPT_LOGGING_CONFIG_PATH = Path(_PROMPT_LOG_TEMP_DIR.name) / "logging.json"
+_PROMPT_LOGGING_CONFIG = deepcopy(DEFAULT_LOGGING_CONFIG)
+_PROMPT_LOGGING_CONFIG["handlers"]["minicpmo_prompt_selection"] = {
+    "class": "logging.FileHandler",
+    "filename": str(_PROMPT_SELECTION_LOG),
+    "formatter": "vllm",
+    "level": "DEBUG",
+}
+_PROMPT_LOGGING_CONFIG["loggers"]["vllm_omni.model_executor.models.minicpmo_4_5.minicpmo_4_5_code2wav"] = {
+    "handlers": ["minicpmo_prompt_selection"],
+    "level": "DEBUG",
+    "propagate": True,
+}
+_PROMPT_LOGGING_CONFIG_PATH.write_text(json.dumps(_PROMPT_LOGGING_CONFIG), encoding="utf-8")
+
 test_params = [
     pytest.param(
         OmniServerParams(
@@ -30,6 +64,10 @@ test_params = [
             stage_config_path=_CI_DEPLOY,
             use_stage_cli=False,
             server_args=["--trust-remote-code", "--async-chunk"],
+            env_dict={
+                "VLLM_CONFIGURE_LOGGING": "1",
+                "VLLM_LOGGING_CONFIG_PATH": str(_PROMPT_LOGGING_CONFIG_PATH),
+            },
         ),
         id="async_chunk",
     ),
@@ -74,6 +112,18 @@ _TTS_EXTRA_BODY = {
 }
 
 
+def _prompt_selection_log_offset() -> int:
+    return _PROMPT_SELECTION_LOG.stat().st_size if _PROMPT_SELECTION_LOG.exists() else 0
+
+
+def _read_prompt_selection_log(offset: int) -> str:
+    if not _PROMPT_SELECTION_LOG.exists():
+        return ""
+    with open(_PROMPT_SELECTION_LOG, "rb") as log_file:
+        log_file.seek(offset)
+        return log_file.read().decode("utf-8")
+
+
 @pytest.mark.core_model
 @pytest.mark.advanced_model
 @pytest.mark.omni
@@ -126,6 +176,51 @@ def test_text_to_audio_001(omni_server, openai_client) -> None:
     openai_client.send_omni_request(request_config)
 
 
+@pytest.mark.advanced_model
+@pytest.mark.omni
+@hardware_test(res={"cuda": "H100", "npu": "A3"}, num_cards=1)
+@pytest.mark.parametrize("omni_server", test_params, indirect=True)
+def test_text_to_audio_with_reference_audio(omni_server, openai_client) -> None:
+    request_config = {
+        "model": omni_server.model,
+        "messages": dummy_messages_from_mix_data(system_prompt=get_system_prompt(), content_text=get_prompt()),
+        "stream": True,
+        "modalities": ["text", "audio"],
+        "extra_body": {
+            **_TTS_EXTRA_BODY,
+            "ref_audio": f"data:audio/wav;base64,{generate_synthetic_audio(5, 1)['base64']}",
+        },
+    }
+
+    log_offset = _prompt_selection_log_offset()
+    responses = openai_client.send_omni_request(request_config)
+    prompt_log = _read_prompt_selection_log(log_offset)
+
+    assert responses[0].success
+    assert responses[0].audio_bytes
+    assert "prompt_cache_id=runtime-ref-" in prompt_log
+    assert "minicpmo45_ref_" in prompt_log
+
+
+@pytest.mark.advanced_model
+@pytest.mark.omni
+@hardware_test(res={"cuda": "H100", "npu": "A3"}, num_cards=1)
+@pytest.mark.parametrize("omni_server", test_params, indirect=True)
+def test_text_to_audio_with_default_reference(omni_server, openai_client) -> None:
+    request_config = {
+        "model": omni_server.model,
+        "messages": dummy_messages_from_mix_data(system_prompt=get_system_prompt(), content_text=get_prompt()),
+        "stream": True,
+        "modalities": ["text", "audio"],
+        "extra_body": _TTS_EXTRA_BODY,
+    }
+
+    responses = openai_client.send_omni_request(request_config)
+
+    assert responses[0].success
+    assert responses[0].audio_bytes
+
+
 @pytest.mark.full_model
 @pytest.mark.omni
 @hardware_test(res={"cuda": "H100", "npu": "A3"}, num_cards=1)