docs: publish PR 6279 benchmark figures

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

.buildkite/cuda/test-nightly.yml

@@ -159,19 +159,7 @@ steps:
           - export VLLM_ALLOW_LONG_MAX_MODEL_LEN="1"
           - export VLLM_USE_DEEP_GEMM="0"
           - export VLLM_MOE_USE_DEEP_GEMM="0"
-          - pytest -s -v tests/e2e/ -m "full_model and L4 and tts" --run-level "full_model" --ignore=tests/e2e/accuracy --ignore=tests/e2e/online_serving/test_voxcpm2_tts_expansion.py
-        mirror_hardwares: l4_1
-
-      - label: ":full_moon: TTS · Accuracy Test · VoxCPM2"
-        timeout_in_minutes: 30
-        commands:
-          - |
-            timeout 30m bash -c "
-              export VLLM_LOGGING_LEVEL=DEBUG
-              export VLLM_WORKER_MULTIPROC_METHOD=spawn
-              export VLLM_ALLOW_LONG_MAX_MODEL_LEN=1
-              pytest -s -v tests/e2e/online_serving/test_voxcpm2_tts_expansion.py -m 'full_model and cuda and tts' --run-level 'full_model'
-            "
+          - 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"
@@ -188,35 +176,6 @@ steps:
             exit $$EXIT
         mirror_hardwares: h100_2
 
-      - label: ":full_moon: TTS · VoxCPM2 · Perf Test"
-        key: nightly-tts-performance-voxcpm2
-        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_voxcpm2.json
-            EXIT=$$?
-            buildkite-agent artifact upload "tests/dfx/perf/results/*.json"
-            exit $$EXIT
-        mirror_hardwares: h100_1
-
-      - label: ":full_moon: TTS · Higgs-Audio-V3 · Perf Test"
-        key: nightly-tts-performance-higgs-audio-v3
-        timeout_in_minutes: 180
-        commands:
-          - export BENCHMARK_DIR=tests/dfx/perf/results
-          - export VLLM_USE_DEEP_GEMM="0"
-          - export VLLM_MOE_USE_DEEP_GEMM="0"
-          - |
-            set +e
-            pytest -s -v tests/dfx/perf/scripts/run_benchmark.py --test-config-file tests/dfx/perf/tests/test_higgs_audio_v3.json
-            EXIT=$$?
-            buildkite-agent artifact upload "tests/dfx/perf/results/*.json"
-            exit $$EXIT
-        mirror_hardwares: h100_1
-
   - group: ":card_index_dividers: Tiny Model Tests [Multi-GPU]"
     key: nightly-tiny-model-test-group
     depends_on: upload-nightly-pipeline
@@ -246,8 +205,7 @@ 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,
-      # and Bagel feature coverage is not duplicated via a broad tests/e2e sweep.
+      # 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
@@ -255,15 +213,6 @@ steps:
           - >-
             pytest -sv
             tests/e2e/online_serving/test_qwen_image_expansion.py
-            tests/e2e/online_serving/test_qwen_image_edit_expansion.py
-            tests/e2e/online_serving/test_qwen_image_layered_expansion.py
-            tests/e2e/online_serving/test_boogu_image.py
-            tests/e2e/online_serving/test_boogu_image_edit.py
-            tests/e2e/online_serving/test_boogu_image_expansion.py
-            tests/e2e/online_serving/test_boogu_image_edit_expansion.py
-            tests/e2e/online_serving/test_bagel_expansion.py
-            tests/e2e/online_serving/test_krea2_expansion.py
-            tests/e2e/offline_inference/test_krea2_expansion.py
             -m "full_model and diffusion and H100 and not distributed_cuda"
             --run-level "full_model"
         mirror_hardwares: h100_1
@@ -274,47 +223,14 @@ steps:
           - >-
             pytest -sv
             tests/e2e/online_serving/test_qwen_image_expansion.py
-            tests/e2e/online_serving/test_qwen_image_edit_expansion.py
-            tests/e2e/offline_inference/test_krea2_expansion.py
             -m "full_model and diffusion and H100 and distributed_cuda"
             --run-level "full_model"
         mirror_hardwares: h100_2
 
-      - label: ":full_moon: Diffusion X2I(&A&T) · Function Test with H100 · Multi-GPU Layered/LongCat/FLUX"
-        timeout_in_minutes: 90
-        commands:
-          - >-
-            pytest -sv
-            tests/e2e/online_serving/test_qwen_image_layered_expansion.py
-            -m "full_model and diffusion and H100 and distributed_cuda"
-            --run-level "full_model"
-        mirror_hardwares: h100_2
-
-      - label: ":full_moon: Diffusion X2I(&A&T) · Function Test with H100 · Multi-GPU Bagel"
-        timeout_in_minutes: 120
-        commands:
-          - >-
-            pytest -sv
-            tests/e2e/online_serving/test_bagel_expansion.py
-            -m "full_model and diffusion and H100 and distributed_cuda"
-            --run-level "full_model"
-        mirror_hardwares: h100_2
-
-      - label: ":full_moon: Diffusion X2I(&A&T) · GLM-Image Function Test with H100"
-        timeout_in_minutes: 120
-        commands:
-          - >-
-            pytest -sv
-            tests/e2e/online_serving/test_glm_image_expansion.py
-            tests/e2e/offline_inference/test_glm_image_autoround_w4a16_expansion.py
-            -m "full_model and diffusion and H100"
-            --run-level "full_model"
-        mirror_hardwares: h100_2
-
       - label: ":full_moon: Diffusion X2I(&A&T) · Function Test with L4"
         timeout_in_minutes: 120
         commands:
-          - pytest -sv tests/e2e/ -k "not test_wan and not test_bagel_expansion 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" --run-level "full_model" --ignore=tests/e2e/accuracy
         mirror_hardwares: l4_4
 
       - label: ":full_moon: Diffusion X2I(&A&T) · Doc Test"
@@ -327,7 +243,7 @@ steps:
         timeout_in_minutes: 180
         commands:
           - export VLLM_HTTP_TIMEOUT_KEEP_ALIVE=120
-          - pytest -s -v tests/e2e/accuracy/test_qwen_image*.py --run-level full_model
+          - pytest -s -v tests/e2e/accuracy/test_qwen_image.py --run-level full_model
           - pytest -s -v tests/e2e/accuracy/test_diffusers_backend_similarity.py -k '2i_matches_diffusers' -m full_model --run-level full_model
         mirror_hardwares: h100_1
 
@@ -355,70 +271,6 @@ steps:
             exit $$EXIT
         mirror_hardwares: h100_4
 
-      - label: ":full_moon: Diffusion X2I(&A&T) · Perf Test · Qwen-Image-Edit"
-        key: nightly-diffusion-x2iat-performance-qwen-image-edit
-        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_edit_vllm_omni.json
-            EXIT=$$?
-            buildkite-agent artifact upload "tests/dfx/perf/results/diffusion_result_*.json"
-            buildkite-agent artifact upload "tests/dfx/perf/results/logs/*.log"
-            exit $$EXIT
-        mirror_hardwares: h100_4
-
-      - label: ":full_moon: Diffusion X2I(&A&T) · Perf Test · Qwen-Image-Edit-2511"
-        key: nightly-diffusion-x2iat-performance-qwen-image-edit-2511
-        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_edit_2511_vllm_omni.json
-            EXIT=$$?
-            buildkite-agent artifact upload "tests/dfx/perf/results/diffusion_result_*.json"
-            buildkite-agent artifact upload "tests/dfx/perf/results/logs/*.log"
-            exit $$EXIT
-        mirror_hardwares: h100_4
-
-      - label: ":full_moon: Diffusion X2I(&A&T) · Perf Test · Qwen-Image-Layered"
-        key: nightly-diffusion-x2iat-performance-qwen-image-layered
-        timeout_in_minutes: 120
-        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_layered_vllm_omni.json
-            EXIT=$$?
-            buildkite-agent artifact upload "tests/dfx/perf/results/diffusion_result_*.json"
-            buildkite-agent artifact upload "tests/dfx/perf/results/logs/*.log"
-            exit $$EXIT
-        mirror_hardwares: h100_1
-
-      - label: ":full_moon: Diffusion X2I(&A&T) · Perf Test · BAGEL"
-        key: nightly-diffusion-x2iat-performance-bagel
-        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_bagel_vllm_omni.json
-            EXIT=$$?
-            buildkite-agent artifact upload "tests/dfx/perf/results/diffusion_result_*.json"
-            buildkite-agent artifact upload "tests/dfx/perf/results/logs/*.log"
-            exit $$EXIT
-        mirror_hardwares: h100_1
-
   - group: ":card_index_dividers: Diffusion X2V Model Test"
     key: nightly-diffusion-x2v-group
     depends_on: upload-nightly-pipeline
@@ -440,12 +292,6 @@ steps:
           - pytest -s -v tests/e2e/online_serving/test_wan22_expansion.py -k "i2v" -m "full_model and cuda" --run-level "full_model"
         mirror_hardwares: h100_2
 
-      - label: ":full_moon: Diffusion X2V · LingBot Video MoE Function Test"
-        timeout_in_minutes: 90
-        commands:
-          - pytest -s -v tests/e2e/online_serving/test_lingbot_video_moe.py -k "video_generation" -m "full_model and cuda" --run-level "full_model"
-        mirror_hardwares: h100_1
-
       - label: ":full_moon: Diffusion X2V · Doc Test"
         timeout_in_minutes: 60
         commands:
@@ -456,15 +302,8 @@ steps:
         timeout_in_minutes: 90
         commands:
           - pytest -s -v tests/e2e/offline_inference/test_wan22_autoround_w4a16_expansion.py -m "full_model and cuda" --run-level "full_model"
-          - pytest -s -v tests/e2e/online_serving/test_wan_2_1_vace_expansion.py tests/e2e/online_serving/test_ltx.py -m "full_model and cuda" --run-level "full_model"
         mirror_hardwares: h100_2
 
-      - label: ":full_moon: Diffusion X2V · LTX-2.5 Online Test"
-        timeout_in_minutes: 60
-        commands:
-          - pytest -s -v tests/e2e/online_serving/test_ltx25.py -m "full_model and cuda" --run-level "full_model"
-        mirror_hardwares: h100_1
-
       - label: ":full_moon: Diffusion X2V · Wan2.2 Accuracy Test"
         timeout_in_minutes: 180
         commands:
@@ -483,23 +322,8 @@ steps:
         commands:
           - pytest -s -v tests/e2e/accuracy/hunyuanvideo15_t2v/test_hunyuanvideo15_t2v_video_similarity.py -m full_model --run-level full_model
           - pytest -s -v tests/e2e/accuracy/hunyuanvideo15_i2v/test_hunyuanvideo15_i2v_video_similarity.py -m full_model --run-level full_model
-          - pytest -s -v tests/e2e/accuracy/test_video_streaming_output_similarity.py -m full_model --run-level full_model
         mirror_hardwares: h100_2
 
-      - label: ":full_moon: Diffusion X2V · LTX Accuracy Test"
-        timeout_in_minutes: 180
-        commands:
-          - pytest -s -v 'tests/e2e/accuracy/ltx/test_ltx_official_similarity.py::test_ltx_matches_official[ltx2_3_i2v]' -m full_model --run-level full_model
-          - pytest -s -v 'tests/e2e/accuracy/ltx/test_ltx_official_similarity.py::test_ltx_matches_official[ltx23_two_stage_layer_fused_i2v]' -m full_model --run-level full_model
-        mirror_hardwares: h100_1
-
-      - label: ":full_moon: Diffusion X2V · LTX-2.5 Official Parity Test"
-        timeout_in_minutes: 180
-        commands:
-          - pytest -s -v tests/e2e/accuracy/ltx/test_ltx25_official_similarity.py::test_ltx25_distilled_two_stage_matches_official -m full_model --run-level full_model
-          - pytest -s -v tests/e2e/accuracy/ltx/test_ltx25_official_similarity.py::test_ltx25_full_matches_official -m full_model --run-level full_model
-        mirror_hardwares: h100_1
-
       - label: ":full_moon: Diffusion X2V · Perf Test"
         key: nightly-diffusion-x2v-performance
         timeout_in_minutes: 180
@@ -575,16 +399,6 @@ steps:
           - pytest -sv tests/diffusion/quantization -m 'full_model and cuda and L4' --run-level "full_model"
         mirror_hardwares: l4_1
 
-  - label: ":bar_chart: Testcase Statistics"
-    key: nightly-testcase-statistics
-    timeout_in_minutes: 120
-    depends_on: upload-nightly-pipeline
-    if: build.env("NIGHTLY") == "1"
-    commands:
-      - python tools/nightly/buildkite_testcase_statistics.py -o tests/dfx/perf/results/buildkite_testcase_statistics.html
-      - buildkite-agent artifact upload "tests/dfx/perf/results/*.html"
-    mirror_hardwares: h100_1
-
   # NOTE: ":email: Nightly Collection & Email" is deprecated and will be removed soon.
   # Do not add new depends_on entries or extend this step.
   - label: ":email: Nightly Collection & Email"
@@ -595,32 +409,20 @@ steps:
       - nightly-omni-performance-vllm-text
       - nightly-omni-performance-multi-replicas
       - nightly-tts-performance
-      - nightly-tts-performance-higgs-audio-v3
       - nightly-diffusion-x2iat-performance-qwen-image
-      - nightly-diffusion-x2iat-performance-qwen-image-edit
-      - nightly-diffusion-x2iat-performance-qwen-image-edit-2511
-      - nightly-diffusion-x2iat-performance-qwen-image-layered
-      - nightly-diffusion-x2iat-performance-bagel
       - nightly-diffusion-x2v-performance
-      - nightly-testcase-statistics
     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-higgs-audio-v3
       - buildkite-agent artifact download "tests/dfx/perf/results/*.json" . --step nightly-omni-performance-no-async-chunk
       - buildkite-agent artifact download "tests/dfx/perf/results/*.json" . --step nightly-omni-performance-async-chunk
       - buildkite-agent artifact download "tests/dfx/perf/results/*.json" . --step nightly-omni-performance-vllm-text
       - buildkite-agent artifact download "tests/dfx/perf/results/*.json" . --step nightly-omni-performance-multi-replicas
       - buildkite-agent artifact download "tests/dfx/perf/results/*.json" . --step nightly-diffusion-x2iat-performance-qwen-image
-      - buildkite-agent artifact download "tests/dfx/perf/results/*.json" . --step nightly-diffusion-x2iat-performance-qwen-image-edit
-      - buildkite-agent artifact download "tests/dfx/perf/results/*.json" . --step nightly-diffusion-x2iat-performance-qwen-image-edit-2511
-      - buildkite-agent artifact download "tests/dfx/perf/results/*.json" . --step nightly-diffusion-x2iat-performance-qwen-image-layered
-      - buildkite-agent artifact download "tests/dfx/perf/results/*.json" . --step nightly-diffusion-x2iat-performance-bagel
       - buildkite-agent artifact download "tests/dfx/perf/results/*.json" . --step nightly-diffusion-x2v-performance
-      - buildkite-agent artifact download "tests/dfx/perf/results/*.html" . --step nightly-testcase-statistics
       - python tools/nightly/generate_nightly_perf_excel.py
       - python tools/nightly/generate_nightly_perf_html.py
       - python tools/nightly/send_nightly_email.py --report-file "tests/dfx/perf/results/*.xlsx, tests/dfx/perf/results/*.html"

.buildkite/cuda/test-weekly.yml

@@ -149,4 +149,4 @@ steps:
             pytest -sv
             tests/e2e/ -m "slow and diffusion and H100 and distributed_cuda"
             --run-level "full_model"
-        mirror_hardwares: h100_2
+        mirror_hardwares: h100_4

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

@@ -69,6 +69,12 @@ steps:
           VLLM_ALLOW_LONG_MAX_MODEL_LEN: "1"
           HF_TOKEN: "${HF_TOKEN}"
         commands:
+          # Pin the Seed-TTS scorer to the second card: _get_eval_device() otherwise
+          # auto-picks npu:0, which the omni server under test already fills with its
+          # KV cache, so Whisper OOMs on `.to(device)` and every clip reports
+          # "ASR / WER failed".
+          - export SEED_TTS_WER_EVAL=1
+          - export SEED_TTS_EVAL_DEVICE=npu:1
           - |
             set +e
             pytest -s -v tests/e2e/accuracy/minicpmo_4_5/test_minicpmo_4_5.py -m "full_model and npu and A3" --run-level full_model
@@ -96,33 +102,6 @@ steps:
             EXIT=$$?
             buildkite-agent artifact upload "tests/dfx/perf/results/*.json"
             exit $$EXIT
-  - group: ":card_index_dividers: Diffusion X2I(&A&T) Model Test"
-    key: nightly-diffusion-x2iat-group
-    depends_on: upload-nightly-pipeline
-    if: >-
-      build.env("NIGHTLY") == "1" ||
-      build.pull_request.labels includes "nightly-test" ||
-      build.pull_request.labels includes "diffusion-x2iat-test"
-    steps:
-      - label: ":full_moon: Diffusion X2I(&A&T) · Perf Test · Qwen-Image-Edit-2511"
-        key: nightly-diffusion-x2iat-performance-qwen-image-edit-2511
-        timeout_in_minutes: 180
-        mirror_hardwares: a3_npu_8
-        env:
-          VLLM_WORKER_MULTIPROC_METHOD: spawn
-          VLLM_ALLOW_LONG_MAX_MODEL_LEN: "1"
-          HF_TOKEN: "${HF_TOKEN}"
-          DIFFUSION_ATTENTION_BACKEND: TORCH_SDPA
-        commands:
-          - export DIFFUSION_BENCHMARK_DIR=tests/dfx/perf/results
-          - export CACHE_DIT_VERSION=1.5.0
-          - |
-            set +e
-            pytest -s -v tests/dfx/perf/scripts/run_diffusion_benchmark.py -m npu --test-config-file tests/dfx/perf/tests/test_qwen_image_edit_2511_vllm_omni.json
-            EXIT=$$?
-            buildkite-agent artifact upload "tests/dfx/perf/results/diffusion_result_*.json"
-            buildkite-agent artifact upload "tests/dfx/perf/results/logs/*.log"
-            exit $$EXIT
   - group: ":card_index_dividers: Diffusion I2I Model Test"
     key: nightly-HunyuanImage3-x2i-test-group
     depends_on: upload-nightly-pipeline

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

@@ -1,4 +1,15 @@
 steps:
+  - label: "Platforms Test"
+    key: ready-platforms-test
+    depends_on: upload-ready-pipeline
+    mirror_hardwares: a3_npu_2
+    commands:
+      - >-
+        pytest -s -v
+        tests/platforms/npu/test_minicpmo_code2wav_npugraph.py
+        -m 'core_model and npu and A3 and omni'
+        --run-level 'core_model'
+
   - group: ":card_index_dividers: Omni Model Test"
     key: ready-omni-group
     depends_on: upload-ready-pipeline

.pre-commit-config.yaml

@@ -66,7 +66,7 @@ repos:
       entry: python tools/pre_commit/check_tts_adapter.py
       language: python
       pass_filenames: false
-      files: ^(vllm_omni/entrypoints/openai/(serving_speech\.py|tts_adapters/__init__\.py)|tools/pre_commit/check_tts_adapter\.py)$
+      files: ^(vllm_omni/entrypoints/openai/serving_speech\.py|tools/pre_commit/check_tts_adapter\.py)$
       # The budget-is-now-slack reminder is printed on a PASSING run, and
       # pre-commit hides output from passing hooks unless the hook is verbose.
       verbose: true

docs/.nav.yml

@@ -43,6 +43,7 @@ nav:
     - Overview: features/README.md
     - Runtime and Stage Execution:
       - Execution Modes and Streaming: user_guide/diffusion/execution_modes.md
+      - Prefill-Decode Disaggregation (experimental): features/pd_disaggregation.md
       - Sleep Mode: features/sleep_mode.md
     - Quantization:
       - Overview: user_guide/quantization/overview.md

docs/configuration/README.md

@@ -14,10 +14,6 @@ For introduction, please check [Introduction for stage config](./stage_configs.m
 
 - **[GPU Memory Calculation and Configuration](./gpu_memory_utilization.md)** - Guide on how to calculate memory requirements and set up `gpu_memory_utilization` for optimal performance
 
-## Multi-Stage Recipes
-
-- **[Prefill-Decode Disaggregation](./pd_disaggregation.md)** - How to derive a PD-aware Qwen3-Omni stage config from the default config without introducing another bundled YAML
-
 ## Optimization Features
 
 - **[Diffusion Features Overview](../user_guide/diffusion_features.md)** - Complete overview of all diffusion model features and supported models

docs/configuration/pd_disaggregation.md

@@ -1,171 +0,0 @@
-# Prefill-Decode (PD) Disaggregation
-
-PD disaggregation splits the Qwen3-Omni thinker into separate prefill and decode
-stages so prompt processing and token generation can run on different workers.
-
-This is documented as a stage-config recipe instead of a bundled YAML because the
-deployment-specific values usually change per environment:
-
-- GPU placement
-- `tensor_parallel_size`
-- connector backend and connector ports
-- connector IPs or bootstrap addresses
-
-Start from the [default Qwen3-Omni stage config](gh-file:vllm_omni/deploy/qwen3_omni_moe.yaml)
-and copy it to your own file, for example `qwen3_omni_pd.yaml`. Then apply the
-changes below.
-
-## Requirements
-
-- 3+ GPUs for a basic layout: prefill, decode, and talker+code2wav
-- A KV connector supported by vLLM, such as `MooncakeConnector`
-- Matching `tensor_parallel_size` on the prefill and decode thinker stages
-
-## 1. Split the thinker into prefill and decode stages
-
-Replace the original thinker stage with two stages:
-
-```yaml
-stage_args:
-  - stage_id: 0
-    stage_type: llm
-    is_prefill_only: true
-    runtime:
-      devices: "0"
-    engine_args:
-      max_num_seqs: 16
-      model_stage: thinker
-      model_arch: Qwen3OmniMoeForConditionalGeneration
-      worker_type: ar
-      scheduler_cls: vllm_omni.core.sched.omni_ar_scheduler.OmniARScheduler
-      gpu_memory_utilization: 0.9
-      enforce_eager: true
-      trust_remote_code: true
-      engine_output_type: latent
-      distributed_executor_backend: "mp"
-      enable_prefix_caching: false
-      max_num_batched_tokens: 32768
-      hf_config_name: thinker_config
-      tensor_parallel_size: 1
-      kv_transfer_config:
-        kv_connector: "MooncakeConnector"
-        kv_role: "kv_producer"
-        kv_rank: 0
-        kv_parallel_size: 2
-        kv_connector_extra_config:
-          mooncake_bootstrap_port: 25201
-    final_output: false
-    is_comprehension: true
-    default_sampling_params:
-      temperature: 0.4
-      top_p: 0.9
-      top_k: 1
-      max_tokens: 2048
-      seed: 42
-      detokenize: True
-      repetition_penalty: 1.05
-
-  - stage_id: 1
-    stage_type: llm
-    is_decode_only: true
-    runtime:
-      devices: "1"
-    engine_args:
-      max_num_seqs: 64
-      model_stage: thinker
-      model_arch: Qwen3OmniMoeForConditionalGeneration
-      worker_type: ar
-      scheduler_cls: vllm_omni.core.sched.omni_ar_scheduler.OmniARScheduler
-      gpu_memory_utilization: 0.9
-      enforce_eager: true
-      trust_remote_code: true
-      engine_output_type: latent
-      distributed_executor_backend: "mp"
-      enable_prefix_caching: false
-      max_num_batched_tokens: 32768
-      hf_config_name: thinker_config
-      tensor_parallel_size: 1
-      kv_transfer_config:
-        kv_connector: "MooncakeConnector"
-        kv_role: "kv_consumer"
-        kv_rank: 1
-        kv_parallel_size: 2
-        kv_connector_extra_config:
-          mooncake_bootstrap_port: 25202
-    engine_input_source: [0]
-    final_output: true
-    final_output_type: text
-    is_comprehension: true
-    default_sampling_params:
-      temperature: 0.4
-      top_p: 0.9
-      top_k: 1
-      max_tokens: 2048
-      seed: 42
-      detokenize: True
-      repetition_penalty: 1.05
-```
-
-Notes:
-
-- `is_prefill_only: true` marks the thinker stage that only saves KV.
-- `is_decode_only: true` marks the thinker stage that resumes from remote KV.
-- `kv_transfer_config` is required on both stages.
-- The orchestrator forces the prefill stage to run with `max_tokens=1`, so the
-  prefill side only processes the prompt and exports KV.
-
-## 2. Shift the downstream stages by one index
-
-After inserting the extra thinker stage, renumber the remaining stages:
-
-```yaml
-  - stage_id: 2
-    runtime:
-      devices: "2"
-    engine_input_source: [1]
-    sync_process_input_func: vllm_omni.model_executor.stage_input_processors.qwen3_omni.thinker2talker_token_only
-
-  - stage_id: 3
-    runtime:
-      devices: "2"
-    engine_args:
-      max_num_seqs: 1
-    engine_input_source: [2]
-    sync_process_input_func: vllm_omni.model_executor.stage_input_processors.qwen3_omni.talker2code2wav_token_only
-```
-
-Compared with the default Qwen3-Omni config:
-
-- the talker becomes stage `2` instead of stage `1`
-- the code2wav stage becomes stage `3` instead of stage `2`
-- the talker now reads from decode stage `1`
-
-## 3. Add runtime edges for the four-stage pipeline
-
-```yaml
-runtime:
-  enabled: true
-  edges:
-    - from: 0
-      to: 1
-    - from: 1
-      to: 2
-    - from: 2
-      to: 3
-```
-
-## 4. Launch with your custom config
-
-```bash
-vllm serve Qwen/Qwen3-Omni-30B-A3B-Instruct --omni --port 8091 \
-    --deploy-config /path/to/qwen3_omni_pd.yaml
-```
-
-## Operational Notes
-
-- `MooncakeConnector` does not support heterogeneous TP sizes across the PD
-  pair. Keep prefill and decode at the same `tensor_parallel_size`.
-- If the thinker requires TP=2, both thinker stages must use TP=2 and be given
-  separate GPU sets, for example `"0,1"` for prefill and `"2,3"` for decode.
-- Choose connector ports and addresses that match your deployment. The values
-  shown above are examples only.

docs/features/README.md

@@ -20,13 +20,19 @@ an implementation contract has a user-facing workflow.
 | Choose serial, batched, step-wise, or streaming diffusion execution | [Execution Modes and Streaming](../user_guide/diffusion/execution_modes.md) | [Diffusion Continuous Batching](../design/feature/diffusion_continuous_batching.md), [Async Diffusion Output](../design/feature/async_diffusion_output.md) |
 | Reclaim stage memory without restarting the server | [Sleep Mode](sleep_mode.md) | Runtime lifecycle behavior is documented in the user guide |
 
+The [Prefill-Decode Disaggregation design reference](pd_disaggregation.md)
+describes the experimental Qwen3-Omni runtime topology and its current
+integration boundaries. It is not yet a supported launch recipe.
+
 Some runtime designs are deliberately not promoted as standalone User Guide
 features yet:
 
-- [Disaggregated Inference](../design/feature/disaggregated_inference.md) and
+- Generic, OmniConnector-based
+  [Disaggregated Inference](../design/feature/disaggregated_inference.md) is not
+  yet a standalone User Guide feature. Its
   [OmniConnector implementations](../design/index.md#communication) describe
-  topology and transport contracts. Practical deployment configuration remains
-  under [Pipeline and deploy configurations](../configuration/stage_configs.md).
+  transport contracts, while practical connector configuration remains under
+  [Pipeline and deploy configurations](../configuration/stage_configs.md).
 - [Async Chunk](../design/feature/async_chunk.md) and
   [Async Omni Output Materialization](../design/feature/omni_async_output_materialization.md)
   are model- and pipeline-dependent. Use the selected model's deploy

docs/features/pd_disaggregation.md

@@ -0,0 +1,140 @@
+# Prefill-Decode (PD) Disaggregation (experimental)
+
+!!! warning "Experimental design reference"
+    PD disaggregation is only partially integrated on the current `main` branch.
+    The runtime contains PD detection and routing scaffolding, but there is no
+    supported deploy overlay or end-to-end validated launch recipe yet. Treat this
+    page as a description of the current design, not as production guidance.
+
+Prefill-decode disaggregation splits the Qwen3-Omni Thinker into two logical
+stages: a prefill worker that processes the prompt and produces KV cache, and a
+decode worker that imports that cache and generates tokens. The Talker and
+Code2Wav stages remain downstream; PD does not disaggregate the entire
+Qwen3-Omni pipeline.
+
+## Current design
+
+### Runtime topology
+
+```mermaid
+flowchart LR
+    client["Client request"] --> orchestrator["Omni Orchestrator"]
+    orchestrator -->|"original prompt<br/>max_tokens = 1"| prefill["Thinker prefill<br/>KV producer"]
+    prefill -->|"KV blocks"| connector[("vLLM KV connector")]
+    prefill -->|"KV routing metadata"| orchestrator
+    orchestrator -->|"original prompt<br/>remote KV parameters"| decode["Thinker decode<br/>KV consumer"]
+    connector -->|"remote KV load"| decode
+    decode -->|"generated text"| client
+```
+
+The orchestrator owns the request lifecycle, while the vLLM KV connector owns
+the bulk KV transfer. The original prompt is submitted to both Thinker stages:
+the decode worker still needs the request tokens and multimodal features to
+construct its request, while the imported KV cache prevents it from repeating
+the completed prefill computation.
+
+For the full Qwen3-Omni pipeline, splitting the Thinker adds one logical stage:
+
+| Stage | Role | Output or handoff |
+| --- | --- | --- |
+| 0 | Thinker prefill | Saves prompt KV and prompt-side multimodal output |
+| 1 | Thinker decode | Loads remote KV, generates text, and produces decode-side conditioning |
+| 2 | Talker | Converts combined Thinker conditioning into codec codes |
+| 3 | Code2Wav | Converts codec codes into audio |
+
+Text remains a final output of the decode stage, and audio remains a final
+output of Code2Wav.
+
+### Request lifecycle
+
+1. At startup, PD detection looks for one stage marked `is_prefill_only` and
+   one downstream stage marked `is_decode_only`. The decode stage must list the
+   prefill stage as an input source.
+2. The orchestrator clones the Thinker sampling parameters for prefill, forces
+   `max_tokens=1`, clears stop conditions, and sets producer-side
+   `kv_transfer_params`. This makes prefill finish by length after exporting KV.
+3. When prefill finishes, the orchestrator captures its KV routing metadata and
+   Qwen multimodal output under the original request ID.
+4. The orchestrator submits the original prompt to decode with consumer-side
+   `kv_transfer_params`. Those parameters identify the transfer, prefill engine,
+   bootstrap endpoint, and, when required by the connector, remote request.
+5. The decode engine imports the KV cache and continues generation. The normal
+   Omni stage path then carries Thinker conditioning through Talker and
+   Code2Wav.
+
+### Control and data paths
+
+PD uses two different connector layers. They solve different handoffs and are
+not interchangeable.
+
+| Path | Payload | Owner |
+| --- | --- | --- |
+| PD control path | Request ID, original prompt, `transfer_id`, bootstrap address, engine ID, and connector-specific request metadata | Omni Orchestrator |
+| PD data path | Thinker attention KV blocks | vLLM KV connector |
+| Downstream model path | Thinker hidden states and embeddings, Talker codec codes, and audio tensors | vLLM-Omni stage connectors and processors |
+
+### Detection and validation
+
+The current runtime accepts one logical prefill/decode pair per pipeline. Each
+stage may use tensor parallelism, but both sides of the pair must satisfy these
+invariants:
+
+- both stages define `kv_transfer_config`
+- prefill uses `kv_producer` or `kv_both`
+- decode uses `kv_consumer` or `kv_both`
+- both stages use the same KV connector
+- `kv_buffer_device` and `kv_buffer_size` agree when set on both stages
+- both stages use the same `tensor_parallel_size`
+
+These checks happen before request processing so an inconsistent PD topology
+fails during engine initialization.
+
+## What is implemented today
+
+| Area | Current state |
+| --- | --- |
+| Pair detection and topology validation | Implemented in the shared PD helper |
+| Prefill sampling preparation | Implemented for sync and async entrypoints |
+| Prefill-to-decode routing | Implemented in the current Orchestrator |
+| Deploy-based configuration | Not yet migrated; the supported `stages:` deploy schema cannot currently expand Qwen3-Omni into the four-stage PD topology |
+| Qwen3 Thinker-to-Talker handoff | Bridge state and embedding-merge helpers exist, but the merge is not wired into the live stage processor |
+| Connector compatibility | Incomplete; the Orchestrator currently requires `remote_request_id`, while the pinned Mooncake connector completes prefill without returning that metadata |
+| Validation coverage | PD entrypoint tests and the PD deploy overlay in online end-to-end tests are temporarily disabled during migration |
+| Performance | No non-regression or throughput/latency result is established for the current path |
+
+Because these pieces are still converging, the previous legacy `stage_args` YAML
+and `vllm serve` command have been removed. The serve CLI now accepts the
+deploy-based `stages:` schema, so retaining the legacy recipe would imply a
+launch path that current `main` rejects.
+
+## Configuration contract under development
+
+The intended deploy integration starts from the
+[default Qwen3-Omni deploy config](gh-file:vllm_omni/deploy/qwen3_omni_moe.yaml)
+and expands its frozen three-stage pipeline into the four stages shown above.
+A complete integration must:
+
+- split the Thinker definition without duplicating the Talker or Code2Wav model
+  contracts
+- remap downstream stage IDs, input sources, and stage connectors
+- inject producer and consumer `kv_transfer_config` values into the two Thinker
+  stages
+- allocate separate device sets for prefill and decode
+- preserve both final outputs: decode text and Code2Wav audio
+- provide connector-specific routing metadata without hard-coding one connector
+  contract
+
+The required number of GPUs depends on tensor-parallel sizes and whether Talker
+and Code2Wav share a device. A basic TP=1 layout uses one device for prefill, one
+for decode, and one for the two downstream audio stages.
+
+## Implementation map
+
+- PD detection, validation, and sampling-parameter preparation:
+  [`PDDisaggregationMixin`](gh-file:vllm_omni/entrypoints/pd_utils.py)
+- startup detection and connector metadata:
+  [`AsyncOmniEngine`](gh-file:vllm_omni/engine/async_omni_engine.py)
+- request-scoped prefill-to-decode routing:
+  [`Orchestrator`](gh-file:vllm_omni/engine/orchestrator.py)
+- Qwen3 downstream conditioning helpers:
+  [Qwen3-Omni stage input processor](gh-file:vllm_omni/model_executor/stage_input_processors/qwen3_omni.py)

docs/models/supported_models.md

@@ -53,6 +53,7 @@ th {
 | `LingBotVideoPipeline` | LingBot-Video dense and MoE T2I, T2V, TI2V | `robbyant/lingbot-video-dense-1.3b`, `robbyant/lingbot-video-moe-30b-a3b` | ✅︎ | | | | — |
 | `MiniMaxH3Pipeline` | MiniMax H3 T2VA, first/last-keyframe FL2VA, and mixed-reference Ref2VA | `MiniMaxAI/MiniMax-H3` | ✅︎ | ✅︎ | | | [Published](https://recipes.vllm.ai/MiniMaxAI/MiniMax-H3) |
 | `DreamZeroPipeline` | DreamZero-DROID | `GEAR-Dreams/DreamZero-DROID` | ✅︎ | ✅︎ | ✅︎ | ✅︎ | — |
+| `Pi0Pipeline` | π0 (Pi-Zero) VLA | `lerobot/pi0_base` | ✅︎ | | | | [Repository](https://github.com/vllm-project/vllm-omni/blob/main/recipes/lerobot/Pi0.md) |
 | `HeliosPipeline`, `HeliosPyramidPipeline` | Helios | `BestWishYsh/Helios-Base`, `BestWishYsh/Helios-Mid`, `BestWishYsh/Helios-Distilled` | ✅︎ | ✅︎ | ✅︎ | | — |
 | `MagiHumanPipeline` | MagiHuman | `SII-GAIR/daVinci-MagiHuman-Base-1080p` | ✅︎ | ✅︎ | | | — |
 | `OvisImagePipeline` | Ovis-Image | `OvisAI/Ovis-Image` | ✅︎ | ✅︎ | | ✅︎ | — |

docs/serving/speech_api.md

@@ -141,7 +141,20 @@ The response shape depends on the streaming parameters:
 
 **Non-streaming (default).** With `stream=false` and no `stream_format`, returns the
 complete clip as binary audio data with an appropriate `Content-Type` header (e.g.
-`audio/wav`). The raw-bytes body has no JSON carrier, so no `usage` is reported.
+`audio/wav`). Because the raw-bytes body has no JSON carrier, successful
+non-streaming responses from non-diffusion speech servers report usage through
+response headers:
+
+| Header | Description |
+| --- | --- |
+| `x-vllm-omni-input-tokens` | Total input tokens (`text_tokens` + `audio_tokens`). |
+| `x-vllm-omni-output-tokens` | Generated codec/audio tokens. |
+| `x-vllm-omni-total-tokens` | `input_tokens` + `output_tokens`. |
+| `x-vllm-omni-input-text-tokens` | Tokens from the synthesized text (`input` plus `instructions`). |
+| `x-vllm-omni-input-audio-tokens` | Reference-audio codec frames, non-zero only for in-context voice cloning. |
+
+Diffusion-mode speech servers route through a separate response path and do not
+emit these headers.
 
 **Raw audio stream** (`stream_format="audio"`). Streams raw audio bytes (PCM or
 WAV) as they are decoded.

docs/serving/videos_api.md

@@ -167,6 +167,14 @@ transfer options such as `control_path`, `control_guidance`,
 `num_conditional_frames`, `show_control_condition`, and `show_input`; see the
 Cosmos3 recipe for complete examples.
 
+HTTP redirects for `image_reference.image_url` follow vLLM's
+`VLLM_MEDIA_URL_ALLOW_REDIRECTS` setting. Before starting the server, set it to
+`1` (the default) to allow redirects or `0` to reject them. A redirect target
+can differ from the original host, and vLLM-Omni does not yet fully implement
+upstream vLLM's media URL allowlist protection. Deployments should therefore
+treat remote media URLs as untrusted and choose this setting as part of their
+URL access policy.
+
 ### Speech-to-Video
 
 For models that support audio-driven generation (e.g., Wan2.2-S2V), pass both

examples/online_serving/pi0/README.md

@@ -0,0 +1,104 @@
+# π0 (Pi-Zero) VLA — OpenPI realtime serving
+
+[π0](https://www.physicalintelligence.company/blog/pi0) is a Vision-Language-Action
+model from Physical Intelligence: multi-camera images + a language instruction +
+robot proprioceptive state → a continuous action chunk via flow-matching denoising
+(it does **not** emit text tokens). This example serves π0 over the OpenPI realtime
+websocket protocol at `/v1/realtime/robot/openpi`.
+
+## Install extras
+
+The core `pip install -e .` does not include the OpenPI client used here:
+
+- `openpi-client` (from the [openpi](https://github.com/Physical-Intelligence/openpi)
+  repo: `pip install -e packages/openpi-client`), `websockets`, `msgpack`, `msgpack-numpy`
+
+## Weights
+
+π0 uses the LeRobot `lerobot/pi0_base` checkpoint (HF, ~13 GB). Either let the server
+download it (`MODEL=lerobot/pi0_base`) or point at a local copy
+(`MODEL=/path/to/pi0_base`).
+
+## Run the server
+
+```bash
+vllm serve lerobot/pi0_base --omni --port 8000 \
+    --served-model-name pi0 \
+    --deploy-config vllm_omni/deploy/pi0.yaml \
+    --enforce-eager --disable-log-stats
+```
+
+The deploy config (`vllm_omni/deploy/pi0.yaml`) declares a single diffusion stage
+(`Pi0Pipeline`), float32, `max_num_seqs: 1`, and the `policy_server_config` handshake
+metadata (3 cameras, `joint_position`, action horizon 50, action dim 32).
+
+### Commonly adjusted `model_config` keys
+
+These live under `stages[0].model_config` in `vllm_omni/deploy/pi0.yaml`:
+
+| Key | Default | Meaning |
+|---|---|---|
+| `chunk_size` | `50` | Action-chunk length (timesteps) the model predicts per inference. |
+| `num_inference_steps` | `10` | Flow-matching Euler denoising steps. |
+| `max_action_dim` | `32` | Action dimensionality (state/action are padded to this). |
+| `max_state_dim` | `32` | Proprioceptive-state dimensionality (zero-padded to this). |
+| `image_resolution` | `[224, 224]` | Per-camera input size (square; SigLIP). |
+| `tokenizer_max_length` | `48` | Max PaliGemma prompt tokens. |
+| `max_cameras` | `3` | Camera slots the model attends to (real + `-1`-padded). |
+| `image_feature_keys` | 3 `observation.images.*` keys | Camera order the model attends to. |
+| `image_key_map` | `{}` | Map raw obs camera keys → `image_feature_keys` (empty = verbatim). |
+
+The `policy_server_config` block below them is the OpenPI handshake metadata
+advertised to the client; keep its `action_horizon` / `action_dim` /
+`image_resolution` in sync with the `model_config` values above. The e2e test
+`tests/e2e/online_serving/test_pi0_expansion.py::test_pi0_openpi_online` connects to a
+live server and asserts the advertised metadata matches these `pi0.yaml` values.
+
+## Run the client
+
+```bash
+python examples/online_serving/pi0/openpi_client.py --host 127.0.0.1 --port 8000 \
+    --prompt "pick up the red block and place it in the bin"
+```
+
+It connects, prints the server metadata, sends robot observations, and prints the
+returned `[action_horizon, action_dim] = [50, 32]` action chunks. Replace the blank
+cameras / zero state in `_make_dummy_obs` with real frames (HWC uint8) and
+proprioceptive state to drive a robot.
+
+### Observation format
+
+The client sends a flat dict per inference:
+
+```python
+{
+    "observation.images.base_0_rgb":       np.uint8[H, W, 3],
+    "observation.images.left_wrist_0_rgb": np.uint8[H, W, 3],
+    "observation.images.right_wrist_0_rgb":np.uint8[H, W, 3],
+    "state":   np.float32[state_dim],   # zero-padded to max_state_dim=32 server-side
+    "prompt":  "pick up the red block",
+    "session_id": "<uuid>",             # accepted but ignored (π0 is stateless)
+}
+```
+
+Camera keys must match the server's `image_feature_keys` (the checkpoint's
+`input_features` order). If your robot uses different camera names, set
+`model_config.image_key_map` in `pi0.yaml` to map raw obs keys → feature keys.
+
+## Correctness
+
+π0's flow-matching kernel is bit-for-bit matched to the LeRobot `PI0Policy` reference
+(`max|Δ| = 7.15e-07`, CPU/float32, fixed noise; see
+`tests/diffusion/models/pi0/test_pi0_parity.py::test_pi0_vllm_omni_vs_lerobot`) and is
+version-stable across transformers releases (the version-stability checks in
+`tests/diffusion/models/pi0/test_pi0_units.py`). An OpenPI websocket online-serving e2e
+lives in `tests/e2e/online_serving/test_pi0_expansion.py::test_pi0_openpi_online`.
+
+## Limitations
+
+- **Normalization stats**: `lerobot/pi0_base` uses identity normalization, which is
+  fully supported (state passes through, actions are returned in the model's space).
+  Per-dataset `norm_stats` declared in a checkpoint's `config.json` are honored, but
+  stats stored only in LeRobot's `policy_preprocessor.json` companion safetensors are
+  not yet loaded — a fine-tuned checkpoint that relies on those would need that bridge
+  added before its actions are in real-world units.

examples/online_serving/pi0/openpi_client.py

@@ -0,0 +1,119 @@
+#!/usr/bin/env python3
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""Minimal OpenPI client for the π0 (Pi-Zero) VLA policy server.
+
+Connects to a running π0 server (see README.md), sends a single robot
+observation (3 cameras + state + language instruction), and prints the returned
+continuous action chunk ``[action_horizon, action_dim]``.
+
+The server speaks the OpenPI websocket protocol:
+    connect -> server sends msgpack(policy_server_config metadata)
+    infer   -> client sends msgpack(obs), server replies msgpack(actions ndarray)
+
+Usage:
+    python openpi_client.py --host 127.0.0.1 --port 8000 \
+        --prompt "pick up the red block"
+"""
+
+from __future__ import annotations
+
+import argparse
+import logging
+import sys
+import uuid
+from pathlib import Path
+from typing import Any
+
+import numpy as np
+
+try:
+    # Avoid shadowing the installed ``openpi_client`` package with this dir.
+    example_dir = str(Path(__file__).resolve().parent)
+    removed = sys.path and sys.path[0] == example_dir
+    if removed:
+        sys.path.pop(0)
+    try:
+        from openpi_client.websocket_client_policy import WebsocketClientPolicy
+    finally:
+        if removed:
+            sys.path.insert(0, example_dir)
+except ImportError as exc:  # pragma: no cover - runtime dependency guard
+    raise ImportError("π0 OpenPI example requires `openpi-client`.") from exc
+
+logger = logging.getLogger("pi0_openpi_client")
+
+DEFAULT_HOST = "127.0.0.1"
+DEFAULT_PORT = 8000
+DEFAULT_PATH = "/v1/realtime/robot/openpi"
+DEFAULT_PROMPT = "pick up the red block and place it in the bin"
+
+# π0 / pi0_base camera identities (must match the server's image_feature_keys).
+CAMERA_KEYS = (
+    "observation.images.base_0_rgb",
+    "observation.images.left_wrist_0_rgb",
+    "observation.images.right_wrist_0_rgb",
+)
+STATE_DIM = 32
+
+
+def _make_dummy_obs(prompt: str, session_id: str, image_size: int = 224) -> dict[str, Any]:
+    """Build a single π0 observation with blank cameras + zero state.
+
+    Replace the zeros with real camera frames (HWC uint8) and proprioceptive
+    state to drive an actual robot.
+    """
+    obs: dict[str, Any] = {cam: np.zeros((image_size, image_size, 3), dtype=np.uint8) for cam in CAMERA_KEYS}
+    obs["state"] = np.zeros(STATE_DIM, dtype=np.float32)
+    obs["prompt"] = prompt
+    obs["session_id"] = session_id
+    return obs
+
+
+def _ws_uri(host: str, port: int, path: str) -> str:
+    """Build the full websocket URI. WebsocketClientPolicy takes the whole URI as
+    ``host`` (it has no separate path arg)."""
+    if host.startswith(("ws://", "wss://")):
+        return host
+    return f"ws://{host}:{port}{path}"
+
+
+def _as_action_array(response: Any) -> np.ndarray:
+    if isinstance(response, dict) and response.get("type") == "error":
+        raise RuntimeError(f"Inference failed: {response.get('message', response)}")
+    return np.asarray(response, dtype=np.float32)
+
+
+def main() -> int:
+    parser = argparse.ArgumentParser(description="π0 OpenPI client")
+    parser.add_argument("--host", default=DEFAULT_HOST)
+    parser.add_argument("--port", type=int, default=DEFAULT_PORT)
+    parser.add_argument("--path", default=DEFAULT_PATH)
+    parser.add_argument("--prompt", default=DEFAULT_PROMPT)
+    parser.add_argument("--num-steps", type=int, default=2, help="Inference observations to send.")
+    args = parser.parse_args()
+    logging.basicConfig(level=logging.INFO, format="%(message)s")
+
+    policy = WebsocketClientPolicy(host=_ws_uri(args.host, args.port, args.path))
+    metadata = dict(policy.get_server_metadata())
+    logger.info("Server metadata: %s", metadata)
+
+    session_id = str(uuid.uuid4())
+    for step in range(args.num_steps):
+        obs = _make_dummy_obs(args.prompt, session_id)
+        actions = _as_action_array(policy.infer(obs))
+        logger.info(
+            "[step %d] actions shape=%s mean=%.4f std=%.4f",
+            step,
+            actions.shape,
+            float(actions.mean()),
+            float(actions.std()),
+        )
+        if not np.isfinite(actions).all():
+            raise RuntimeError("Server returned non-finite actions.")
+    logger.info("Done — π0 OpenPI round-trip OK.")
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())

mkdocs.yml

@@ -79,6 +79,7 @@ plugins:
         user_guide/diffusion/step_execution.md: user_guide/diffusion/execution_modes.md
         user_guide/diffusion/cpu_offload_diffusion.md: user_guide/diffusion/cpu_offload.md
         user_guide/quantization/index.md: user_guide/quantization/overview.md
+        configuration/pd_disaggregation.md: features/pd_disaggregation.md
         design/feature/diffusion_request_level_batching.md: design/feature/diffusion_continuous_batching.md
         design/feature/diffusion_step_execution.md: design/feature/diffusion_continuous_batching.md
         design/feature/cpu_offloading.md: design/feature/offloader/README.md

recipes/MammothModa2/MammothModa2.md

@@ -81,7 +81,7 @@ MammothModa2 generation parameters as a JSON object through `--extra-body`:
 ```bash
 python examples/offline_inference/text_to_image/text_to_image.py \
   --model ./MammothModa2-Preview \
-  --stage-configs-path vllm_omni/deploy/mammoth_moda2.yaml \
+  --deploy-config vllm_omni/deploy/mammoth_moda2.yaml \
   --prompt "A stylish woman riding a motorcycle in NYC, movie poster style" \
   --height 1024 \
   --width 1024 \
@@ -132,6 +132,44 @@ ls -lh mammoth_t2i.png
 python -c "from PIL import Image; print(Image.open('mammoth_t2i.png').size)"
 ```
 
+### 1x AMD MI300X, MammothModa2 Preview
+
+#### Environment
+
+- OS: Linux 6.8.0-134-generic, x86_64
+- Container: official ROCm image built from `docker/Dockerfile.rocm`
+- Python: 3.12.13
+- PyTorch: 2.11.0+gitd0c8b1f
+- Driver / runtime: AMD 6.19.14.31400000 / ROCm 7.2.53211
+- GPU: one AMD Instinct MI300X, `gfx942:sramecc+:xnack-`, 191.69 GiB visible HBM
+- vLLM version: 0.27.0+rocm723
+- vLLM Omni version or commit: `73e1368c7bb940efe1a025859c9d6c8eeeb2e3f0`
+- Installed vLLM Omni package metadata: `0.27.0rc2.dev44+g55abdade9.rocm`
+
+#### Offline Commands
+
+The checked run used the committed stage split, with `gpu_memory_utilization` set to 0.5 for AR and 0.3 for DiT:
+
+```bash
+python3 examples/offline_inference/text_to_image/text_to_image.py \
+    --model bytedance-research/MammothModa2-Preview \
+    --deploy-config vllm_omni/deploy/mammoth_moda2.yaml \
+    --prompt "A stylish woman riding a motorcycle in NYC, movie poster style" \
+    --height 1024 \
+    --width 1024 \
+    --seed 42 \
+    --extra-body '{"text_guidance_scale": 4.0, "cfg_range": [0.0, 1.0], "num_inference_steps": 50}' \
+    --enable-diffusion-pipeline-profiler \
+    --log-stats \
+    --output mammoth_t2i.png
+```
+
+#### Verification
+
+The first request took 85.224 seconds. The AR stage generated 4,161 visual tokens in 72.996 seconds, and the DiT stage took 12.163 seconds. AR weight loading used 21.4 GiB and took 8.250 seconds. DiT weight loading used 5.49 GiB and took 1.824 seconds. The largest one second whole device memory sample was 106.57 GiB, including the AR KV cache reserved by the 0.5 memory setting.
+
+The output was a valid 1024 by 1024 RGB PNG.
+
 ## MammothModa2-Dev unified inference
 
 MammothModa2-Dev uses a Qwen3-VL AR backbone, while MammothModa2-Preview uses

recipes/OpenBMB/MiniCPM-o-4_5.md

@@ -71,6 +71,35 @@ single-GPU entry point; `minicpmo_4_5_2gpu.yaml` is the recommended
 two-GPU profile. The removed fused two-stage implementation is not retained as
 a fallback because it would duplicate state machines and correctness paths.
 
+### Graph execution
+
+Graph boundaries follow each stage's state model:
+
+| Stage | Graph mode on Ascend | Eager boundary |
+| --- | --- | --- |
+| 0 Thinker | vLLM `PIECEWISE` | multimodal preprocessing and output routing |
+| 1 Talker | vLLM `PIECEWISE` | conditioning preprocessing, codec sampling, request-state updates |
+| 2 Code2Wav | inner exact-shape NPUGraph for the CFM DiT estimator | encoder, timestep embedding, HiFT/RNG, request parsing, stream-state commit |
+
+Stage 2 keeps `enforce_eager: true` for the generation runner because its
+Python request metadata and per-request cache dictionaries are not valid
+outer-graph inputs. The backend captures only the deterministic CFM DiT
+estimator, with an exact graph key for each tensor shape. Timestep embedding
+stays eager because the upstream implementation creates a CPU frequency tensor;
+HiFT stays eager because it generates random phase. The graph cache stores up
+to 32 entries by default, after which unseen shapes run eagerly.
+
+An ACL graph capture failure is fatal to that Stage-2 process because older
+torch-npu releases can leave allocator and RNG capture state invalid. Restart
+the service after a capture failure. To run without capture, set
+`code2wav_enable_npu_graph: false` under the Stage-2
+`platforms.npu.stages[].additional_config` block before startup. Tune the cache
+limit there with `code2wav_max_npu_graphs`. Graph mode also requires
+`ASCEND_LAUNCH_BLOCKING` to be unset or set to `0`.
+
+The inner NPUGraph is independent of the outer runner setting, so do not use a
+global `--enforce-eager` override when Stage 0/1 `PIECEWISE` replay is desired.
+
 ## GPU
 
 ### 1 x GPU (default — single command)
@@ -211,9 +240,9 @@ speech output (TTS)"** checkbox on / off.
   model processes still share one CUDA device.
 - `--trust-remote-code` is required — the HF repo ships a custom
   `MiniCPMO` config / model class.
-- Stage 0 Thinker and Stage 1 Talker enable vLLM CUDA Graphs. Stage 2 remains
-  eager because its request-owned Flow/HiFT caches and variable chunk/cache
-  shapes are not yet exposed through a static exact-shape graph wrapper.
+- Stage 0 Thinker and Stage 1 Talker enable vLLM CUDA Graphs. Stage 2 keeps its
+  request orchestration eager while using inner CFM and HiFT CUDA Graphs. On
+  Ascend, the exact-shape CFM DiT estimator instead uses inner NPUGraph replay.
 - All default stages use `max_num_seqs: 4` to reduce cross-process GPU
   contention. Talker AR
   state and Code2Wav caches are request-owned; Code2Wav batches only
@@ -224,8 +253,8 @@ speech output (TTS)"** checkbox on / off.
 - `StageRequestStats.batch_size` is a request-scoped placeholder, not the
   scheduler's execution batch.
 - Single-GPU co-location trades throughput for hardware density: Stage 0/1
-  CUDA Graph replay and eager Stage 2 vocoder kernels compete across three
-  CUDA contexts. Use the 8x4090 config or a custom multi-GPU mapping for
+  CUDA Graph replay and Stage 2 vocoder kernels compete across three CUDA
+  contexts. Use the 8x4090 config or a custom multi-GPU mapping for
   throughput-sensitive serving.
 
 ### 8 x RTX 4090 24GB (consumer-GPU layout)

recipes/Qwen/Qwen3-TTS.md

@@ -252,3 +252,53 @@ curl -X POST http://localhost:8091/v1/audio/speech \
     - stage_id: 1
       gpu_memory_utilization: 0.15
   ```
+
+### 1x AMD MI300X, 1.7B checkpoints
+
+#### Environment
+
+- OS: Linux 6.8.0-134-generic, x86_64
+- Container: official ROCm image built from `docker/Dockerfile.rocm`
+- Python: 3.12.13
+- PyTorch: 2.11.0+gitd0c8b1f
+- Driver / runtime: AMD 6.19.14.31400000 / ROCm 7.2.53211
+- GPU: one AMD Instinct MI300X, `gfx942:sramecc+:xnack-`, 191.69 GiB visible HBM
+- vLLM version: 0.27.0+rocm723
+- vLLM Omni version or commit: `3fecb6953ca8dc51210cc0421ef24552267a41ef`
+- Installed vLLM Omni package metadata: `0.27.0rc2.dev44+g55abdade9.rocm`
+- ONNX Runtime: onnxruntime-rocm 1.22.2.post3 with `ROCMExecutionProvider`
+- transformers: 5.15.0
+
+#### Command
+
+The MI300X tests also worked without `enforce_eager`. To use that setup, remove `enforce_eager: true` from stage 1 under `platforms.rocm` in `vllm_omni/deploy/qwen3_tts.yaml`.
+
+Set this environment variable before you start Qwen3-TTS:
+
+```bash
+export MIOPEN_FIND_MODE=FAST
+```
+
+#### Verification
+
+Each row records one offline request with one prompt and batch size one. Request time is the logged `e2e_wall_time_ms`, and stage 1 time is the logged `e2e_stage_1_wall_time_ms`.
+
+| Checkpoint | Code2Wav configuration | `MIOPEN_FIND_MODE` | Request time | Stage 1 time | Output duration | Real time factor | Maximum sampled device memory |
+| --- | --- | --- | ---: | ---: | ---: | ---: | ---: |
+| CustomVoice | Eager | Unset | 1.683 s | 1.613 s | 5.68 s | 0.30 | 60.91 GiB |
+| CustomVoice | Graphs allowed | `FAST` | 1.326 s | 1.259 s | 5.68 s | 0.23 | 62.19 GiB |
+| VoiceDesign | Eager | Unset | 1.195 s | 1.180 s | 4.40 s | 0.27 | 60.91 GiB |
+| VoiceDesign | Graphs allowed | `FAST` | 1.127 s | 1.054 s | 4.40 s | 0.26 | 62.21 GiB |
+| Base | Eager | Unset | 13.330 s | 13.263 s | 4.32 s | 3.09 | 61.74 GiB |
+| Base | Graphs allowed | `FAST` | 14.314 s | 14.245 s | 4.80 s | 2.98 | 62.84 GiB |
+
+All six processes finished successfully, and each output passed the 24 kHz mono WAV checks.
+
+#### Notes
+
+- The current ROCm config keeps Code2Wav in eager mode.
+- The tested alternative allows graphs and sets [`MIOPEN_FIND_MODE=FAST`](https://rocm.docs.amd.com/projects/MIOpen/en/develop/reference/env_variables.html). MIOpen uses a saved kernel choice when available and uses its immediate fallback otherwise.
+- CustomVoice used a graph for one of four logged Code2Wav batches. VoiceDesign used a graph for two of four batches. Base used no graphs for its four logged batches.
+- The recorded request time with graphs allowed was lower for CustomVoice and VoiceDesign and higher for Base. The Base runs generated different amounts of audio, so their request times are not a direct comparison.
+- Each setup was tested with one request, so the timing is not a performance benchmark.
+- GPU memory was sampled once per second with `rocm-smi`.

recipes/README.md

@@ -49,6 +49,7 @@ recipes/
 | [`Helios/Helios.md`](./Helios/Helios.md) | Text-to-video, image-to-video, and video-to-video generation | 1x NVIDIA H20 |
 | [`inclusionAI/Ming-flash-omni-2.0.md`](./inclusionAI/Ming-flash-omni-2.0.md) | Online serving for multimodal chat + standalone TTS | 4x H100 / 1x H100 80GB |
 | [`inclusionAI/Ming-omni-tts.md`](./inclusionAI/Ming-omni-tts.md) | Offline + online dense Ming TTS/audio generation | 1x H100 80GB / 1x AMD MI300X (ROCm 7.2) |
+| [`k2-fsa/OmniVoice.md`](./k2-fsa/OmniVoice.md) | Offline and online multilingual text-to-speech | 1x AMD MI300X (ROCm 7.2) |
 | [`IndexTeam/IndexTTS-2.md`](./IndexTeam/IndexTTS-2.md) | Online serving for voice-cloned TTS with optional emotion control | 1x L4 24GB or larger CUDA GPU |
 | [`IndexTeam/IndexTTS-2_5.md`](./IndexTeam/IndexTTS-2_5.md) | Online + offline multilingual voice-cloned TTS with native speed and emotion control | 1x NVIDIA H20 96GB |
 | [`MiniMaxAI/MiniMax-H3.md`](./MiniMaxAI/MiniMax-H3.md) | T2VA, FL2VA, image+audio Ref2VA, and multi-video Ref2VA serving | 1x GPU with CPU offload / 4x B300 |
@@ -73,9 +74,9 @@ recipes/
 | [`Qwen/Qwen-Image-2512.md`](./Qwen/Qwen-Image-2512.md) | Text-to-image serving with step-wise continuous batching replay and ModelOpt FP8 / mixed FP8/NVFP4 | 1x A800 80GB / 2x B200 |
 | [`Qwen/Qwen-Image-Edit.md`](./Qwen/Qwen-Image-Edit.md) | Text-guided single-image editing | 1x or 2x H200 141GB |
 | [`Qwen/Qwen3-Omni.md`](./Qwen/Qwen3-Omni.md) | Online serving for multimodal chat | 1x A100 80GB |
-| [`Qwen/Qwen3-TTS.md`](./Qwen/Qwen3-TTS.md) | Text-to-speech serving (CustomVoice / VoiceDesign / Base) | 1x H100/A100 80GB |
-| [`SenseNova/SenseNova-U1.md`](./SenseNova/SenseNova-U1.md) | Unified image generation and understanding | 1x H200 (144GB) |
-| [`StabilityAI/Stable-Audio-Open.md`](./StabilityAI/Stable-Audio-Open.md) | Offline + online text-to-audio generation (Stable Audio Open) | 1x RTX 4090 24GB |
+| [`Qwen/Qwen3-TTS.md`](./Qwen/Qwen3-TTS.md) | Text-to-speech serving (CustomVoice / VoiceDesign / Base) | 1x H100/A100 80GB / 1x AMD MI300X |
+| [`SenseNova/SenseNova-U1.md`](./SenseNova/SenseNova-U1.md) | Unified image generation and understanding | 1x or 2x H200 / 1x AMD MI300X |
+| [`StabilityAI/Stable-Audio-Open.md`](./StabilityAI/Stable-Audio-Open.md) | Offline + online text-to-audio generation (Stable Audio Open) | 1x RTX 4090 24GB / 1x AMD MI300X |
 | [`Tencent/Covo-Audio-Chat.md`](./Tencent/Covo-Audio-Chat.md) | Online serving for audio chat | 1x A100 80GB |
 | [`Tencent/HunyuanImage-3.0-Instruct.md`](./Tencent/HunyuanImage-3.0-Instruct.md) | DiT-only text-to-image serving and benchmark, including ModelOpt mixed FP8/NVFP4 | 4x H100/H800 80GB / 2x B200 |
 | [`Wan-AI/Wan2.2-T2V.md`](./Wan-AI/Wan2.2-T2V.md) | Text-to-video serving (Wan2.2 14B) with Skip-Softmax sparse attention | 1x datacenter Blackwell (B200/B300) |

recipes/SenseNova/SenseNova-U1.md

@@ -178,6 +178,48 @@ with the expected 1536×2720 resolution.
 - The LLM transformer uses `QKVParallelLinear` and `MergedColumnParallelLinear`
   for fused QKV and gate/up projections with TP support.
 
+### 1x AMD MI300X 192GB
+
+#### Environment
+
+- OS: Linux 6.8.0-134-generic, x86_64
+- Container: official ROCm image built from `docker/Dockerfile.rocm`
+- Python: 3.12.13
+- PyTorch: 2.11.0+gitd0c8b1f
+- Driver / runtime: AMD 6.19.14.31400000 / ROCm 7.2.53211
+- GPU: one AMD Instinct MI300X, `gfx942:sramecc+:xnack-`, 191.69 GiB visible HBM
+- vLLM version: 0.27.0+rocm723
+- vLLM Omni version or commit: `a704c8759c96e123c0d7c89b11f120b1c0f120cf`
+- Installed vLLM Omni package metadata: `0.27.0rc2.dev44+g55abdade9.rocm`
+
+#### Command
+
+```bash
+python3 examples/offline_inference/text_to_image/text_to_image.py \
+    --model SenseNova/SenseNova-U1-8B-MoT \
+    --prompt "Close portrait of an elderly woman by a farmhouse window, textured skin, gentle smile, warm natural light, emotional documentary look." \
+    --width 1536 \
+    --height 2720 \
+    --seed 42 \
+    --num-inference-steps 50 \
+    --cfg-scale 4.0 \
+    --extra-body '{"think": true, "cfg_norm": "none", "timestep_shift": 3.0, "t_eps": 0.02}' \
+    --enable-diffusion-pipeline-profiler \
+    --log-stats \
+    --output sensenova_u1_mi300x.png
+```
+
+#### Verification
+
+The command completed and wrote a valid 1536 by 2720 RGB PNG.
+
+#### Notes
+
+- Generation with 50 inference steps took 34.011 seconds.
+- Model loading used 32.774 GiB and took 13.987 seconds.
+- The internal profiler recorded 35.67 GB reserved and 35.10 GB allocated for the request.
+- The highest one second whole device memory sample was 37.93 GiB.
+
 ## Online Serving
 
 SenseNova-U1 supports all four modalities via the OpenAI-compatible

recipes/StabilityAI/Stable-Audio-Open.md

@@ -164,7 +164,50 @@ PY
   `torchaudio==2.11.0+cu130`.
 - The `NIXL is not available`, `GLOO_SOCKET_IFNAME`, and `torchsde` boundary
   warnings observed during validation did not prevent successful generation.
-- This recipe was validated on one RTX 4090 24GB GPU only. Other GPU counts,
-  ROCm, XPU, and NPU setups are not covered here.
+- The RTX 4090 entry was validated on one 24 GB GPU. The MI300X entry below covers one 192 GB GPU.
 - Long generations, higher inference-step counts, and non-WAV response formats
   were not benchmarked in this recipe.
+
+#### 1x AMD MI300X 192GB
+
+##### Environment
+
+- OS: Linux 6.8.0-134-generic, x86_64
+- Container: official ROCm image built from `docker/Dockerfile.rocm`
+- Python: 3.12.13
+- PyTorch: 2.11.0+gitd0c8b1f
+- Driver / runtime: AMD 6.19.14.31400000 / ROCm 7.2.53211
+- GPU: one AMD Instinct MI300X, `gfx942:sramecc+:xnack-`, 191.69 GiB visible HBM
+- vLLM version: 0.27.0+rocm723
+- vLLM Omni version or commit: `73e1368c7bb940efe1a025859c9d6c8eeeb2e3f0`
+- Installed vLLM Omni package metadata: `0.27.0rc2.dev44+g55abdade9.rocm`
+
+##### Commands
+
+```bash
+python3 examples/offline_inference/text_to_audio/text_to_audio.py \
+    --model stabilityai/stable-audio-open-1.0 \
+    --prompt "A gentle piano melody with soft room ambience" \
+    --negative-prompt "Low quality, distorted, noisy" \
+    --seed 42 \
+    --guidance-scale 7.0 \
+    --audio-length 10.0 \
+    --num-inference-steps 50 \
+    --cache-backend tea_cache \
+    --enable-diffusion-pipeline-profiler \
+    --output stable_audio_10s.wav
+```
+
+##### Verification
+
+The command completed and wrote a valid 44.1 kHz stereo WAV with 10.00 seconds of audio.
+
+##### Notes
+
+- TeaCache ran with `rel_l1_thresh=0.2`.
+- Model loading used 2.7891 GiB and took 3.706 seconds.
+- Generation took 4.750 seconds, which gives a real time factor of 0.475 for the 10.00 second output.
+- The internal profiler recorded 15.65 GB reserved and 9.68 GB allocated for the request.
+- The highest one second whole device memory sample was 19.61 GiB.
+- The output RMS was 0.0887, and its peak absolute amplitude was 0.5761.
+- The full process took 384 seconds, including startup and compilation.

recipes/inclusionAI/Ming-omni-tts.md

@@ -170,6 +170,35 @@ python examples/online_serving/text_to_speech/ming_tts/openai_speech_client.py \
 `--ref-audio` matches upstream `use_spk_emb=True`; do not add `--ref-text`
 for the dialect case.
 
+**Offline smoke result on ROCm 7.2:**
+
+A later offline smoke run used the same MI300X model with the following software:
+
+- GPU: one AMD Instinct MI300X with 191.69 GiB visible HBM and gfx942
+- Kernel: Linux 6.8.0-134-generic, x86_64
+- Python: 3.12.13
+- PyTorch: 2.11.0+gitd0c8b1f
+- ROCm or HIP: 7.2.53211
+- vLLM: 0.27.0+rocm723
+- vLLM Omni checkout: `73e1368c7bb940efe1a025859c9d6c8eeeb2e3f0`
+
+```bash
+python3 examples/offline_inference/text_to_speech/ming_tts/end2end.py \
+    --model inclusionAI/Ming-omni-tts-0.5B \
+    --deploy-config vllm_omni/deploy/ming_tts.yaml \
+    --case basic \
+    --text "你好，这是 AMD MI300X ROCm 配方验证。" \
+    --output-dir ./ming_tts_output \
+    --output-name ming_omni_tts_mi300x.wav \
+    --enforce-eager \
+    --log-stats \
+    --metadata-json ./ming_tts_manifest.json
+```
+
+The checked run produced a valid 44.1 kHz mono WAV with 4.80 seconds of audio, RMS 0.1449, and peak absolute amplitude 0.8621. The first request took 20.406 seconds, which gives a real time factor of 4.25. The stage weight loads used 1.31 GiB and 1.47 GiB and took 1.756 seconds and 1.264 seconds. The largest one second whole device memory sample was 91.05 GiB, including the reserved KV cache. Both stages used eager mode, and the AR stage selected `TRITON_ATTN`.
+
+The run used the official ROCm image built from `docker/Dockerfile.rocm`.
+
 ## Notes
 
 - The official ROCm image includes the platform dependencies.

recipes/k2-fsa/OmniVoice.md

@@ -0,0 +1,98 @@
+# OmniVoice
+
+> Offline and online text to speech with automatic voice, voice design, and reference voice cloning
+
+## Summary
+
+- Vendor: k2-fsa
+- Model: `k2-fsa/OmniVoice`
+- Task: Multilingual text to speech
+- Mode: Offline inference for all modes and online serving for automatic voice
+- Maintainer: Community
+
+## References
+
+- [Hugging Face model](https://huggingface.co/k2-fsa/OmniVoice)
+- [Offline text to speech examples](../../docs/user_guide/examples/offline_inference/text_to_speech.md#omnivoice)
+- [Online text to speech examples](../../docs/user_guide/examples/online_serving/text_to_speech.md#omnivoice)
+- [Deploy config](../../vllm_omni/deploy/omnivoice.yaml)
+
+## Requirements
+
+Download the checkpoint before the first run:
+
+```bash
+huggingface-cli download k2-fsa/OmniVoice
+```
+
+Automatic voice and voice design require `transformers>=4.57.0`. Reference voice cloning requires `transformers>=5.3.0`.
+
+The vLLM Omni deploy config runs the OmniVoice generator and decoder inside one float32 diffusion stage.
+
+## ROCm
+
+### 1x AMD MI300X
+
+#### Environment
+
+- GPU: one AMD Instinct MI300X with 191.69 GiB visible HBM and gfx942
+- Kernel: Linux 6.8.0-134-generic, x86_64
+- Python: 3.12.13
+- PyTorch: 2.11.0+gitd0c8b1f
+- ROCm or HIP: 7.2.53211
+- vLLM: 0.27.0+rocm723
+- vLLM Omni checkout: `73e1368c7bb940efe1a025859c9d6c8eeeb2e3f0`
+- transformers: 5.15.0
+
+The run used the official ROCm image built from `docker/Dockerfile.rocm`.
+
+#### Automatic voice
+
+Run the offline example from the repository root:
+
+```bash
+python3 examples/offline_inference/text_to_speech/omnivoice/end2end.py \
+    --model k2-fsa/OmniVoice \
+    --deploy-config vllm_omni/deploy/omnivoice.yaml \
+    --text "Hello, this is OmniVoice running on one AMD MI300X." \
+    --seed 42 \
+    --output omnivoice_mi300x.wav
+```
+
+The checked run produced a valid 24 kHz mono WAV with 3.60 seconds of audio, RMS 0.1194, and peak absolute amplitude 0.9129. The first request took 0.685 seconds, which gives a real time factor of 0.19. Model loading used 3.5739 GiB and took 9.564 seconds. The request used 4.56 GB reserved and 3.64 GB allocated according to the internal profiler, while the largest one second whole device sample was 8.33 GiB. The log states that the OmniVoice Triton kernels loaded.
+
+#### Reference voice cloning
+
+The offline interface also accepts a reference WAV and its transcript:
+
+```bash
+python3 examples/offline_inference/text_to_speech/omnivoice/end2end.py \
+    --model k2-fsa/OmniVoice \
+    --text "Hello, this is a cloned voice." \
+    --ref-audio ref.wav \
+    --ref-text "This is the reference transcription." \
+    --output cloned.wav
+```
+
+#### Voice design
+
+```bash
+python3 examples/offline_inference/text_to_speech/omnivoice/end2end.py \
+    --model k2-fsa/OmniVoice \
+    --text "Hello, this is a designed voice." \
+    --instruct "female, low pitch, British accent" \
+    --output designed.wav
+```
+
+## Online serving
+
+Online serving currently supports automatic voice:
+
+```bash
+vllm serve k2-fsa/OmniVoice --omni --port 8091 --trust-remote-code
+```
+
+```bash
+cd examples/online_serving/text_to_speech/omnivoice
+python speech_client.py --text "Hello, how are you?"
+```

recipes/lerobot/Pi0.md

@@ -0,0 +1,108 @@
+# Pi0 (π0, Pi-Zero) VLA
+
+> Vision-Language-Action policy serving over the OpenPI realtime websocket
+
+## Summary
+
+- Vendor: Physical Intelligence (checkpoint redistributed by LeRobot)
+- Model: `lerobot/pi0_base`
+- Task: Vision-Language-Action — multi-camera images + a language instruction +
+  robot proprioceptive state → a continuous action chunk
+  `[action_horizon, action_dim] = [50, 32]` via flow-matching denoising. π0 does
+  **not** emit text tokens.
+- Mode: Online serving via the OpenPI realtime robot API
+  (`/v1/realtime/robot/openpi` websocket)
+- Maintainer: Community
+
+## When to use this recipe
+
+Use this recipe to deploy `lerobot/pi0_base` as a robot policy server: a robot
+client connects over the OpenPI websocket and, per control step, sends an
+observation (camera frames + state + instruction) and receives an action chunk.
+
+A single pipeline class (`Pi0Pipeline`) serves the policy. π0 is stateless across
+calls (no KV reuse), so the protocol's `session_id` / `reset` are accepted but
+ignored.
+
+## Weights
+
+π0 uses the LeRobot `lerobot/pi0_base` checkpoint (HF, ~13 GB). Either let the
+server download it (`MODEL=lerobot/pi0_base`) or point at a local copy.
+
+## Hardware Support
+
+### 1x H200 141GB (Online serving)
+
+#### Environment
+
+- OS: Ubuntu 22.04+
+- Python: 3.12+
+- Driver / runtime: NVIDIA CUDA environment
+- vLLM / vLLM-Omni: use the commit you are deploying from
+- OpenPI client extras (for the example client): `openpi-client`
+  (`pip install -e packages/openpi-client` from the
+  [openpi](https://github.com/Physical-Intelligence/openpi) repo), `websockets`,
+  `msgpack`, `msgpack-numpy`
+
+#### Command
+
+```bash
+vllm serve lerobot/pi0_base \
+  --omni \
+  --host 0.0.0.0 --port 8000 \
+  --served-model-name pi0 \
+  --deploy-config vllm_omni/deploy/pi0.yaml \
+  --enforce-eager --disable-log-stats
+```
+
+The deploy config (`vllm_omni/deploy/pi0.yaml`) declares a single diffusion stage
+(`Pi0Pipeline`), `float32`, `max_num_seqs: 1`, and the `policy_server_config`
+handshake metadata (3 cameras, `joint_position`, action horizon 50, action dim
+32) that enables the OpenPI realtime serving layer.
+
+#### Verification
+
+Run the example client (sends a synthetic observation and prints the returned
+action chunk):
+
+```bash
+python examples/online_serving/pi0/openpi_client.py \
+  --host 127.0.0.1 --port 8000 \
+  --prompt "pick up the red block and place it in the bin"
+```
+
+It connects, prints the server metadata, sends robot observations, and prints the
+returned `[action_horizon, action_dim] = [50, 32]` action chunks. The observation
+is a flat dict per inference:
+
+```python
+{
+    "observation.images.base_0_rgb":        np.uint8[H, W, 3],
+    "observation.images.left_wrist_0_rgb":  np.uint8[H, W, 3],
+    "observation.images.right_wrist_0_rgb": np.uint8[H, W, 3],
+    "state":   np.float32[state_dim],   # zero-padded to max_state_dim=32 server-side
+    "prompt":  "pick up the red block",
+    "session_id": "<uuid>",             # accepted but ignored (π0 is stateless)
+}
+```
+
+Camera keys must match the server's `image_feature_keys` (the checkpoint's
+`input_features` order). If your robot uses different camera names, set
+`model_config.image_key_map` in `pi0.yaml` to map raw obs keys → feature keys.
+
+## Correctness
+
+π0's flow-matching kernel is bit-for-bit matched to the LeRobot `PI0Policy`
+reference (`max|Δ| = 7.15e-07`, CPU/float32, fixed noise; see
+`tests/diffusion/models/pi0/test_pi0_parity.py::test_pi0_vllm_omni_vs_lerobot`).
+
+## References
+
+- Model blog: <https://www.physicalintelligence.company/blog/pi0>
+- Checkpoint: <https://huggingface.co/lerobot/pi0_base>
+- Pipeline: [`vllm_omni/diffusion/models/pi0/pipeline_pi0.py`](../../vllm_omni/diffusion/models/pi0/pipeline_pi0.py)
+- Deploy config: [`vllm_omni/deploy/pi0.yaml`](../../vllm_omni/deploy/pi0.yaml)
+- Example client: [`examples/online_serving/pi0/`](../../examples/online_serving/pi0/)
+- Tests: CPU units + LeRobot parity in [`tests/diffusion/models/pi0/`](../../tests/diffusion/models/pi0/),
+  OpenPI websocket e2e in
+  [`tests/e2e/online_serving/test_pi0_expansion.py`](../../tests/e2e/online_serving/test_pi0_expansion.py)

tests/core/sched/test_omni_scheduler_mixin_shared.py

@@ -4,7 +4,10 @@ from types import SimpleNamespace
 import pytest
 from vllm.v1.engine import FinishReason
 
-from vllm_omni.core.sched.omni_scheduler_mixin import OmniSchedulerMixin
+from vllm_omni.core.sched.omni_scheduler_mixin import (
+    DEFAULT_INPUT_WAIT_TIMEOUT_S,
+    OmniSchedulerMixin,
+)
 from vllm_omni.core.sched.output import OmniChunkRecvHandle
 
 pytestmark = [pytest.mark.core_model, pytest.mark.cpu]
@@ -22,13 +25,25 @@ def test_schedule_lifecycle_helpers_process_and_restore_both_input_paths():
     scheduler.requests = {"request": object()}
     scheduler._consume_pending_connector_output = lambda mode: calls.append(("consume", mode))
     scheduler._process_pending_input_timeouts = lambda: calls.append(("timeouts",))
+
+    def _collect_timed_out(timeout_s):
+        calls.append(("chunk-timeouts", timeout_s))
+        return set()
+
+    def _collect_failed_sends():
+        calls.append(("failed-sends",))
+        return {}
+
     scheduler.chunk_transfer_adapter = SimpleNamespace(
+        receives_chunks=True,
         process_pending_chunks=lambda waiting, running, scheduler_requests: calls.append(
             ("process", waiting, running, scheduler_requests)
         ),
         restore_queues=lambda waiting, running, scheduler_requests: calls.append(
             ("restore-chunks", waiting, running, scheduler_requests)
         ),
+        collect_timed_out_request_ids=_collect_timed_out,
+        collect_failed_send_request_ids=_collect_failed_sends,
     )
     scheduler.input_coordinator = SimpleNamespace(
         restore_queues=lambda waiting: calls.append(("restore-full", waiting))
@@ -41,6 +56,10 @@ def test_schedule_lifecycle_helpers_process_and_restore_both_input_paths():
         ("consume", "ar"),
         ("timeouts",),
         ("process", scheduler.waiting, scheduler.running, scheduler.requests),
+        # The chunk deadline runs after chunks are applied, so a chunk that
+        # arrived this cycle resets the clock before it is measured (R1.1).
+        ("chunk-timeouts", DEFAULT_INPUT_WAIT_TIMEOUT_S),
+        ("failed-sends",),
         ("restore-chunks", scheduler.waiting, scheduler.running, scheduler.requests),
         ("restore-full", scheduler.waiting),
     ]

tests/core/sched/test_omni_scheduler_mixin_timeouts.py

@@ -1,6 +1,10 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-"""Unit coverage for _process_pending_input_timeouts.
+"""Unit coverage for the two stage-input timeout hooks.
+
+``_process_pending_input_timeouts`` covers the full-payload transport;
+``_process_pending_chunk_timeouts`` covers async-chunk. Both share
+``VLLM_OMNI_INPUT_WAIT_TIMEOUT_S``.
 
 Verifies that the mixin correctly *delegates* timed-out requests to the
 base scheduler's ``finish_requests`` API with ``RequestStatus.FINISHED_ERROR``.
@@ -12,6 +16,10 @@ this file only asserts the wiring from the mixin to that API.
 
 from __future__ import annotations
 
+import importlib
+import logging
+import math
+import os
 from types import SimpleNamespace
 
 import pytest
@@ -102,5 +110,248 @@ def test_process_pending_input_timeouts_disabled_when_timeout_zero(monkeypatch):
     assert scheduler.finish_calls == []
 
 
+class _FakeChunkAdapter:
+    """Stands in for OmniChunkTransferAdapter's timeout-collection surface."""
+
+    def __init__(self, timed_out_ids, *, receives_chunks=True):
+        self._timed_out_ids = set(timed_out_ids)
+        self.receives_chunks = receives_chunks
+        self.calls = []
+
+    def collect_timed_out_request_ids(self, timeout_s):
+        self.calls.append(timeout_s)
+        return set(self._timed_out_ids)
+
+
+class _FakeChunkScheduler(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_pending_chunk_timeouts_delegates_to_finish_requests():
+    """A request stalled in WAITING_FOR_CHUNK is failed, not left parked.
+
+    This is the async-chunk half of the safety net (RFC #4855 R1.1); before it
+    existed, the chunk path had no deadline at all and issue #3833 could park a
+    request forever.
+    """
+    req_id = "stalled-stream"
+    adapter = _FakeChunkAdapter(timed_out_ids={req_id})
+    scheduler = _FakeChunkScheduler({req_id: SimpleNamespace(request_id=req_id)}, adapter)
+
+    scheduler._process_pending_chunk_timeouts()
+
+    assert len(adapter.calls) == 1
+    assert adapter.calls[0] > 0
+    assert len(scheduler.finish_calls) == 1
+    finished_ids, status = scheduler.finish_calls[0]
+    assert finished_ids == {req_id}
+    assert getattr(status, "name", str(status)).endswith("FINISHED_ERROR")
+
+
+def test_process_pending_chunk_timeouts_skips_already_freed_request():
+    adapter = _FakeChunkAdapter(timed_out_ids={"already-freed"})
+    scheduler = _FakeChunkScheduler(requests={}, adapter=adapter)
+
+    scheduler._process_pending_chunk_timeouts()
+
+    assert scheduler.finish_calls == []
+
+
+def test_process_pending_chunk_timeouts_noop_on_a_producer_stage():
+    """Stage 0 does not receive chunks, so it has nothing to expire."""
+    adapter = _FakeChunkAdapter(timed_out_ids={"r1"}, receives_chunks=False)
+    scheduler = _FakeChunkScheduler({"r1": SimpleNamespace(request_id="r1")}, adapter)
+
+    scheduler._process_pending_chunk_timeouts()
+
+    assert adapter.calls == []
+    assert scheduler.finish_calls == []
+
+
+def test_process_pending_chunk_timeouts_noop_without_adapter():
+    class _NoAdapter(OmniSchedulerMixin):
+        def __init__(self):
+            self.requests = {}
+            self.chunk_transfer_adapter = None
+            self.finish_calls = []
+
+        def finish_requests(self, req_ids, status):
+            self.finish_calls.append((set(req_ids), status))
+
+    scheduler = _NoAdapter()
+    scheduler._process_pending_chunk_timeouts()
+    assert scheduler.finish_calls == []
+
+
+def test_process_pending_chunk_timeouts_disabled_when_timeout_zero(monkeypatch):
+    """The shared knob disables both transports' nets together."""
+    from vllm_omni.core.sched import omni_scheduler_mixin
+
+    monkeypatch.setattr(omni_scheduler_mixin, "DEFAULT_INPUT_WAIT_TIMEOUT_S", 0.0)
+
+    adapter = _FakeChunkAdapter(timed_out_ids={"r1"})
+    scheduler = _FakeChunkScheduler({"r1": SimpleNamespace(request_id="r1")}, adapter)
+    scheduler._process_pending_chunk_timeouts()
+
+    assert adapter.calls == []
+    assert scheduler.finish_calls == []
+
+
+class _FakeSendFailureAdapter:
+    """Stands in for the adapter's drained send-failure map (R1.2)."""
+
+    def __init__(self, failures):
+        self._failures = dict(failures)
+        self.collect_calls = 0
+
+    def collect_failed_send_request_ids(self):
+        self.collect_calls += 1
+        failures, self._failures = self._failures, {}
+        return failures
+
+
+class _FakeSendFailureScheduler(OmniSchedulerMixin):
+    def __init__(self, requests, adapter):
+        self.requests = requests
+        self.chunk_transfer_adapter = adapter
+
+
+def test_log_failed_chunk_sends_reports_a_live_request(caplog):
+    adapter = _FakeSendFailureAdapter({"req-live": "OSError: No space left on device"})
+    scheduler = _FakeSendFailureScheduler({"req-live": SimpleNamespace(request_id="req-live")}, adapter)
+
+    with caplog.at_level(logging.ERROR):
+        scheduler._log_failed_chunk_sends()
+
+    assert "req-live" in caplog.text
+    assert "No space left on device" in caplog.text
+
+
+def test_log_failed_chunk_sends_still_reports_a_departed_request(caplog):
+    """``collect_*`` drains the map, so a failure for a request that already left
+    this scheduler is the one place a drop could still go unrecorded."""
+    adapter = _FakeSendFailureAdapter({"req-gone": "OSError: No space left on device"})
+    scheduler = _FakeSendFailureScheduler({}, adapter)
+
+    with caplog.at_level(logging.WARNING):
+        scheduler._log_failed_chunk_sends()
+
+    assert adapter.collect_calls == 1
+    assert "req-gone" in caplog.text
+    assert "No space left on device" in caplog.text
+    assert "finished or aborted" in caplog.text
+
+
+def test_log_failed_chunk_sends_noop_without_adapter():
+    scheduler = _FakeSendFailureScheduler({}, None)
+    scheduler._log_failed_chunk_sends()
+
+
+# --- 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
+# when only the full-payload path used it.
+
+
+@pytest.fixture
+def reload_with(monkeypatch):
+    """Reload the mixin module under a given env value, then put it back.
+
+    ``importlib.reload`` re-executes into the live module dict, so whatever the
+    last test parsed is what every later test in the session sees -- ``-k
+    zero_warns`` alone would leave ``DEFAULT_INPUT_WAIT_TIMEOUT_S = 0.0`` and
+    silently disable both deadlines. The rejection cases are worse: the
+    ValueError fires *after* the rebind, so ``nan`` lingers in the module.
+    ``monkeypatch`` restores the environment but not the module, so reload once
+    more on the way out.
+    """
+    import vllm_omni.core.sched.omni_scheduler_mixin as mod
+
+    def _reload(raw):
+        if raw is None:
+            monkeypatch.delenv("VLLM_OMNI_INPUT_WAIT_TIMEOUT_S", raising=False)
+        else:
+            monkeypatch.setenv("VLLM_OMNI_INPUT_WAIT_TIMEOUT_S", raw)
+        return importlib.reload(mod)
+
+    yield _reload
+
+    monkeypatch.undo()
+    importlib.reload(mod)
+
+
+@pytest.mark.parametrize(
+    "raw,expected",
+    [
+        (None, 600.0),
+        ("30", 30.0),
+        ("0.5", 0.5),
+        ("0", 0.0),  # explicit opt-out is still honoured
+    ],
+)
+def test_timeout_accepts_valid_values(reload_with, raw, expected):
+    mod = reload_with(raw)
+    assert mod.DEFAULT_INPUT_WAIT_TIMEOUT_S == expected
+
+
+@pytest.mark.parametrize("raw", ["-1", "-600", "-0.5"])
+def test_negative_timeout_is_rejected(reload_with, raw):
+    """`-1` is a common "no limit" idiom; substituting 600 would give an operator
+    who meant "never time out" mysterious failures ten minutes in. Fail loudly."""
+    with pytest.raises(ValueError, match="negative"):
+        reload_with(raw)
+
+
+@pytest.mark.parametrize("raw", ["nan", "inf", "-inf", "1e999"])
+def test_non_finite_timeout_is_rejected(reload_with, raw):
+    """nan compares false against every deadline check and inf is unreachable;
+    both disable the net while looking like a number."""
+    with pytest.raises(ValueError, match="not finite"):
+        reload_with(raw)
+
+
+def test_zero_warns_and_names_both_transports(reload_with, caplog):
+    with caplog.at_level(logging.WARNING):
+        mod = reload_with("0")
+    assert mod.DEFAULT_INPUT_WAIT_TIMEOUT_S == 0.0
+    text = caplog.text
+    assert "DISABLED" in text
+    assert "WAITING_FOR_INPUT" in text and "WAITING_FOR_CHUNK" in text
+
+
+def test_unparsable_timeout_falls_back(reload_with):
+    mod = reload_with("not-a-number")
+    assert mod.DEFAULT_INPUT_WAIT_TIMEOUT_S == 600.0
+
+
+def test_a_rejected_value_does_not_outlive_its_test(monkeypatch):
+    """The reason ``reload_with`` reloads on the way out, done here by hand.
+
+    ``nan`` is bound before the ValueError fires, so the reload that rejects it
+    still leaves it in the module -- and nan compares false against every
+    deadline check, which is exactly the silent-disable this item exists to
+    stop. Restoring the environment is not enough; the module has to be reloaded
+    too.
+    """
+    import vllm_omni.core.sched.omni_scheduler_mixin as mod
+
+    ambient = float(os.environ.get("VLLM_OMNI_INPUT_WAIT_TIMEOUT_S", "600"))
+
+    monkeypatch.setenv("VLLM_OMNI_INPUT_WAIT_TIMEOUT_S", "nan")
+    with pytest.raises(ValueError, match="not finite"):
+        importlib.reload(mod)
+    assert math.isnan(mod.DEFAULT_INPUT_WAIT_TIMEOUT_S)
+
+    monkeypatch.undo()
+    importlib.reload(mod)
+    assert mod.DEFAULT_INPUT_WAIT_TIMEOUT_S == ambient
+
+
 if __name__ == "__main__":
     pytest.main([__file__, "-v"])

tests/dfx/perf/tests/test_bagel_vllm_omni.json

@@ -10,7 +10,7 @@
           "num_cards": 1
         }
       },
-      "full_model",
+      "local_model",
       "diffusion"
     ],
     "description": "Single-stage BAGEL (TP=1, CFG=1), t2i Task",
@@ -61,7 +61,7 @@
           "num_cards": 1
         }
       },
-      "full_model",
+      "local_model",
       "diffusion"
     ],
     "description": "Single-stage BAGEL (TP=1, CFG=1), i2i Task",
@@ -113,7 +113,7 @@
           "num_cards": 1
         }
       },
-      "full_model",
+      "local_model",
       "diffusion"
     ],
     "description": "Two-stage BAGEL (bagel.yaml), TP=1, CFG=1, t2i Task",
@@ -164,7 +164,7 @@
           "num_cards": 1
         }
       },
-      "full_model",
+      "local_model",
       "diffusion"
     ],
     "description": "Two-stage BAGEL (bagel.yaml), TP=1, CFG=1, i2i Task",

tests/dfx/perf/tests/test_higgs_audio_v3.json

@@ -10,7 +10,7 @@
           "num_cards": 1
         }
       },
-      "full_model",
+      "local_model",
       "tts"
     ],
     "server_params": {

tests/dfx/perf/tests/test_ltx2_vllm_omni.json

@@ -10,7 +10,7 @@
           "num_cards": 1
         }
       },
-      "full_model",
+      "local_model",
       "diffusion"
     ],
     "description": "Single-device baseline with enforce-eager (no torch.compile)",
@@ -64,7 +64,7 @@
           "num_cards": 1
         }
       },
-      "full_model",
+      "local_model",
       "diffusion"
     ],
     "description": "Single-device with torch.compile (default, no enforce-eager)",
@@ -117,7 +117,7 @@
           "num_cards": 2
         }
       },
-      "full_model",
+      "local_model",
       "diffusion"
     ],
     "description": "CFG-parallel=2 with enforce-eager",
@@ -172,7 +172,7 @@
           "num_cards": 2
         }
       },
-      "full_model",
+      "local_model",
       "diffusion"
     ],
     "description": "CFG-parallel=2 with torch.compile",
@@ -226,7 +226,7 @@
           "num_cards": 1
         }
       },
-      "full_model",
+      "local_model",
       "diffusion"
     ],
     "description": "CacheDiT with enforce-eager",

tests/dfx/perf/tests/test_qwen_image_edit_2511_vllm_omni.json

@@ -10,7 +10,7 @@
           "num_cards": 1
         }
       },
-      "full_model",
+      "local_model",
       "diffusion"
     ],
     "description": "Single-device baseline (two input images)",
@@ -76,7 +76,7 @@
           "num_cards": 4
         }
       },
-      "full_model",
+      "local_model",
       "diffusion"
     ],
     "description": "Ulysses SP=2 + CFG=2 + VAE patch parallel=4",
@@ -125,7 +125,7 @@
           "num_cards": 4
         }
       },
-      "full_model",
+      "local_model",
       "diffusion"
     ],
     "description": "Ulysses SP=2 + CFG=2 + CacheDiT",

tests/dfx/perf/tests/test_qwen_image_edit_vllm_omni.json

@@ -10,7 +10,7 @@
           "num_cards": 1
         }
       },
-      "full_model",
+      "local_model",
       "diffusion"
     ],
     "description": "Single-device baseline",
@@ -73,7 +73,7 @@
           "num_cards": 4
         }
       },
-      "full_model",
+      "local_model",
       "diffusion"
     ],
     "description": "Ulysses SP=2 + CFG=2 + VAE patch parallel=4",
@@ -121,7 +121,7 @@
           "num_cards": 4
         }
       },
-      "full_model",
+      "local_model",
       "diffusion"
     ],
     "description": "Ulysses SP=2 + CFG=2 + CacheDiT",

tests/dfx/perf/tests/test_qwen_image_layered_vllm_omni.json

@@ -10,7 +10,7 @@
           "num_cards": 1
         }
       },
-      "full_model",
+      "local_model",
       "diffusion"
     ],
     "description": "Single-device baseline",

tests/dfx/perf/tests/test_voxcpm2.json

@@ -10,7 +10,7 @@
           "num_cards": 1
         }
       },
-      "full_model",
+      "local_model",
       "tts"
     ],
     "server_params": {

tests/diffusion/models/pi0/__init__.py

@@ -0,0 +1,2 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project

tests/diffusion/models/pi0/test_pi0_parity.py

@@ -0,0 +1,366 @@
+#!/usr/bin/env python
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""π0 LeRobot parity (in-process): the bit-for-bit correctness oracle.
+
+Verifies that vllm-omni's ``Pi0ForActionPrediction`` produces bit-for-bit
+matching action chunks with LeRobot's ``PI0Policy`` when fed:
+  * the same weights (``lerobot/pi0_base``)
+  * the same pre-processed inputs (images, masks, tokens, state)
+  * the same initial noise tensor
+
+π0 is a flow-matching model (Euler-integrated ODE from t=1 → t=0 with a fixed
+``num_steps``), so the output is deterministic once the noise is fixed and
+``torch.allclose`` on the final action chunk is a valid oracle. This is the
+authoritative correctness oracle for the vllm-omni π0 port (max|Δ| < 1e-4).
+
+The OpenPI websocket online-serving e2e lives separately in
+``tests/e2e/online_serving/test_pi0_expansion.py``.
+
+Run in a SEPARATE ``lerobot[pi]`` venv (avoids dep conflict with the vllm-omni
+env), with the vllm-omni ``pi0`` package importable::
+
+    python -m pytest tests/diffusion/models/pi0/test_pi0_parity.py -v -s
+
+Skipped automatically when LeRobot is not installed (e.g. the vllm-omni env).
+The run is CPU/float32 with fixed defaults; the only override is
+``PI0_PARITY_MODEL_PATH`` (a local pi0_base dir in LeRobot format, to skip the
+HF download; defaults to ``lerobot/pi0_base``).
+"""
+
+from __future__ import annotations
+
+import copy
+import importlib.util
+import os
+
+import pytest
+import torch
+
+# local_model: needs real weights + a lerobot venv (transformers 5.3.0), so it
+# runs locally rather than in the ready-CI (see docs/contributing/ci/CI_5levels.md).
+# Additionally gated on lerobot being importable.
+_HAS_LEROBOT = importlib.util.find_spec("lerobot") is not None
+
+pytestmark = [pytest.mark.local_model, pytest.mark.diffusion]
+
+
+# ─── Config (fixed; matches LeRobot defaults for ``lerobot/pi0_base``) ──
+DEVICE = "cpu"
+DTYPE_STR = "float32"
+ATOL = 1e-4
+NUM_STEPS = 10
+BATCH_SIZE = 2
+ACTION_DIM = 32
+STATE_DIM = 32
+ACTION_HORIZON = 50
+MAX_TOKEN_LEN = 48
+
+# The only knob: point at a local pi0_base dir (LeRobot format) to skip the HF
+# download; defaults to the HF repo id.
+MODEL_PATH = os.environ.get("PI0_PARITY_MODEL_PATH", "lerobot/pi0_base")
+
+
+def _resolve_checkpoint_dir() -> str:
+    """Return a local dir containing the pi0_base checkpoint (download if needed)."""
+    if os.path.isdir(MODEL_PATH):
+        return MODEL_PATH
+    from huggingface_hub import snapshot_download
+
+    return snapshot_download(repo_id=MODEL_PATH, repo_type="model")
+
+
+# ─── Dummy dataset stats (identity) ──────────────────────────────────
+def _dummy_dataset_stats() -> dict:
+    return {
+        "observation.state": {
+            "mean": torch.zeros(STATE_DIM),
+            "std": torch.ones(STATE_DIM),
+            "q01": torch.zeros(STATE_DIM),
+            "q99": torch.ones(STATE_DIM),
+        },
+        "action": {
+            "mean": torch.zeros(ACTION_DIM),
+            "std": torch.ones(ACTION_DIM),
+            "q01": torch.zeros(ACTION_DIM),
+            "q99": torch.ones(ACTION_DIM),
+        },
+        "images": {
+            cam: {
+                "mean": torch.zeros(3, 224, 224),
+                "std": torch.ones(3, 224, 224),
+                "q01": torch.zeros(3, 224, 224),
+                "q99": torch.ones(3, 224, 224),
+            }
+            for cam in ("base_0_rgb", "left_wrist_0_rgb", "right_wrist_0_rgb")
+        },
+    }
+
+
+def _create_dummy_batch(batch_size: int = BATCH_SIZE, device: str = DEVICE) -> dict:
+    """Reproducible dummy inputs — identical across both implementations."""
+    g = torch.Generator(device="cpu").manual_seed(0)
+    prompt = "Pick up the red block and place it in the bin"
+    return {
+        "observation.state": torch.randn(batch_size, STATE_DIM, generator=g, dtype=torch.float32).to(device),
+        "action": torch.randn(batch_size, ACTION_HORIZON, ACTION_DIM, generator=g, dtype=torch.float32).to(device),
+        "observation.images.base_0_rgb": torch.rand(batch_size, 3, 224, 224, generator=g, dtype=torch.float32).to(
+            device
+        ),
+        "observation.images.left_wrist_0_rgb": torch.rand(batch_size, 3, 224, 224, generator=g, dtype=torch.float32).to(
+            device
+        ),
+        "observation.images.right_wrist_0_rgb": torch.rand(
+            batch_size, 3, 224, 224, generator=g, dtype=torch.float32
+        ).to(device),
+        "task": [prompt for _ in range(batch_size)],
+    }
+
+
+# ─── LeRobot instantiation ────────────────────────────────────────────
+def _instantiate_lerobot():
+    from lerobot.policies.pi0 import PI0Policy
+    from lerobot.policies.pi0.processor_pi0 import make_pi0_pre_post_processors
+
+    policy = PI0Policy.from_pretrained(MODEL_PATH, strict=True)
+    policy.to(DEVICE)
+    policy.config.device = DEVICE
+    policy.eval()
+
+    pre, post = make_pi0_pre_post_processors(config=policy.config, dataset_stats=_dummy_dataset_stats())
+    return policy, pre, post
+
+
+# ─── vllm-omni instantiation ──────────────────────────────────────────
+def _instantiate_vllm_omni():
+    """Build the vllm-omni π0 model in isolation (no pipeline, no engine)."""
+    from vllm_omni.diffusion.models.pi0 import Pi0Config, Pi0ForActionPrediction
+
+    cfg = Pi0Config(
+        max_action_dim=ACTION_DIM,
+        max_state_dim=STATE_DIM,
+        chunk_size=ACTION_HORIZON,
+        num_inference_steps=NUM_STEPS,
+        dtype=DTYPE_STR,
+    )
+    model = Pi0ForActionPrediction(cfg)
+    model.to(DEVICE).eval()
+    _load_lerobot_weights(model)
+    return model
+
+
+def _load_lerobot_weights(model):
+    """Feed the ``lerobot/pi0_base`` safetensors into the vllm-omni model.
+
+    ``Pi0ForActionPrediction.load_weights`` handles the leading ``model.``
+    prefix and the flat→nested / lm_head→embed_tokens remaps, so we can pass
+    the raw checkpoint dict.
+    """
+    import safetensors.torch
+
+    cache_dir = _resolve_checkpoint_dir()
+    path = os.path.join(cache_dir, "model.safetensors")
+    state = safetensors.torch.load_file(path)
+    model.load_weights(list(state.items()))
+
+
+# ─── Helpers to extract LeRobot's pre-processed inputs ────────────────
+def _extract_lerobot_model_inputs(lerobot_policy, processed_batch):
+    """Mimic what ``PI0Policy.predict_action_chunk`` feeds into
+    ``self.model.sample_actions``. We use these *exact* tensors for vllm-omni
+    so any divergence must come from the core model, not preprocessing.
+    """
+    images, img_masks = lerobot_policy._preprocess_images(processed_batch)
+    from lerobot.utils.constants import (
+        OBS_LANGUAGE_ATTENTION_MASK,
+        OBS_LANGUAGE_TOKENS,
+    )
+
+    lang_tokens = processed_batch[OBS_LANGUAGE_TOKENS]
+    lang_masks = processed_batch[OBS_LANGUAGE_ATTENTION_MASK]
+    state = lerobot_policy.prepare_state(processed_batch)
+    return images, img_masks, lang_tokens, lang_masks, state
+
+
+# ─── Shared fixed-noise sampler ───────────────────────────────────────
+def _make_fixed_noise(batch_size: int, device: str) -> torch.Tensor:
+    g = torch.Generator(device="cpu").manual_seed(42)
+    return torch.randn(batch_size, ACTION_HORIZON, ACTION_DIM, generator=g, dtype=torch.float32).to(device)
+
+
+# ─── Main test ────────────────────────────────────────────────────────
+@pytest.mark.skipif(not _HAS_LEROBOT, reason="lerobot not installed (run in a lerobot venv).")
+def test_pi0_vllm_omni_vs_lerobot():
+    print("\n[parity] Instantiating LeRobot…")
+    lerobot_policy, lerobot_pre, _ = _instantiate_lerobot()
+
+    print("[parity] Instantiating vllm-omni…")
+    omni_model = _instantiate_vllm_omni()
+
+    print("[parity] Preparing shared inputs…")
+    raw_batch = _create_dummy_batch()
+    processed_batch = lerobot_pre(copy.deepcopy(raw_batch))
+    images, img_masks, lang_tokens, lang_masks, state = _extract_lerobot_model_inputs(lerobot_policy, processed_batch)
+    noise = _make_fixed_noise(raw_batch["observation.state"].shape[0], DEVICE)
+
+    print(f"[parity] state.shape={state.shape}  lang_tokens.shape={lang_tokens.shape}")
+    print(f"[parity] images[0].shape={images[0].shape} (num_cams={len(images)})")
+    print(f"[parity] noise.shape={noise.shape}  dtype={noise.dtype}")
+
+    # ── LeRobot forward ──
+    print("[parity] Running LeRobot sample_actions…")
+    with torch.no_grad():
+        lerobot_actions = lerobot_policy.model.sample_actions(
+            images,
+            img_masks,
+            lang_tokens,
+            lang_masks,
+            state,
+            noise=noise,
+            num_steps=NUM_STEPS,
+        )
+    print(
+        f"[parity] LeRobot actions: shape={lerobot_actions.shape} "
+        f"mean={lerobot_actions.mean().item():.6f} std={lerobot_actions.std().item():.6f}"
+    )
+
+    # ── vllm-omni forward ──
+    print("[parity] Running vllm-omni sample_actions…")
+    with torch.no_grad():
+        omni_actions = omni_model.sample_actions(
+            images=images,
+            image_masks=img_masks,
+            lang_tokens=lang_tokens,
+            lang_masks=lang_masks,
+            state=state,
+            noise=noise,
+            num_steps=NUM_STEPS,
+        )
+    print(
+        f"[parity] vllm-omni actions: shape={omni_actions.shape} "
+        f"mean={omni_actions.mean().item():.6f} std={omni_actions.std().item():.6f}"
+    )
+
+    # ── Compare ──
+    diff = (lerobot_actions.float() - omni_actions.float()).abs()
+    max_diff = diff.max().item()
+    mean_diff = diff.mean().item()
+    print(f"[parity] |Δ| max={max_diff:.2e}  mean={mean_diff:.2e}  atol={ATOL:.1e}")
+    close = torch.allclose(lerobot_actions.float(), omni_actions.float(), atol=ATOL)
+    print(f"[parity] torch.allclose(atol={ATOL}): {close}")
+
+    if not close:
+        print("\n[parity] ⚠️  Outputs diverge — running per-stage diagnostics…")
+        _diagnose_divergence(
+            lerobot_policy.model,
+            omni_model,
+            images,
+            img_masks,
+            lang_tokens,
+            lang_masks,
+            state,
+            noise,
+        )
+
+    assert close, (
+        f"vllm-omni vs LeRobot actions differ beyond atol={ATOL}. max_diff={max_diff:.2e}  mean_diff={mean_diff:.2e}"
+    )
+
+
+# ─── Per-stage divergence diagnostics ─────────────────────────────────
+@torch.no_grad()
+def _diagnose_divergence(
+    lerobot_flow_model,
+    omni_model,
+    images,
+    img_masks,
+    lang_tokens,
+    lang_masks,
+    state,
+    noise,
+):
+    """Localize a numerical mismatch to a specific pipeline stage:
+    1. Prefix embeddings (SigLIP / embed_tokens / projector) — image vs lang.
+    2. Prefix KV cache layer 0 — PaliGemma LM attention.
+    3. A single denoise_step velocity at t=1.0 — action expert forward.
+    """
+    from vllm_omni.diffusion.models.pi0.modeling_pi0 import (
+        make_att_2d_masks,
+        prepare_attention_masks_4d,
+    )
+
+    # ── Stage 1: prefix embeddings ──
+    lr_prefix_embs, lr_prefix_pad, lr_prefix_att = lerobot_flow_model.embed_prefix(
+        images, img_masks, lang_tokens, lang_masks
+    )
+    sg_prefix_embs, sg_prefix_pad, sg_prefix_att = omni_model.embed_prefix(images, img_masks, lang_tokens, lang_masks)
+    total_diff = (lr_prefix_embs.float() - sg_prefix_embs.float()).abs().max().item()
+    print(f"[diag] prefix_embs max |Δ| = {total_diff:.2e}   (shape={tuple(sg_prefix_embs.shape)})")
+    print(f"[diag] prefix_pad_masks equal: {torch.equal(lr_prefix_pad, sg_prefix_pad)}")
+    print(f"[diag] prefix_att_masks equal: {torch.equal(lr_prefix_att.bool(), sg_prefix_att.bool())}")
+
+    num_cams = len(images)
+    img_len = 256 * num_cams
+    lang_len = lr_prefix_embs.shape[1] - img_len
+    img_diff = (lr_prefix_embs[:, :img_len].float() - sg_prefix_embs[:, :img_len].float()).abs().max().item()
+    lang_diff = (lr_prefix_embs[:, img_len:].float() - sg_prefix_embs[:, img_len:].float()).abs().max().item()
+    print(f"[diag]   image slice [:{img_len}] max|Δ| = {img_diff:.2e}   (num_cams={num_cams})")
+    print(f"[diag]   lang slice  [{img_len}:] max|Δ| = {lang_diff:.2e}   (len={lang_len})")
+
+    def _stats(name, t):
+        t = t.float()
+        print(
+            f"[diag] {name} prefix_embs: mean={t.mean().item():+.4f} "
+            f"std={t.std().item():.4f} min={t.min().item():+.2f} "
+            f"max={t.max().item():+.2f}"
+        )
+
+    _stats("LeRobot ", lr_prefix_embs)
+    _stats("vllm-omni", sg_prefix_embs)
+
+    # ── Stage 2: prefix KV cache ──
+    prefix_att_2d = make_att_2d_masks(sg_prefix_pad, sg_prefix_att)
+    prefix_pos = torch.cumsum(sg_prefix_pad, dim=1) - 1
+    prefix_att_4d = prepare_attention_masks_4d(prefix_att_2d)
+
+    lerobot_flow_model.paligemma_with_expert.paligemma.language_model.config._attn_implementation = "eager"
+    _, lr_kv = lerobot_flow_model.paligemma_with_expert.forward(
+        attention_mask=prefix_att_4d,
+        position_ids=prefix_pos,
+        past_key_values=None,
+        inputs_embeds=[lr_prefix_embs, None],
+        use_cache=True,
+    )
+    _, sg_kv = omni_model.paligemma_with_expert.forward(
+        attention_mask=prefix_att_4d,
+        position_ids=prefix_pos,
+        past_key_values=None,
+        inputs_embeds=[sg_prefix_embs, None],
+        use_cache=True,
+    )
+
+    def _layer0_kv(cache):
+        if isinstance(cache, list):
+            return cache[0]
+        layer0 = cache.layers[0]
+        return layer0.keys, layer0.values
+
+    try:
+        lr_k, lr_v = _layer0_kv(lr_kv)
+        sg_k, sg_v = _layer0_kv(sg_kv)
+        dk = (lr_k.float() - sg_k.float()).abs().max().item()
+        dv = (lr_v.float() - sg_v.float()).abs().max().item()
+        print(f"[diag] prefix KV layer0  K max|Δ|={dk:.2e}  V max|Δ|={dv:.2e}")
+    except Exception as e:  # noqa: BLE001
+        print(f"[diag] could not extract prefix KV for comparison: {e}")
+
+    # ── Stage 3: a single denoise_step at t=1.0 ──
+    bsize = state.shape[0]
+    t = torch.ones(bsize, dtype=torch.float32, device=state.device)
+    lr_vt = lerobot_flow_model.denoise_step(state, sg_prefix_pad, lr_kv, noise, t)
+    sg_vt = omni_model.denoise_step(state, sg_prefix_pad, sg_kv, noise, t)
+    print(f"[diag] denoise_step(t=1) v_t max|Δ| = {(lr_vt.float() - sg_vt.float()).abs().max().item():.2e}")
+
+
+if __name__ == "__main__":
+    test_pi0_vllm_omni_vs_lerobot()

tests/diffusion/models/pi0/test_pi0_units.py

@@ -0,0 +1,585 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""CPU unit tests for the π0 (Pi-Zero) VLA model.
+
+Deliberately lightweight: pure utility code (config plumbing, mask building,
+sinusoidal embeddings, normalization, image preprocessing, weight-load remap)
+that runs on CPU with no weights and no lerobot. The full LeRobot parity check
+lives in ``tests/diffusion/models/pi0/test_pi0_parity.py`` and requires a model download.
+
+    pytest tests/diffusion/models/pi0/test_pi0_units.py -v
+"""
+
+from __future__ import annotations
+
+import logging
+import math
+
+import pytest
+import torch
+
+from vllm_omni.diffusion.models.pi0.config import Pi0Config
+from vllm_omni.diffusion.models.pi0.modeling_pi0 import (
+    OPENPI_ATTENTION_MASK_VALUE,
+    Pi0ForActionPrediction,
+    _apply_norm,
+    _build_norm_buffers,
+    create_sinusoidal_pos_embedding,
+    get_gemma_config,
+    make_att_2d_masks,
+    prepare_attention_masks_4d,
+)
+from vllm_omni.diffusion.models.pi0.processor_pi0 import (
+    Pi0ImageProcessor,
+    build_model_inputs,
+    pil_image_to_tensor,
+    resize_with_pad,
+)
+
+pytestmark = [pytest.mark.core_model, pytest.mark.cpu]
+
+
+# ----------------------------------------------------------------------------
+# Pi0Config dataclass + checkpoint resolver
+# ----------------------------------------------------------------------------
+_LEROBOT_CFG = {
+    "type": "pi0",
+    "paligemma_variant": "gemma_2b",
+    "action_expert_variant": "gemma_300m",
+    "chunk_size": 50,
+    "max_action_dim": 32,
+    "max_state_dim": 32,
+    "num_inference_steps": 10,
+    "image_resolution": [224, 224],
+    "tokenizer_max_length": 48,
+    "dtype": "float32",
+    # Training-only keys that must be filtered out of the dataclass.
+    "optimizer_lr": 2.5e-5,
+    "scheduler_warmup_steps": 1000,
+    "input_features": {
+        "observation.images.base_0_rgb": {"type": "VISUAL", "shape": [3, 224, 224]},
+        "observation.images.left_wrist_0_rgb": {"type": "VISUAL", "shape": [3, 224, 224]},
+        "observation.images.right_wrist_0_rgb": {"type": "VISUAL", "shape": [3, 224, 224]},
+        "observation.state": {"type": "STATE", "shape": [32]},
+    },
+}
+
+_EXPECTED_CAMERA_ORDER = [
+    "observation.images.base_0_rgb",
+    "observation.images.left_wrist_0_rgb",
+    "observation.images.right_wrist_0_rgb",
+]
+
+
+def test_config_camera_order_from_input_features():
+    c = Pi0Config.from_model_config(_LEROBOT_CFG)
+    assert c.image_feature_keys == _EXPECTED_CAMERA_ORDER
+
+
+def test_config_filters_training_only_keys():
+    c = Pi0Config.from_model_config(_LEROBOT_CFG)
+    assert not hasattr(c, "optimizer_lr")
+    assert not hasattr(c, "scheduler_warmup_steps")
+
+
+def test_config_coerces_image_resolution_to_tuple():
+    c = Pi0Config.from_model_config(_LEROBOT_CFG)
+    assert c.image_resolution == (224, 224)
+    assert isinstance(c.image_resolution, tuple)
+
+
+def test_config_non_square_resolution_raises():
+    with pytest.raises(ValueError):
+        Pi0Config(image_resolution=(224, 256))
+
+
+@pytest.mark.parametrize("bad", [(224,), (224, 224, 3), 224])
+def test_config_malformed_resolution_raises(bad):
+    with pytest.raises(ValueError):
+        Pi0Config(image_resolution=bad)
+
+
+def test_config_defaults_and_empty():
+    c = Pi0Config.from_model_config(None)
+    assert c.chunk_size == 50
+    assert c.max_action_dim == 32
+    assert c.max_state_dim == 32
+    assert c.num_inference_steps == 10
+    assert c.image_resolution == (224, 224)
+    assert c.tokenizer_max_length == 48
+    assert c.max_cameras == 3
+    assert c.norm_stats is None
+    assert c.image_key_map == {}
+
+
+def test_config_deploy_yaml_override():
+    """Deploy-yaml model_config is authoritative (e.g. force dtype/variant)."""
+    c = Pi0Config.from_model_config({**_LEROBOT_CFG, "dtype": "bfloat16", "num_inference_steps": 4})
+    assert c.dtype == "bfloat16"
+    assert c.num_inference_steps == 4
+
+
+# ----------------------------------------------------------------------------
+# Kernel utilities: sinusoidal timestep embedding
+# ----------------------------------------------------------------------------
+def test_sinusoidal_embedding_shape():
+    emb = create_sinusoidal_pos_embedding(torch.tensor([0.0, 0.5, 1.0]), dimension=64)
+    assert emb.shape == (3, 64)
+
+
+def test_sinusoidal_embedding_values_at_zero():
+    emb = create_sinusoidal_pos_embedding(torch.tensor([0.0]), dimension=4)
+    assert torch.allclose(emb[0, :2].float(), torch.zeros(2), atol=1e-6)
+    assert torch.allclose(emb[0, 2:].float(), torch.ones(2), atol=1e-6)
+
+
+def test_sinusoidal_embedding_odd_dim_raises():
+    with pytest.raises(ValueError):
+        create_sinusoidal_pos_embedding(torch.tensor([1.0]), dimension=3)
+
+
+def test_sinusoidal_embedding_non_1d_raises():
+    with pytest.raises(ValueError):
+        create_sinusoidal_pos_embedding(torch.tensor([[0.0, 0.5]]), dimension=4)
+
+
+def test_sinusoidal_embedding_distinct_timesteps_differ():
+    emb = create_sinusoidal_pos_embedding(torch.tensor([0.1, 0.2, 0.5, 0.9]), dimension=16)
+    for i in range(emb.shape[0]):
+        for j in range(i + 1, emb.shape[0]):
+            assert not torch.allclose(emb[i], emb[j])
+
+
+# ----------------------------------------------------------------------------
+# Kernel utilities: attention mask construction
+# ----------------------------------------------------------------------------
+def test_make_att_2d_masks_bidirectional():
+    result = make_att_2d_masks(torch.ones(1, 4, dtype=torch.bool), torch.zeros(1, 4, dtype=torch.long))
+    assert result.all()
+
+
+def test_make_att_2d_masks_causal():
+    result = make_att_2d_masks(torch.ones(1, 4, dtype=torch.bool), torch.ones(1, 4, dtype=torch.long))
+    expected = torch.tril(torch.ones(4, 4, dtype=torch.bool))
+    assert torch.equal(result[0], expected)
+
+
+def test_make_att_2d_masks_prefix_lm():
+    result = make_att_2d_masks(torch.ones(1, 5, dtype=torch.bool), torch.tensor([[0, 0, 0, 1, 1]], dtype=torch.long))
+    assert result[0, 0, 0]
+    assert not result[0, 0, 3]
+    assert result[0, 3, 0]
+    assert not result[0, 3, 4]
+
+
+def test_make_att_2d_masks_respects_padding():
+    pad = torch.tensor([[1, 1, 1, 0]], dtype=torch.bool)  # last token is padding
+    att = torch.zeros(1, 4, dtype=torch.long)  # full bidirectional
+    result = make_att_2d_masks(pad, att)
+    assert not result[0, :, 3].any(), "Nothing should attend to padded key"
+    assert not result[0, 3, :].any(), "Padded query should attend to nothing"
+
+
+def test_prepare_attention_masks_4d():
+    mask_2d = torch.tensor([[[True, False], [True, True]]])
+    mask_4d = prepare_attention_masks_4d(mask_2d)
+    assert mask_4d.shape == (1, 1, 2, 2)
+    assert mask_4d[0, 0, 0, 0].item() == 0.0
+    assert mask_4d[0, 0, 1, 1].item() == 0.0
+    expected = torch.tensor(OPENPI_ATTENTION_MASK_VALUE, dtype=mask_4d.dtype)
+    assert torch.equal(mask_4d[0, 0, 0, 1], expected)
+
+
+def test_attention_mask_value_finite_and_negative():
+    assert math.isfinite(OPENPI_ATTENTION_MASK_VALUE)
+    assert OPENPI_ATTENTION_MASK_VALUE < -1e30
+
+
+# ----------------------------------------------------------------------------
+# Normalization round-trips
+# ----------------------------------------------------------------------------
+def test_build_norm_buffers_missing():
+    assert _build_norm_buffers(None, "state") is None
+    assert _build_norm_buffers({}, "state") is None
+    assert _build_norm_buffers({"state": None}, "state") is None
+
+
+def test_build_norm_buffers_mean_std():
+    stats = {"state": {"mode": "mean_std", "mean": [1.0, 2.0], "std": [0.5, 0.5]}}
+    out = _build_norm_buffers(stats, "state")
+    assert out["mode"] == "mean_std"
+    assert torch.equal(out["mean"], torch.tensor([1.0, 2.0]))
+    assert torch.equal(out["std"], torch.tensor([0.5, 0.5]))
+
+
+def test_build_norm_buffers_min_max():
+    stats = {"action": {"mode": "min_max", "min": [-1.0, 0.0], "max": [1.0, 2.0]}}
+    out = _build_norm_buffers(stats, "action")
+    assert out["mode"] == "min_max"
+    assert torch.equal(out["min"], torch.tensor([-1.0, 0.0]))
+    assert torch.equal(out["max"], torch.tensor([1.0, 2.0]))
+
+
+def test_apply_norm_mean_std_forward_inverse():
+    stats = _build_norm_buffers({"s": {"mode": "mean_std", "mean": [0.5, -0.2], "std": [2.0, 0.1]}}, "s")
+    x = torch.tensor([[1.0, 0.3]])
+    z = _apply_norm(x, stats, inverse=False)
+    assert torch.allclose(z, torch.tensor([[0.25, 5.0]]), atol=1e-5)
+    x_back = _apply_norm(z, stats, inverse=True)
+    assert torch.allclose(x_back, x, atol=1e-5)
+
+
+def test_apply_norm_min_max_forward_inverse():
+    stats = _build_norm_buffers({"s": {"mode": "min_max", "min": [0.0, -1.0], "max": [1.0, 1.0]}}, "s")
+    x = torch.tensor([[0.25, 0.5]])
+    z = _apply_norm(x, stats, inverse=False)
+    assert torch.allclose(z, torch.tensor([[-0.5, 0.5]]), atol=1e-5)
+    x_back = _apply_norm(z, stats, inverse=True)
+    assert torch.allclose(x_back, x, atol=1e-5)
+
+
+def test_apply_norm_preserves_padded_tail():
+    stats = _build_norm_buffers({"s": {"mode": "mean_std", "mean": [0.0, 0.0], "std": [1.0, 1.0]}}, "s")
+    x = torch.tensor([[1.0, 2.0, 99.0, -99.0]])
+    z = _apply_norm(x, stats, inverse=False)
+    assert torch.allclose(z[:, :2], x[:, :2])
+    assert torch.allclose(z[:, 2:], x[:, 2:])
+
+
+def test_apply_norm_noop_when_stats_none():
+    x = torch.randn(2, 5)
+    assert torch.equal(_apply_norm(x, None, inverse=False), x)
+    assert torch.equal(_apply_norm(x, None, inverse=True), x)
+
+
+# ----------------------------------------------------------------------------
+# Gemma variant configs
+# ----------------------------------------------------------------------------
+def test_gemma_2b_dims():
+    c = get_gemma_config("gemma_2b")
+    assert c.width == 2048
+    assert c.depth == 18
+
+
+def test_gemma_300m_dims():
+    c = get_gemma_config("gemma_300m")
+    assert c.width == 1024
+    assert c.depth == 18
+
+
+def test_gemma_unknown_variant_raises():
+    with pytest.raises(ValueError):
+        get_gemma_config("gemma_7b")
+
+
+# ----------------------------------------------------------------------------
+# load_weights remap rules (tiny model; needs torch + transformers)
+# ----------------------------------------------------------------------------
+def _tiny_pi0_model():
+    """Construct a tiny π0 model for remap tests (full Gemma depth but small
+    action/state dims — the PaliGemma backbone is always full-size)."""
+    cfg = Pi0Config(max_action_dim=8, max_state_dim=8, chunk_size=2)
+    return Pi0ForActionPrediction(cfg)
+
+
+@pytest.fixture(autouse=True)
+def _silence_pi0_loader(caplog):
+    # The remap tests feed a single synthetic weight; the loader's
+    # "missing params" warning is expected noise here.
+    logging.getLogger("vllm_omni.diffusion.models.pi0.modeling_pi0").setLevel(logging.ERROR)
+    yield
+
+
+@pytest.mark.slow
+def test_remap_paligemma_submodules_to_nested_layout():
+    model = _tiny_pi0_model()
+    params = dict(model.named_parameters())
+    target = next(k for k in params if k.startswith("paligemma_with_expert.paligemma.model.vision_tower."))
+    flat_key = "model." + target.replace(
+        "paligemma_with_expert.paligemma.model.vision_tower.",
+        "paligemma_with_expert.paligemma.vision_tower.",
+    )
+    payload = torch.full(params[target].shape, 0.12345)
+    before = params[target].detach().clone()
+    model.load_weights([(flat_key, payload)])
+    after = dict(model.named_parameters())[target]
+    assert not torch.equal(before, after), "flat→nested remap broken"
+    assert torch.allclose(after, payload)
+
+
+@pytest.mark.slow
+def test_lm_head_remap_to_embed_tokens():
+    model = _tiny_pi0_model()
+    params = dict(model.named_parameters())
+    target = "paligemma_with_expert.paligemma.model.language_model.embed_tokens.weight"
+    assert target in params
+    payload = torch.full(params[target].shape, 0.5)
+    before = params[target].detach().clone()
+    model.load_weights([("model.paligemma_with_expert.paligemma.lm_head.weight", payload)])
+    after = dict(model.named_parameters())[target]
+    assert not torch.equal(before, after), "lm_head→embed_tokens remap broken"
+    assert torch.allclose(after, payload)
+
+
+@pytest.mark.slow
+def test_vision_tower_version_robust_remap():
+    """transformers >=5.4 flattens SigLIP from vision_tower.vision_model.* to
+    vision_tower.*; the lerobot/pi0_base checkpoint uses the vision_model infix.
+    load_weights must adapt so vision weights aren't silently dropped (which
+    would leave the vision encoder at random init)."""
+    model = _tiny_pi0_model()
+    params = dict(model.named_parameters())
+    # A representative vision-tower param as it exists on the model.
+    target = next(
+        k
+        for k in params
+        if k.startswith("paligemma_with_expert.paligemma.model.vision_tower.") and "patch_embedding.weight" in k
+    )
+    vt = "paligemma_with_expert.paligemma.model.vision_tower."
+    rest = target[len(vt) :]
+    # Build the checkpoint key in whichever layout differs from the model's.
+    if rest.startswith("vision_model."):
+        ckpt_key = vt + rest[len("vision_model.") :]
+    else:
+        ckpt_key = vt + "vision_model." + rest
+    payload = torch.full(params[target].shape, 0.073)
+    before = params[target].detach().clone()
+    model.load_weights([("model." + ckpt_key, payload)])
+    after = dict(model.named_parameters())[target]
+    assert not torch.equal(before, after), "vision_tower version-robust remap broken"
+    assert torch.allclose(after, payload)
+
+
+# ----------------------------------------------------------------------------
+# Image processor
+# ----------------------------------------------------------------------------
+def test_resize_with_pad_square():
+    out = resize_with_pad(torch.ones(1, 3, 224, 224), 224, 224)
+    assert out.shape == (1, 3, 224, 224)
+
+
+def test_resize_with_pad_wide():
+    """Wide source (H < W): padding applied to top/bottom (rows)."""
+    out = resize_with_pad(torch.zeros(1, 3, 100, 200), 224, 224)
+    assert out.shape == (1, 3, 224, 224)
+    assert out[0, 0, 0, 112].item() == -1.0  # top row is padded
+
+
+def test_resize_with_pad_tall():
+    """Tall source (H > W): padding applied to left/right (cols)."""
+    out = resize_with_pad(torch.zeros(1, 3, 400, 100), 224, 224)
+    assert out.shape == (1, 3, 224, 224)
+    assert out[0, 0, 112, 0].item() == -1.0  # left-most column padded
+
+
+def test_resize_with_pad_rejects_non_4d():
+    with pytest.raises(ValueError):
+        resize_with_pad(torch.zeros(3, 100, 200), 224, 224)
+
+
+def test_pil_to_tensor():
+    import numpy as np
+    from PIL import Image
+
+    img = Image.fromarray(np.full((10, 10, 3), 128, dtype=np.uint8))
+    t = pil_image_to_tensor(img)
+    assert t.shape == (1, 3, 10, 10)
+    expected = 128.0 / 255.0 * 2.0 - 1.0
+    assert abs(t[0, 0, 5, 5].item() - expected) < 1e-4
+
+
+def test_pil_to_tensor_rgba_to_rgb():
+    import numpy as np
+    from PIL import Image
+
+    rgba = np.full((8, 8, 4), 200, dtype=np.uint8)
+    img = Image.fromarray(rgba, mode="RGBA")
+    t = pil_image_to_tensor(img)
+    assert t.shape == (1, 3, 8, 8)
+
+
+def test_make_empty_image():
+    proc = Pi0ImageProcessor(image_size=224)
+    empty = proc.make_empty_image()
+    assert empty.shape == (1, 3, 224, 224)
+    assert (empty == -1.0).all()
+
+
+def test_preprocess_single_resizes_ndarray():
+    import numpy as np
+
+    proc = Pi0ImageProcessor(image_size=224)
+    arr = np.full((120, 120, 3), 100, dtype=np.uint8)
+    t = proc.preprocess_single(arr)
+    assert t.shape == (1, 3, 224, 224)
+
+
+# ----------------------------------------------------------------------------
+# build_model_inputs: camera ordering, padding, masks, state
+# ----------------------------------------------------------------------------
+class _FakeTokenizer:
+    """Minimal stand-in for the PaliGemma tokenizer (no download)."""
+
+    def __call__(
+        self, text, padding=None, max_length=None, truncation=None, add_special_tokens=None, return_tensors=None
+    ):
+        ids = [2] + [ord(c) % 100 for c in text][: max_length - 1]
+        ids = ids[:max_length]
+        attn = [1] * len(ids)
+        while len(ids) < max_length:
+            ids.append(0)
+            attn.append(0)
+        return {"input_ids": ids, "attention_mask": attn}
+
+
+def test_build_model_inputs_camera_order_and_padding():
+    import numpy as np
+
+    config = Pi0Config.from_model_config(_LEROBOT_CFG)  # 3-camera config
+    device = torch.device("cpu")
+    # Provide only 2 of the 3 declared cameras.
+    robot_obs = {
+        "images": {
+            "observation.images.base_0_rgb": np.full((224, 224, 3), 50, dtype=np.uint8),
+            "observation.images.left_wrist_0_rgb": np.full((224, 224, 3), 80, dtype=np.uint8),
+        },
+        "state": [0.1] * 14,  # raw state shorter than max_state_dim=32
+        "prompt": "pick up the block",
+    }
+    images, image_masks, lang_tokens, lang_masks, state = build_model_inputs(
+        robot_obs, config, _FakeTokenizer(), device
+    )
+    assert len(images) == 3
+    assert all(img.shape == (1, 3, 224, 224) for img in images)
+    masks = [bool(m.item()) for m in image_masks]
+    assert masks == [True, True, False]
+    # The padded (3rd) camera must be all -1.
+    assert (images[2] == -1.0).all()
+    assert lang_tokens.shape == (1, 48)
+    assert lang_masks.shape == (1, 48)
+    assert state.shape == (1, 32)
+    # State padded with zeros beyond the 14 real entries.
+    assert torch.allclose(state[0, 14:], torch.zeros(18))
+
+
+def test_build_model_inputs_state_truncated():
+    config = Pi0Config.from_model_config(_LEROBOT_CFG)
+    robot_obs = {"images": {}, "state": [0.5] * 40, "prompt": "x"}
+    _, _, _, _, state = build_model_inputs(robot_obs, config, _FakeTokenizer(), torch.device("cpu"))
+    assert state.shape == (1, 32)
+
+
+def test_build_model_inputs_image_key_map():
+    import numpy as np
+
+    cfg = Pi0Config.from_model_config({**_LEROBOT_CFG, "image_key_map": {"cam_high": "observation.images.base_0_rgb"}})
+    robot_obs = {
+        "images": {"cam_high": np.full((224, 224, 3), 60, dtype=np.uint8)},
+        "prompt": "go",
+    }
+    _, image_masks, _, _, _ = build_model_inputs(robot_obs, cfg, _FakeTokenizer(), torch.device("cpu"))
+    masks = [bool(m.item()) for m in image_masks]
+    assert masks == [True, False, False]  # only base_0_rgb (mapped) is present
+
+
+# ----------------------------------------------------------------------------
+# Version-stability / self-consistency (gated on a real pi0_base checkpoint)
+#
+# The LeRobot parity gate (test_pi0_parity.py) only validates correctness at
+# transformers 5.3.0 (where LeRobot loads pi0_base cleanly). Production vllm-omni
+# ships transformers 5.6.2, and LeRobot is not a usable reference there (it
+# incorrectly loads the SigLIP vision tower at >=5.4). So we pin the kernel's deterministic
+# output to golden values established at 5.3.0 and re-check them here: running on
+# the production transformers proves the port is version-stable and guards the
+# vision_tower / embed_scale version-robust paths against regressions.
+#
+#   PI0_SELFCONSIST_CKPT=/path/to/pi0_base \
+#       pytest tests/diffusion/models/pi0/test_pi0_units.py -k version_stable -v -s
+# ----------------------------------------------------------------------------
+import os  # noqa: E402
+
+_SELFCONSIST_CKPT = os.environ.get("PI0_SELFCONSIST_CKPT")
+
+# Golden values established at transformers 5.3.0 (where parity = 7.15e-07).
+_GOLDEN_SUM = -67.808113
+_GOLDEN_STD = 0.252789
+_GOLDEN_MEAN = -0.021190
+_GOLDEN_EMBED_IMAGE_STD = 2.17603
+
+
+def _selfconsist_inputs(device):
+    # NB: draw order is load-bearing — the golden values were established with
+    # state drawn first, then the 3 images, then lang (shared RNG, seed 0).
+    g = torch.Generator(device="cpu").manual_seed(0)
+    b = 2
+    state = torch.randn(b, 32, generator=g)
+    images = [torch.rand(b, 3, 224, 224, generator=g) for _ in range(3)]
+    masks = [torch.ones(b, dtype=torch.bool) for _ in range(3)]
+    lang = torch.randint(0, 1000, (b, 48), generator=g)
+    lmask = torch.ones(b, 48, dtype=torch.bool)
+    ng = torch.Generator(device="cpu").manual_seed(42)
+    noise = torch.randn(b, 50, 32, generator=ng)
+    return (
+        [im.to(device) for im in images],
+        [m.to(device) for m in masks],
+        lang.to(device),
+        lmask.to(device),
+        state.to(device),
+        noise.to(device),
+    )
+
+
+def _selfconsist_model(device):
+    import safetensors.torch
+
+    model = Pi0ForActionPrediction(
+        Pi0Config(max_action_dim=32, max_state_dim=32, chunk_size=50, num_inference_steps=10, dtype="float32")
+    )
+    state = safetensors.torch.load_file(os.path.join(_SELFCONSIST_CKPT, "model.safetensors"))
+    filled = model.load_weights(list(state.items()))
+    expected = {n for n, _ in model.named_parameters() if "rotary_emb" not in n and not n.endswith(".inv_freq")}
+    missing = expected - (filled or set())
+    assert not missing, f"{len(missing)} params received no weight (e.g. {sorted(missing)[:3]})"
+    return model.to(device).eval()
+
+
+@pytest.mark.slow
+@pytest.mark.skipif(not _SELFCONSIST_CKPT, reason="Set PI0_SELFCONSIST_CKPT to a pi0_base dir.")
+@torch.no_grad()
+def test_version_stable_actions():
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    model = _selfconsist_model(device)
+    images, masks, lang, lmask, state, noise = _selfconsist_inputs(device)
+    actions = (
+        model.sample_actions(
+            images=images,
+            image_masks=masks,
+            lang_tokens=lang,
+            lang_masks=lmask,
+            state=state,
+            noise=noise,
+            num_steps=10,
+        )
+        .float()
+        .cpu()
+    )
+    s, m, std = actions.sum().item(), actions.mean().item(), actions.std().item()
+    assert abs(s - _GOLDEN_SUM) < 0.5, f"action sum drifted: {s} vs {_GOLDEN_SUM}"
+    assert abs(std - _GOLDEN_STD) < 0.01, f"action std drifted: {std} vs {_GOLDEN_STD}"
+    assert abs(m - _GOLDEN_MEAN) < 0.01, f"action mean drifted: {m} vs {_GOLDEN_MEAN}"
+
+
+@pytest.mark.slow
+@pytest.mark.skipif(not _SELFCONSIST_CKPT, reason="Set PI0_SELFCONSIST_CKPT to a pi0_base dir.")
+@torch.no_grad()
+def test_version_stable_embed_image():
+    """Directly guards the SigLIP vision-tower load across the >=5.4 flattening."""
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    model = _selfconsist_model(device)
+    g = torch.Generator(device="cpu").manual_seed(7)
+    img = torch.rand(1, 3, 224, 224, generator=g, dtype=torch.float32).to(device)
+    std = model.paligemma_with_expert.embed_image(img).float().std().item()
+    assert abs(std - _GOLDEN_EMBED_IMAGE_STD) < 0.05, (
+        f"embed_image std drifted: {std} vs {_GOLDEN_EMBED_IMAGE_STD} — "
+        "likely a vision_tower weight-load regression under this transformers version."
+    )

tests/distributed/omni_connectors/test_chunk_transfer_adapter.py

@@ -1728,3 +1728,112 @@ def test_purge_is_noop_on_empty_deques(build_adapter):
     adapter.restore_queues(waiting_queue, running_queue, scheduler_requests={})
     assert running_queue == []
     assert waiting_queue == []
+
+
+# --------------------------------------------------------------------------- #
+#  Chunk-wait deadline (RFC #4855 R1.1, issue #3833)
+#
+#  Before this, the async-chunk path had no deadline of any kind, so a dropped
+#  terminal chunk or an upstream stage that died mid-stream parked the request
+#  in WAITING_FOR_CHUNK forever. None of these scenarios were covered.
+# --------------------------------------------------------------------------- #
+
+
+def _park_in_chunk_wait(adapter, request, *, waiting=True):
+    """Drive a request through one process_pending_chunks round into the wait."""
+    queue = DummyWaitingQueue([request]) if waiting else [request]
+    if waiting:
+        adapter.process_pending_chunks(queue, [])
+    else:
+        adapter.process_pending_chunks(DummyWaitingQueue(), queue)
+    return queue
+
+
+def test_chunk_wait_clock_starts_when_a_request_parks(build_adapter):
+    adapter, _ = build_adapter(stage_id=1, model_mode="ar")
+    request = _req("r1", RequestStatus.WAITING)
+
+    _park_in_chunk_wait(adapter, request)
+
+    assert request.status == RequestStatus.WAITING_FOR_CHUNK
+    assert "r1" in adapter._waiting_since
+
+
+def test_chunk_wait_does_not_expire_before_the_deadline(build_adapter):
+    adapter, _ = build_adapter(stage_id=1, model_mode="ar")
+    _park_in_chunk_wait(adapter, _req("r1", RequestStatus.WAITING))
+
+    assert adapter.collect_timed_out_request_ids(timeout_s=600.0) == set()
+    assert "r1" in adapter._waiting_since
+
+
+def test_dropped_terminal_chunk_expires_the_request(build_adapter):
+    """The #3833 scenario: upstream stops sending and never marks the stream done."""
+    adapter, _ = build_adapter(stage_id=1, model_mode="ar")
+    _park_in_chunk_wait(adapter, _req("r1", RequestStatus.WAITING))
+
+    adapter._waiting_since["r1"] -= 601.0
+
+    assert adapter.collect_timed_out_request_ids(timeout_s=600.0) == {"r1"}
+    # Cleared, so a second sweep does not re-report the same request.
+    assert adapter.collect_timed_out_request_ids(timeout_s=600.0) == set()
+
+
+def test_arriving_chunk_resets_the_clock(build_adapter):
+    """A slow but healthy stream must never expire: the deadline measures stall
+    time between chunks, not the lifetime of the stream."""
+    adapter, _ = build_adapter(stage_id=1, model_mode="ar")
+    request = _req("r1", RequestStatus.WAITING)
+    queue = _park_in_chunk_wait(adapter, request)
+    adapter.restore_queues(queue, [], scheduler_requests={"r1": request})
+
+    # Age the wait to just under the deadline, then deliver a chunk.
+    adapter._waiting_since["r1"] -= 599.0
+    adapter._finished_load_reqs.add("r1")
+    adapter.process_pending_chunks(queue, [])
+
+    assert "r1" not in adapter._waiting_since
+    assert adapter.collect_timed_out_request_ids(timeout_s=600.0) == set()
+
+
+def test_a_disabled_deadline_never_expires(build_adapter):
+    adapter, _ = build_adapter(stage_id=1, model_mode="ar")
+    _park_in_chunk_wait(adapter, _req("r1", RequestStatus.WAITING))
+    adapter._waiting_since["r1"] -= 10_000.0
+
+    assert adapter.collect_timed_out_request_ids(timeout_s=0.0) == set()
+    assert adapter.collect_timed_out_request_ids(timeout_s=-1.0) == set()
+
+
+def test_finished_request_leaves_no_stale_timestamp(build_adapter):
+    """Otherwise a completed request would be reported as timed out later."""
+    adapter, _ = build_adapter(stage_id=1, model_mode="ar")
+    request = _req("r1", RequestStatus.WAITING)
+    _park_in_chunk_wait(adapter, request)
+
+    adapter.finish_requests(["r1"], RequestStatus.FINISHED_STOPPED, {"r1": request})
+
+    assert "r1" not in adapter._waiting_since
+    assert adapter.collect_timed_out_request_ids(timeout_s=0.001) == set()
+
+
+def test_aborted_request_leaves_no_stale_timestamp(build_adapter):
+    adapter, _ = build_adapter(stage_id=1, model_mode="ar")
+    _park_in_chunk_wait(adapter, _req("r1", RequestStatus.WAITING))
+
+    adapter.cleanup_receiver("r1")
+
+    assert "r1" not in adapter._waiting_since
+
+
+def test_expiry_is_per_request(build_adapter):
+    """One stalled stream must not take down its healthy neighbours."""
+    adapter, _ = build_adapter(stage_id=1, model_mode="ar", max_num_seqs=4)
+    stalled = _req("stalled", RequestStatus.WAITING)
+    healthy = _req("healthy", RequestStatus.WAITING)
+    adapter.process_pending_chunks(DummyWaitingQueue([stalled, healthy]), [])
+
+    adapter._waiting_since["stalled"] -= 601.0
+
+    assert adapter.collect_timed_out_request_ids(timeout_s=600.0) == {"stalled"}
+    assert "healthy" in adapter._waiting_since

tests/e2e/accuracy/ltx/test_ltx25_official_similarity.py

@@ -463,7 +463,7 @@ def _audio_metrics(reference: np.ndarray, prediction: np.ndarray) -> dict[str, f
     }
 
 
-@pytest.mark.full_model
+@pytest.mark.slow
 @pytest.mark.benchmark
 @pytest.mark.diffusion
 @hardware_test(res={"cuda": "H100"}, num_cards=1)
@@ -591,7 +591,7 @@ def test_ltx25_distilled_two_stage_matches_official(accuracy_artifact_root: Path
     assert audio_metrics["relative_l2"] <= LTX25_AUDIO_RELATIVE_L2_THRESHOLD
 
 
-@pytest.mark.full_model
+@pytest.mark.slow
 @pytest.mark.benchmark
 @pytest.mark.diffusion
 @hardware_test(res={"cuda": "H100"}, num_cards=1)

tests/e2e/accuracy/ltx/test_ltx_official_similarity.py

@@ -544,7 +544,7 @@ def _audio_metrics(reference: np.ndarray, prediction: np.ndarray) -> dict[str, f
 
 
 @pytest.mark.parametrize("case", CASES, ids=lambda case: case.name)
-@pytest.mark.full_model
+@pytest.mark.slow
 @pytest.mark.benchmark
 @pytest.mark.diffusion
 @hardware_test(res={"cuda": "H100"}, num_cards=1)

tests/e2e/accuracy/test_qwen_image_edit.py

@@ -17,7 +17,7 @@ from tests.helpers.mark import hardware_test
 from tests.helpers.media import get_asset_path
 from tests.helpers.runtime import OmniServer
 
-pytestmark = [pytest.mark.full_model, pytest.mark.diffusion]
+pytestmark = [pytest.mark.diffusion]
 
 
 SINGLE_MODEL = "Qwen/Qwen-Image-Edit"
@@ -253,6 +253,7 @@ def _diffusers_output_single_image(accuracy_artifact_root: Path, qwen_bear_image
     )
 
 
+@pytest.mark.slow
 @pytest.mark.benchmark
 @hardware_test(res={"cuda": "H100"}, num_cards=1)
 def test_qwen_image_edit_single_matches_diffusers(
@@ -278,6 +279,7 @@ def test_qwen_image_edit_single_matches_diffusers(
     )
 
 
+@pytest.mark.slow
 @pytest.mark.benchmark
 @hardware_test(res={"cuda": "H100"}, num_cards=1)
 def test_qwen_image_edit_2511_matches_diffusers_pixelwise(accuracy_artifact_root: Path) -> None:

tests/e2e/accuracy/test_qwen_image_layered.py

@@ -17,7 +17,7 @@ from tests.helpers.env import run_post_test_cleanup, run_pre_test_cleanup
 from tests.helpers.mark import hardware_test
 from tests.helpers.runtime import OmniServer
 
-pytestmark = [pytest.mark.full_model, pytest.mark.diffusion]
+pytestmark = [pytest.mark.slow, pytest.mark.diffusion]
 
 
 MODEL_ID = "Qwen/Qwen-Image-Layered"

tests/e2e/accuracy/test_video_streaming_output_similarity.py

@@ -11,7 +11,7 @@ from tests.e2e.accuracy.helpers import assert_video_similarity_metrics, probe_vi
 from tests.helpers.mark import hardware_marks
 from tests.helpers.runtime import DiffusionResponse, OmniServer, OpenAIClientHandler
 
-pytestmark = [pytest.mark.diffusion, pytest.mark.full_model]
+pytestmark = [pytest.mark.diffusion, pytest.mark.slow]
 
 MODEL = "BestWishYsh/Helios-Distilled"
 PROMPT = "A serene lakeside sunrise with mist over the water."

tests/e2e/offline_inference/test_bagel_expansion.py

@@ -138,7 +138,7 @@ def _make_file_lora_request(adapter_dir: Path) -> LoRARequest:
 
 
 pytestmark = [
-    pytest.mark.full_model,
+    pytest.mark.slow,
     pytest.mark.diffusion,
     pytest.mark.parametrize("omni_runner", [_OMNI_RUNNER_PARAM], indirect=True),
 ]

tests/e2e/offline_inference/test_glm_image_autoround_w4a16_expansion.py

@@ -109,7 +109,7 @@ def _diffusion_sampling_params(
 
 
 pytestmark = [
-    pytest.mark.full_model,
+    pytest.mark.slow,
     pytest.mark.diffusion,
 ]
 

tests/e2e/offline_inference/test_moss_tts_realtime.py

@@ -96,7 +96,7 @@ def _get_test_config() -> str:
 
 pytestmark = [
     pytest.mark.skip(reason="https://github.com/vllm-project/vllm-omni/issues/4700"),
-    pytest.mark.full_model,
+    pytest.mark.slow,
     pytest.mark.tts,
     pytest.mark.parametrize(
         "omni_runner",
@@ -271,7 +271,6 @@ def _collect_audio(omni_runner: OmniRunner, request: dict) -> tuple[torch.Tensor
 # ---------------------------------------------------------------------------
 
 
-@pytest.mark.advanced_model
 @hardware_test(res={"cuda": "L4"}, num_cards=1)
 def test_moss_tts_realtime_english(omni_runner: OmniRunner, ref_audio_path: str) -> None:
     """MossTTSRealtime: English voice_clone produces non-empty 24 kHz audio."""
@@ -283,7 +282,6 @@ def test_moss_tts_realtime_english(omni_runner: OmniRunner, ref_audio_path: str)
     assert not torch.all(audio == 0), "Audio is silence"
 
 
-@pytest.mark.advanced_model
 @hardware_test(res={"cuda": "L4"}, num_cards=1)
 def test_moss_tts_realtime_chinese(omni_runner: OmniRunner, ref_audio_path: str) -> None:
     """MossTTSRealtime: Chinese input produces non-empty audio."""

tests/e2e/online_serving/test_bagel_expansion.py

@@ -22,7 +22,7 @@ from tests.helpers.mark import hardware_marks
 from tests.helpers.runtime import OmniServer, OmniServerParams, OpenAIClientHandler, dummy_messages_from_mix_data
 from tests.helpers.stage_config import get_deploy_config_path, modify_stage_config
 
-pytestmark = [pytest.mark.diffusion, pytest.mark.full_model]
+pytestmark = [pytest.mark.diffusion, pytest.mark.slow]
 
 PROMPT = "A futuristic city skyline at twilight, cyberpunk style, ultra-detailed, high resolution."
 NEGATIVE_PROMPT = "low quality, blurry, distorted, deformed, watermark"

tests/e2e/online_serving/test_bagel_multi_replicas.py

@@ -1,6 +1,8 @@
-"""Nightly-only e2e coverage for BAGEL diffusion multi-replica serving.
+"""Weekly e2e coverage for BAGEL diffusion multi-replica serving.
 
 This test needs 4 H100 GPUs; keep it out of generic test_bagel_* jobs.
+It is covered by the weekly Diffusion · H100 · Multi-GPU lane
+(``.buildkite/cuda/test-weekly.yml``, ``mirror_hardwares: h100_4``).
 """
 
 import os
@@ -17,7 +19,7 @@ MODEL = "ByteDance-Seed/BAGEL-7B-MoT"
 MULTI_REPLICA_DEPLOY = get_deploy_config_path("ci/bagel_multi_replicas_4gpu.yaml")
 ROUTE_STRESS_REQUESTS = 6
 
-pytestmark = [pytest.mark.full_model, pytest.mark.diffusion]
+pytestmark = [pytest.mark.slow, pytest.mark.diffusion]
 
 test_params = [
     OmniServerParams(

tests/e2e/online_serving/test_boogu_image.py

@@ -16,7 +16,7 @@ parity vs. the Diffusers baseline is covered separately (see the support plan, s
 From ``tests/``::
 
     pytest -s -v e2e/online_serving/test_boogu_image.py \
-        -m "full_model and diffusion and H100" --run-level=full_model
+        -m "slow and diffusion and H100"
 """
 
 import os
@@ -28,7 +28,7 @@ from tests.helpers.runtime import OmniServer, OmniServerParams, OpenAIClientHand
 
 os.environ["VLLM_WORKER_MULTIPROC_METHOD"] = "spawn"
 
-pytestmark = [pytest.mark.diffusion, pytest.mark.full_model]
+pytestmark = [pytest.mark.diffusion, pytest.mark.slow]
 
 MODEL = "Boogu/Boogu-Image-0.1-Base"
 T2I_PROMPT = "A mountain lake at sunset, photorealistic, cinematic lighting"

tests/e2e/online_serving/test_boogu_image_edit.py

@@ -19,7 +19,7 @@ reference image is supported.
 From ``tests/``::
 
     pytest -s -v e2e/online_serving/test_boogu_image_edit.py \
-        -m "full_model and diffusion and H100" --run-level=full_model
+        -m "slow and diffusion and H100"
 """
 
 import base64
@@ -35,7 +35,7 @@ from tests.helpers.runtime import (
     dummy_messages_from_mix_data,
 )
 
-pytestmark = [pytest.mark.diffusion, pytest.mark.full_model]
+pytestmark = [pytest.mark.diffusion, pytest.mark.slow]
 
 EDIT_PROMPT = "Change the style to a colored pencil drawing."
 SINGLE_CARD_FEATURE_MARKS = hardware_marks(res={"cuda": "H100"})

tests/e2e/online_serving/test_boogu_image_edit_expansion.py

@@ -25,7 +25,7 @@ model and are intentionally omitted.
 From ``tests/``::
 
     pytest -s -v e2e/online_serving/test_boogu_image_edit_expansion.py \
-        -m "full_model and diffusion and H100" --run-level=full_model
+        -m "slow and diffusion and H100"
 """
 
 import os
@@ -38,7 +38,7 @@ from tests.helpers.runtime import OmniServer, OmniServerParams, OpenAIClientHand
 
 os.environ["VLLM_WORKER_MULTIPROC_METHOD"] = "spawn"
 
-pytestmark = [pytest.mark.diffusion, pytest.mark.full_model]
+pytestmark = [pytest.mark.diffusion, pytest.mark.slow]
 
 MODEL = "Boogu/Boogu-Image-0.1-Edit"
 EDIT_PROMPT = "Change the style to a colored pencil drawing."

tests/e2e/online_serving/test_boogu_image_expansion.py

@@ -30,7 +30,7 @@ omitted (add rows here when those features land).
 From ``tests/``::
 
     pytest -s -v e2e/online_serving/test_boogu_image_expansion.py \
-        -m "full_model and diffusion and H100" --run-level=full_model
+        -m "slow and diffusion and H100"
 """
 
 import os
@@ -42,7 +42,7 @@ from tests.helpers.runtime import OmniServer, OmniServerParams, OpenAIClientHand
 
 os.environ["VLLM_WORKER_MULTIPROC_METHOD"] = "spawn"
 
-pytestmark = [pytest.mark.diffusion, pytest.mark.full_model]
+pytestmark = [pytest.mark.diffusion, pytest.mark.slow]
 
 MODEL = "Boogu/Boogu-Image-0.1-Base"
 T2I_PROMPT = "A mountain lake at sunset, photorealistic, cinematic lighting"

tests/e2e/online_serving/test_glm_image_expansion.py

@@ -30,7 +30,7 @@ from tests.helpers.runtime import (
 )
 from tests.helpers.stage_config import get_deploy_config_path, modify_stage_config
 
-pytestmark = [pytest.mark.diffusion, pytest.mark.full_model]
+pytestmark = [pytest.mark.diffusion, pytest.mark.slow]
 
 MODEL = os.environ.get("GLM_IMAGE_MODEL_PATH", "zai-org/GLM-Image")
 

tests/e2e/online_serving/test_lingbot_video_moe.py

@@ -63,7 +63,7 @@ def test_text_to_image_moe(omni_server: OmniServer, openai_client: OpenAIClientH
     )
 
 
-@pytest.mark.full_model
+@pytest.mark.slow
 @pytest.mark.diffusion
 @pytest.mark.parametrize("omni_server", _get_server_cases(MODEL), indirect=True)
 def test_video_generation_modes_moe(omni_server: OmniServer, openai_client: OpenAIClientHandler) -> None:

tests/e2e/online_serving/test_ltx.py

@@ -10,7 +10,7 @@ from tests.helpers.mark import hardware_marks
 from tests.helpers.media import generate_synthetic_image
 from tests.helpers.runtime import OmniServer, OmniServerParams, OpenAIClientHandler
 
-pytestmark = [pytest.mark.diffusion, pytest.mark.full_model]
+pytestmark = [pytest.mark.diffusion, pytest.mark.slow]
 
 DISTILLED_MODEL = os.getenv("VLLM_TEST_LTX23_DISTILLED_MODEL", "diffusers/LTX-2.3-Distilled-Diffusers")
 BASE_MODEL = os.getenv("VLLM_TEST_LTX23_MODEL", "diffusers/LTX-2.3-Diffusers")

tests/e2e/online_serving/test_ltx25.py

@@ -20,7 +20,7 @@ MODEL = os.getenv("VLLM_TEST_LTX25_MODEL", DEFAULT_MODEL)
 MODEL_REVISION = os.getenv("VLLM_TEST_LTX25_MODEL_REVISION", DEFAULT_REVISION if MODEL == DEFAULT_MODEL else "")
 PROMPT = "A red fox walks through a snowy forest while the camera remains fixed."
 
-pytestmark = [pytest.mark.diffusion, pytest.mark.full_model]
+pytestmark = [pytest.mark.diffusion, pytest.mark.slow]
 SINGLE_CARD_MARKS = hardware_marks(res={"cuda": "H100"})
 
 

tests/e2e/online_serving/test_ming_flash_omni_expansion.py

@@ -20,7 +20,7 @@ from tests.helpers.stage_config import get_deploy_config_path
 
 os.environ["VLLM_WORKER_MULTIPROC_METHOD"] = "spawn"
 
-pytestmark = [pytest.mark.omni, pytest.mark.full_model]
+pytestmark = [pytest.mark.omni, pytest.mark.slow]
 
 _SKIP_NEED_4_H100_NOT_CI = pytest.mark.skip(
     reason="Requires 4x H100 GPUs; skipped in CI for now.",

tests/e2e/online_serving/test_pi0_expansion.py

@@ -0,0 +1,85 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+"""E2E online serving test for π0 (Pi-Zero) OpenPI websocket serving.
+
+Boots ``vllm serve --omni --deploy-config pi0.yaml`` and drives the real OpenPI
+websocket (``/v1/realtime/robot/openpi``) — the same wire path a robot uses
+(handshake metadata → send observation → receive action chunk). Mirrors
+``tests/e2e/online_serving/test_dreamzero_expansion.py``. Needs a GPU + a
+pi0_base checkpoint; skipped in CI unless explicitly run.
+
+The in-process LeRobot parity oracle lives separately in
+``tests/diffusion/models/pi0/test_pi0_parity.py``.
+"""
+
+from __future__ import annotations
+
+import os
+
+import pytest
+
+from tests.helpers.mark import hardware_test
+from tests.helpers.runtime import (
+    OmniServerParams,
+    get_open_port,
+    pi0_openpi_require_dependencies,
+    pi0_openpi_run_policy_session,
+    pi0_openpi_validate_session_result,
+)
+
+os.environ["VLLM_WORKER_MULTIPROC_METHOD"] = "spawn"
+os.environ["VLLM_TEST_CLEAN_GPU_MEMORY"] = "0"
+
+MODEL = "lerobot/pi0_base"
+
+
+test_params = [
+    OmniServerParams(
+        model=MODEL,
+        port=8092,
+        server_args=[
+            "--deploy-config",
+            "vllm_omni/deploy/pi0.yaml",
+            "--served-model-name",
+            "pi0",
+            "--enforce-eager",
+            "--disable-log-stats",
+        ],
+        env_dict={
+            "ATTENTION_BACKEND": "torch",
+            "DIFFUSION_ATTENTION_BACKEND": "TORCH_SDPA",
+            "VLLM_DISABLE_COMPILE_CACHE": "1",
+            "MASTER_PORT": str(get_open_port()),
+        },
+    )
+]
+
+
+@pytest.mark.full_model
+@pytest.mark.diffusion
+@hardware_test(res={"cuda": "H100"})
+@pytest.mark.parametrize("omni_server", test_params, indirect=True)
+def test_pi0_openpi_online(omni_server):
+    try:
+        pi0_openpi_require_dependencies()
+    except ModuleNotFoundError as exc:
+        pytest.skip(str(exc))
+
+    result = pi0_openpi_run_policy_session(
+        host=omni_server.host,
+        port=omni_server.port,
+        prompt="pick up the red block and place it in the bin",
+        session_id="pi0-online-e2e",
+        num_steps=2,
+    )
+
+    # Asserts every returned chunk is [50, 32] + finite, and the handshake
+    # metadata matches pi0.yaml's policy_server_config.
+    pi0_openpi_validate_session_result(result)
+
+    metadata = result["metadata"]
+    assert tuple(metadata["image_resolution"]) == (224, 224)
+    assert metadata["needs_wrist_camera"] is True
+    assert metadata["needs_session_id"] is False
+    assert metadata["action_space"] == "joint_position"

tests/e2e/online_serving/test_qwen3_omni_expansion.py

@@ -452,11 +452,31 @@ def test_audio_in_video_default_loader_sampling_regression(omni_server, openai_c
 @hardware_test(res={"cuda": "H100", "rocm": "MI325"}, num_cards=2)
 @pytest.mark.parametrize("omni_server", test_params, indirect=True)
 def test_one_word_prompt_001(omni_server, openai_client) -> None:
-    """
+    """Catastrophic-regression gate on one-word pronunciation.
+
+    Whether a one-word answer is pronounced intelligibly is sampled, not guaranteed: the
+    talker runs at temperature 0.9 (the upstream default, see qwen3_omni_moe.yaml stage 1),
+    so asserting it per request gates on a random variable, which is what the old 10-retry
+    loop was compensating for.
+
+    Sends a fixed 40 requests and requires at least 29 to transcribe as the expected
+    word. 85.6% is a predeclared minimum acceptable success probability, taken from a
+    measured HF reference run (77/90); vLLM-Omni itself measured 92.2%. Assuming 40
+    independent Bernoulli trials, >=29/40 has a nominal rejection probability of 0.9%
+    at p=.856, with 92.9% power at p=.60 and 55.9% at p=.70.
+
+    Those are conditional on p, not a measured false-failure rate for this CI: 77/90 is
+    one point estimate (Wilson 76.8%-91.4%), and at the low end of that interval the gate
+    would reject about 20% of runs. Pinning down a real rate would take repeated
+    report-only runs on the target hardware.
+
+    A drop to 70% is caught only about half the time, so this is a smoke gate for gross
+    pipeline breakage, not a quality tracker -- resolving moderate drift needs ~80+
+    samples and belongs in an accuracy benchmark. See #5395.
+
     Input Modal: text only (one-word answer constraint).
     Output Modal: text, audio (default ``modalities``); ``key_words`` only assert on text.
     Input Setting: stream=True
-    Datasets: single request
     """
     messages = dummy_messages_from_mix_data(
         system_prompt=get_system_prompt(),
@@ -474,17 +494,13 @@ def test_one_word_prompt_001(omni_server, openai_client) -> None:
         "transcript_language": "en",
     }
 
-    # Retry only when assert_omni_response fails on text/audio cosine similarity (see tests/helpers/assertions.py).
-    _similarity_assert_msg = "The audio content is not same as the text"
-    _max_retries = 10
-    for attempt in range(_max_retries):
-        try:
-            openai_client.send_omni_request(request_config, request_num=get_max_batch_size())
-            break
-        except AssertionError as e:
-            if _similarity_assert_msg not in str(e) or attempt == _max_retries - 1:
-                raise
-            print(f"Similarity assertion failed, retrying {attempt + 2}/{_max_retries}: {e!r}")
+    # The judge runs per request so mismatches can be counted, which also transcribes
+    # concurrently where the previous request_num=5 path judged serially -- worth watching
+    # on tighter cards, since each judge loads whisper on the same device as the talker and
+    # code2wav stages.
+    openai_client.send_omni_request(
+        request_config, request_num=40, min_successes=29, max_concurrency=get_max_batch_size()
+    )
 
 
 @hardware_test(res={"cuda": "H100", "rocm": "MI325"}, num_cards=2)

tests/e2e/online_serving/test_qwen_image_edit.py

@@ -2,20 +2,19 @@
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
 """
-Online serving tests for the Qwen-Image-Edit family (image-to-image via chat completions).
+Online serving tests for ``Qwen/Qwen-Image-Edit-2511`` (image-to-image via chat completions).
 
-- ``test_single_image_to_image_001``: ``Qwen/Qwen-Image-Edit`` — one reference image, fixed 512×512.
-- ``test_multi_images_to_image_001``: ``Qwen/Qwen-Image-Edit-2511`` — two reference images, fixed 512×512.
-- ``test_different_sizes_001``: ``Qwen/Qwen-Image-Edit-2511`` only, ``slow`` — mixed input
-  resolutions; ``extra_body`` uses per-output ``width``/``height`` lists; the test client sends one
-  scalar-size request per list index in parallel and merges images (see ``OpenAIClientHandler.send_diffusion_request``).
+- ``test_single_image_to_image_001``: one reference image, fixed 512×512.
+- ``test_multi_images_to_image_001``: two reference images, fixed 512×512.
+- ``test_different_sizes_001``: ``slow`` — mixed input resolutions; ``extra_body`` uses
+  per-output ``width``/``height`` lists; the test client sends one scalar-size request
+  per list index in parallel and merges images (see ``OpenAIClientHandler.send_diffusion_request``).
 
-``_get_diffusion_feature_cases`` registers a single ``default`` ``OmniServerParams`` row per model.
+``_get_diffusion_feature_cases`` registers a single ``default`` ``OmniServerParams`` row.
 
 From ``tests/``::
 
-    pytest -s -v e2e/online_serving/test_qwen_image_edit.py -m "core_model and diffusion" --run-level=core_model
-    pytest -s -v e2e/online_serving/test_qwen_image_edit.py -m "advanced_model and diffusion" --run-level=advanced_model
+    pytest -s -v e2e/online_serving/test_qwen_image_edit.py -m "slow and diffusion" --run-level=full_model
 """
 
 import pytest
@@ -29,6 +28,7 @@ from tests.helpers.runtime import (
     dummy_messages_from_mix_data,
 )
 
+MODEL = "Qwen/Qwen-Image-Edit-2511"
 EDIT_PROMPT = "Transform this modern, geometrist image into a Vincent van Gogh style impressionist painting."
 MULTI_EDIT_PROMPT = (
     "Transform the first image into a Dadaism collage art. "
@@ -39,7 +39,7 @@ NEGATIVE_PROMPT = "blurry, low quality, modern, geometrist"
 SINGLE_CARD_FEATURE_MARKS = hardware_marks(res={"cuda": "H100"})
 
 
-def _get_diffusion_feature_cases(model: str):
+def _get_diffusion_feature_cases(model: str = MODEL):
     """Return one ``default`` ``OmniServerParams`` row for ``model`` (no extra ``server_args``)."""
     return [
         pytest.param(
@@ -56,11 +56,11 @@ def _get_diffusion_feature_cases(model: str):
 @pytest.mark.diffusion
 @pytest.mark.parametrize(
     "omni_server",
-    _get_diffusion_feature_cases("Qwen/Qwen-Image-Edit"),
+    _get_diffusion_feature_cases(),
     indirect=True,
 )
 def test_single_image_to_image_001(omni_server: OmniServer, openai_client: OpenAIClientHandler):
-    """Single-reference edit smoke for ``Qwen/Qwen-Image-Edit`` (CFG when negative prompt + ``true_cfg_scale`` > 1)."""
+    """Single-reference edit smoke for ``Qwen/Qwen-Image-Edit-2511`` (CFG when negative prompt + ``true_cfg_scale`` > 1)."""
     image_data_url = f"data:image/jpeg;base64,{generate_synthetic_image(512, 512)['base64']}"
 
     messages = dummy_messages_from_mix_data(image_data_url=image_data_url, content_text=EDIT_PROMPT)
@@ -86,7 +86,7 @@ def test_single_image_to_image_001(omni_server: OmniServer, openai_client: OpenA
 @pytest.mark.diffusion
 @pytest.mark.parametrize(
     "omni_server",
-    _get_diffusion_feature_cases("Qwen/Qwen-Image-Edit-2511"),
+    _get_diffusion_feature_cases(),
     indirect=True,
 )
 def test_multi_images_to_image_001(omni_server: OmniServer, openai_client: OpenAIClientHandler):
@@ -117,7 +117,7 @@ def test_multi_images_to_image_001(omni_server: OmniServer, openai_client: OpenA
 @pytest.mark.diffusion
 @pytest.mark.parametrize(
     "omni_server",
-    _get_diffusion_feature_cases("Qwen/Qwen-Image-Edit-2511"),
+    _get_diffusion_feature_cases(),
     indirect=True,
 )
 def test_different_sizes_001(omni_server: OmniServer, openai_client: OpenAIClientHandler):

tests/e2e/online_serving/test_qwen_image_edit_expansion.py

@@ -1,8 +1,6 @@
 """
 Comprehensive tests of diffusion features that are available in online serving mode
-and are supported by the following models:
-- Qwen-Image-Edit: single image input
-- Qwen-Image-Edit-2511: single image input and two image inputs
+and are supported by Qwen-Image-Edit-2511 (single image input and two image inputs).
 """
 
 import pytest
@@ -11,10 +9,10 @@ from tests.helpers.mark import hardware_marks
 from tests.helpers.media import generate_synthetic_image
 from tests.helpers.runtime import OmniServer, OmniServerParams, OpenAIClientHandler, dummy_messages_from_mix_data
 
-pytestmark = [pytest.mark.diffusion, pytest.mark.full_model]
+pytestmark = [pytest.mark.diffusion, pytest.mark.slow]
 
-EDIT_PROMPT = "Transform this modern, geometrist image into a Vincent van Gogh style impressionist painting."
-SINGLE_EDIT_PROMPT_2511 = "Restyle this image into a Vincent van Gogh style impressionist painting."
+MODEL = "Qwen/Qwen-Image-Edit-2511"
+SINGLE_EDIT_PROMPT = "Restyle this image into a Vincent van Gogh style impressionist painting."
 MULTI_EDIT_PROMPT = (
     "Transform the first image into a Dadaism collage art. "
     "Transform the second image into a Vincent van Gogh style painting. "
@@ -25,9 +23,7 @@ SINGLE_CARD_FEATURE_MARKS = hardware_marks(res={"cuda": "H100"})
 PARALLEL_FEATURE_MARKS = hardware_marks(res={"cuda": "H100"}, num_cards=2)
 
 
-# This test file targets two models, so I write a helper function.
-# If a similar test only involves one model, one can just define a global list variable.
-def _get_diffusion_feature_cases(model: str):
+def _get_diffusion_feature_cases(model: str = MODEL):
     return [
         pytest.param(
             OmniServerParams(
@@ -123,35 +119,7 @@ def _get_diffusion_feature_cases(model: str):
 
 @pytest.mark.parametrize(
     "omni_server",
-    _get_diffusion_feature_cases("Qwen/Qwen-Image-Edit"),
-    indirect=True,
-)
-def test_qwen_image_edit(omni_server: OmniServer, openai_client: OpenAIClientHandler):
-    """Test all diffusion features with Qwen-Image-Edit in regular end-user scenarios."""
-    image_data_url = f"data:image/jpeg;base64,{generate_synthetic_image(512, 512)['base64']}"
-
-    messages = dummy_messages_from_mix_data(image_data_url=image_data_url, content_text=EDIT_PROMPT)
-
-    # CFG parallel is only activated when a negative prompt and true_cfg_scale > 1.0 are both present
-    request_config = {
-        "model": omni_server.model,
-        "messages": messages,
-        "extra_body": {
-            "height": 512,
-            "width": 512,
-            "num_inference_steps": 2,
-            "negative_prompt": NEGATIVE_PROMPT,
-            "true_cfg_scale": 4.0,
-            "seed": 42,
-        },
-    }
-
-    openai_client.send_diffusion_request(request_config)
-
-
-@pytest.mark.parametrize(
-    "omni_server",
-    _get_diffusion_feature_cases("Qwen/Qwen-Image-Edit-2511"),
+    _get_diffusion_feature_cases(),
     indirect=True,
 )
 def test_qwen_image_edit_2511_single_image(omni_server: OmniServer, openai_client: OpenAIClientHandler):
@@ -166,7 +134,7 @@ def test_qwen_image_edit_2511_single_image(omni_server: OmniServer, openai_clien
     """
     image_data_url = f"data:image/jpeg;base64,{generate_synthetic_image(512, 512)['base64']}"
 
-    messages = dummy_messages_from_mix_data(image_data_url=image_data_url, content_text=SINGLE_EDIT_PROMPT_2511)
+    messages = dummy_messages_from_mix_data(image_data_url=image_data_url, content_text=SINGLE_EDIT_PROMPT)
 
     request_config = {
         "model": omni_server.model,
@@ -186,7 +154,7 @@ def test_qwen_image_edit_2511_single_image(omni_server: OmniServer, openai_clien
 
 @pytest.mark.parametrize(
     "omni_server",
-    _get_diffusion_feature_cases("Qwen/Qwen-Image-Edit-2511"),
+    _get_diffusion_feature_cases(),
     indirect=True,
 )
 def test_qwen_image_edit_2511_two_images(omni_server: OmniServer, openai_client: OpenAIClientHandler):

tests/e2e/online_serving/test_qwen_image_layered_expansion.py

@@ -18,7 +18,7 @@ from tests.helpers.mark import hardware_marks
 from tests.helpers.media import decode_b64_image, generate_synthetic_image
 from tests.helpers.runtime import OmniServer, OmniServerParams, OpenAIClientHandler, dummy_messages_from_mix_data
 
-pytestmark = [pytest.mark.diffusion, pytest.mark.full_model]
+pytestmark = [pytest.mark.diffusion, pytest.mark.slow]
 
 MODEL = "Qwen/Qwen-Image-Layered"
 EDIT_PROMPT = "Decompose this image into layers."

tests/e2e/online_serving/test_voxcpm2_tts_expansion.py

@@ -16,7 +16,7 @@ from tests.helpers.media import load_test_audio_data_url
 from tests.helpers.runtime import OmniServerParams
 from tests.helpers.stage_config import get_deploy_config_path
 
-pytestmark = [pytest.mark.full_model, pytest.mark.tts]
+pytestmark = [pytest.mark.slow, pytest.mark.tts]
 
 os.environ["VLLM_WORKER_MULTIPROC_METHOD"] = "spawn"
 

tests/e2e/online_serving/test_wan_2_1_vace_expansion.py

@@ -26,7 +26,7 @@ import pytest
 from tests.helpers.mark import hardware_marks
 from tests.helpers.runtime import OmniServer, OmniServerParams, OpenAIClientHandler
 
-pytestmark = [pytest.mark.diffusion, pytest.mark.full_model]
+pytestmark = [pytest.mark.diffusion, pytest.mark.slow]
 
 MODEL = "Wan-AI/Wan2.1-VACE-1.3B-diffusers"
 PROMPT = "A cat walking slowly across a sunlit garden path"

tests/engine/test_orchestrator_error_handling.py

@@ -411,6 +411,51 @@ async def test_async_chunk_prewarm_failure_reports_failing_downstream_stage(orch
             orchestrator_fixture.thread.join(timeout=5)
 
 
+@pytest.mark.asyncio
+async def test_async_chunk_prewarm_without_prompt_token_ids_fails_only_that_request(
+    orchestrator_factory,
+) -> None:
+    """R1.3 of #4855: an async-chunk request whose stage-0 prompt carries no
+    token ids cannot prewarm anything, so it must fail with a client error
+    instead of stalling.
+
+    Non-fatal is the point: ``fatal=True`` reads as "the engine died" and the
+    entrypoints act on it (``OmniLLM.generate`` raises out of its batch loop,
+    the serving layer calls ``terminate_if_errored``). One client's embeds-only
+    prompt must not reach other requests.
+    """
+    stage0 = FakeStageClient(stage_type="llm", final_output=False)
+    stage1 = FakeStageClient(stage_type="llm", final_output=True)
+    orchestrator_fixture = orchestrator_factory([stage0, stage1], async_chunk=True)
+
+    try:
+        await _enqueue_add_request(
+            orchestrator_fixture,
+            request_id="req-embeds",
+            # No prompt_token_ids: what an embeds-only prompt looks like here.
+            prompt=SimpleNamespace(request_id="req-embeds"),
+            original_prompt={"prompt": "embeds only"},
+            sampling_params_list=[_sampling_params(), _sampling_params()],
+            final_stage_id=1,
+        )
+
+        error_msg = await _wait_for_error_message(orchestrator_fixture, request_id="req-embeds")
+        assert error_msg.fatal is False
+        assert error_msg.status_code == 400
+        assert error_msg.error_type == "BadRequestError"
+        assert error_msg.stage_id == 0
+        assert "prompt_token_ids" in error_msg.error
+
+        # Nothing was prewarmed downstream, and the request is gone.
+        assert stage1.add_request_calls == []
+        await _wait_for(lambda: "req-embeds" not in orchestrator_fixture.orchestrator.request_states)
+        assert orchestrator_fixture.thread.is_alive()
+    finally:
+        if orchestrator_fixture.thread.is_alive():
+            orchestrator_fixture.request_sync_q.put_nowait(ShutdownRequestMessage())
+            orchestrator_fixture.thread.join(timeout=5)
+
+
 # ───────── Direct unit tests for the fault-isolation helpers ─────────
 
 

tests/engine/test_orchestrator_stage_input_bridge.py

@@ -256,12 +256,13 @@ async def test_async_prewarm_skips_outgoing_only_stage() -> None:
         duplex_identity=SimpleNamespace(),
     )
 
-    await orchestrator._prewarm_async_chunk_stages(
+    prewarmed = await orchestrator._prewarm_async_chunk_stages(
         "req-prewarm",
         SimpleNamespace(prompt_token_ids=[1, 2], resumable=True),
         req_state,
     )
 
+    assert prewarmed is True
     assert stage1.submitted == []
     assert len(stage2.submitted) == 1
     assert 1 not in req_state.stage_submit_ts
@@ -277,6 +278,7 @@ async def test_async_prewarm_skips_outgoing_only_stage() -> None:
 @pytest.mark.asyncio
 async def test_duplex_prewarm_runs_after_first_stage0_submission() -> None:
     port, stage_pools, request_states, prewarm, submission = _duplex_stage_port_submission()
+    prewarm.return_value = True
 
     result = await port.submit(submission)
 
@@ -285,6 +287,30 @@ async def test_duplex_prewarm_runs_after_first_stage0_submission() -> None:
     prewarm.assert_awaited_once_with("req-duplex", ANY, request_states["req-duplex"])
 
 
+@pytest.mark.asyncio
+async def test_duplex_submit_bails_out_when_prewarm_failed_the_request() -> None:
+    """A failed prewarm has already aborted the request and popped its state.
+
+    Returning a success result here would hand the control plane a replica for a
+    request that no longer exists, and the trailing bookkeeping would re-register
+    a running counter the cleanup just released -- a leak that never decrements.
+    """
+    port, stage_pools, request_states, prewarm, submission = _duplex_stage_port_submission()
+    counter = MagicMock()
+    port._running_counter = counter
+    prewarm.return_value = False
+    request_state = request_states["req-duplex"]
+
+    with pytest.raises(RuntimeError, match="prewarm failed"):
+        await port.submit(submission)
+
+    prewarm.assert_awaited_once()
+    assert request_state.duplex_stage_fences == {}
+    assert request_state.stage_submit_ts == {}
+    assert request_state.running_counter_registered is False
+    counter.increment.assert_not_called()
+
+
 @pytest.mark.asyncio
 async def test_async_route_forwards_to_outgoing_only_stage() -> None:
     orchestrator = object.__new__(Orchestrator)

tests/entrypoints/openai_api/test_audex_serving_guards.py

@@ -26,8 +26,13 @@ pytestmark = [pytest.mark.core_model, pytest.mark.cpu]
 
 
 def _serving(model_type: str = "audex") -> OmniOpenAIServingSpeech:
+    from vllm_omni.entrypoints.openai.tts_adapters import resolve_adapter
+    from vllm_omni.entrypoints.openai.tts_adapters.base import SpeechServingContext
+
     serving = OmniOpenAIServingSpeech.__new__(OmniOpenAIServingSpeech)
     serving._tts_model_type = model_type
+    adapter_cls = resolve_adapter(model_type)
+    serving._adapter = adapter_cls(SpeechServingContext(server=serving)) if adapter_cls is not None else None
     serving._mark_ref_audio_artifact_ready_for_request = lambda request_id: None
     serving._discard_ref_audio_artifact_warmup = lambda request_id: None
     return serving
@@ -154,7 +159,7 @@ class TestAudexCfgValidation:
         assert _adapter().validate(_speech_request(cfg_scale=None)) is None
         serving = TestAudexCfgInjection()._serving_with_fake_tokenizer()
         params = SimpleNamespace(extra_args={"cfg_scale": None})
-        out = serving._inject_audex_cfg_pair_args("req-1", {"prompt": "p"}, params)
+        out = serving._adapter._inject_audex_cfg_pair_args("req-1", {"prompt": "p"}, params)
         assert out.extra_args == {}
 
     @pytest.mark.parametrize("key", ["cfg_role", "cfg_pair_id", "cfg_null_prompt"])
@@ -177,21 +182,21 @@ class TestAudexCfgInjection:
     def test_no_extra_args_is_untouched(self):
         serving = self._serving_with_fake_tokenizer()
         params = SimpleNamespace(extra_args=None)
-        out = serving._inject_audex_cfg_pair_args("req-1", {"prompt": self._cond_prompt()}, params)
+        out = serving._adapter._inject_audex_cfg_pair_args("req-1", {"prompt": self._cond_prompt()}, params)
         assert out.extra_args is None
         assert params.extra_args is None
 
     def test_scale_one_leaves_params_byte_identical(self):
         serving = self._serving_with_fake_tokenizer()
         params = SimpleNamespace(extra_args={"cfg_scale": 1.0})
-        out = serving._inject_audex_cfg_pair_args("req-1", {"prompt": self._cond_prompt()}, params)
+        out = serving._adapter._inject_audex_cfg_pair_args("req-1", {"prompt": self._cond_prompt()}, params)
         assert out.extra_args == {}
 
     def test_guided_request_gains_pair_contract(self):
         serving = self._serving_with_fake_tokenizer()
         params = SimpleNamespace(extra_args={"cfg_scale": 1.5})
         cond = self._cond_prompt("Some transcription text here.")
-        out = serving._inject_audex_cfg_pair_args("req-42", {"prompt": cond}, params)
+        out = serving._adapter._inject_audex_cfg_pair_args("req-42", {"prompt": cond}, params)
 
         extra = out.extra_args
         assert extra["cfg_scale"] == 1.5
@@ -207,14 +212,14 @@ class TestAudexCfgInjection:
         serving = self._serving_with_fake_tokenizer()
         params = SimpleNamespace(extra_args={"cfg_scale": 1.5})
         with pytest.raises(ValueError, match="adapter-built text prompt"):
-            serving._inject_audex_cfg_pair_args("req-1", {"prompt_token_ids": [1]}, params)
+            serving._adapter._inject_audex_cfg_pair_args("req-1", {"prompt_token_ids": [1]}, params)
 
     def test_injection_never_mutates_input_params(self):
         """The input may be the engine's SHARED default params (review P2):
         pair state must land only on the returned clone."""
         serving = self._serving_with_fake_tokenizer()
         shared = SimpleNamespace(extra_args={"cfg_scale": 1.5}, temperature=0.1)
-        out = serving._inject_audex_cfg_pair_args("req-7", {"prompt": self._cond_prompt()}, shared)
+        out = serving._adapter._inject_audex_cfg_pair_args("req-7", {"prompt": self._cond_prompt()}, shared)
         assert out is not shared
         assert shared.extra_args == {"cfg_scale": 1.5}
         assert shared.temperature == 0.1
@@ -252,11 +257,11 @@ def test_guided_injection_sets_guided_temperature():
     serving = TestAudexCfgInjection()._serving_with_fake_tokenizer()
     cond = TestAudexCfgInjection()._cond_prompt()
     params = SimpleNamespace(extra_args={"cfg_scale": 1.5}, temperature=0.1)
-    out = serving._inject_audex_cfg_pair_args("req-1", {"prompt": cond}, params)
+    out = serving._adapter._inject_audex_cfg_pair_args("req-1", {"prompt": cond}, params)
     assert out.temperature == 0.05
 
     unguided = SimpleNamespace(extra_args={"cfg_scale": 1.0}, temperature=0.1)
-    out = serving._inject_audex_cfg_pair_args("req-2", {"prompt": cond}, unguided)
+    out = serving._adapter._inject_audex_cfg_pair_args("req-2", {"prompt": cond}, unguided)
     assert out.temperature == 0.1
 
 
@@ -340,7 +345,7 @@ class TestAudexTTAInjection:
     def test_default_injection_applies_rvq_and_cfg3(self):
         serving = self._serving()
         params = SimpleNamespace(extra_args=None)
-        out = serving._inject_audex_tta_args("req-9", self._prompt(), params)
+        out = serving._adapter._inject_audex_tta_args("req-9", self._prompt(), params)
 
         extra = out.extra_args
         rvq = extra["tta_rvq"]
@@ -355,15 +360,15 @@ class TestAudexTTAInjection:
     def test_explicit_scale_one_disables_cfg_but_keeps_rvq(self):
         serving = self._serving()
         params = SimpleNamespace(extra_args={"cfg_scale": 1.0})
-        out = serving._inject_audex_tta_args("req-1", self._prompt(), params)
+        out = serving._adapter._inject_audex_tta_args("req-1", self._prompt(), params)
         assert "tta_rvq" in out.extra_args
         assert "cfg_scale" not in out.extra_args
         assert "cfg_role" not in out.extra_args
 
     def test_rvq_contract_cached_per_process(self):
         serving = self._serving()
-        out_a = serving._inject_audex_tta_args("a", self._prompt(), SimpleNamespace(extra_args=None))
-        out_b = serving._inject_audex_tta_args("b", self._prompt(), SimpleNamespace(extra_args=None))
+        out_a = serving._adapter._inject_audex_tta_args("a", self._prompt(), SimpleNamespace(extra_args=None))
+        out_b = serving._adapter._inject_audex_tta_args("b", self._prompt(), SimpleNamespace(extra_args=None))
         assert out_a.extra_args["tta_rvq"] is out_b.extra_args["tta_rvq"]
 
     def test_injection_never_mutates_input_params(self):
@@ -372,8 +377,8 @@ class TestAudexTTAInjection:
         per-request pair state must land only on the returned clone."""
         serving = self._serving()
         shared = SimpleNamespace(extra_args=None)
-        out_1 = serving._inject_audex_tta_args("req-1", self._prompt(), shared)
-        out_2 = serving._inject_audex_tta_args("req-2", self._prompt(), shared)
+        out_1 = serving._adapter._inject_audex_tta_args("req-1", self._prompt(), shared)
+        out_2 = serving._adapter._inject_audex_tta_args("req-2", self._prompt(), shared)
         assert shared.extra_args is None
         assert out_1 is not shared and out_2 is not shared
         assert out_1.extra_args["cfg_pair_id"] == "req-1"

tests/entrypoints/openai_api/test_serving_speech.py

@@ -33,6 +33,8 @@ from vllm_omni.entrypoints.openai.protocol.audio import (
     OpenAICreateAudioGenerateRequest,
     OpenAICreateSpeechRequest,
     SpeechBatchItem,
+    SpeechInputTokenDetails,
+    SpeechTokenUsage,
     StreamingSpeechSessionConfig,
 )
 from vllm_omni.entrypoints.openai.serving_speech import (
@@ -52,6 +54,7 @@ from vllm_omni.model_executor.models.ming_tts.constants import (
     KEY_SPEAKER_WAVEFORM,
     KEY_SPEAKER_WAVEFORM_LENGTHS,
     SPEAKER_EMBEDDING_DIM,
+    TEXT_EOS_TOKEN_ID,
 )
 from vllm_omni.outputs import OmniRequestOutput
 
@@ -154,6 +157,7 @@ class TestAudioMixin:
 # Helper to create mock model output for endpoint tests
 def create_mock_audio_output_for_test(
     request_id: str = "speech-mock-123",
+    metrics: dict | None = None,
 ) -> OmniRequestOutput:
     class MockCompletionOutput:
         def __init__(self, index: int = 0):
@@ -180,11 +184,16 @@ def create_mock_audio_output_for_test(
     audio_tensor = torch.sin(torch.linspace(0, 440 * 2 * torch.pi, num_samples))
     mock_request_output = MockRequestOutput(request_id=request_id, audio_tensor=audio_tensor)
 
-    return OmniRequestOutput.from_stage_output(
+    if metrics is None:
+        metrics = {"stage_metrics": {"0": {"num_tokens_in": 0, "num_tokens_out": 5}}}
+
+    output = OmniRequestOutput.from_stage_output(
         mock_request_output,
         stage_id=0,
         final_output_type="audio",
     )
+    output.metrics = metrics
+    return output
 
 
 def _write_custom_voice_manifest(root: Path, *, model_type: str, voices: dict) -> None:
@@ -232,6 +241,7 @@ def test_app(mocker: MockerFixture):
         models=mock_models,
         request_logger=mock_request_logger,
     )
+    speech_server._tts_tokenizer = lambda text, padding=False: {"input_ids": text.split()}
 
     # Skip TTS validation in tests (mock doesn't set up supported_speakers)
     speech_server._validate_tts_request = mocker.MagicMock(return_value=None)
@@ -369,6 +379,37 @@ class TestSpeechAPI:
         assert response.headers["content-type"] == "audio/wav"
         assert len(response.content) > 0
 
+    def test_create_speech_includes_token_usage_and_detail_headers(self, client):
+        payload = {
+            "input": "Hello world",
+            "model": "tts-model",
+            "voice": "alloy",
+            "response_format": "wav",
+        }
+        response = client.post("/v1/audio/speech", json=payload)
+        assert response.status_code == 200
+        assert response.headers["x-vllm-omni-input-tokens"] == "2"
+        assert response.headers["x-vllm-omni-output-tokens"] == "5"
+        assert response.headers["x-vllm-omni-total-tokens"] == "7"
+        assert response.headers["x-vllm-omni-input-text-tokens"] == "2"
+        assert response.headers["x-vllm-omni-input-audio-tokens"] == "0"
+
+    def test_build_speech_usage_headers_uses_usage_and_detail_field_names(self):
+        usage = SpeechTokenUsage(
+            input_tokens=7,
+            output_tokens=11,
+            total_tokens=18,
+            input_token_details=SpeechInputTokenDetails(text_tokens=3, audio_tokens=4),
+        )
+        headers = OmniOpenAIServingSpeech._build_speech_usage_headers(usage)
+        assert headers == {
+            "X-VLLM-OMNI-INPUT-TOKENS": "7",
+            "X-VLLM-OMNI-OUTPUT-TOKENS": "11",
+            "X-VLLM-OMNI-TOTAL-TOKENS": "18",
+            "X-VLLM-OMNI-INPUT-TEXT-TOKENS": "3",
+            "X-VLLM-OMNI-INPUT-AUDIO-TOKENS": "4",
+        }
+
     def test_create_speech_mp3_format(self, client):
         payload = {
             "input": "Hello world",
@@ -3618,7 +3659,21 @@ class TestCosyVoice3Serving:
         assert error is not None
         assert "max_new_tokens" in error
 
-    def test_prepare_speech_generation_cosyvoice3(self, cosyvoice3_server, mocker: MockerFixture):
+    @pytest.mark.parametrize(
+        ("max_new_tokens", "expected_min_tokens", "expected_max_tokens"),
+        [
+            (None, 10, 2000),
+            (5, 5, 5),
+        ],
+    )
+    def test_prepare_speech_generation_cosyvoice3(
+        self,
+        cosyvoice3_server,
+        mocker: MockerFixture,
+        max_new_tokens: int | None,
+        expected_min_tokens: int,
+        expected_max_tokens: int,
+    ):
         cosyvoice3_server._build_cosyvoice3_prompt = mocker.AsyncMock(
             return_value={
                 "prompt": "Hello",
@@ -3626,15 +3681,23 @@ class TestCosyVoice3Serving:
                 "mm_processor_kwargs": {"prompt_text": "ref text", "sample_rate": 24000},
             }
         )
-        cosyvoice3_server._apply_cosyvoice3_dynamic_tokens = mocker.MagicMock(side_effect=lambda spl, req: spl)
+        cosyvoice3_server.model_config.hf_config = SimpleNamespace(
+            min_token_text_ratio=1, max_token_text_ratio=200, allowed_special=True
+        )
+        cosyvoice3_server._cosyvoice3_tokenizer = mocker.MagicMock()
+        mocker.patch("vllm_omni.model_executor.models.cosyvoice3.utils.extract_text_token", return_value=[None, 10])
 
         request = OpenAICreateSpeechRequest(
             input="Hello",
             ref_audio="data:audio/wav;base64,abc",
             ref_text="Reference text",
+            max_new_tokens=max_new_tokens,
         )
         request_id, generator, tts_params = asyncio.run(cosyvoice3_server._prepare_speech_generation(request))
+        sampling_params = cosyvoice3_server.engine_client.generate.call_args.kwargs["sampling_params_list"][0]
 
+        assert sampling_params.min_tokens == expected_min_tokens
+        assert sampling_params.max_tokens == expected_max_tokens
         assert request_id.startswith("speech-")
         assert generator == "generator"
         assert tts_params == {}
@@ -3718,6 +3781,90 @@ class TestGLMTTSServing:
         assert text_token_len == 3
         load_tokenizer.assert_called_once()
 
+    def test_prepare_speech_generation_glm_tts(self, glm_tts_server, mocker: MockerFixture):
+        glm_tts_server._build_glm_tts_prompt = mocker.AsyncMock(
+            return_value={
+                "prompt": "Hello",
+                "multi_modal_data": {"audio": (np.zeros(24000), 24000)},
+                "mm_processor_kwargs": {"prompt_text": "ref text", "sample_rate": 24000},
+            }
+        )
+        request = OpenAICreateSpeechRequest(
+            input="Hello",
+            ref_audio="data:audio/wav;base64,abc",
+            ref_text="Reference text",
+        )
+        glm_tts_server.model_config.hf_config = SimpleNamespace(min_token_text_ratio=1, max_token_text_ratio=200)
+        glm_tts_server._estimate_glm_tts_text_token_len = mocker.MagicMock(return_value=10)
+        request_id, generator, tts_params = asyncio.run(glm_tts_server._prepare_speech_generation(request))
+        sampling_params = glm_tts_server.engine_client.generate.call_args.kwargs["sampling_params_list"][0]
+
+        assert sampling_params.min_tokens == 10
+        assert sampling_params.max_tokens == 2000
+        assert request_id.startswith("speech-")
+        assert generator == "generator"
+        assert tts_params == {}
+        glm_tts_server._build_glm_tts_prompt.assert_awaited_once()
+
+
+@pytest.fixture
+def ming_tts_server(mocker: MockerFixture):
+    mocker.patch.object(OmniOpenAIServingSpeech, "_load_supported_speakers", return_value=set())
+    mocker.patch.object(OmniOpenAIServingSpeech, "_load_codec_frame_rate", return_value=None)
+
+    mock_engine_client = mocker.MagicMock()
+    mock_engine_client.errored = False
+    mock_engine_client.model_config = mocker.MagicMock(
+        model="inclusionAI/Ming-omni-tts-0.5B",
+        hf_config=SimpleNamespace(min_token_text_ratio=2, max_token_text_ratio=20),
+    )
+    mock_engine_client.default_sampling_params_list = [
+        SimpleNamespace(max_tokens=2048, min_tokens=None, extra_args=None)
+    ]
+    mock_engine_client.tts_batch_max_items = 32
+    mock_engine_client.generate = mocker.MagicMock(return_value="generator")
+    mock_engine_client.stage_configs = [
+        SimpleNamespace(
+            engine_args=SimpleNamespace(
+                model_stage="llm", model_arch="MingTTSForConditionalGeneration", worker_type="ar"
+            ),
+            tts_args={},
+        )
+    ]
+
+    mock_models = mocker.MagicMock()
+    mock_models.is_base_model.return_value = True
+    return OmniOpenAIServingSpeech(
+        engine_client=mock_engine_client,
+        models=mock_models,
+        request_logger=mocker.MagicMock(),
+    )
+
+
+class TestMingTTSServing:
+    def test_prepare_speech_generation_ming(self, ming_tts_server, mocker: MockerFixture):
+        ming_tts_server._adapter.build = mocker.AsyncMock(
+            return_value=PreparedRequest(
+                prompt={"prompt": "hello"},
+                tts_params={},
+                model_type="ming_tts",
+            )
+        )
+
+        request = OpenAICreateSpeechRequest(
+            input="Hello",
+            ref_audio="data:audio/wav;base64,abc",
+            ref_text="Reference text",
+            max_new_tokens=7,
+        )
+        asyncio.run(ming_tts_server._prepare_speech_generation(request))
+
+        assert ming_tts_server._tts_model_type == "ming_tts"
+        ming_tts_server._adapter.build.assert_awaited_once()
+        sampling_params = ming_tts_server.engine_client.generate.call_args.kwargs["sampling_params_list"][0]
+        assert sampling_params.stop_token_ids == [TEXT_EOS_TOKEN_ID]
+        assert sampling_params.max_tokens == 8
+
 
 @pytest.fixture
 def ming_flash_omni_tts_server(mocker: MockerFixture):
@@ -3901,6 +4048,9 @@ class TestTTSAsyncOffloading:
                     model_type=adapter_model_type,
                 )
 
+            def apply_sampling_overrides(self, sampling_params_list, request, prompt, request_id=None):
+                return sampling_params_list
+
         voxtral_server._tts_model_type = legacy_tts_model_type
         mocker.patch.object(voxtral_server, "_get_tts_adapter", return_value=FakeAdapter())
         log_info = mocker.patch("vllm_omni.entrypoints.openai.serving_speech.logger.info")

tests/entrypoints/openai_api/test_tts_detection.py

@@ -14,7 +14,6 @@ import itertools
 import pytest
 
 from vllm_omni.entrypoints.openai.tts_adapters import (
-    LEGACY_TTS_DETECTORS,
     TTS_ADAPTER_REGISTRY,
     all_tts_stage_keys,
     detect_tts_model_type,
@@ -282,27 +281,17 @@ def test_entry_stage_archs_is_ming_only():
     assert tts_entry_stage_archs() == frozenset({"MingTTSForConditionalGeneration"})
 
 
-def test_every_detected_type_is_adapter_backed_or_declared_legacy():
-    """Detection may only name a model that has an adapter or an explicit
-    :data:`LEGACY_TTS_DETECTORS` entry — never an undeclared string."""
-    legacy_names = {d.name for d in LEGACY_TTS_DETECTORS}
+def test_every_detected_type_is_adapter_backed():
+    """Detection may only name a model that has a registered adapter."""
     for stage, arch in itertools.product(_STAGES, _ARCHS):  # full cross product on purpose
         detected = detect_tts_model_type(stage, arch)
         if detected is None:
             continue
-        assert detected in TTS_ADAPTER_REGISTRY or detected in legacy_names, (
+        assert detected in TTS_ADAPTER_REGISTRY, (
             f"detection produced undeclared model type {detected!r} for stage={stage!r} arch={arch!r}"
         )
 
 
-def test_legacy_detectors_have_no_adapter():
-    """A legacy entry that gained an adapter must be deleted from the list."""
-    for detector in LEGACY_TTS_DETECTORS:
-        assert resolve_adapter(detector.name) is None, (
-            f"{detector.name!r} now has an adapter; remove it from LEGACY_TTS_DETECTORS"
-        )
-
-
 def test_no_ambiguous_detectors_at_equal_priority():
     """Two detectors that can match the same deployment must be ordered.
 

tests/entrypoints/openai_api/test_video_api_utils.py

@@ -2,16 +2,122 @@
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 """Unit tests for OpenAI-compatible video API encoding helpers."""
 
+import base64
+from io import BytesIO
+
+import httpx
 import numpy as np
 import pytest
 import torch
+from PIL import Image
+from vllm import envs
 
 from vllm_omni.diffusion.postprocess import rife_interpolator
 from vllm_omni.entrypoints.openai import video_api_utils
+from vllm_omni.entrypoints.openai.errors import InvalidInputReferenceError
 
 pytestmark = [pytest.mark.core_model, pytest.mark.cpu]
 
 
+def _png_bytes() -> bytes:
+    buffer = BytesIO()
+    Image.new("RGB", (2, 1), color=(12, 34, 56)).save(buffer, format="PNG")
+    return buffer.getvalue()
+
+
+def _install_http_transport(monkeypatch, handler, client_kwargs):
+    async_client = httpx.AsyncClient
+
+    def _client_factory(*args, **kwargs):
+        client_kwargs.append(kwargs.copy())
+        return async_client(*args, transport=httpx.MockTransport(handler), **kwargs)
+
+    monkeypatch.setattr(video_api_utils.httpx, "AsyncClient", _client_factory)
+
+
+@pytest.mark.asyncio
+async def test_decode_image_url_follows_redirects_when_allowed(monkeypatch):
+    requested_paths = []
+    client_kwargs = []
+
+    def _handler(request):
+        requested_paths.append(request.url.path)
+        if request.url.path == "/redirect.png":
+            return httpx.Response(302, headers={"location": "/image.png"})
+        return httpx.Response(200, content=_png_bytes(), headers={"content-type": "image/png"})
+
+    monkeypatch.setattr(envs, "VLLM_MEDIA_URL_ALLOW_REDIRECTS", True)
+    _install_http_transport(monkeypatch, _handler, client_kwargs)
+
+    image = await video_api_utils.decode_image_url("https://example.com/redirect.png")
+
+    assert image.size == (2, 1)
+    assert image.mode == "RGB"
+    assert requested_paths == ["/redirect.png", "/image.png"]
+    assert client_kwargs == [{"timeout": 60, "follow_redirects": True}]
+
+
+@pytest.mark.asyncio
+async def test_decode_image_url_rejects_redirects_when_disabled(monkeypatch):
+    requested_paths = []
+    client_kwargs = []
+
+    def _handler(request):
+        requested_paths.append(request.url.path)
+        return httpx.Response(302, headers={"location": "/image.png"})
+
+    monkeypatch.setattr(envs, "VLLM_MEDIA_URL_ALLOW_REDIRECTS", False)
+    _install_http_transport(monkeypatch, _handler, client_kwargs)
+
+    with pytest.raises(InvalidInputReferenceError, match="redirect.*VLLM_MEDIA_URL_ALLOW_REDIRECTS"):
+        await video_api_utils.decode_image_url("https://example.com/redirect.png")
+
+    assert requested_paths == ["/redirect.png"]
+    assert client_kwargs == [{"timeout": 60, "follow_redirects": False}]
+
+
+@pytest.mark.asyncio
+async def test_decode_image_url_reports_http_status(monkeypatch):
+    client_kwargs = []
+
+    def _handler(request):
+        return httpx.Response(404)
+
+    monkeypatch.setattr(envs, "VLLM_MEDIA_URL_ALLOW_REDIRECTS", True)
+    _install_http_transport(monkeypatch, _handler, client_kwargs)
+
+    with pytest.raises(InvalidInputReferenceError, match="server returned HTTP 404"):
+        await video_api_utils.decode_image_url("https://example.com/missing.png")
+
+
+@pytest.mark.asyncio
+async def test_decode_image_url_reports_connection_failure(monkeypatch):
+    client_kwargs = []
+
+    def _handler(request):
+        raise httpx.ConnectError("connection refused", request=request)
+
+    monkeypatch.setattr(envs, "VLLM_MEDIA_URL_ALLOW_REDIRECTS", True)
+    _install_http_transport(monkeypatch, _handler, client_kwargs)
+
+    with pytest.raises(InvalidInputReferenceError, match="failed to download image"):
+        await video_api_utils.decode_image_url("https://example.com/unreachable.png")
+
+
+@pytest.mark.asyncio
+async def test_decode_image_url_keeps_data_urls_local(monkeypatch):
+    def _unexpected_http_client(*args, **kwargs):
+        pytest.fail("data URLs must not create an HTTP client")
+
+    monkeypatch.setattr(video_api_utils.httpx, "AsyncClient", _unexpected_http_client)
+    encoded_image = base64.b64encode(_png_bytes()).decode()
+
+    image = await video_api_utils.decode_image_url(f"data:image/png;base64,{encoded_image}")
+
+    assert image.size == (2, 1)
+    assert image.mode == "RGB"
+
+
 def _install_fake_video_mux(monkeypatch, mux_calls):
     def _fake_mux_video_audio_bytes(frames, audio, fps, audio_sample_rate, video_codec_options=None):
         mux_calls.append(

tests/entrypoints/openai_api/test_video_server.py

@@ -16,15 +16,17 @@ from pathlib import Path
 from types import SimpleNamespace
 
 import av
+import httpx
 import numpy as np
 import pytest
 from fastapi import FastAPI, HTTPException
 from fastapi.testclient import TestClient
 from PIL import Image
 from pytest_mock import MockerFixture
+from vllm import envs
 
 from vllm_omni.diffusion.utils.media_utils import mux_video_audio_bytes
-from vllm_omni.entrypoints.openai import api_server
+from vllm_omni.entrypoints.openai import api_server, video_api_utils
 from vllm_omni.entrypoints.openai.api_server import router
 from vllm_omni.entrypoints.openai.protocol.videos import (
     VideoGenerationRequest,
@@ -599,6 +601,45 @@ def test_i2v_video_generation_with_image_reference_form(test_client, mocker: Moc
     assert input_image.size == (40, 24)
 
 
+def test_i2v_video_generation_follows_allowed_image_redirect(test_client, mocker: MockerFixture, monkeypatch):
+    requested_paths = []
+    async_client = httpx.AsyncClient
+
+    def _handler(request):
+        requested_paths.append(request.url.path)
+        if request.url.path == "/redirect.png":
+            return httpx.Response(302, headers={"location": "/image.png"})
+        return httpx.Response(200, content=_make_test_image_bytes((40, 24)))
+
+    def _client_factory(*args, **kwargs):
+        return async_client(*args, transport=httpx.MockTransport(_handler), **kwargs)
+
+    monkeypatch.setattr(envs, "VLLM_MEDIA_URL_ALLOW_REDIRECTS", True)
+    monkeypatch.setattr(video_api_utils.httpx, "AsyncClient", _client_factory)
+    mocker.patch(
+        "vllm_omni.entrypoints.openai.serving_video._encode_video_bytes",
+        return_value=b"fake-video",
+    )
+
+    response = test_client.post(
+        "/v1/videos",
+        data={
+            "prompt": "A fox running through snow.",
+            "image_reference": json.dumps({"image_url": "https://example.com/redirect.png"}),
+        },
+    )
+
+    assert response.status_code == 200
+    video_id = response.json()["id"]
+    _wait_for_status(test_client, video_id, VideoGenerationStatus.COMPLETED.value)
+    assert requested_paths == ["/redirect.png", "/image.png"]
+
+    engine = test_client.app.state.openai_serving_video._engine_client
+    input_image = engine.captured_prompt["multi_modal_data"]["image"]
+    assert isinstance(input_image, Image.Image)
+    assert input_image.size == (40, 24)
+
+
 def test_v2v_video_generation_form(test_client, mocker: MockerFixture):
     video_bytes = _make_test_video_bytes((32, 24), num_frames=3)
 

tests/helpers/assertions.py

@@ -2,12 +2,16 @@
 
 import io
 import json
+import math
 import tempfile
 import threading
 import wave
+from collections.abc import Callable
+from concurrent.futures import ThreadPoolExecutor
+from dataclasses import dataclass
 from io import BytesIO
 from pathlib import Path
-from typing import TYPE_CHECKING, Any, Literal
+from typing import TYPE_CHECKING, Any, Literal, TypeVar
 
 if TYPE_CHECKING:
     from tests.helpers.runtime import DiffusionResponse
@@ -24,8 +28,109 @@ from tests.helpers.media import (
     preprocess_text,
 )
 
+_ResponseT = TypeVar("_ResponseT")
+
 _GENDER_PIPELINE = None
 _GENDER_PIPELINE_LOCK = threading.Lock()
+
+# Assertions that sample a random variable rather than testing whether serving works:
+# whether TTS audio is intelligible enough to transcribe back, and whether a preset voice's
+# timbre estimates as the expected gender. Both are decided by the model and the estimator,
+# so a caller measuring a success rate has to tell them apart from a serving failure. The
+# messages are constants because a caller matching the wording by hand would silently stop
+# matching if it were reworded, reclassifying every quality failure as a hard failure.
+AUDIO_MISMATCH_MESSAGE = "The audio content is not same as the text"
+GENDER_MISMATCH_MESSAGE = "estimated gender is"
+_QUALITY_FAILURE_MESSAGES = (AUDIO_MISMATCH_MESSAGE, GENDER_MISMATCH_MESSAGE)
+
+
+@dataclass(frozen=True)
+class SuccessRateGate:
+    """Gate a sampled quality check on how many requests pass, not on each one passing.
+
+    Built by the send helpers from their own arguments, so a test states a policy and never
+    touches this class. See ``collect_at_success_rate``.
+
+    Attributes:
+        min_successes: Fail below this many successes out of ``request_num``. Predeclare it
+            from a measured baseline; tuning it until CI passes defeats the gate.
+        concurrency: In-flight requests, in batches until the sample count is reached.
+            Defaults to sending all of them at once.
+    """
+
+    min_successes: int
+    concurrency: int | None = None
+
+
+def is_quality_failure(exc: BaseException) -> bool:
+    """True when ``exc`` is one of the sampled quality assertions above.
+
+    A caller absorbing a few of these into a success-rate budget must not absorb an HTTP
+    failure, missing audio or a text-keyword miss as well, or a broken server passes.
+    """
+    if not isinstance(exc, AssertionError):
+        return False
+    return any(message in str(exc) for message in _QUALITY_FAILURE_MESSAGES)
+
+
+def collect_at_success_rate(
+    sample: Callable[[], _ResponseT],
+    gate: SuccessRateGate,
+    *,
+    request_num: int,
+    report: Callable[[str], None] = print,
+) -> list[_ResponseT]:
+    """Send ``request_num`` samples and gate on how many passed, returning the successes.
+
+    ``sample`` sends one request and asserts it. Only the sampled quality assertions are
+    counted; anything else propagates immediately. Does not retry and does not exit early,
+    since both would bias the rate being measured.
+    """
+    if gate.min_successes > request_num:
+        raise ValueError(f"min_successes={gate.min_successes} exceeds request_num={request_num}, gate can never pass")
+    concurrency = min(gate.concurrency or request_num, request_num)
+    responses: list[_ResponseT] = []
+    failures: list[str] = []
+    lock = threading.Lock()
+
+    def _one_sample(_: int) -> None:
+        try:
+            response = sample()
+        except AssertionError as exc:
+            if not is_quality_failure(exc):
+                raise
+            with lock:
+                failures.append(str(exc))
+            return
+        with lock:
+            responses.append(response)
+
+    for start in range(0, request_num, concurrency):
+        size = min(concurrency, request_num - start)
+        with ThreadPoolExecutor(max_workers=size) as executor:
+            for _ in executor.map(_one_sample, range(size)):
+                pass
+
+    successes = request_num - len(failures)
+    report(f"success rate: {successes}/{request_num} succeeded")
+    assert successes >= gate.min_successes, (
+        f"success rate {successes}/{request_num} is below the "
+        f"{gate.min_successes}/{request_num} gate "
+        f"(95% CI {wilson_interval(successes, request_num)}). Failures:\n"
+        + "\n".join(f"  - {failure}" for failure in failures)
+    )
+    return responses
+
+
+def wilson_interval(successes: int, total: int, z: float = 1.96) -> str:
+    """Format a 95% Wilson score interval, so a rate failure shows how marginal it is."""
+    p = successes / total
+    denom = 1 + z * z / total
+    centre = (p + z * z / (2 * total)) / denom
+    half = z / denom * math.sqrt(p * (1 - p) / total + z * z / (4 * total * total))
+    return f"{max(0.0, centre - half):.1%}-{min(1.0, centre + half):.1%}"
+
+
 # Transcript gates default to whisper ``small`` for speed. ``small`` mishears a
 # short TTS clip ~0.5% of the time (e.g. "Hello"->"fellow", or hallucinating a
 # leading SFX token), which flakes the deterministic similarity gate. Short
@@ -474,7 +579,7 @@ def _assert_preset_voice_gender_from_audio(
     print(f"Preset voice gender check: preset={key!r}, estimated={estimated_gender!r}, expected={expected_gender!r}")
     if estimated_gender != "unknown":
         assert estimated_gender == expected_gender, (
-            f"{voice_name!r} is expected {expected_gender}, but estimated gender is {estimated_gender!r}"
+            f"{voice_name!r} is expected {expected_gender}, but {GENDER_MISMATCH_MESSAGE} {estimated_gender!r}"
         )
 
 
@@ -655,7 +760,7 @@ def assert_omni_response(response: Any, request_config: dict[str, Any], run_leve
                     shorter_clean = _re.sub(r"[^\w\s]", "", shorter).strip()
                     longer_clean = _re.sub(r"[^\w\s]", "", longer).strip()
                     assert shorter_clean and (shorter_clean in longer_clean), (
-                        f"The audio content is not same as the text "
+                        f"{AUDIO_MISMATCH_MESSAGE} "
                         f"(short-text containment check failed: "
                         f"text={text_output!r}, transcript={transcript!r})"
                     )
@@ -666,7 +771,7 @@ def assert_omni_response(response: Any, request_config: dict[str, Any], run_leve
                         text_output.lower(),
                     )
                     print(f"similarity is: {similarity}")
-                    assert similarity > similarity_threshold, "The audio content is not same as the text"
+                    assert similarity > similarity_threshold, AUDIO_MISMATCH_MESSAGE
             if audio_ref_text:
                 assert transcript is not None, "No audio transcript for reference-text validation"
                 audio_similarity = cosine_similarity_text(

tests/helpers/runtime.py

@@ -38,11 +38,13 @@ from vllm.distributed.parallel_state import (
 from vllm.logger import init_logger
 
 from tests.helpers.assertions import (
+    SuccessRateGate,
     assert_audio_speech_response,
     assert_diffusion_response,
     assert_http_error,
     assert_images_generations_response,
     assert_omni_response,
+    collect_at_success_rate,
 )
 from tests.helpers.env import run_post_test_cleanup, run_pre_test_cleanup
 from tests.helpers.media import (
@@ -1891,8 +1893,20 @@ class OpenAIClientHandler:
             )
         ]
 
-    def send_omni_request(self, request_config: dict[str, Any], request_num: int = 1) -> list[OmniResponse]:
-        """Chat completions via the OpenAI Python SDK (not raw HTTP)."""
+    def send_omni_request(
+        self,
+        request_config: dict[str, Any],
+        request_num: int = 1,
+        *,
+        min_successes: int | None = None,
+        max_concurrency: int | None = None,
+    ) -> list[OmniResponse]:
+        """Chat completions via the OpenAI Python SDK (not raw HTTP).
+
+        ``min_successes`` gates the batch on how many of the ``request_num`` requests pass
+        rather than requiring each one to, and returns only the successes; ``max_concurrency``
+        caps how many are in flight at once. See ``SuccessRateGate``.
+        """
         responses: list[OmniResponse] = []
         stream = request_config.get("stream", False)
         modalities = request_config.get("modalities", ["text", "audio"])
@@ -1923,22 +1937,6 @@ class OpenAIClientHandler:
         if extra_body:
             create_kwargs["extra_body"] = extra_body
 
-        if request_num == 1:
-            wall_start = time.perf_counter()
-            chat_completion = self.client.chat.completions.create(**create_kwargs)
-            resp = (
-                self._process_stream_omni_response(chat_completion, wall_start=wall_start)
-                if stream
-                else self._process_non_stream_omni_response(chat_completion, wall_start=wall_start)
-            )
-            assert_omni_response(resp, request_config, run_level=self.run_level)
-            if resp.e2e_latency is not None:
-                self._print_client_stat(f"[omni] request#1 success in {resp.e2e_latency:.3f}s")
-            else:
-                self._print_client_stat("[omni] request#1 completed")
-            responses.append(resp)
-            return responses
-
         def _one():
             wall_start = time.perf_counter()
             chat_completion = self.client.chat.completions.create(**create_kwargs)
@@ -1948,6 +1946,33 @@ class OpenAIClientHandler:
                 else self._process_non_stream_omni_response(chat_completion, wall_start=wall_start)
             )
 
+        if min_successes is not None:
+            gate = SuccessRateGate(min_successes=min_successes, concurrency=max_concurrency)
+
+            def _sample() -> OmniResponse:
+                resp = _one()
+                assert_omni_response(resp, request_config, run_level=self.run_level)
+                return resp
+
+            # Not _print_client_stat: the count is the gate's verdict, so it has to survive
+            # log_stats=False.
+            return collect_at_success_rate(
+                _sample,
+                gate,
+                request_num=request_num,
+                report=lambda line: print(f"[omni] {line}", flush=True),
+            )
+
+        if request_num == 1:
+            resp = _one()
+            assert_omni_response(resp, request_config, run_level=self.run_level)
+            if resp.e2e_latency is not None:
+                self._print_client_stat(f"[omni] request#1 success in {resp.e2e_latency:.3f}s")
+            else:
+                self._print_client_stat("[omni] request#1 completed")
+            responses.append(resp)
+            return responses
+
         with concurrent.futures.ThreadPoolExecutor(max_workers=request_num) as executor:
             futures = {executor.submit(_one): i + 1 for i in range(request_num)}
             for future in concurrent.futures.as_completed(futures):
@@ -3240,6 +3265,146 @@ def iter_omni_runner(
         print("OmniRunner stopped")
 
 
+# ─────────────────────────────────────────────────────────────────────
+# π0 (Pi-Zero) OpenPI websocket policy client
+#
+# Minimal client for the OpenPI realtime robot protocol
+# (``/v1/realtime/robot/openpi``): connect → read handshake metadata → send
+# observation → receive an action chunk. Used by the π0 online-serving e2e
+# (tests/e2e/online_serving/test_pi0_expansion.py). π0's observation is 3 RGB
+# cameras + proprioceptive state + a language prompt, built as synthetic numpy
+# frames here (no video assets).
+# ─────────────────────────────────────────────────────────────────────
+PI0_OPENPI_PATH = "/v1/realtime/robot/openpi"
+PI0_OPENPI_DEFAULT_PROMPT = "pick up the red block and place it in the bin"
+# π0 / pi0_base camera identities (must match the server's image_feature_keys,
+# i.e. vllm_omni/deploy/pi0.yaml, whose image_key_map is empty).
+PI0_CAMERA_KEYS = (
+    "observation.images.base_0_rgb",
+    "observation.images.left_wrist_0_rgb",
+    "observation.images.right_wrist_0_rgb",
+)
+PI0_ACTION_HORIZON = 50
+PI0_ACTION_DIM = 32
+PI0_STATE_DIM = 32
+PI0_IMAGE_SIZE = 224
+
+
+def pi0_openpi_require_dependencies() -> None:
+    """Raise ModuleNotFoundError if the OpenPI websocket client deps are missing."""
+    missing = []
+    try:
+        import websockets.sync.client  # noqa: F401
+    except ImportError:
+        missing.append("websockets")
+    try:
+        from openpi_client import msgpack_numpy  # noqa: F401
+    except ImportError:
+        missing.append("openpi-client")
+    if missing:
+        raise ModuleNotFoundError(f"π0 OpenPI test dependencies are missing: {', '.join(missing)}")
+
+
+def _pi0_decode_action_response(response: bytes | str) -> np.ndarray:
+    from openpi_client import msgpack_numpy
+
+    if isinstance(response, str):
+        raise RuntimeError(f"Inference failed: {response}")
+    decoded = msgpack_numpy.unpackb(response)
+    if isinstance(decoded, dict) and decoded.get("type") == "error":
+        raise RuntimeError(f"Inference failed: {decoded.get('message', decoded)}")
+    return np.asarray(decoded, dtype=np.float32)
+
+
+def pi0_make_dummy_obs(*, prompt: str, session_id: str, image_size: int = PI0_IMAGE_SIZE) -> dict[str, Any]:
+    """A single π0 observation: 3 blank cameras (HWC uint8) + zero state + prompt."""
+    obs: dict[str, Any] = {cam: np.zeros((image_size, image_size, 3), dtype=np.uint8) for cam in PI0_CAMERA_KEYS}
+    obs["state"] = np.zeros(PI0_STATE_DIM, dtype=np.float32)
+    obs["prompt"] = prompt
+    obs["session_id"] = session_id
+    return obs
+
+
+def pi0_openpi_run_policy_session(
+    *,
+    host: str = "127.0.0.1",
+    port: int = 8000,
+    path: str = PI0_OPENPI_PATH,
+    prompt: str = PI0_OPENPI_DEFAULT_PROMPT,
+    session_id: str | None = None,
+    num_steps: int = 2,
+) -> dict[str, Any]:
+    """Connect, read handshake metadata, send ``num_steps`` observations."""
+    import uuid
+
+    import websockets.sync.client as websockets_client
+    from openpi_client import msgpack_numpy
+
+    session_id = session_id or str(uuid.uuid4())
+    uri = f"ws://{host}:{port}{path}"
+    packer = msgpack_numpy.Packer()
+    conn = websockets_client.connect(uri, compression=None, max_size=None, ping_interval=300, ping_timeout=3600)
+    try:
+        metadata = msgpack_numpy.unpackb(conn.recv())
+        if not isinstance(metadata, dict):
+            raise TypeError(f"Expected dict metadata from server, got {type(metadata)!r}")
+        actions = []
+        for _ in range(num_steps):
+            payload = pi0_make_dummy_obs(prompt=prompt, session_id=session_id)
+            payload["endpoint"] = "infer"
+            conn.send(packer.pack(payload))
+            actions.append(_pi0_decode_action_response(conn.recv()))
+        return {"metadata": dict(metadata), "actions": actions, "session_id": session_id}
+    finally:
+        conn.close()
+
+
+def pi0_openpi_validate_session_result(
+    result: dict[str, Any],
+    *,
+    expected_action_horizon: int = PI0_ACTION_HORIZON,
+    expected_action_dim: int = PI0_ACTION_DIM,
+) -> None:
+    """Assert the handshake metadata + every returned action chunk for π0."""
+    metadata = result["metadata"]
+    required_keys = (
+        "image_resolution",
+        "needs_wrist_camera",
+        "needs_session_id",
+        "action_space",
+        "action_horizon",
+        "action_dim",
+    )
+    missing = [key for key in required_keys if key not in metadata]
+    if missing:
+        raise AssertionError(f"Missing π0 metadata keys: {missing}")
+
+    if tuple(metadata["image_resolution"]) != (PI0_IMAGE_SIZE, PI0_IMAGE_SIZE):
+        raise AssertionError(f"Unexpected image_resolution: {metadata['image_resolution']!r}")
+    if not metadata["needs_wrist_camera"]:
+        raise AssertionError("π0 test expects needs_wrist_camera=True")
+    if metadata["needs_session_id"]:
+        raise AssertionError("π0 is stateless; needs_session_id must be False")
+    if metadata["action_space"] != "joint_position":
+        raise AssertionError(f"Unexpected action_space: {metadata['action_space']!r}")
+    if int(metadata["action_horizon"]) != expected_action_horizon:
+        raise AssertionError(f"Unexpected action_horizon: {metadata['action_horizon']!r}")
+    if int(metadata["action_dim"]) != expected_action_dim:
+        raise AssertionError(f"Unexpected action_dim: {metadata['action_dim']!r}")
+
+    actions = result["actions"]
+    if not actions:
+        raise AssertionError("No actions returned from the server")
+    for index, action in enumerate(actions):
+        if action.shape != (expected_action_horizon, expected_action_dim):
+            raise AssertionError(
+                f"Action {index} shape mismatch: expected "
+                f"{(expected_action_horizon, expected_action_dim)}, got {action.shape}"
+            )
+        if not np.isfinite(action).all():
+            raise AssertionError(f"Action {index} contains non-finite values")
+
+
 __all__ = [
     "DiffusionResponse",
     "HttpResponse",
@@ -3261,4 +3426,8 @@ __all__ = [
     "OpenAIClientHandler",
     "get_open_port",
     "dummy_messages_from_mix_data",
+    "pi0_openpi_require_dependencies",
+    "pi0_openpi_run_policy_session",
+    "pi0_openpi_validate_session_result",
+    "pi0_make_dummy_obs",
 ]

tests/helpers/tests/test_success_rate_helpers.py

@@ -0,0 +1,110 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import threading
+from types import SimpleNamespace
+
+import pytest
+
+from tests.helpers import assertions
+from tests.helpers.assertions import (
+    AUDIO_MISMATCH_MESSAGE,
+    SuccessRateGate,
+    assert_omni_response,
+    collect_at_success_rate,
+    is_quality_failure,
+)
+
+pytestmark = [pytest.mark.core_model, pytest.mark.cpu]
+
+_GATE = SuccessRateGate(min_successes=7, concurrency=2)
+
+
+def _fake_response(text: str) -> SimpleNamespace:
+    return SimpleNamespace(success=True, text_content=text, audio_content=None, audio_bytes=b"fake-wav")
+
+
+def _omni_response_failure(monkeypatch, *, text: str, transcript: str) -> AssertionError:
+    """Drive the real assert_omni_response to failure and hand back what it raised."""
+    monkeypatch.setattr(assertions, "convert_audio_bytes_to_text", lambda *a, **kw: transcript)
+    request_config = {"modalities": ["text", "audio"], "key_words": {"text": ["london"]}}
+    with pytest.raises(AssertionError) as excinfo:
+        assert_omni_response(_fake_response(text), request_config, run_level="full_model")
+    return excinfo.value
+
+
+def test_real_audio_mismatch_is_tolerated(monkeypatch):
+    # Ties the classifier to the actual producer: if assert_omni_response's wording drifts,
+    # this fails instead of the gate silently turning every quality failure into a hard one.
+    assert is_quality_failure(_omni_response_failure(monkeypatch, text="London", transcript="런던"))
+
+
+def test_real_keyword_failure_is_not_tolerated(monkeypatch):
+    assert not is_quality_failure(_omni_response_failure(monkeypatch, text="Paris", transcript="London"))
+
+
+@pytest.mark.parametrize("exc", [RuntimeError(AUDIO_MISMATCH_MESSAGE), ValueError("boom")])
+def test_non_assertion_failures_are_not_tolerated(exc):
+    # A transport error carrying the same text is still a serving failure, not a sample.
+    assert not is_quality_failure(exc)
+
+
+class _ScriptedSampler:
+    """One scripted outcome per call: raised if it is an exception, else returned."""
+
+    def __init__(self, outcomes: list) -> None:
+        self._outcomes = outcomes
+        self._lock = threading.Lock()
+        self.calls = 0
+
+    def __call__(self):
+        with self._lock:
+            outcome = self._outcomes[self.calls]
+            self.calls += 1
+        if isinstance(outcome, BaseException):
+            raise outcome
+        return outcome
+
+
+def _mismatch(detail: str) -> AssertionError:
+    return AssertionError(f"{AUDIO_MISMATCH_MESSAGE} (transcript={detail!r})")
+
+
+def test_tolerated_failures_are_counted_and_every_sample_is_sent():
+    sampler = _ScriptedSampler(["ok"] * 7 + [_mismatch("a"), _mismatch("b"), _mismatch("c")])
+
+    responses = collect_at_success_rate(sampler, _GATE, request_num=10, report=lambda _: None)
+
+    # No early exit once the gate is already satisfied: it would bias the measured rate.
+    assert sampler.calls == 10
+    assert responses == ["ok"] * 7
+
+
+def test_gate_failure_reports_the_count_and_every_failing_sample():
+    outcomes = ["ok"] * 6 + [_mismatch("first"), _mismatch("second"), _mismatch("third"), _mismatch("fourth")]
+
+    with pytest.raises(AssertionError) as excinfo:
+        collect_at_success_rate(_ScriptedSampler(outcomes), _GATE, request_num=10, report=lambda _: None)
+
+    message = str(excinfo.value)
+    assert "6/10" in message and "7/10 gate" in message
+    assert all(detail in message for detail in ("first", "second", "third", "fourth"))
+
+
+def test_serving_failure_is_not_absorbed_into_the_budget():
+    sampler = _ScriptedSampler(["ok", AssertionError("No audio output is generated"), *["ok"] * 8])
+
+    with pytest.raises(AssertionError, match="No audio output"):
+        collect_at_success_rate(sampler, _GATE, request_num=10, report=lambda _: None)
+
+    # Stopped on the serving failure rather than spending the remaining samples.
+    assert sampler.calls < 10
+
+
+def test_unreachable_gate_is_rejected_before_sending():
+    sampler = _ScriptedSampler(["ok"] * 3)
+
+    with pytest.raises(ValueError, match="can never pass"):
+        collect_at_success_rate(sampler, _GATE, request_num=3, report=lambda _: None)
+
+    assert sampler.calls == 0

tests/model_executor/models/minicpmo_4_5/test_pipeline.py

@@ -24,6 +24,7 @@ from vllm_omni.config.pipeline_registry import OMNI_PIPELINES
 from vllm_omni.config.stage_config import (
     PipelineConfig,
     StageExecutionType,
+    _apply_platform_overrides,
     load_deploy_config,
     merge_pipeline_deploy,
 )
@@ -184,6 +185,30 @@ class TestDeployTopology:
                 0.35,
             ]
 
+    @pytest.mark.parametrize(
+        "filename",
+        [
+            "minicpmo_4_5.yaml",
+            "minicpmo_4_5_2gpu.yaml",
+            "minicpmo_4_5_3gpu.yaml",
+        ],
+    )
+    def test_npu_graph_configuration_is_stage_scoped(self, filename: str) -> None:
+        deploy = load_deploy_config(_DEPLOY_DIR / filename)
+        connector_extra = deploy.connectors["connector_of_shared_memory"]["extra"]
+        assert "code2wav_enable_npu_graph" not in connector_extra
+        assert "code2wav_max_npu_graphs" not in connector_extra
+
+        deploy = _apply_platform_overrides(deploy, platform="npu")
+        stages = merge_pipeline_deploy(OMNI_PIPELINES[deploy.pipeline], deploy)
+        assert stages[0].yaml_engine_args["compilation_config"]["cudagraph_mode"] == "PIECEWISE"
+        assert stages[1].yaml_engine_args["compilation_config"]["cudagraph_mode"] == "PIECEWISE"
+        assert stages[2].yaml_engine_args["enforce_eager"] is True
+        assert stages[2].yaml_engine_args["additional_config"] == {
+            "code2wav_enable_npu_graph": True,
+            "code2wav_max_npu_graphs": 32,
+        }
+
     def test_pipeline_exposes_full_and_async_payload_hooks(self) -> None:
         pipeline = OMNI_PIPELINES[_PIPELINE_KEY]
         talker = pipeline.get_stage(1)

tests/model_tests/diffusion/test_alignment.py

@@ -77,6 +77,7 @@ EXCLUDED_MODELS = [
     "DreamZeroPipeline",
     "StableDiffusionXLPipeline",
     "Gr00tN1d7Pipeline",
+    "Pi0Pipeline",
     "SoulXSingerPipeline",
     "SoulXSingerSVCPipeline",
 ]

tests/platforms/npu/test_graph_tools.py

@@ -0,0 +1,333 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+from contextlib import contextmanager
+from types import SimpleNamespace
+
+import pytest
+import torch
+
+from vllm_omni.platforms.npu.graph_tools import (
+    CapturedDeviceGraph,
+    NPUExactGraphRunner,
+)
+from vllm_omni.platforms.npu.models import minicpmo_4_5_code2wav as code2wav_patch
+
+pytestmark = [pytest.mark.core_model, pytest.mark.cpu]
+
+
+def test_captured_graph_replay_clones_persistent_outputs():
+    static_input = torch.zeros(1)
+    static_output = torch.zeros(1)
+
+    class _Graph:
+        def replay(self):
+            static_output.copy_(static_input * 2)
+
+    graph = CapturedDeviceGraph(
+        graph=_Graph(),
+        static_inputs=(static_input,),
+        static_outputs=(static_output,),
+    )
+
+    first = graph.replay((torch.tensor([2.0]),))[0]
+    second = graph.replay((torch.tensor([3.0]),))[0]
+
+    torch.testing.assert_close(first, torch.tensor([4.0]))
+    torch.testing.assert_close(second, torch.tensor([6.0]))
+    assert first.data_ptr() != second.data_ptr()
+
+
+def test_capture_uses_vllm_global_graph_pool(monkeypatch):
+    runner = NPUExactGraphRunner()
+    global_pool = object()
+    seen_pools = []
+    synchronizations = 0
+
+    class _Graph:
+        def replay(self):
+            pass
+
+    @contextmanager
+    def graph_context(graph, *, pool):
+        del graph
+        seen_pools.append(pool)
+        yield
+
+    def synchronize():
+        nonlocal synchronizations
+        synchronizations += 1
+
+    fake_npu = SimpleNamespace(
+        NPUGraph=_Graph,
+        graph=graph_context,
+        synchronize=synchronize,
+    )
+    monkeypatch.setattr(torch, "npu", fake_npu, raising=False)
+
+    import vllm.platforms
+
+    monkeypatch.setattr(
+        vllm.platforms,
+        "current_platform",
+        SimpleNamespace(get_global_graph_pool=lambda: global_pool),
+    )
+
+    captured = runner.capture(
+        (torch.tensor([2.0]),),
+        lambda value: (value + 1,),
+    )
+
+    assert seen_pools == [global_pool]
+    assert synchronizations == 2
+    torch.testing.assert_close(captured.static_outputs[0], torch.tensor([3.0]))
+
+
+def test_exact_signature_dispatch_captures_then_replays(monkeypatch):
+    runner = NPUExactGraphRunner(max_graphs=2)
+    replay_count = 0
+
+    class _FunctionalGraph:
+        def __init__(self, compute):
+            self.compute = compute
+
+        def replay(self, inputs):
+            nonlocal replay_count
+            replay_count += 1
+            return tuple(output.detach().clone() for output in self.compute(*inputs))
+
+    monkeypatch.setattr(runner, "_eligible", lambda inputs: True)
+    monkeypatch.setattr(
+        runner,
+        "capture",
+        lambda inputs, compute: _FunctionalGraph(compute),
+    )
+
+    def compute(value):
+        return (value * 2,)
+
+    first = runner.run("unit", (torch.tensor([2.0]),), (False,), compute)
+    second = runner.run("unit", (torch.tensor([3.0]),), (False,), compute)
+
+    torch.testing.assert_close(first[0], torch.tensor([4.0]))
+    torch.testing.assert_close(second[0], torch.tensor([6.0]))
+    assert runner.stats == {"captures": 1, "failed": 0, "hits": 1}
+    assert replay_count == 1
+
+
+def test_capture_failure_is_fatal_for_stage_process(monkeypatch):
+    runner = NPUExactGraphRunner(
+        component_name="test component",
+        disable_config_hint="disable the test graph",
+    )
+    captures = 0
+    computes = 0
+
+    monkeypatch.setattr(runner, "_eligible", lambda inputs: True)
+
+    def fail_capture(inputs, compute):
+        nonlocal captures
+        captures += 1
+        raise RuntimeError("unsupported op")
+
+    monkeypatch.setattr(runner, "capture", fail_capture)
+
+    def compute(value):
+        nonlocal computes
+        computes += 1
+        return (value + 1,)
+
+    with pytest.raises(RuntimeError, match="restart the stage process"):
+        runner.run("unit", (torch.tensor([1.0]),), (False,), compute)
+    with pytest.raises(RuntimeError, match="cannot continue"):
+        runner.run(
+            "different-shape",
+            (torch.tensor([1.0, 2.0]),),
+            (True,),
+            compute,
+        )
+
+    assert captures == 1
+    assert computes == 1
+    assert runner.stats == {"captures": 0, "failed": 1, "hits": 0}
+
+
+def test_code2wav_runtime_disables_internal_format_and_jit(monkeypatch):
+    monkeypatch.delenv("ASCEND_LAUNCH_BLOCKING", raising=False)
+    compile_modes = []
+    fake_npu = SimpleNamespace(
+        config=SimpleNamespace(allow_internal_format=True),
+        set_compile_mode=lambda **kwargs: compile_modes.append(kwargs),
+    )
+    monkeypatch.setattr(torch, "npu", fake_npu, raising=False)
+
+    code2wav_patch.prepare_code2wav_graph_runtime()
+
+    assert fake_npu.config.allow_internal_format is False
+    assert compile_modes == [{"jit_compile": False}]
+
+
+def test_code2wav_runtime_rejects_launch_blocking(monkeypatch):
+    monkeypatch.setenv("ASCEND_LAUNCH_BLOCKING", "1")
+
+    with pytest.raises(RuntimeError, match="ASCEND_LAUNCH_BLOCKING=1"):
+        code2wav_patch.prepare_code2wav_graph_runtime()
+
+
+@pytest.mark.parametrize(
+    ("enabled", "max_graphs", "expected_prepared"),
+    [
+        (True, 7, 1),
+        (False, 7, 0),
+        (True, 0, 0),
+    ],
+)
+def test_code2wav_patch_reads_stage_additional_config(
+    monkeypatch,
+    enabled,
+    max_graphs,
+    expected_prepared,
+):
+    prepared = 0
+
+    class _Backend:
+        speech_window = torch.zeros(1)
+        flow = SimpleNamespace(training=False)
+
+    model = SimpleNamespace(
+        backend=None,
+        vllm_config=SimpleNamespace(
+            additional_config={
+                "code2wav_enable_npu_graph": enabled,
+                "code2wav_max_npu_graphs": max_graphs,
+            }
+        ),
+    )
+
+    def build_backend(instance):
+        instance.backend = _Backend()
+
+    def prepare_runtime():
+        nonlocal prepared
+        prepared += 1
+
+    monkeypatch.setattr(code2wav_patch, "_original_build_backend", build_backend)
+    monkeypatch.setattr(code2wav_patch, "prepare_code2wav_graph_runtime", prepare_runtime)
+    code2wav_patch._patched_build_backend(model)
+
+    assert prepared == expected_prepared
+    if expected_prepared:
+        graph_runner = code2wav_patch._backend_graph_runners[model.backend]
+        assert isinstance(graph_runner, NPUExactGraphRunner)
+        assert graph_runner.max_graphs == max_graphs
+    else:
+        assert model.backend not in code2wav_patch._backend_graph_runners
+
+
+@pytest.mark.parametrize("with_cache", [False, True])
+def test_code2wav_estimator_graph_dispatch_is_platform_owned(monkeypatch, with_cache):
+    calls = []
+
+    class _Backend:
+        _trt_stepper = None
+        _cfm_graph_wrapper = None
+
+    class _Runner:
+        def run(self, operation, inputs, constants, compute):
+            calls.append((operation, inputs, constants))
+            return compute(*inputs)
+
+    backend = _Backend()
+    runner = _Runner()
+    code2wav_patch._backend_graph_runners[backend] = runner
+
+    def graphable_estimator_step(
+        instance,
+        estimator,
+        *,
+        x,
+        mu,
+        time_embedding,
+        speakers,
+        cond,
+        cnn_cache,
+        att_cache,
+    ):
+        del instance, estimator, time_embedding, speakers, cond
+        cache_marker = x.new_tensor(0 if cnn_cache is None else 1)
+        return x + mu, cache_marker, cache_marker.clone()
+
+    monkeypatch.setattr(code2wav_patch, "_original_estimator_step", lambda *args, **kwargs: None)
+    monkeypatch.setattr(code2wav_patch, "_graphable_estimator_step", graphable_estimator_step)
+    value = torch.tensor([2.0])
+    cache = torch.tensor([1.0]) if with_cache else None
+    estimator = SimpleNamespace(t_embedder=lambda time: time + 1)
+    outputs = code2wav_patch._patched_estimator_step(
+        backend,
+        estimator,
+        x=value,
+        mu=value,
+        time=value,
+        speakers=value,
+        cond=value,
+        cnn_cache=cache,
+        att_cache=cache,
+    )
+
+    assert calls[0][0] == "cfm_estimator"
+    assert calls[0][2] == (with_cache,)
+    assert len(calls[0][1]) == (7 if with_cache else 5)
+    torch.testing.assert_close(calls[0][1][2], torch.tensor([[3.0]]))
+    torch.testing.assert_close(outputs[0], torch.tensor([4.0]))
+    torch.testing.assert_close(outputs[1], torch.tensor(1.0 if with_cache else 0.0))
+
+
+def test_code2wav_platform_wraps_flow_execution_context(monkeypatch):
+    entered = []
+
+    class _Backend:
+        pass
+
+    class _Context:
+        def __init__(self, device, require_math):
+            self.device = device
+            self.require_math = require_math
+
+        def __enter__(self):
+            entered.append((self.device, self.require_math))
+
+        def __exit__(self, exc_type, exc, traceback):
+            return False
+
+    backend = _Backend()
+    code2wav_patch._backend_graph_runners[backend] = NPUExactGraphRunner()
+    monkeypatch.setattr(
+        code2wav_patch,
+        "_flow_execution_context",
+        lambda device, *, require_math: _Context(device, require_math),
+    )
+    monkeypatch.setattr(
+        code2wav_patch,
+        "_original_setup_batch",
+        lambda instance, features, batch_size: ("setup", batch_size),
+    )
+    monkeypatch.setattr(
+        code2wav_patch,
+        "_original_decode_batch",
+        lambda instance, tokens, features, states, *, last_chunk, flush_encoder: (
+            "decode",
+            last_chunk,
+            flush_encoder,
+        ),
+    )
+    features = SimpleNamespace(speech_tokens=torch.zeros(1))
+
+    assert code2wav_patch._patched_setup_batch(backend, features, 3) == ("setup", 3)
+    assert code2wav_patch._patched_decode_batch(
+        backend,
+        torch.zeros(1),
+        features,
+        [],
+        last_chunk=True,
+    ) == ("decode", True, False)
+    assert entered == [(torch.device("cpu"), True), (torch.device("cpu"), True)]

tests/platforms/npu/test_minicpmo_code2wav_npugraph.py

@@ -0,0 +1,143 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""Ascend hardware validation for MiniCPM-o Code2Wav NPUGraph replay."""
+
+from math import prod
+from types import SimpleNamespace
+
+import pytest
+import torch
+import torch.nn as nn
+
+from tests.helpers.mark import hardware_marks
+from vllm_omni.model_executor.models.minicpmo_4_5.batched_token2wav import (
+    BatchedToken2Wav,
+)
+from vllm_omni.platforms.npu.graph_tools import NPUExactGraphRunner
+from vllm_omni.platforms.npu.models.minicpmo_4_5_code2wav import (
+    _graphable_estimator_step,
+    prepare_code2wav_graph_runtime,
+)
+from vllm_omni.platforms.npu.models.step_audio2_token2wav import (
+    npu_token2wav_sdpa_context,
+)
+
+pytestmark = [
+    pytest.mark.core_model,
+    pytest.mark.omni,
+    *hardware_marks(res={"npu": "A3"}, num_cards=1),
+]
+
+
+class _Flow(nn.Module):
+    def __init__(self, estimator: nn.Module):
+        super().__init__()
+        self.decoder = SimpleNamespace(estimator=estimator)
+
+
+class _Token2Wav:
+    def __init__(self, estimator: nn.Module):
+        self.flow = _Flow(estimator).eval()
+        self.hift = nn.Identity().eval()
+        self.float16 = False
+        self.n_timesteps = 2
+        self.mel_cache_len = 1
+        self.source_cache_len = 2
+        self.speech_window = torch.ones(4, device="npu")
+
+
+def test_minicpmo_cfm_estimator_npugraph_matches_eager():
+    decoder_dit = pytest.importorskip("cosyvoice2.flow.decoder_dit")
+    prepare_code2wav_graph_runtime()
+
+    estimator = (
+        decoder_dit.DiT(
+            in_channels=6,
+            out_channels=2,
+            depth=2,
+            num_heads=2,
+            head_dim=8,
+            hidden_size=16,
+            mlp_ratio=2.0,
+        )
+        .npu()
+        .eval()
+    )
+    graph_runner = NPUExactGraphRunner(max_graphs=4)
+    adapter = BatchedToken2Wav(_Token2Wav(estimator))
+
+    def inputs(seed: int):
+        def make(shape: tuple[int, ...], offset: float):
+            values = torch.arange(
+                prod(shape),
+                device="npu",
+                dtype=torch.float32,
+            )
+            return torch.sin(values * 0.17 + seed + offset).reshape(shape)
+
+        return (
+            make((2, 2, 6), 0.0),
+            make((2, 2, 6), 0.5),
+            make((2, 1, 16), 1.0),
+            make((2, 1), 1.5),
+            make((2, 1, 6), 2.0),
+        )
+
+    def compute(*values, caches=None):
+        cnn_cache, att_cache = (None, None) if caches is None else caches
+        return _graphable_estimator_step(
+            adapter,
+            estimator,
+            x=values[0],
+            mu=values[1],
+            time_embedding=values[2],
+            speakers=values[3],
+            cond=values[4],
+            cnn_cache=cnn_cache,
+            att_cache=att_cache,
+        )
+
+    with torch.inference_mode(), npu_token2wav_sdpa_context(require_math=True):
+        warm_inputs = inputs(11)
+        warm_outputs = graph_runner.run(
+            "cfm_estimator",
+            warm_inputs,
+            (False,),
+            lambda *values: compute(*values),
+        )
+        replay_inputs = inputs(13)
+        expected = compute(*replay_inputs)
+        actual = graph_runner.run(
+            "cfm_estimator",
+            replay_inputs,
+            (False,),
+            lambda *values: compute(*values),
+        )
+        for eager, replayed in zip(expected, actual, strict=True):
+            torch.testing.assert_close(replayed, eager, rtol=1e-3, atol=1e-3)
+
+        cached_inputs = (*inputs(17), warm_outputs[1], warm_outputs[2])
+        graph_runner.run(
+            "cfm_estimator",
+            cached_inputs,
+            (True,),
+            lambda *values: compute(*values[:5], caches=(values[5], values[6])),
+        )
+        replay_cached_inputs = (
+            *inputs(19),
+            warm_outputs[1] + 0.1,
+            warm_outputs[2] + 0.1,
+        )
+        expected_cached = compute(
+            *replay_cached_inputs[:5],
+            caches=(replay_cached_inputs[5], replay_cached_inputs[6]),
+        )
+        actual_cached = graph_runner.run(
+            "cfm_estimator",
+            replay_cached_inputs,
+            (True,),
+            lambda *values: compute(*values[:5], caches=(values[5], values[6])),
+        )
+        for eager, replayed in zip(expected_cached, actual_cached, strict=True):
+            torch.testing.assert_close(replayed, eager, rtol=1e-3, atol=1e-3)
+        assert graph_runner.stats == {"captures": 2, "failed": 0, "hits": 2}

tests/platforms/npu/test_step_audio2_token2wav.py

@@ -7,6 +7,7 @@ from __future__ import annotations
 import importlib.util
 import sys
 import types
+from contextlib import contextmanager
 from pathlib import Path
 from types import SimpleNamespace
 
@@ -194,3 +195,71 @@ def test_hift_patch_reports_incompatible_layout(monkeypatch: pytest.MonkeyPatch)
 
     with pytest.raises(TypeError, match=r"m_source\.l_sin_gen\._f02sine"):
         module.patch_step_audio2_hift_for_npu(SimpleNamespace())
+
+
+def test_sdpa_context_preserves_exceptions_from_token2wav_body(
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    module = _load_patch_module(monkeypatch)
+    fake_attention_patch = types.ModuleType("vllm_omni.platforms.npu.models.cosyvoice2_dit_attn")
+    fake_attention_patch.apply_cosyvoice2_dit_attn_npu_patch = lambda: None
+
+    @contextmanager
+    def fake_math_context(*, require_available=False):
+        del require_available
+        yield
+
+    fake_attention_patch.npu_math_sdpa_context = fake_math_context
+    monkeypatch.setitem(
+        sys.modules,
+        "vllm_omni.platforms.npu.models.cosyvoice2_dit_attn",
+        fake_attention_patch,
+    )
+
+    with pytest.raises(RuntimeError, match="original graph failure"):
+        with module.npu_token2wav_sdpa_context():
+            raise RuntimeError("original graph failure")
+
+
+def test_sdpa_context_requires_math_for_npu_graph(monkeypatch: pytest.MonkeyPatch) -> None:
+    module = _load_patch_module(monkeypatch)
+    required = []
+    fake_attention_patch = types.ModuleType("vllm_omni.platforms.npu.models.cosyvoice2_dit_attn")
+    fake_attention_patch.apply_cosyvoice2_dit_attn_npu_patch = lambda: None
+
+    @contextmanager
+    def fake_math_context(*, require_available=False):
+        required.append(require_available)
+        yield
+
+    fake_attention_patch.npu_math_sdpa_context = fake_math_context
+    monkeypatch.setitem(
+        sys.modules,
+        "vllm_omni.platforms.npu.models.cosyvoice2_dit_attn",
+        fake_attention_patch,
+    )
+
+    with module.npu_token2wav_sdpa_context(require_math=True):
+        pass
+
+    assert required == [True]
+
+
+def test_sdpa_context_does_not_hide_patch_setup_failures(monkeypatch: pytest.MonkeyPatch) -> None:
+    module = _load_patch_module(monkeypatch)
+    fake_attention_patch = types.ModuleType("vllm_omni.platforms.npu.models.cosyvoice2_dit_attn")
+
+    def fail_patch():
+        raise RuntimeError("attention patch failed")
+
+    fake_attention_patch.apply_cosyvoice2_dit_attn_npu_patch = fail_patch
+    fake_attention_patch.npu_math_sdpa_context = contextmanager(lambda: iter((None,)))
+    monkeypatch.setitem(
+        sys.modules,
+        "vllm_omni.platforms.npu.models.cosyvoice2_dit_attn",
+        fake_attention_patch,
+    )
+
+    with pytest.raises(RuntimeError, match="attention patch failed"):
+        with module.npu_token2wav_sdpa_context():
+            pass

tests/tools/test_check_tts_adapter.py

@@ -17,9 +17,7 @@ import pytest
 sys.path.insert(0, str(Path(__file__).resolve().parents[2] / "tools" / "pre_commit"))
 
 from check_tts_adapter import (  # noqa: E402
-    MAX_LEGACY_DETECTORS,
     MAX_MODEL_TYPE_BRANCHES,
-    _legacy_detectors,
     _model_type_branches,
     main,
 )
@@ -60,12 +58,6 @@ def test_dict_dispatch_is_a_known_gap():
     assert _count('TABLE = {"foo": 1}\nvalue = TABLE[self._tts_model_type]') == 0
 
 
-def test_legacy_detector_counting():
-    src = 'X = (LegacyDetector(name="a", stage_keys=frozenset({"s"})), LegacyDetector(name="b"))'
-    found = _legacy_detectors(ast.parse(src))
-    assert [name for _, name in found] == ["a", "b"]
-
-
 def test_gate_passes_on_current_tree():
     """The recorded budgets must match reality, or the ratchet is decorative."""
     assert main([]) == 0
@@ -74,6 +66,4 @@ def test_gate_passes_on_current_tree():
 def test_budgets_are_the_real_counts():
     root = Path(__file__).resolve().parents[2]
     serving = root / "vllm_omni" / "entrypoints" / "openai" / "serving_speech.py"
-    adapters = root / "vllm_omni" / "entrypoints" / "openai" / "tts_adapters" / "__init__.py"
     assert len(_model_type_branches(ast.parse(serving.read_text()))) == MAX_MODEL_TYPE_BRANCHES
-    assert len(_legacy_detectors(ast.parse(adapters.read_text()))) == MAX_LEGACY_DETECTORS

tests/worker/test_gpu_ar_model_runner.py

@@ -2,6 +2,7 @@ import asyncio
 import copy
 from collections.abc import Sequence
 from types import SimpleNamespace
+from typing import Any
 
 import numpy as np
 import pytest
@@ -200,6 +201,15 @@ def test_speech_extra_params_reach_model_sampler_as_sampling_metadata(monkeypatc
         async def build(self, request, sampling_params_list, has_inline_ref_audio):
             return PreparedRequest(prompt={"prompt": request.input}, tts_params={}, model_type="higgs_audio_v3")
 
+        def apply_sampling_overrides(
+            self,
+            sampling_params_list: list,
+            request: "OpenAICreateSpeechRequest",
+            prompt: dict[str, Any] | None = None,
+            request_id: str | None = None,
+        ) -> list:
+            return sampling_params_list
+
     engine = SimpleNamespace(
         errored=False,
         default_sampling_params_list=[SamplingParams(temperature=1.0, top_p=0.95, top_k=50)],

tools/nightly/buildkite_testcase_statistics.py

@@ -1,8 +1,7 @@
 #!/usr/bin/env python3
 """
-Parse pytest commands from Buildkite test-ready.yml, test-merge.yml, test-nightly.yml,
-and test-weekly.yml; collect test cases (including parametrized) via pytest --collect-only -q
-and produce an HTML report.
+Parse pytest commands from Buildkite .buildkite/cuda/test-{ready,merge,nightly,weekly}.yml;
+collect test cases (including parametrized) via pytest --collect-only -q and produce an HTML report.
 
 Leaf steps may live under ``group:`` blocks (nested ``steps``); those are flattened with the
 group title carried into the report.
@@ -30,7 +29,7 @@ import yaml
 
 # Repo root (parent of the directory containing this script)
 REPO_ROOT = Path(__file__).resolve().parent.parent.parent
-BUILDKITE_DIR = REPO_ROOT / ".buildkite"
+BUILDKITE_DIR = REPO_ROOT / ".buildkite" / "cuda"
 PIPELINE_FILES = ["test-ready.yml", "test-merge.yml", "test-nightly.yml", "test-weekly.yml"]
 
 
@@ -256,13 +255,34 @@ def _resolve_pytest_target(target: str, repo_root: Path, extra: list[str], raw_l
         rel_dir = Path(target.rstrip("/"))
         dir_path = (repo_root / rel_dir).resolve()
         if dir_path.exists() and dir_path.is_dir():
-            return [str(dir_path)], target.replace("\\", "/").rstrip("/"), 120
+            return [str(dir_path)], target.replace("\\", "/").rstrip("/"), 300
 
     if not extra:
         raise RuntimeError(f"Failed to parse pytest sidecar args from line: {raw_line!r}")
     if not (repo_root / "tests").exists():
         raise FileNotFoundError("tests/ directory not found under repo root")
-    return ["tests/"], "tests/", 120
+    # Broad ``tests/`` collection loads many conftests; allow more time on busy CI agents.
+    return ["tests/"], "tests/", 300
+
+
+def _format_pytest_collect_failure(
+    path_args: list[str],
+    extra: list[str],
+    result: subprocess.CompletedProcess[str],
+) -> str:
+    """pytest often puts collection errors on stdout; include both streams + exit code."""
+    stdout = (result.stdout or "").strip()
+    stderr = (result.stderr or "").strip()
+    parts = [
+        f"pytest --collect-only failed for {path_args} with args {extra} (exit={result.returncode})",
+    ]
+    if stderr:
+        parts.append(f"stderr:\n{stderr}")
+    if stdout:
+        parts.append(f"stdout:\n{stdout}")
+    if not stderr and not stdout:
+        parts.append("(no stdout/stderr captured)")
+    return "\n".join(parts)
 
 
 def collect_test_names(target: str, repo_root: Path, raw_line: str) -> list[str]:
@@ -273,6 +293,8 @@ def collect_test_names(target: str, repo_root: Path, raw_line: str) -> list[str]
     flags ``--ignore``, ``--test-config-file``, ``-m``, ``-k``, ``--run-level`` are taken from the
     full ``raw_line`` and appended to the same invocation so collection matches CI (perf scripts
     parametrize from ``--test-config-file`` at import time).
+
+    Pytest exit code 5 (no tests collected) is treated as an empty result, not a hard failure.
     """
     extra = _pytest_collect_sidecar_args(raw_line)
     path_args, _fallback, timeout_quiet = _resolve_pytest_target(target, repo_root, extra, raw_line)
@@ -286,8 +308,15 @@ def collect_test_names(target: str, repo_root: Path, raw_line: str) -> list[str]
         text=True,
         timeout=timeout_quiet,
     )
+    # 5 == no tests collected under the given -m/-k filters.
+    if result.returncode == 5:
+        print(
+            f"pytest --collect-only: no tests matched for {path_args} with args {extra}",
+            file=sys.stderr,
+        )
+        return []
     if result.returncode != 0:
-        raise RuntimeError(f"pytest --collect-only failed for {path_args} with args {extra}: {result.stderr.strip()}")
+        raise RuntimeError(_format_pytest_collect_failure(path_args, extra, result))
     print(f"pytest --collect-only success for {path_args} with args {extra}: {result.stdout.strip()}")
     return _parse_collect_only_stdout(result.stdout or "", stderr=result.stderr or "")
 
@@ -962,7 +991,7 @@ def main() -> None:
         "--buildkite-dir",
         type=Path,
         default=BUILDKITE_DIR,
-        help="Path to .buildkite directory",
+        help="Path to Buildkite CUDA pipeline directory (default: .buildkite/cuda)",
     )
     default_out = REPO_ROOT / "buildkite_testcase_statistics.html"
     parser.add_argument(
@@ -1023,7 +1052,12 @@ def main() -> None:
             names = []
             skipped_ids: set[str] = set()
             for target, raw in targets:
-                collected = collect_test_names(target, REPO_ROOT, raw)
+                try:
+                    collected = collect_test_names(target, REPO_ROOT, raw)
+                except (RuntimeError, FileNotFoundError, subprocess.TimeoutExpired) as exc:
+                    # Keep generating the report for other steps; surface the root cause.
+                    print(f"WARNING: collect failed for {module_key} target={target!r}: {exc}", file=sys.stderr)
+                    collected = []
                 skipped_ids.update(get_skip_status(target, raw, REPO_ROOT))
                 names.extend(collected)
             count = len(names) - len(skipped_ids)

tools/pre_commit/check_tts_adapter.py

@@ -6,10 +6,8 @@ Per-model behaviour is being moved out of ``serving_speech.py`` and into
 nothing stopped a new model from adding one more ``self._tts_model_type == ...``
 branch faster than the migration removed them.
 
-This gate makes the two debt counters monotonically non-increasing:
-
-1. ``self._tts_model_type`` comparisons in ``serving_speech.py``
-2. entries in ``tts_adapters.LEGACY_TTS_DETECTORS`` (models with no adapter)
+This gate makes the number of ``self._tts_model_type`` comparisons in
+``serving_speech.py`` monotonically non-increasing.
 
 Lowering a budget after removing branches is expected and encouraged — the check
 insists on it, so the recorded numbers cannot silently drift upward. Raising one
@@ -28,11 +26,9 @@ from pathlib import Path
 
 REPO_ROOT = Path(__file__).resolve().parents[2]
 SERVING_SPEECH = Path("vllm_omni/entrypoints/openai/serving_speech.py")
-ADAPTERS_INIT = Path("vllm_omni/entrypoints/openai/tts_adapters/__init__.py")
 
 # Budgets. These may only go DOWN. See the module docstring.
-MAX_MODEL_TYPE_BRANCHES = 27
-MAX_LEGACY_DETECTORS = 0
+MAX_MODEL_TYPE_BRANCHES = 20
 
 _GUIDANCE = """
 Move the per-model behaviour into its adapter under
@@ -109,20 +105,6 @@ def _model_type_branches(tree: ast.AST) -> list[tuple[int, str]]:
     return found
 
 
-def _legacy_detectors(tree: ast.AST) -> list[tuple[int, str]]:
-    """Every ``LegacyDetector(...)`` construction."""
-    found: list[tuple[int, str]] = []
-    for node in ast.walk(tree):
-        if isinstance(node, ast.Call) and isinstance(node.func, ast.Name) and node.func.id == "LegacyDetector":
-            name = "?"
-            for kw in node.keywords:
-                if kw.arg == "name" and isinstance(kw.value, ast.Constant):
-                    name = str(kw.value.value)
-            found.append((node.lineno, name))
-    found.sort()
-    return found
-
-
 def _check(
     label: str,
     path: Path,
@@ -151,21 +133,18 @@ def _check(
 
 def main(argv: list[str] | None = None) -> int:
     parser = argparse.ArgumentParser(description=__doc__)
-    # pre-commit passes the staged filenames; we always audit the same two files,
+    # pre-commit passes the staged filenames; we always audit the same file,
     # so they are accepted and ignored.
     parser.add_argument("filenames", nargs="*")
     parser.parse_args(argv)
 
     serving_path = REPO_ROOT / SERVING_SPEECH
-    adapters_path = REPO_ROOT / ADAPTERS_INIT
-    for path in (serving_path, adapters_path):
-        if not path.is_file():
-            print(f"check_tts_adapter: expected file not found: {path}", file=sys.stderr)
-            return 1
+    if not serving_path.is_file():
+        print(f"check_tts_adapter: expected file not found: {serving_path}", file=sys.stderr)
+        return 1
 
     notices: list[str] = []
     branches = _model_type_branches(ast.parse(serving_path.read_text()))
-    detectors = _legacy_detectors(ast.parse(adapters_path.read_text()))
 
     errors = _check(
         "`_tts_model_type` comparisons",
@@ -176,15 +155,6 @@ def main(argv: list[str] | None = None) -> int:
         "MAX_MODEL_TYPE_BRANCHES",
         notices,
     )
-    errors += _check(
-        "unmigrated model types in LEGACY_TTS_DETECTORS",
-        ADAPTERS_INIT,
-        len(detectors),
-        MAX_LEGACY_DETECTORS,
-        [f"line {lineno}: {name}" for lineno, name in detectors],
-        "MAX_LEGACY_DETECTORS",
-        notices,
-    )
 
     for notice in notices:
         print(f"check_tts_adapter: {notice}", file=sys.stderr)

vllm_omni/config/pipeline_registry.py

@@ -33,6 +33,7 @@ from vllm.logger import init_logger
 from vllm_omni.config.stage_config import (
     PipelineConfig,
 )
+from vllm_omni.diffusion.models.pi0.pipeline_pi0 import PI0_PIPELINE
 from vllm_omni.model_executor.models.audex.pipeline import (
     AUDEX_S2S_PIPELINE,
     AUDEX_THINKER_ONLY_PIPELINE,
@@ -139,6 +140,7 @@ OMNI_PIPELINES: dict[str, PipelineConfig | PipelineResolverFunc] = {
     "lance": LANCE_PIPELINE,
     "dreamzero": DREAMZERO_PIPELINE,
     "Gr00tN1d7": GR00T_N1D7_PIPELINE,
+    "pi0": PI0_PIPELINE,
     "gepard": GEPARD_PIPELINE,
     "glm_image": GLM_IMAGE_PIPELINE,
     "hunyuan_image_3_moe": HUNYUAN_IMAGE3_PIPELINE,

vllm_omni/core/sched/omni_scheduler_mixin.py

@@ -1,5 +1,6 @@
 from __future__ import annotations
 
+import math
 import os
 import time
 from collections.abc import Iterable
@@ -42,25 +43,56 @@ logger = init_logger(__name__)
 
 _STATS_INTERVAL_S = 1.0
 
-# Upper bound on how long a request may sit in full-payload-input wait
-# (the state ``OmniSchedulingCoordinator`` records via ``_waiting_since``)
-# before the scheduler force-fails it.  Defends against stuck consumer-side
-# requests when the producer drops a full-payload, send fails, or recv
-# never arrives.  Override per-deployment via
-# VLLM_OMNI_INPUT_WAIT_TIMEOUT_S; set <=0 to disable the safety net.
+# Upper bound on how long a request may wait for stage input before the
+# scheduler force-fails it.  Defends against stuck consumer-side requests when
+# the producer drops a payload, send fails, or recv never arrives.  Override
+# per-deployment via VLLM_OMNI_INPUT_WAIT_TIMEOUT_S; set exactly 0 to disable
+# the safety net. Negative and non-finite values are rejected at startup.
 #
-# Scope: this constant only covers the full-payload coordinator path
-# (``input_coordinator``).  The async-chunk path uses
-# ``chunk_transfer_adapter`` and is not affected by this constant.
+# Scope: both transports.  The full-payload path measures time parked in
+# WAITING_FOR_INPUT (``OmniSchedulingCoordinator._waiting_since``); the
+# async-chunk path measures time stalled in WAITING_FOR_CHUNK
+# (``OmniChunkTransferAdapter._waiting_since``, reset on each chunk arrival).
+# One knob, because the question it answers -- "how long may a request wait for
+# stage input?" -- does not depend on which transport carries it.
+_INPUT_WAIT_TIMEOUT_DEFAULT = 600.0
 _INPUT_WAIT_TIMEOUT_RAW = os.environ.get("VLLM_OMNI_INPUT_WAIT_TIMEOUT_S", "600")
 try:
     DEFAULT_INPUT_WAIT_TIMEOUT_S: float = float(_INPUT_WAIT_TIMEOUT_RAW)
 except ValueError:
     logger.warning(
-        "Invalid VLLM_OMNI_INPUT_WAIT_TIMEOUT_S=%r; falling back to 600 seconds.",
+        "Invalid VLLM_OMNI_INPUT_WAIT_TIMEOUT_S=%r; falling back to %s seconds.",
         _INPUT_WAIT_TIMEOUT_RAW,
+        _INPUT_WAIT_TIMEOUT_DEFAULT,
+    )
+    DEFAULT_INPUT_WAIT_TIMEOUT_S = _INPUT_WAIT_TIMEOUT_DEFAULT
+
+if not math.isfinite(DEFAULT_INPUT_WAIT_TIMEOUT_S):
+    # nan compares false against everything, so every deadline check silently
+    # passes; inf makes the deadline unreachable. Both disable the net while
+    # looking like a number, which is the failure this item exists to stop.
+    raise ValueError(
+        f"VLLM_OMNI_INPUT_WAIT_TIMEOUT_S={_INPUT_WAIT_TIMEOUT_RAW!r} is not finite. "
+        "Use a positive number of seconds, or exactly 0 to disable the deadline."
+    )
+if DEFAULT_INPUT_WAIT_TIMEOUT_S < 0:
+    # Fail at startup rather than substituting a default. `-1` is a common idiom
+    # for "no limit", so silently turning it into 600 would give an operator who
+    # meant "never time out" mysterious failures ten minutes in.
+    raise ValueError(
+        f"VLLM_OMNI_INPUT_WAIT_TIMEOUT_S={_INPUT_WAIT_TIMEOUT_RAW!r} is negative. "
+        "Use a positive number of seconds, or exactly 0 to disable the deadline."
+    )
+elif DEFAULT_INPUT_WAIT_TIMEOUT_S == 0:
+    # Zero stays a supported opt-out, but it is no longer silent: it disables
+    # the deadline on BOTH transports, so a request whose stage input never
+    # arrives waits forever. Say which nets just went away.
+    logger.warning(
+        "VLLM_OMNI_INPUT_WAIT_TIMEOUT_S=0: stage-input deadlines are DISABLED for "
+        "both the full-payload path (WAITING_FOR_INPUT) and the async-chunk path "
+        "(WAITING_FOR_CHUNK). A request whose input never arrives will wait "
+        "indefinitely instead of failing."
     )
-    DEFAULT_INPUT_WAIT_TIMEOUT_S = 600.0
 
 
 class OmniSchedulerMixin:
@@ -184,6 +216,8 @@ class OmniSchedulerMixin:
                 self.running,
                 scheduler_requests=self.requests,
             )
+            self._process_pending_chunk_timeouts()
+            self._log_failed_chunk_sends()
 
     def _restore_omni_wait_queues(self) -> None:
         """Restore requests temporarily parked by Omni input gates."""
@@ -211,9 +245,9 @@ class OmniSchedulerMixin:
         Disabled when ``DEFAULT_INPUT_WAIT_TIMEOUT_S`` is <= 0.
 
         Scope: only covers ``input_coordinator`` (full-payload path).
-        Async-chunk requests park in ``chunk_transfer_adapter`` instead
-        and are not handled here -- if a similar safety net is needed
-        for the chunk path, it belongs in the chunk adapter.
+        Async-chunk requests park in ``chunk_transfer_adapter`` instead and
+        are handled by ``_process_pending_chunk_timeouts`` below, which shares
+        this timeout.
         """
         if DEFAULT_INPUT_WAIT_TIMEOUT_S <= 0:
             return
@@ -234,6 +268,84 @@ class OmniSchedulerMixin:
         )
         self.finish_requests(present_ids, RequestStatus.FINISHED_ERROR)
 
+    def _log_failed_chunk_sends(self) -> None:
+        """Surface chunks the sender gave up on (R1.2 of #4855).
+
+        ``save_loop`` pops a task with ``popleft()`` and never re-queues it, and
+        ``connector.put`` can report failure without raising, so a dropped chunk
+        is a give-up rather than a retry. Until this change the only trace was a
+        warning that printed ``None`` for the request id, because the task dict
+        has no ``request_id`` key.
+
+        This only reports. Failing the request from here does not work: the
+        producer is not the ``final_output`` stage, so an abort synthesized by
+        ``finish_requests`` is not turned into a client-visible termination by
+        ``Orchestrator._route_output`` (see the ``final_output`` guards there).
+        Making a producer-side drop client-visible needs an orchestrator-side
+        path like ``_fail_request_dead_stage``; the consumer's R1.1 deadline is
+        what actually ends the request today.
+        """
+        adapter = getattr(self, "chunk_transfer_adapter", None)
+        if adapter is None:
+            return
+        failures = adapter.collect_failed_send_request_ids()
+        for request_id, reason in failures.items():
+            if request_id in self.requests:
+                logger.error(
+                    "[OmniScheduler] req=%s: chunk send gave up (%s); the consumer will "
+                    "fail on the stage-input deadline",
+                    request_id,
+                    reason,
+                )
+            else:
+                # Drained but no longer scheduled here: the request finished or
+                # was aborted between the failed send and this sweep. Still say
+                # so -- collect_* empties the map, so staying quiet would make
+                # this the one drop path in a change whose point is visibility.
+                logger.warning(
+                    "[OmniScheduler] req=%s: chunk send gave up (%s) after the request "
+                    "left this scheduler (finished or aborted)",
+                    request_id,
+                    reason,
+                )
+
+    def _process_pending_chunk_timeouts(self) -> None:
+        """Force-fail requests stalled in ``WAITING_FOR_CHUNK`` too long.
+
+        The async-chunk counterpart of ``_process_pending_input_timeouts``,
+        called right after ``chunk_transfer_adapter.process_pending_chunks``.
+        Until now the chunk path had no safety net at all: the scheduler-side
+        comment on the full-payload net said a similar guard "belongs in the
+        chunk adapter", while the worker mixin assumed the full-payload net
+        already covered it.  Only the first was true, so a dropped terminal
+        chunk or a producer that exhausted its send retries parked the request
+        forever (vllm-project/vllm-omni#3833).  Since ``async_chunk: true`` is
+        the default for the flagship TTS pipelines, that was the default
+        streaming path.
+
+        Shares ``VLLM_OMNI_INPUT_WAIT_TIMEOUT_S`` with the full-payload net:
+        one knob for "how long may a request wait for stage input", regardless
+        of which transport carries it.  Disabled when the value is <= 0.
+        """
+        if DEFAULT_INPUT_WAIT_TIMEOUT_S <= 0:
+            return
+        adapter = getattr(self, "chunk_transfer_adapter", None)
+        if adapter is None or not getattr(adapter, "receives_chunks", False):
+            return
+        timed_out_ids = adapter.collect_timed_out_request_ids(timeout_s=DEFAULT_INPUT_WAIT_TIMEOUT_S)
+        if not timed_out_ids:
+            return
+        present_ids = {req_id for req_id in timed_out_ids if req_id in self.requests}
+        if not present_ids:
+            return
+        logger.warning(
+            "Marking %d request(s) as FINISHED_ERROR after stalling > %.0fs waiting for a chunk: %s",
+            len(present_ids),
+            DEFAULT_INPUT_WAIT_TIMEOUT_S,
+            sorted(present_ids),
+        )
+        self.finish_requests(present_ids, RequestStatus.FINISHED_ERROR)
+
     def _capture_omni_connector_output(self, model_runner_output: Any) -> None:
         """Stash the model runner's omni_connector_output for next schedule().
 

vllm_omni/deploy/minicpmo_4_5.yaml

@@ -111,3 +111,9 @@ platforms:
         max_num_batched_tokens: 8192
         compilation_config:
           cudagraph_mode: PIECEWISE
+      - stage_id: 2
+        # Stage 2 stays outer-eager and captures only exact-shape CFM DiT
+        # estimator kernels through the NPU platform patch.
+        additional_config:
+          code2wav_enable_npu_graph: true
+          code2wav_max_npu_graphs: 32

vllm_omni/deploy/minicpmo_4_5_2gpu.yaml

@@ -93,3 +93,9 @@ platforms:
         max_num_batched_tokens: 8192
         compilation_config:
           cudagraph_mode: PIECEWISE
+      - stage_id: 2
+        # Stage 2 stays outer-eager and captures only exact-shape CFM DiT
+        # estimator kernels through the NPU platform patch.
+        additional_config:
+          code2wav_enable_npu_graph: true
+          code2wav_max_npu_graphs: 32

vllm_omni/deploy/minicpmo_4_5_3gpu.yaml

@@ -86,4 +86,20 @@ stages:
       max_tokens: 65536
       detokenize: true
 
-platforms: {}
+platforms:
+  npu:
+    stages:
+      - stage_id: 0
+        max_num_batched_tokens: 8192
+        compilation_config:
+          cudagraph_mode: PIECEWISE
+      - stage_id: 1
+        max_num_batched_tokens: 8192
+        compilation_config:
+          cudagraph_mode: PIECEWISE
+      - stage_id: 2
+        # Stage 2 stays outer-eager and captures only exact-shape CFM DiT
+        # estimator kernels through the NPU platform patch.
+        additional_config:
+          code2wav_enable_npu_graph: true
+          code2wav_max_npu_graphs: 32

vllm_omni/deploy/pi0.yaml

@@ -0,0 +1,52 @@
+# π0 (Pi-Zero) VLA deploy: single diffusion stage.
+#
+# Topology (PI0_PIPELINE) is declared in vllm_omni/diffusion/models/pi0/pipeline_pi0.py.
+# π0 takes multi-camera images + a language instruction + robot state and emits
+# a continuous action chunk [chunk_size, max_action_dim] via flow matching.
+# Actions are exposed through the OpenPI realtime serving API
+# (/v1/realtime/robot/openpi); they ride in multimodal_output["actions"].
+#
+# Default: one GPU, TP=1, eager (flow matching needs no CUDA graph), bsz 1
+# (OpenPI policy serving is one observation -> one action reply). float32 to
+# match the LeRobot pi0_base reference numerics.
+
+pipeline: pi0
+async_chunk: false
+distributed_executor_backend: mp
+dtype: float32
+
+stages:
+  - stage_id: 0
+    devices: "0"
+    max_num_seqs: 1
+    enforce_eager: true
+    model_class_name: Pi0Pipeline
+    model_config:
+      paligemma_variant: gemma_2b
+      action_expert_variant: gemma_300m
+      chunk_size: 50
+      max_action_dim: 32
+      max_state_dim: 32
+      num_inference_steps: 10
+      image_resolution: [224, 224]
+      tokenizer_max_length: 48
+      max_cameras: 3
+      # Camera identities (the order the model attends to). Serving maps raw obs
+      # keys onto these via image_key_map; leave image_key_map empty to match
+      # incoming keys verbatim.
+      image_feature_keys:
+        - observation.images.base_0_rgb
+        - observation.images.left_wrist_0_rgb
+        - observation.images.right_wrist_0_rgb
+      image_key_map: {}
+      # OpenPI handshake metadata advertised to the robot client.
+      policy_server_config:
+        image_resolution: [224, 224]
+        n_external_cameras: 1
+        needs_wrist_camera: true
+        needs_stereo_camera: false
+        needs_session_id: false
+        action_space: joint_position
+        action_horizon: 50
+        action_dim: 32
+        max_cameras: 3

vllm_omni/diffusion/data.py

@@ -1318,6 +1318,12 @@ class OmniDiffusionConfig:
                         self.update_multimodal_support()
                     else:
                         raise
+                elif cfg.get("type") == "pi0":
+                    # π0 (Pi-Zero) VLA — the LeRobot config.json uses ``type: "pi0"``.
+                    if self.model_class_name is None:
+                        self.model_class_name = "Pi0Pipeline"
+                    self.set_tf_model_config(TransformerConfig())
+                    self.update_multimodal_support()
                 elif architectures and len(architectures) == 1:
                     architecture = architectures[0]
                     from vllm_omni.diffusion.registry import DiffusionModelRegistry

vllm_omni/diffusion/models/ltx2/ltx2_phase_adapter.py

@@ -203,6 +203,11 @@ def _apply_unquantized_gemm_with_weight(
     weight: torch.Tensor,
     bias: torch.Tensor | None,
 ) -> torch.Tensor:
+    # GEMM dispatch is selected from the host platform, not the tensor device.
+    # In CPU tests on a ROCm host that selects rocm_unquantized_gemm, which has
+    # no CPU kernel. Keep that combination on PyTorch's portable implementation.
+    if current_platform.is_rocm() and input_.device.type == "cpu":
+        return F.linear(input_, weight, bias)
     if envs.VLLM_BATCH_INVARIANT and current_platform.is_cuda_alike():
         return linear_batch_invariant(input_, weight, bias)
     return dispatch_unquantized_gemm()(layer, input_, weight, bias)

vllm_omni/diffusion/models/pi0/__init__.py

@@ -0,0 +1,48 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""π0 (Pi-Zero) VLA model for vllm-omni.
+
+PaliGemma (SigLIP vision + Gemma-2B LM) + Gemma-300M action expert + a
+flow-matching action head. Outputs a continuous action chunk
+``[horizon, action_dim]`` rather than tokens.
+"""
+
+from vllm_omni.diffusion.models.pi0.config import Pi0Config
+from vllm_omni.diffusion.models.pi0.modeling_pi0 import (
+    GemmaVariantConfig,
+    PaliGemmaWithActionExpert,
+    Pi0ForActionPrediction,
+    create_sinusoidal_pos_embedding,
+    get_gemma_config,
+    make_att_2d_masks,
+    prepare_attention_masks_4d,
+)
+from vllm_omni.diffusion.models.pi0.pipeline_pi0 import (
+    Pi0Pipeline,
+    get_pi0_post_process_func,
+)
+from vllm_omni.diffusion.models.pi0.processor_pi0 import (
+    Pi0ImageProcessor,
+    build_model_inputs,
+    pil_image_to_tensor,
+    resize_with_pad,
+    tokenize_prompt,
+)
+
+__all__ = [
+    "Pi0Config",
+    "Pi0ForActionPrediction",
+    "PaliGemmaWithActionExpert",
+    "GemmaVariantConfig",
+    "get_gemma_config",
+    "create_sinusoidal_pos_embedding",
+    "make_att_2d_masks",
+    "prepare_attention_masks_4d",
+    "Pi0ImageProcessor",
+    "build_model_inputs",
+    "pil_image_to_tensor",
+    "resize_with_pad",
+    "tokenize_prompt",
+    "Pi0Pipeline",
+    "get_pi0_post_process_func",
+]

vllm_omni/diffusion/models/pi0/config.py

@@ -0,0 +1,125 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""Config surface for the π0 (Pi-Zero) VLA model in vllm-omni.
+
+Only the parameters that actually shape runtime behaviour live here; the
+transformer dimensions (hidden size, head count, ...) are derived from
+``paligemma_variant`` / ``action_expert_variant`` inside the model via
+``get_gemma_config`` (see ``modeling_pi0``).
+
+The resolver reads the **raw LeRobot ``config.json``** (the field surface of
+``lerobot.policies.pi0.PI0Config``): only the recognized dataclass fields are
+used; any other keys in the file are ignored.
+"""
+
+from __future__ import annotations
+
+import json
+from dataclasses import dataclass, field
+from dataclasses import fields as dataclass_fields
+from pathlib import Path
+from typing import Any
+
+# LeRobot / OpenPI observation key conventions.
+ACTION = "action"
+OBS_STR = "observation"
+OBS_STATE = OBS_STR + ".state"
+OBS_IMAGES = OBS_STR + ".images"
+
+
+@dataclass
+class Pi0Config:
+    """π0 VLA config (dataclass, not an HF ``PretrainedConfig``).
+
+    Mirrors the runtime-relevant subset of LeRobot ``PI0Config`` plus a couple
+    of serving-side knobs (``max_cameras``, ``image_feature_keys``,
+    ``image_key_map``, ``norm_stats``).
+    """
+
+    # Backbone variants — mapped to Gemma dimensions by ``get_gemma_config``.
+    paligemma_variant: str = "gemma_2b"
+    action_expert_variant: str = "gemma_300m"
+
+    # Action chunk shape.
+    chunk_size: int = 50
+    max_action_dim: int = 32
+    max_state_dim: int = 32
+
+    # Flow-matching denoising schedule.
+    num_inference_steps: int = 10
+    # Sinusoidal timestep embedding periods (must match OpenPI/LeRobot).
+    min_period: float = 4e-3
+    max_period: float = 4.0
+
+    # Image preprocessing. π0/SigLIP only support square inputs.
+    image_resolution: tuple[int, int] = (224, 224)
+    tokenizer_max_length: int = 48
+    # Number of camera slots the model attends to (real + padded).
+    max_cameras: int = 3
+
+    # Weight dtype the checkpoint was saved in; parity passes this through to
+    # match LeRobot's ``PI0Config.dtype``.
+    dtype: str = "float32"
+
+    # Per-dataset normalization stats, optional (schema matches LeRobot's
+    # ``NormalizerProcessorStep``; consumed by ``_build_norm_buffers``). ``None``
+    # means identity / pass-through (the ``lerobot/pi0_base`` case).
+    norm_stats: dict | None = None
+
+    # Ordered list of image feature keys (e.g. ``observation.images.base_0_rgb``)
+    # taken from the checkpoint's ``input_features``. This is the **camera order**
+    # that must be reproduced for LeRobot parity.
+    image_feature_keys: list[str] | None = None
+
+    # Optional map from raw OpenPI obs keys → ``image_feature_keys`` entries, so
+    # serving can translate wire obs (e.g. ``observation/exterior_image_0_left``)
+    # into the checkpoint's camera identities. Empty → identity match on key.
+    image_key_map: dict[str, str] = field(default_factory=dict)
+
+    # Stored for reference / camera-order derivation; not used at inference.
+    input_features: dict[str, Any] = field(default_factory=dict)
+    output_features: dict[str, Any] = field(default_factory=dict)
+
+    def __post_init__(self) -> None:
+        # Coerce list → tuple (JSON has no tuples) and validate squareness.
+        res = self.image_resolution
+        if not isinstance(res, (tuple, list)) or len(res) != 2 or res[0] != res[1]:
+            raise ValueError(f"π0 expects a square image_resolution (H == W); got {res!r}.")
+        self.image_resolution = (int(res[0]), int(res[1]))
+
+        # Derive the camera order from input_features if not given explicitly.
+        if self.image_feature_keys is None and self.input_features:
+            self.image_feature_keys = [key for key in self.input_features if key.startswith(OBS_IMAGES + ".")]
+
+    # ------------------------------------------------------------------
+    # Constructors
+    # ------------------------------------------------------------------
+    @classmethod
+    def from_pretrained(cls, checkpoint_dir: str | Path) -> Pi0Config:
+        """Build from a checkpoint directory's ``config.json``."""
+        checkpoint_dir = Path(checkpoint_dir)
+        config_path = checkpoint_dir / "config.json"
+        if not config_path.exists():
+            return cls()
+        with open(config_path, encoding="utf-8") as f:
+            raw = json.load(f)
+        return cls.from_model_config(raw)
+
+    @classmethod
+    def from_model_config(cls, model_config: dict[str, Any] | None) -> Pi0Config:
+        """Build from a config dict (LeRobot ``config.json`` or deploy yaml).
+
+        Keeps only the recognized dataclass fields (the LeRobot config also
+        carries many training-only keys) and coerces ``image_resolution`` to a
+        tuple.
+        """
+        if not model_config:
+            return cls()
+
+        raw = dict(model_config)
+        if "image_resolution" in raw:
+            raw["image_resolution"] = tuple(raw["image_resolution"])
+
+        allowed = {item.name for item in dataclass_fields(cls)}
+        filtered = {key: value for key, value in raw.items() if key in allowed}
+        return cls(**filtered)

vllm_omni/diffusion/models/pi0/modeling_pi0.py

@@ -0,0 +1,944 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""Inference-only π0 (Pi-Zero) VLA math kernel for vllm-omni.
+
+A self-contained inference kernel: only the math that turns a robot observation
+into an action chunk, with no serving/request glue — validated bit-for-bit
+against the LeRobot ``PI0Policy`` reference (``max|Δ| < 1e-4``).
+
+π0 = PaliGemma (SigLIP vision + Gemma 2B LM) prefix + Gemma 300M action expert
+suffix + flow-matching head. Inference:
+
+1. Embed prefix (images + language) → bidirectional prefix tokens.
+2. Forward prefix through PaliGemma → a per-layer ``list[(k, v)]`` KV cache.
+3. For each Euler step ``t = 1.0, 1-dt, ..., 0``: embed the suffix
+   (state + noisy actions + timestep), run the action expert with cross
+   attention over the cached prefix K/V, predict velocity ``v_t``, and
+   integrate ``x_t = x_t + dt * v_t``.
+4. Return ``x_0`` as the action chunk ``(batch, action_horizon, action_dim)``.
+
+We walk Gemma decoder layers ourselves in ``_compute_layer_*`` so we never go
+through ``GemmaModel.forward`` (which has version-dependent mask/KV-cache
+behaviour). Targets modern transformers (PaliGemmaForConditionalGeneration with
+an inner PaliGemmaModel + DynamicCache).
+
+Reference implementations:
+   - OpenPI: openpi/src/openpi/models_pytorch/pi0_pytorch.py, gemma_pytorch.py
+   - LeRobot: lerobot/src/lerobot/policies/pi0/modeling_pi0.py
+Weight source: https://huggingface.co/lerobot/pi0_base
+"""
+
+from __future__ import annotations
+
+import logging
+import math
+from collections.abc import Iterable
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from transformers.models.auto import CONFIG_MAPPING
+from transformers.models.gemma.modeling_gemma import (
+    GemmaForCausalLM,
+    apply_rotary_pos_emb,
+)
+from transformers.models.paligemma.modeling_paligemma import (
+    PaliGemmaForConditionalGeneration,
+)
+from vllm.model_executor.model_loader.weight_utils import default_weight_loader
+
+logger = logging.getLogger(__name__)
+
+# ──────────────────────────────────────────────────────────────────────
+# Constants
+# ──────────────────────────────────────────────────────────────────────
+DEFAULT_ACTION_DIM = 32
+DEFAULT_ACTION_HORIZON = 50
+DEFAULT_MAX_TOKEN_LEN = 48
+DEFAULT_NUM_INFERENCE_STEPS = 10
+DEFAULT_IMAGE_RESOLUTION = (224, 224)
+
+# Large negative value to fill masked-out positions in a float attention mask.
+# Matches OpenPI's constant exactly so that numerics line up during parity.
+# Ref: openpi/src/openpi/models/gemma.py
+OPENPI_ATTENTION_MASK_VALUE = -2.3819763e38
+
+
+# ──────────────────────────────────────────────────────────────────────
+# Gemma variant configs (matches openpi/models/gemma.py get_config)
+# ──────────────────────────────────────────────────────────────────────
+
+
+class GemmaVariantConfig:
+    def __init__(self, width, depth, mlp_dim, num_heads, num_kv_heads, head_dim):
+        self.width = width
+        self.depth = depth
+        self.mlp_dim = mlp_dim
+        self.num_heads = num_heads
+        self.num_kv_heads = num_kv_heads
+        self.head_dim = head_dim
+
+
+def get_gemma_config(variant: str) -> GemmaVariantConfig:
+    if variant == "gemma_2b":
+        return GemmaVariantConfig(2048, 18, 16384, 8, 1, 256)
+    elif variant == "gemma_300m":
+        return GemmaVariantConfig(1024, 18, 4096, 8, 1, 256)
+    else:
+        raise ValueError(f"Unknown variant: {variant}")
+
+
+# ──────────────────────────────────────────────────────────────────────
+# Utility functions (match openpi/models_pytorch/pi0_pytorch.py)
+# ──────────────────────────────────────────────────────────────────────
+
+
+def create_sinusoidal_pos_embedding(
+    time: torch.Tensor,
+    dimension: int,
+    min_period: float = 4e-3,
+    max_period: float = 4.0,
+    device: torch.device = None,
+) -> torch.Tensor:
+    """Compute a sine/cosine positional embedding for scalar timesteps.
+
+    Ref: openpi/models_pytorch/pi0_pytorch.py create_sinusoidal_pos_embedding
+    """
+    if dimension % 2 != 0:
+        raise ValueError(f"dimension ({dimension}) must be divisible by 2")
+    if time.ndim != 1:
+        raise ValueError("time tensor must be 1-D (batch_size,)")
+    if device is None:
+        device = time.device
+
+    # Use float64 for the log-linear sweep and for the inner products, to
+    # match the reference implementation's numerical behaviour exactly.
+    dtype = torch.float64
+    fraction = torch.linspace(0.0, 1.0, dimension // 2, dtype=dtype, device=device)
+    period = min_period * (max_period / min_period) ** fraction
+    scaling_factor = 1.0 / period * 2 * math.pi
+    sin_input = scaling_factor[None, :] * time[:, None].to(dtype)
+    return torch.cat([torch.sin(sin_input), torch.cos(sin_input)], dim=1)
+
+
+def make_att_2d_masks(pad_masks: torch.Tensor, att_masks: torch.Tensor) -> torch.Tensor:
+    """Build a 2D attention mask from a padding mask and an autoregressive mask.
+
+    Ref: openpi/models_pytorch/pi0_pytorch.py make_att_2d_masks
+    """
+    if att_masks.ndim != 2:
+        raise ValueError(f"att_masks must be 2-D, got {att_masks.ndim}-D")
+    if pad_masks.ndim != 2:
+        raise ValueError(f"pad_masks must be 2-D, got {pad_masks.ndim}-D")
+
+    cumsum = torch.cumsum(att_masks, dim=1)
+    att_2d_masks = cumsum[:, None, :] <= cumsum[:, :, None]
+    pad_2d_masks = pad_masks[:, None, :] * pad_masks[:, :, None]
+    return att_2d_masks & pad_2d_masks
+
+
+def _build_norm_buffers(norm_stats: dict | None, key: str) -> dict[str, torch.Tensor] | None:
+    """Parse a ``norm_stats[key]`` entry into CPU tensors, or ``None``.
+
+    Matches LeRobot's ``NormalizationMode``:
+      - ``mean_std`` : ``forward = (x - mean) / std``, ``inverse = x * std + mean``
+      - ``min_max``  : forward maps into ``[-1, 1]``, inverse maps back.
+    """
+    if not norm_stats or not isinstance(norm_stats, dict):
+        return None
+    entry = norm_stats.get(key)
+    if not entry:
+        return None
+    mode = str(entry.get("mode", "mean_std")).lower()
+    if mode == "mean_std":
+        mean = entry.get("mean")
+        std = entry.get("std")
+        if mean is None or std is None:
+            return None
+        return {
+            "mode": mode,
+            "mean": torch.as_tensor(mean, dtype=torch.float32),
+            "std": torch.as_tensor(std, dtype=torch.float32),
+        }
+    if mode == "min_max":
+        lo = entry.get("min")
+        hi = entry.get("max")
+        if lo is None or hi is None:
+            return None
+        return {
+            "mode": mode,
+            "min": torch.as_tensor(lo, dtype=torch.float32),
+            "max": torch.as_tensor(hi, dtype=torch.float32),
+        }
+    return None
+
+
+def _apply_norm(
+    x: torch.Tensor,
+    stats: dict[str, torch.Tensor] | None,
+    inverse: bool,
+    eps: float = 1e-8,
+) -> torch.Tensor:
+    """Apply (un)normalization using the given stats. No-op if ``stats`` is None.
+
+    Broadcasts over leading dims; stats vectors align with the last dim. When
+    ``x`` has more last-dim entries than the stats (π0 pads state/action to
+    ``max_dim``), only the first ``len(stats)`` entries are transformed and
+    the padded tail is left untouched.
+    """
+    if stats is None:
+        return x
+    mode = stats["mode"]
+    if mode == "mean_std":
+        mean = stats["mean"].to(device=x.device, dtype=x.dtype)
+        std = stats["std"].to(device=x.device, dtype=x.dtype)
+        valid = mean.shape[0]
+        head = x[..., :valid]
+        if inverse:
+            head = head * std + mean
+        else:
+            head = (head - mean) / (std + eps)
+        if valid == x.shape[-1]:
+            return head
+        return torch.cat([head, x[..., valid:]], dim=-1)
+    if mode == "min_max":
+        lo = stats["min"].to(device=x.device, dtype=x.dtype)
+        hi = stats["max"].to(device=x.device, dtype=x.dtype)
+        valid = lo.shape[0]
+        head = x[..., :valid]
+        denom = (hi - lo).clamp_min(eps)
+        if inverse:
+            head = (head + 1.0) * 0.5 * denom + lo
+        else:
+            head = 2.0 * (head - lo) / denom - 1.0
+        if valid == x.shape[-1]:
+            return head
+        return torch.cat([head, x[..., valid:]], dim=-1)
+    return x
+
+
+def prepare_attention_masks_4d(att_2d_masks: torch.Tensor) -> torch.Tensor:
+    """Convert ``(B, S, S)`` bool masks to ``(B, 1, S, S)`` float masks.
+
+    ``True`` → 0.0 (attend), ``False`` → ``OPENPI_ATTENTION_MASK_VALUE``.
+    Ref: openpi PI0Pytorch._prepare_attention_masks_4d
+    """
+    att_2d_masks_4d = att_2d_masks[:, None, :, :]
+    return torch.where(att_2d_masks_4d, 0.0, OPENPI_ATTENTION_MASK_VALUE)
+
+
+# ──────────────────────────────────────────────────────────────────────
+# Dual-backbone: PaliGemma + Action Expert
+# Ref: openpi/models_pytorch/gemma_pytorch.py PaliGemmaWithExpertModel
+# Ref: lerobot/policies/pi0/modeling_pi0.py PaliGemmaWithExpertModel
+# ──────────────────────────────────────────────────────────────────────
+
+
+def _repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
+    """Repeat KV heads for grouped-query attention.
+
+    ``(B, num_kv_heads, S, D) → (B, num_kv_heads * n_rep, S, D)``.
+    Matches ``transformers.models.gemma.modeling_gemma.repeat_kv``.
+    """
+    if n_rep == 1:
+        return hidden_states
+    b, nh, s, d = hidden_states.shape
+    hidden_states = hidden_states[:, :, None, :, :].expand(b, nh, n_rep, s, d)
+    return hidden_states.reshape(b, nh * n_rep, s, d)
+
+
+def _attend(query_states, key_states, value_states, attention_mask, num_kv_groups, scaling):
+    """Manual eager attention: ``softmax(Q Kᵀ · scale + mask) · V``.
+
+    Important: we slice ``attention_mask[..., :key_states.shape[-2]]`` so that
+    the same ``(B, 1, Q, prefix+suffix)`` mask produced by ``denoise_step``
+    can be re-used both in the prefix pass (K length = prefix) and in the
+    suffix pass (K length = prefix + suffix after we concat the cached prefix).
+    Stock transformers' ``eager_attention_forward`` adds the mask without
+    slicing and therefore breaks when the two dims disagree.
+    """
+    k = _repeat_kv(key_states, num_kv_groups)
+    v = _repeat_kv(value_states, num_kv_groups)
+    attn_weights = torch.matmul(query_states, k.transpose(2, 3)) * scaling
+    if attention_mask is not None:
+        attn_weights = attn_weights + attention_mask[:, :, :, : k.shape[-2]]
+    attn_weights = F.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
+    return torch.matmul(attn_weights, v)
+
+
+def _compute_layer_prefix_only(layer_idx, hidden_states, attention_mask, position_ids, paligemma):
+    """Run one PaliGemma LM layer on the prefix only, returning the layer
+    output **and** the post-RoPE ``(k, v)`` so the caller can cache them for
+    the suffix pass."""
+    model = paligemma.model.language_model
+    layer = model.layers[layer_idx]
+    residual = hidden_states
+    x = layer.input_layernorm(hidden_states)
+
+    hidden_shape = (*x.shape[:-1], -1, layer.self_attn.head_dim)
+    q = layer.self_attn.q_proj(x).view(hidden_shape).transpose(1, 2)
+    k = layer.self_attn.k_proj(x).view(hidden_shape).transpose(1, 2)
+    v = layer.self_attn.v_proj(x).view(hidden_shape).transpose(1, 2)
+
+    cos, sin = model.rotary_emb(v, position_ids)
+    q, k = apply_rotary_pos_emb(q, k, cos, sin, unsqueeze_dim=1)
+
+    att = _attend(
+        q,
+        k,
+        v,
+        attention_mask,
+        num_kv_groups=layer.self_attn.num_key_value_groups,
+        scaling=1.0 / math.sqrt(layer.self_attn.head_dim),
+    )
+    att = att.transpose(1, 2).reshape(q.shape[0], -1, q.shape[1] * layer.self_attn.head_dim)
+
+    out = layer.self_attn.o_proj(att) + residual
+    after_resid = out
+    out = layer.mlp(layer.post_attention_layernorm(out)) + after_resid
+    return out, (k, v)
+
+
+def _compute_layer_suffix_only(
+    layer_idx,
+    hidden_states,
+    prefix_kv,
+    attention_mask,
+    position_ids,
+    paligemma,
+    gemma_expert,
+):
+    """Run one action-expert layer on the suffix, attending to the cached
+    prefix K/V concatenated with freshly computed suffix K/V.
+
+    ``prefix_kv`` is a ``(k_prefix, v_prefix)`` tuple, shape
+    ``(B, num_kv_heads, prefix_len, head_dim)``, post-RoPE, produced by the
+    corresponding ``_compute_layer_prefix_only`` call. Bypasses
+    ``GemmaModel.forward`` entirely — no HF mask rebuild, no cache format
+    gymnastics.
+    """
+    layer = gemma_expert.model.layers[layer_idx]
+    residual = hidden_states
+    x = layer.input_layernorm(hidden_states)
+
+    hidden_shape = (*x.shape[:-1], -1, layer.self_attn.head_dim)
+    q = layer.self_attn.q_proj(x).view(hidden_shape).transpose(1, 2)
+    k_suf = layer.self_attn.k_proj(x).view(hidden_shape).transpose(1, 2)
+    v_suf = layer.self_attn.v_proj(x).view(hidden_shape).transpose(1, 2)
+
+    # RoPE frequencies are shared between PaliGemma and the expert.
+    cos, sin = gemma_expert.model.rotary_emb(v_suf, position_ids)
+    q, k_suf = apply_rotary_pos_emb(q, k_suf, cos, sin, unsqueeze_dim=1)
+
+    # Concatenate cached prefix K/V (possibly different dtype) with suffix K/V.
+    k_prefix, v_prefix = prefix_kv
+    k = torch.cat([k_prefix.to(k_suf.dtype), k_suf], dim=2)
+    v = torch.cat([v_prefix.to(v_suf.dtype), v_suf], dim=2)
+
+    att = _attend(
+        q,
+        k,
+        v,
+        attention_mask,
+        num_kv_groups=layer.self_attn.num_key_value_groups,
+        scaling=1.0 / math.sqrt(layer.self_attn.head_dim),
+    )
+    att = att.transpose(1, 2).reshape(q.shape[0], -1, q.shape[1] * layer.self_attn.head_dim)
+
+    out = layer.self_attn.o_proj(att) + residual
+    after_resid = out
+    out = layer.mlp(layer.post_attention_layernorm(out)) + after_resid
+    return out
+
+
+class PaliGemmaWithActionExpert(nn.Module):
+    """Dual-backbone transformer: PaliGemma (Gemma 2B) + Action Expert (Gemma 300M).
+
+    ``forward`` has two inference modes:
+      - **prefix_only**: ``inputs_embeds=[prefix, None]`` + ``use_cache=True``
+        → compute prefix hidden states + a layer-wise K/V cache.
+      - **suffix_only**: ``inputs_embeds=[None, suffix]`` + the cache from the
+        previous prefix pass → compute expert hidden states with cross-attention
+        over the concatenated (prefix, suffix) K/V.
+
+    Both modes walk Gemma decoder layers manually through ``_compute_layer_*``
+    instead of ``GemmaModel.forward`` — that way we own the attention mask
+    handling and the KV cache format (a plain ``list[(k, v)]`` one entry
+    per layer).
+
+    Ref: lerobot/policies/pi0/modeling_pi0.py PaliGemmaWithExpertModel
+    """
+
+    def __init__(self, vlm_config, action_expert_config):
+        super().__init__()
+
+        # Build HF PaliGemma config from the variant dims we expose.
+        # NOTE: we do NOT set use_adarms / adarms_cond_dim — those are
+        # OpenPI-only attributes used by π0.5, not π0.
+        vlm_config_hf = CONFIG_MAPPING["paligemma"]()
+        vlm_config_hf._vocab_size = 257152
+        vlm_config_hf.image_token_index = 257152
+        vlm_config_hf.text_config.hidden_size = vlm_config.width
+        vlm_config_hf.text_config.intermediate_size = vlm_config.mlp_dim
+        vlm_config_hf.text_config.num_attention_heads = vlm_config.num_heads
+        vlm_config_hf.text_config.head_dim = vlm_config.head_dim
+        vlm_config_hf.text_config.num_hidden_layers = vlm_config.depth
+        vlm_config_hf.text_config.num_key_value_heads = vlm_config.num_kv_heads
+        vlm_config_hf.text_config.hidden_activation = "gelu_pytorch_tanh"
+        # transformers ≥ 5 uses ``dtype``; older versions still accept
+        # ``torch_dtype``. We set the canonical name; HF's PretrainedConfig
+        # normalizes one to the other internally.
+        vlm_config_hf.text_config.dtype = "float32"
+        vlm_config_hf.text_config.vocab_size = 257152
+        vlm_config_hf.vision_config.intermediate_size = 4304
+        vlm_config_hf.vision_config.projection_dim = 2048
+        vlm_config_hf.vision_config.projector_hidden_act = "gelu_fast"
+        vlm_config_hf.vision_config.dtype = "float32"
+
+        action_expert_config_hf = CONFIG_MAPPING["gemma"](
+            head_dim=action_expert_config.head_dim,
+            hidden_size=action_expert_config.width,
+            intermediate_size=action_expert_config.mlp_dim,
+            num_attention_heads=action_expert_config.num_heads,
+            num_hidden_layers=action_expert_config.depth,
+            num_key_value_heads=action_expert_config.num_kv_heads,
+            vocab_size=257152,
+            hidden_activation="gelu_pytorch_tanh",
+            dtype="float32",
+        )
+
+        self.paligemma = PaliGemmaForConditionalGeneration(config=vlm_config_hf)
+        self.gemma_expert = GemmaForCausalLM(config=action_expert_config_hf)
+        # The action expert doesn't embed tokens — it only consumes the
+        # suffix state/action embeddings we feed in.
+        self.gemma_expert.model.embed_tokens = None
+
+    def embed_image(self, pixel_values: torch.Tensor) -> torch.Tensor:
+        """Encode images with SigLIP vision tower + PaliGemma projector.
+
+        We run the two steps explicitly instead of calling
+        ``PaliGemmaModel.get_image_features`` because that convenience
+        method historically has returned either the projected tensor (old)
+        or the raw vision-tower output (new). Being explicit makes us
+        independent of which transformers release we're on.
+        """
+        # Shapes: pixel_values (B, 3, 224, 224) → SigLIP (B, 256, 1152)
+        #                                      → projector (B, 256, 2048)
+        vision_outputs = self.paligemma.model.vision_tower(pixel_values)
+        image_features = vision_outputs.last_hidden_state
+        return self.paligemma.model.multi_modal_projector(image_features)
+
+    def embed_language_tokens(self, tokens: torch.Tensor) -> torch.Tensor:
+        """Embed language tokens with PaliGemma's embedding table, returning the
+        ``* sqrt(hidden)``-scaled embedding (PaliGemma/Gemma convention).
+
+        The scaling location moved across transformers releases:
+          * ≤ 5.3: ``embed_tokens`` is a plain ``nn.Embedding`` and the
+            ``* sqrt(hidden)`` normalizer lives inside ``GemmaModel.forward``
+            (which we bypass) — so we apply it explicitly here.
+          * ≥ 5.4: ``embed_tokens`` is a ``GemmaTextScaledWordEmbedding`` that
+            self-applies ``embed_scale = hidden_size ** 0.5`` — applying it again
+            would double-scale (≈45×). We detect this and skip the manual scale.
+        This makes ``embed_language_tokens`` return the canonical scaled embedding
+        on every transformers version.
+        """
+        embed_tokens = self.paligemma.model.language_model.embed_tokens
+        lang_emb = embed_tokens(tokens)
+        # If the embedding already self-scales (transformers ≥ 5.4), don't repeat it.
+        if getattr(embed_tokens, "embed_scale", None) is None:
+            lang_emb = lang_emb * math.sqrt(lang_emb.shape[-1])
+        return lang_emb
+
+    def forward(
+        self,
+        attention_mask: torch.Tensor | None = None,
+        position_ids: torch.LongTensor | None = None,
+        past_key_values=None,
+        inputs_embeds: list[torch.Tensor | None] | None = None,
+        use_cache: bool = False,
+    ):
+        """Dispatch to prefix_only / suffix_only and return
+        ``([prefix_out, suffix_out], past_key_values_or_None)``.
+        """
+        num_layers = self.paligemma.config.text_config.num_hidden_layers
+        pali_lm = self.paligemma.model.language_model
+        expert_lm = self.gemma_expert.model
+
+        if inputs_embeds[1] is None:
+            # Prefix-only: PaliGemma LM on (images + language) tokens; the
+            # per-layer post-RoPE K/V is collected into a list that the
+            # suffix pass will consume directly.
+            hidden_states = inputs_embeds[0]
+            kv_list: list[tuple[torch.Tensor, torch.Tensor]] = []
+            for layer_idx in range(num_layers):
+                hidden_states, kv = _compute_layer_prefix_only(
+                    layer_idx,
+                    hidden_states,
+                    attention_mask,
+                    position_ids,
+                    paligemma=self.paligemma,
+                )
+                kv_list.append(kv)
+            hidden_states = pali_lm.norm(hidden_states)
+            return [hidden_states, None], (kv_list if use_cache else None)
+
+        if inputs_embeds[0] is not None:
+            raise ValueError(
+                "PaliGemmaWithActionExpert.forward only supports prefix-only "
+                "or suffix-only dispatch; got both inputs_embeds populated."
+            )
+        # Suffix-only: action expert on (state + noisy_actions + time),
+        # attending to the cached prefix K/V.
+        if not isinstance(past_key_values, list):
+            raise TypeError(
+                "suffix_only forward expects past_key_values to be the "
+                "list[(k, v)] produced by a previous prefix_only forward; "
+                f"got {type(past_key_values)}"
+            )
+        hidden_states = inputs_embeds[1]
+        for layer_idx in range(num_layers):
+            hidden_states = _compute_layer_suffix_only(
+                layer_idx,
+                hidden_states,
+                past_key_values[layer_idx],
+                attention_mask,
+                position_ids,
+                paligemma=self.paligemma,
+                gemma_expert=self.gemma_expert,
+            )
+        hidden_states = expert_lm.norm(hidden_states)
+        return [None, hidden_states], None
+
+
+# ──────────────────────────────────────────────────────────────────────
+# Main π0 Model
+# ──────────────────────────────────────────────────────────────────────
+
+
+class Pi0ForActionPrediction(nn.Module):
+    """π0 VLA model for robot action prediction via flow matching.
+
+    Inference flow:
+      1. Embed prefix (images + language) → prefix tokens.
+      2. Forward prefix through PaliGemma → layer-wise KV cache.
+      3. For each denoising step ``t = 1.0, 1-dt, ..., 0``:
+         a. Embed suffix (state + x_t + timestep) → suffix tokens.
+         b. Forward suffix through the action expert with the prefix cache.
+         c. ``x_t = x_t + dt * v_t`` (Euler integration).
+      4. Return ``x_0`` as the predicted action chunk.
+    """
+
+    def __init__(
+        self,
+        config,
+        quant_config=None,
+        prefix: str = "",
+    ):
+        super().__init__()
+        # ``quant_config`` is accepted for interface compatibility but unused —
+        # π0 runs in full precision for flow-matching parity.
+        del quant_config
+        self.config = config
+
+        self.action_dim = getattr(config, "max_action_dim", DEFAULT_ACTION_DIM)
+        # ``max_state_dim`` is independent from ``max_action_dim`` — e.g. Aloha
+        # has state_dim=14, action_dim=32, both padded to their own max.  Fall
+        # back to action_dim for older configs that only expose a single size.
+        self.max_state_dim = getattr(config, "max_state_dim", self.action_dim)
+        self.action_horizon = getattr(config, "chunk_size", DEFAULT_ACTION_HORIZON)
+        self.num_inference_steps = getattr(config, "num_inference_steps", DEFAULT_NUM_INFERENCE_STEPS)
+
+        paligemma_variant = getattr(config, "paligemma_variant", "gemma_2b")
+        action_expert_variant = getattr(config, "action_expert_variant", "gemma_300m")
+        vlm_config = get_gemma_config(paligemma_variant)
+        expert_config = get_gemma_config(action_expert_variant)
+        self.vlm_width = vlm_config.width
+        self.expert_width = expert_config.width
+
+        # Dual backbone
+        self.paligemma_with_expert = PaliGemmaWithActionExpert(vlm_config, expert_config)
+
+        # Action chunk projections (openpi pi0_pytorch.py).
+        self.action_in_proj = nn.Linear(self.action_dim, self.expert_width)
+        self.action_out_proj = nn.Linear(self.expert_width, self.action_dim)
+
+        # State is projected to the expert dim once (π0 only, not π0.5).
+        # Ref: lerobot modeling_pi0.py ``nn.Linear(config.max_state_dim, ...)``
+        self.state_proj = nn.Linear(self.max_state_dim, self.expert_width)
+
+        # Timestep + action fusion MLP (2W → W → W).
+        self.action_time_mlp_in = nn.Linear(2 * self.expert_width, self.expert_width)
+        self.action_time_mlp_out = nn.Linear(self.expert_width, self.expert_width)
+
+        # Optional mean/std normalization stats (LeRobot NormalizerProcessorStep
+        # semantics). Format on the config (all optional):
+        #     norm_stats = {
+        #         "state":  {"mode": "mean_std", "mean": [...], "std": [...]},
+        #         "action": {"mode": "mean_std", "mean": [...], "std": [...]},
+        #     }
+        # ``mode`` may also be ``"min_max"``.
+        #
+        # Missing norm_stats is logged at INFO (not WARNING) because the most
+        # common setup is "client pre-normalizes state and post-normalizes
+        # actions using dataset stats" — that's a supported mode, not an error.
+        self._state_norm = _build_norm_buffers(getattr(config, "norm_stats", None), "state")
+        self._action_norm = _build_norm_buffers(getattr(config, "norm_stats", None), "action")
+        if self._state_norm is None:
+            logger.info(
+                "π0: no state normalization stats on config.norm_stats — state values will pass through unchanged."
+            )
+        if self._action_norm is None:
+            logger.info(
+                "π0: no action normalization stats on config.norm_stats — "
+                "returned actions are in the model's normalized space."
+            )
+
+    # ── State / action normalization ─────────────────────────────────
+
+    def _normalize_state(self, state: torch.Tensor) -> torch.Tensor:
+        return _apply_norm(state, self._state_norm, inverse=False)
+
+    def _unnormalize_actions(self, actions: torch.Tensor) -> torch.Tensor:
+        return _apply_norm(actions, self._action_norm, inverse=True)
+
+    # ── Prefix embedding ─────────────────────────────────────────────
+
+    def embed_prefix(
+        self,
+        images: list[torch.Tensor],
+        image_masks: list[torch.Tensor],
+        lang_tokens: torch.Tensor,
+        lang_masks: torch.Tensor,
+    ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+        """Build the prefix embeddings, per-token padding mask, and AR mask.
+
+        Prefix tokens form a contiguous sequence
+        ``[img_cam_0..., img_cam_1..., ..., lang_tokens...]`` with
+        bidirectional attention; the returned ``att_masks`` are all zeros
+        because each token is free to attend to every other prefix token (the
+        suffix pass will put a causal boundary right before the state token).
+
+        Ref: openpi PI0Pytorch.embed_prefix
+        """
+        embs: list[torch.Tensor] = []
+        pad_masks: list[torch.Tensor] = []
+        att_masks: list[int] = []
+
+        for img, img_mask in zip(images, image_masks):
+            img_emb = self.paligemma_with_expert.embed_image(img)
+            bsize, num_img_embs = img_emb.shape[:2]
+            embs.append(img_emb)
+            pad_masks.append(img_mask[:, None].expand(bsize, num_img_embs))
+            att_masks += [0] * num_img_embs
+
+        # ``embed_language_tokens`` already returns the ``* sqrt(hidden)``-scaled
+        # embedding (version-robust: self-scaled at transformers ≥ 5.4, manually
+        # scaled at ≤ 5.3). The image slice went through the projector and is
+        # NOT scaled. See embed_language_tokens for the version details.
+        lang_emb = self.paligemma_with_expert.embed_language_tokens(lang_tokens)
+
+        embs.append(lang_emb)
+        pad_masks.append(lang_masks)
+        att_masks += [0] * lang_emb.shape[1]
+
+        embs = torch.cat(embs, dim=1)
+        pad_masks = torch.cat(pad_masks, dim=1)
+        att_masks = torch.tensor(att_masks, dtype=torch.bool, device=embs.device)
+        att_masks = att_masks[None, :].expand(pad_masks.shape[0], -1)
+
+        return embs, pad_masks, att_masks
+
+    # ── Suffix embedding ─────────────────────────────────────────────
+
+    def embed_suffix(
+        self,
+        state: torch.Tensor,
+        noisy_actions: torch.Tensor,
+        timestep: torch.Tensor,
+    ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+        """Build the suffix embeddings + masks: ``[state_token, action_tokens×H]``.
+
+        AR mask layout: ``[1, 1, 0, 0, ..., 0]`` — the state token is a causal
+        boundary (no later token attends backwards through it onto prefix
+        *by mistake*), the first action token starts a new causal block, and
+        the rest of the action tokens attend to each other bidirectionally.
+
+        Ref: openpi PI0Pytorch.embed_suffix
+        """
+        model_dtype = self.state_proj.weight.dtype
+        state = state.to(dtype=model_dtype)
+        noisy_actions = noisy_actions.to(dtype=model_dtype)
+        device = state.device
+
+        # State → (B, 1, W)
+        state_emb = self.state_proj(state)[:, None, :]
+        bsize = state_emb.shape[0]
+
+        # Sinusoidal timestep → (B, W) → expand across the horizon.
+        time_emb = create_sinusoidal_pos_embedding(
+            timestep,
+            self.action_in_proj.out_features,
+            min_period=4e-3,
+            max_period=4.0,
+            device=device,
+        ).to(dtype=model_dtype)
+
+        # Fuse action and time via (2W → W → W) MLP with SiLU.
+        action_emb = self.action_in_proj(noisy_actions)  # (B, H, W)
+        action_time_emb = torch.cat([action_emb, time_emb[:, None, :].expand_as(action_emb)], dim=2)
+        action_time_emb = self.action_time_mlp_in(action_time_emb)
+        action_time_emb = F.silu(action_time_emb)
+        action_time_emb = self.action_time_mlp_out(action_time_emb)
+
+        embs = torch.cat([state_emb, action_time_emb], dim=1)
+        pad_masks = torch.ones(bsize, embs.shape[1], dtype=torch.bool, device=device)
+        att_masks = torch.tensor(
+            [1] + [1] + [0] * (self.action_horizon - 1),
+            dtype=embs.dtype,
+            device=device,
+        )[None, :].expand(bsize, -1)
+        return embs, pad_masks, att_masks
+
+    # ── Denoising step ───────────────────────────────────────────────
+
+    def denoise_step(
+        self,
+        state: torch.Tensor,
+        prefix_pad_masks: torch.Tensor,
+        past_key_values,
+        x_t: torch.Tensor,
+        timestep: torch.Tensor,
+    ) -> torch.Tensor:
+        """Apply one flow-matching denoising step: predict ``v_t`` from ``x_t``.
+
+        Uses the prefix KV cache from ``sample_actions`` and only runs the
+        action expert. Ref: openpi PI0Pytorch.denoise_step.
+        """
+        suffix_embs, suffix_pad_masks, suffix_att_masks = self.embed_suffix(state, x_t, timestep)
+
+        # Build the full (B, suffix_len, prefix_len + suffix_len) boolean mask:
+        #   * suffix queries can see every *valid* prefix key (padded camera
+        #     slots and padded language tokens are blocked).
+        #   * within the suffix, the state token is a causal boundary; action
+        #     tokens attend to each other bidirectionally.
+        batch_size = prefix_pad_masks.shape[0]
+        suffix_len = suffix_pad_masks.shape[1]
+        prefix_len = prefix_pad_masks.shape[1]
+
+        prefix_pad_2d_masks = prefix_pad_masks[:, None, :].expand(batch_size, suffix_len, prefix_len)
+        suffix_att_2d_masks = make_att_2d_masks(suffix_pad_masks, suffix_att_masks)
+        full_att_2d_masks = torch.cat([prefix_pad_2d_masks, suffix_att_2d_masks], dim=2)
+
+        # Position IDs continue from where the prefix's last valid token left off.
+        prefix_offsets = torch.sum(prefix_pad_masks, dim=-1)[:, None]
+        position_ids = prefix_offsets + torch.cumsum(suffix_pad_masks, dim=1) - 1
+
+        full_att_2d_masks_4d = prepare_attention_masks_4d(full_att_2d_masks)
+
+        outputs_embeds, _ = self.paligemma_with_expert.forward(
+            attention_mask=full_att_2d_masks_4d,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            inputs_embeds=[None, suffix_embs],
+            use_cache=False,
+        )
+
+        # Drop the state token, keep only the action tokens, project down.
+        suffix_out = outputs_embeds[1][:, -self.action_horizon :]
+        suffix_out = suffix_out.to(dtype=self.action_out_proj.weight.dtype)
+        return self.action_out_proj(suffix_out)
+
+    # ── Full action generation ───────────────────────────────────────
+
+    @torch.no_grad()
+    def sample_actions(
+        self,
+        images: list[torch.Tensor],
+        image_masks: list[torch.Tensor],
+        lang_tokens: torch.Tensor,
+        lang_masks: torch.Tensor,
+        state: torch.Tensor,
+        noise: torch.Tensor | None = None,
+        num_steps: int | None = None,
+    ) -> torch.Tensor:
+        """Generate an action chunk via iterative flow-matching denoising.
+
+        Convention: ``t=1`` is noise, ``t=0`` is the target — opposite of the
+        published π0 paper but matches both OpenPI and LeRobot.
+        Ref: openpi PI0Pytorch.sample_actions
+        """
+        if num_steps is None:
+            num_steps = self.num_inference_steps
+
+        bsize = state.shape[0]
+        device = state.device
+        if noise is None:
+            noise = torch.randn(
+                bsize,
+                self.action_horizon,
+                self.action_dim,
+                dtype=torch.float32,
+                device=device,
+            )
+
+        # 1. Prefix embeddings + mask building.
+        prefix_embs, prefix_pad_masks, prefix_att_masks = self.embed_prefix(
+            images, image_masks, lang_tokens, lang_masks
+        )
+        prefix_att_2d_masks = make_att_2d_masks(prefix_pad_masks, prefix_att_masks)
+        prefix_position_ids = torch.cumsum(prefix_pad_masks, dim=1) - 1
+        prefix_att_2d_masks_4d = prepare_attention_masks_4d(prefix_att_2d_masks)
+
+        # 2. Forward prefix through PaliGemma LM, producing a list[(k, v)] cache.
+        _, past_key_values = self.paligemma_with_expert.forward(
+            attention_mask=prefix_att_2d_masks_4d,
+            position_ids=prefix_position_ids,
+            past_key_values=None,
+            inputs_embeds=[prefix_embs, None],
+            use_cache=True,
+        )
+
+        # 3. Euler-integrated denoising from t=1 down to t=0.
+        dt = -1.0 / num_steps
+        x_t = noise
+        for step in range(num_steps):
+            t = 1.0 + step * dt
+            time_tensor = torch.full((bsize,), t, dtype=torch.float32, device=device)
+            v_t = self.denoise_step(
+                state=state,
+                prefix_pad_masks=prefix_pad_masks,
+                past_key_values=past_key_values,
+                x_t=x_t,
+                timestep=time_tensor,
+            )
+            x_t = x_t + dt * v_t
+        return x_t
+
+    # ── Weight loading ───────────────────────────────────────────────
+
+    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]):
+        """Load weights from an ``lerobot/pi0_base`` safetensors checkpoint.
+
+        Checkpoint keys (after stripping the leading ``model.`` prefix) look
+        like ``paligemma_with_expert.paligemma.<sub>.*``. Modern transformers
+        nests those sub-modules one level deeper (``paligemma.model.<sub>``),
+        and PaliGemma ties ``lm_head.weight`` with ``embed_tokens.weight`` at
+        ``post_init`` time. Two remap rules make the load lossless:
+
+          1. ``paligemma.{vision_tower,multi_modal_projector,language_model}.*``
+             → ``paligemma.model.{...}.*``
+          2. ``paligemma.lm_head.weight``
+             → ``paligemma.model.language_model.embed_tokens.weight``
+             (the checkpoint does not store ``embed_tokens.weight`` on its
+             own — only the tied ``lm_head.weight`` copy — and
+             ``PaliGemmaForConditionalGeneration`` does not register
+             ``lm_head.weight`` as a Parameter when tied, so without this
+             rule the language embedding would silently remain at its
+             random init.)
+
+        On any remaining mismatch the loader logs a warning listing a sample
+        of the unmatched checkpoint keys and model params; downstream parity
+        tests surface these as a hard failure.
+        """
+        params_dict = dict(self.named_parameters())
+        buffers_dict = dict(self.named_buffers())
+
+        _PALIGEMMA_SUBMODULES = (
+            "vision_tower",
+            "multi_modal_projector",
+            "language_model",
+        )
+
+        def _remap(name: str) -> str:
+            # Strip the leading "model." that LeRobot's PI0Policy wrapper adds.
+            if name.startswith("model."):
+                name = name[len("model.") :]
+
+            # Historical MLP alias used by some early π0 checkpoints.
+            if name.startswith("time_mlp_in."):
+                name = "action_time_mlp_in." + name[len("time_mlp_in.") :]
+            elif name.startswith("time_mlp_out."):
+                name = "action_time_mlp_out." + name[len("time_mlp_out.") :]
+
+            # Nested PaliGemma layout.
+            for sub in _PALIGEMMA_SUBMODULES:
+                flat = f"paligemma_with_expert.paligemma.{sub}."
+                nested = f"paligemma_with_expert.paligemma.model.{sub}."
+                if name.startswith(flat) and not name.startswith(nested):
+                    return nested + name[len(flat) :]
+
+            # Tied lm_head → embed_tokens redirect (see docstring).
+            if name == "paligemma_with_expert.paligemma.lm_head.weight":
+                return "paligemma_with_expert.paligemma.model.language_model.embed_tokens.weight"
+
+            return name
+
+        def _fix_vision_tower(name: str) -> str:
+            """Reconcile the SigLIP vision-tower nesting across transformers
+            versions. transformers ≤5.3 wraps the encoder in an extra
+            ``vision_tower.vision_model.*`` module; ≥5.4 flattens it to
+            ``vision_tower.*``. The ``lerobot/pi0_base`` checkpoint uses the
+            ``vision_model`` layout, so on newer transformers we must drop that
+            infix (else 437 vision params silently load nothing). Adaptive:
+            only rewrite when the rewritten key actually exists on the model.
+            """
+            vt = "paligemma_with_expert.paligemma.model.vision_tower."
+            if not name.startswith(vt):
+                return name
+            rest = name[len(vt) :]
+            if name in params_dict or name in buffers_dict:
+                return name  # model already expects this exact layout
+            if rest.startswith("vision_model."):
+                # Checkpoint has the infix; model (≥5.4) doesn't → strip it.
+                candidate = vt + rest[len("vision_model.") :]
+            else:
+                # Checkpoint lacks the infix; model (≤5.3) has it → insert it.
+                candidate = vt + "vision_model." + rest
+            return candidate if (candidate in params_dict or candidate in buffers_dict) else name
+
+        loaded = 0
+        skipped: list[str] = []
+        filled_params: set = set()
+        for name, loaded_weight in weights:
+            mapped = _fix_vision_tower(_remap(name))
+            if mapped in params_dict:
+                param = params_dict[mapped]
+                weight_loader = getattr(param, "weight_loader", default_weight_loader)
+                weight_loader(param, loaded_weight)
+                loaded += 1
+                filled_params.add(mapped)
+            elif mapped in buffers_dict:
+                buffers_dict[mapped].copy_(loaded_weight)
+                loaded += 1
+                filled_params.add(mapped)
+            else:
+                skipped.append(mapped)
+
+        # Reverse audit: any model param that got no checkpoint tensor at all
+        # would be running with random init. ``rotary_emb.inv_freq`` and
+        # friends are config-derived buffers, not stored in the checkpoint.
+        missing_params: list[str] = []
+        for pname in params_dict:
+            if pname in filled_params:
+                continue
+            if "rotary_emb" in pname or pname.endswith(".inv_freq"):
+                continue
+            missing_params.append(pname)
+
+        if missing_params or skipped:
+            parts: list[str] = []
+            if skipped:
+                parts.append(f"{len(skipped)} checkpoint key(s) had no home in the model (first 5: {skipped[:5]})")
+            if missing_params:
+                parts.append(f"{len(missing_params)} model param(s) received NO weight (first 5: {missing_params[:5]})")
+            logger.warning(
+                "π0 load_weights: %d tensors loaded — %s.",
+                loaded,
+                "; ".join(parts),
+            )
+        else:
+            logger.info(
+                "π0 load_weights: %d tensors loaded, 0 skipped, 0 missing.",
+                loaded,
+            )
+        return filled_params

vllm_omni/diffusion/models/pi0/pipeline_pi0.py

@@ -0,0 +1,277 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""π0 (Pi-Zero) VLA pipeline for vllm-omni.
+
+Entry point for ``DiffusionEngine.step() → pipeline.forward(req)``. Mirrors the
+DreamZero contract: the pipeline owns ALL preprocessing. It reads the raw robot
+observation from ``req.sampling_params.extra_args["robot_obs"]`` (delivered by
+the OpenPI realtime serving layer), builds model inputs, runs flow-matching
+denoising, and returns ``DiffusionOutput(output={"actions": ndarray})`` — which
+``diffusion_engine`` promotes to ``multimodal_output["actions"]`` for the client.
+
+π0 is stateless across calls (no KV reuse), so ``session_id`` / ``reset`` from
+the OpenPI protocol are accepted but ignored.
+"""
+
+from __future__ import annotations
+
+import os
+
+import numpy as np
+import torch
+from torch import nn
+from vllm.logger import init_logger
+
+from vllm_omni.config.stage_config import (
+    PipelineConfig,
+    StageExecutionType,
+    StagePipelineConfig,
+)
+from vllm_omni.diffusion.data import DiffusionOutput, OmniDiffusionConfig
+from vllm_omni.diffusion.models.pi0.config import Pi0Config
+from vllm_omni.diffusion.models.pi0.modeling_pi0 import Pi0ForActionPrediction
+from vllm_omni.diffusion.models.pi0.processor_pi0 import build_model_inputs
+from vllm_omni.diffusion.request import OmniDiffusionRequest
+
+logger = init_logger(__name__)
+
+# Default tokenizer for the PaliGemma prefix (matches LeRobot Pi0).
+DEFAULT_PI0_TOKENIZER = "google/paligemma-3b-pt-224"
+
+
+def _pi0_post_process(x):
+    """Module-level identity post-process (picklable across the orchestrator's
+    multiprocess boundary — a local closure is not)."""
+    return x
+
+
+def get_pi0_post_process_func(od_config: OmniDiffusionConfig):
+    """π0 returns actions directly; post-processing is identity. The diffusion
+    engine resolves this via the registry (``_DIFFUSION_POST_PROCESS_FUNCS``) and
+    applies it engine-side, so the pipeline does NOT attach it to DiffusionOutput.
+    """
+    del od_config
+    return _pi0_post_process
+
+
+# Single-stage diffusion topology for online OpenPI serving.
+#
+# π0 is one diffusion stage (robot observation -> action chunk via flow
+# matching). It is registered in ``OMNI_PIPELINES`` (rather than relying on the
+# default single-stage fallback) because π0 is *online-served* via
+# ``vllm serve --deploy-config pi0.yaml``; that path
+# (``OmniStagePipelineFactory._create_from_registry``) requires a registered
+# pipeline so the deploy yaml's ``policy_server_config`` reaches the OpenPI
+# websocket layer. Offline-only models (e.g. ``internvla_a1``) need no topology.
+PI0_PIPELINE = PipelineConfig(
+    model_type="pi0",
+    model_arch="Pi0Pipeline",
+    stages=(
+        StagePipelineConfig(
+            stage_id=0,
+            model_stage="diffusion",
+            execution_type=StageExecutionType.DIFFUSION,
+            input_sources=(),
+            final_output=True,
+            final_output_type="action",
+            model_arch="Pi0Pipeline",
+        ),
+    ),
+)
+
+
+class Pi0Pipeline(nn.Module):
+    """π0 VLA pipeline: raw robot obs → continuous action chunk.
+
+    Registered as ``"Pi0Pipeline"`` in the diffusion registry. Weights are
+    self-loaded in ``__init__`` from the checkpoint's ``model.safetensors`` via
+    the kernel's ``load_weights`` (which handles the LeRobot key remaps).
+    """
+
+    def __init__(self, *, od_config: OmniDiffusionConfig, prefix: str = ""):
+        super().__init__()
+        self.od_config = od_config
+        self.prefix = prefix
+        # Resolve od_config.model to a LOCAL directory. A bare HF repo id (e.g.
+        # the documented ``lerobot/pi0_base``) must be snapshot-downloaded first;
+        # otherwise config/tokenizer/weights would silently fall back to random
+        # init (this pipeline self-loads from a local dir and has no
+        # ``weights_sources`` for the framework loader to download from).
+        self.model_dir = self._resolve_model_dir(od_config.model)
+        self.config = self._build_config(od_config)
+
+        custom_args = od_config.custom_pipeline_args or {}
+        self.tokenizer_source = str(custom_args.get("tokenizer", self._resolve_tokenizer_source()))
+
+        # Torch dtype/device from od_config.
+        self._torch_dtype = self._resolve_dtype(od_config)
+        self._device = self._resolve_device(od_config)
+
+        self.tokenizer = self._load_tokenizer()
+        self.model = self._initialize_model()
+
+    # ------------------------------------------------------------------
+    # Construction helpers
+    # ------------------------------------------------------------------
+    @staticmethod
+    def _resolve_model_dir(model: str | None) -> str | None:
+        """Return a local directory for ``model``; download an HF repo id if needed.
+
+        Limits the snapshot to the files π0 actually reads (config, weights,
+        tokenizer) so a repo id doesn't pull unrelated artifacts.
+        """
+        if not model:
+            return None
+        if os.path.isdir(model):
+            return model
+        from huggingface_hub import snapshot_download
+
+        return snapshot_download(
+            repo_id=model,
+            allow_patterns=["*.json", "*.safetensors", "*.model", "tokenizer*"],
+        )
+
+    def _build_config(self, od_config: OmniDiffusionConfig) -> Pi0Config:
+        """Build Pi0Config from deploy-yaml model_config, falling back to the
+        checkpoint's config.json (raw LeRobot format)."""
+        if od_config.model_config:
+            config = Pi0Config.from_model_config(dict(od_config.model_config))
+            # The deploy yaml is authoritative, but must not silently drop
+            # checkpoint-derived fields it omits. Backfill the camera order and
+            # any normalization stats the yaml didn't specify.
+            if self.model_dir and (not config.image_feature_keys or config.norm_stats is None):
+                ckpt = Pi0Config.from_pretrained(self.model_dir)
+                if not config.image_feature_keys:
+                    config.image_feature_keys = ckpt.image_feature_keys
+                    if not config.input_features:
+                        config.input_features = ckpt.input_features
+                if config.norm_stats is None:
+                    config.norm_stats = ckpt.norm_stats
+            return config
+        if self.model_dir:
+            return Pi0Config.from_pretrained(self.model_dir)
+        return Pi0Config()
+
+    def _resolve_tokenizer_source(self) -> str:
+        """Prefer the checkpoint dir if it ships tokenizer files; else PaliGemma."""
+        if self.model_dir and os.path.isdir(self.model_dir):
+            if os.path.exists(os.path.join(self.model_dir, "tokenizer_config.json")):
+                return self.model_dir
+        return DEFAULT_PI0_TOKENIZER
+
+    @staticmethod
+    def _resolve_dtype(od_config: OmniDiffusionConfig) -> torch.dtype:
+        dt = od_config.dtype
+        if isinstance(dt, torch.dtype):
+            return dt
+        return getattr(torch, str(dt).split(".")[-1], torch.float32)
+
+    @staticmethod
+    def _resolve_device(od_config: OmniDiffusionConfig) -> torch.device:
+        from vllm_omni.diffusion.distributed.utils import get_local_device
+
+        try:
+            return get_local_device()
+        except Exception:  # noqa: BLE001
+            return torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+    def _load_tokenizer(self):
+        from transformers import AutoTokenizer
+
+        return AutoTokenizer.from_pretrained(self.tokenizer_source)
+
+    def has_real_checkpoint(self) -> bool:
+        return bool(self.model_dir) and os.path.exists(os.path.join(self.model_dir, "model.safetensors"))
+
+    def _initialize_model(self) -> Pi0ForActionPrediction:
+        model = Pi0ForActionPrediction(self.config)
+        if self.has_real_checkpoint():
+            self._load_checkpoint(model)
+        else:
+            logger.info("Pi0Pipeline: no model.safetensors under %s; using random init.", self.model_dir)
+        model.to(device=self._device, dtype=self._torch_dtype)
+        model.eval()
+        return model
+
+    def _load_checkpoint(self, model: Pi0ForActionPrediction) -> None:
+        import safetensors.torch
+
+        path = os.path.join(self.model_dir, "model.safetensors")
+        logger.info("Pi0Pipeline: loading π0 weights from %s", path)
+        state = safetensors.torch.load_file(path)
+        model.load_weights(list(state.items()))
+
+    # ------------------------------------------------------------------
+    # Framework weight-loading hook
+    # ------------------------------------------------------------------
+    def load_weights(self, weights=()):  # noqa: D401
+        """No-op for the diffusion loader: π0 self-loads its checkpoint in
+        ``__init__`` (the kernel's ``load_weights`` handles the LeRobot remaps).
+        We expose no ``weights_sources``, so the loader passes an empty iterator
+        here; returning ``None`` skips its strict unloaded-weights check.
+        """
+        for _ in weights:
+            pass
+        return None
+
+    # ------------------------------------------------------------------
+    # Inference
+    # ------------------------------------------------------------------
+    @torch.inference_mode()
+    def forward(self, req: OmniDiffusionRequest, **kwargs) -> DiffusionOutput:
+        extra_args = getattr(req.sampling_params, "extra_args", None) or {}
+        robot_obs = extra_args.get("robot_obs")
+
+        if robot_obs is None:
+            # Dummy warmup path (no obs): return zeros so engine warmup/capture
+            # doesn't crash. Mirrors DreamZero's dummy-run handling.
+            first_prompt = req.prompts[0] if req.prompts else ""
+            prompt = first_prompt if isinstance(first_prompt, str) else (first_prompt.get("prompt") or "")
+            num_steps = getattr(req.sampling_params, "num_inference_steps", None)
+            if prompt == "dummy run" or num_steps == 1:
+                logger.info("Pi0Pipeline: dummy warmup request without robot_obs — returning zeros.")
+                return DiffusionOutput(
+                    output={
+                        "actions": np.zeros(
+                            (self.config.chunk_size, self.config.max_action_dim),
+                            dtype=np.float32,
+                        )
+                    },
+                )
+            return DiffusionOutput(
+                error="Pi0Pipeline.forward requires sampling_params.extra_args['robot_obs'].",
+            )
+
+        images, image_masks, lang_tokens, lang_masks, state = build_model_inputs(
+            robot_obs, self.config, self.tokenizer, self._device
+        )
+
+        # State normalization (identity for pi0_base) inside the model.
+        state = self.model._normalize_state(state)
+
+        noise = extra_args.get("noise")
+        if noise is not None and not isinstance(noise, torch.Tensor):
+            noise = torch.as_tensor(noise, dtype=torch.float32, device=self._device)
+        elif isinstance(noise, torch.Tensor):
+            noise = noise.to(device=self._device, dtype=torch.float32)
+
+        num_steps = extra_args.get("num_inference_steps")
+
+        actions = self.model.sample_actions(
+            images=images,
+            image_masks=image_masks,
+            lang_tokens=lang_tokens,
+            lang_masks=lang_masks,
+            state=state,
+            noise=noise,
+            num_steps=num_steps,
+        )
+        actions = self.model._unnormalize_actions(actions)
+
+        # (B=1, horizon, action_dim) → (horizon, action_dim) numpy for the wire.
+        actions_np = actions.squeeze(0).float().cpu().numpy()
+
+        # Note: post-processing is applied engine-side via the registry
+        # (_DIFFUSION_POST_PROCESS_FUNCS), so we don't attach it here (a local
+        # closure wouldn't survive the orchestrator's multiprocess pickling).
+        return DiffusionOutput(output={"actions": actions_np})

vllm_omni/diffusion/models/pi0/processor_pi0.py

@@ -0,0 +1,222 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""Preprocessing for the π0 VLA model.
+
+Converts a raw robot observation (multi-camera images + language instruction +
+proprioceptive state) into the tensors that ``Pi0ForActionPrediction.sample_actions``
+consumes:
+
+  * images       : list of ``(1, 3, 224, 224)`` float tensors in ``[-1, 1]``,
+                   one per camera slot (real cameras first, then ``-1``-filled
+                   padding) in the checkpoint's ``image_feature_keys`` order
+  * image_masks  : list of ``(1,)`` bool tensors, ``True`` = real camera
+  * lang_tokens  : ``(1, L)`` long, PaliGemma tokenizer, ``\\n``-terminated
+  * lang_masks   : ``(1, L)`` bool
+  * state        : ``(1, max_state_dim)`` float32, zero-padded
+
+Image/prompt preprocessing matches OpenPI and LeRobot bit-for-bit — see the
+parity test (``test_pi0_parity.py``). A set of stateless helpers the diffusion
+pipeline calls directly (the DreamZero contract: the pipeline owns its
+preprocessing).
+
+Reference:
+  - OpenPI: openpi/src/openpi/shared/image_tools.py (resize_with_pad)
+  - OpenPI: openpi/src/openpi/models_pytorch/preprocessing_pytorch.py
+  - LeRobot: lerobot/src/lerobot/policies/pi0/modeling_pi0.py (Pi0NewLineProcessor)
+"""
+
+from __future__ import annotations
+
+import logging
+from typing import Any
+
+import numpy as np
+import torch
+import torch.nn.functional as F
+from PIL import Image
+
+logger = logging.getLogger(__name__)
+
+# Defaults straight from the π0 reference configs.
+PI0_IMAGE_SIZE = 224  # openpi/models/model.py IMAGE_RESOLUTION
+PI0_NUM_IMAGE_TOKENS = 256  # SigLIP So400m/14 on 224×224 → (224/14)**2
+PI0_IMAGE_TOKEN_INDEX = 257152  # openpi/models_pytorch/gemma_pytorch.py
+PI0_MAX_CAMERAS = 3  # openpi/models/model.py (3 camera slots)
+PI0_MAX_TOKEN_LEN = 48  # openpi/models/pi0_config.py
+
+
+def resize_with_pad(
+    images: torch.Tensor,
+    target_height: int,
+    target_width: int,
+    mode: str = "bilinear",
+) -> torch.Tensor:
+    """Resize ``(B, C, H, W)`` images to the target shape, preserving aspect
+    ratio with -1 padding on the short side.
+
+    Matches openpi ``image_tools.resize_with_pad_torch`` — the clamp to
+    [-1, 1] is what lets the padded region blend with SigLIP-normalized
+    pixels without adding signal at the boundary.
+    """
+    if images.ndim != 4:
+        raise ValueError(f"Expected 4-D (B,C,H,W), got {images.ndim}-D")
+    _, _, cur_h, cur_w = images.shape
+    ratio = max(cur_w / target_width, cur_h / target_height)
+    rh, rw = int(cur_h / ratio), int(cur_w / ratio)
+    align_corners = False if mode == "bilinear" else None
+    resized = F.interpolate(images, size=(rh, rw), mode=mode, align_corners=align_corners)
+    resized = resized.clamp(-1.0, 1.0)
+    ph, rem_h = divmod(target_height - rh, 2)
+    pw, rem_w = divmod(target_width - rw, 2)
+    return F.pad(resized, (pw, pw + rem_w, ph, ph + rem_h), value=-1.0)
+
+
+def pil_image_to_tensor(image: Image.Image) -> torch.Tensor:
+    """PIL → ``(1, C, H, W)`` float32 in ``[-1, 1]`` (SigLIP normalization)."""
+    if image.mode != "RGB":
+        image = image.convert("RGB")
+    arr = np.array(image, dtype=np.float32) / 255.0 * 2.0 - 1.0
+    return torch.from_numpy(arr).permute(2, 0, 1).unsqueeze(0)
+
+
+class Pi0ImageProcessor:
+    """Minimal image preprocessor: image → normalized + padded ``[-1,1]`` tensor."""
+
+    def __init__(self, image_size: int = PI0_IMAGE_SIZE):
+        self.image_size = image_size
+
+    def preprocess_single(self, image: Any) -> torch.Tensor:
+        """Accept a PIL image, an HWC uint8/float ndarray, or a CHW tensor and
+        return a ``(1, 3, image_size, image_size)`` float tensor in ``[-1, 1]``."""
+        t = self._to_tensor(image)
+        if t.shape[2] != self.image_size or t.shape[3] != self.image_size:
+            t = resize_with_pad(t, self.image_size, self.image_size)
+        return t
+
+    def _to_tensor(self, image: Any) -> torch.Tensor:
+        if isinstance(image, Image.Image):
+            return pil_image_to_tensor(image)
+        if isinstance(image, np.ndarray):
+            arr = image
+            # HWC → normalize uint8/[0,255] to [-1,1]; assume already [-1,1] if float.
+            if arr.ndim == 3 and arr.shape[-1] in (1, 3):
+                if np.issubdtype(arr.dtype, np.integer) or arr.max() > 1.0:
+                    arr = arr.astype(np.float32) / 255.0 * 2.0 - 1.0
+                else:
+                    arr = arr.astype(np.float32)
+                return torch.from_numpy(arr).permute(2, 0, 1).unsqueeze(0)
+            return torch.as_tensor(arr, dtype=torch.float32)
+        if isinstance(image, torch.Tensor):
+            t = image
+            if t.ndim == 3:  # (C, H, W) → (1, C, H, W)
+                t = t.unsqueeze(0)
+            return t.to(dtype=torch.float32)
+        raise TypeError(f"Unsupported image type for π0 preprocessing: {type(image)}")
+
+    def make_empty_image(self) -> torch.Tensor:
+        """Fill tensor for an unused camera slot — pure -1, matches OpenPI/LeRobot."""
+        return torch.full((1, 3, self.image_size, self.image_size), -1.0)
+
+
+def tokenize_prompt(tokenizer, text: str, max_token_len: int = PI0_MAX_TOKEN_LEN):
+    """Return ``(input_ids, attention_mask)`` lists, length ``max_token_len``.
+
+    Matches LeRobot's ``Pi0NewLineProcessor``: append ``\\n`` if the caller
+    didn't, then run the PaliGemma tokenizer with right-padding.
+    """
+    prompt = text or ""
+    if not prompt.endswith("\n"):
+        prompt = prompt + "\n"
+    enc = tokenizer(
+        prompt,
+        padding="max_length",
+        max_length=max_token_len,
+        truncation=True,
+        add_special_tokens=True,
+        return_tensors=None,
+    )
+    return list(enc["input_ids"]), list(enc["attention_mask"])
+
+
+def _extract_images(robot_obs: dict, config) -> dict[str, Any]:
+    """Pull a ``{feature_key: image}`` map out of a raw robot obs.
+
+    Accepts two shapes:
+      * ``robot_obs["images"]`` is a dict keyed by camera name; or
+      * flat ``observation/<cam>`` style keys on ``robot_obs`` itself.
+    Keys are translated through ``config.image_key_map`` (raw obs key →
+    checkpoint feature key) so serving wire names map onto ``input_features``.
+    """
+    images = robot_obs.get("images")
+    if not isinstance(images, dict):
+        # Fall back to flat keys (anything that isn't a known scalar field).
+        images = {k: v for k, v in robot_obs.items() if k not in ("state", "prompt", "session_id", "reset", "images")}
+    key_map = getattr(config, "image_key_map", None) or {}
+    return {key_map.get(k, k): v for k, v in images.items()}
+
+
+def build_model_inputs(robot_obs: dict, config, tokenizer, device: torch.device):
+    """Convert a raw robot observation into ``sample_actions`` inputs.
+
+    Reproduces the LeRobot camera ordering exactly: iterate
+    ``config.image_feature_keys`` (the ordered camera identities from the
+    checkpoint's ``input_features``); for each, use the supplied image (mask
+    ``True``) or a ``-1``-filled empty image (mask ``False``). Then tokenize
+    the prompt and zero-pad/truncate the state to ``max_state_dim``.
+
+    Returns ``(images, image_masks, lang_tokens, lang_masks, state)`` with a
+    leading batch dim of 1 on the tensors.
+    """
+    image_size = int(config.image_resolution[0])
+    img_proc = Pi0ImageProcessor(image_size=image_size)
+
+    feature_keys = config.image_feature_keys or []
+    if not feature_keys:
+        # No declared camera order — fall back to whatever the obs provides,
+        # capped at max_cameras (preserves insertion order).
+        provided = _extract_images(robot_obs, config)
+        feature_keys = list(provided.keys())[: config.max_cameras]
+
+    obs_images = _extract_images(robot_obs, config)
+
+    images: list[torch.Tensor] = []
+    image_masks: list[torch.Tensor] = []
+    for key in feature_keys[: config.max_cameras]:
+        img = obs_images.get(key)
+        if img is not None:
+            tensor = img_proc.preprocess_single(img).to(device=device)
+            mask = True
+        else:
+            tensor = img_proc.make_empty_image().to(device=device)
+            mask = False
+        images.append(tensor)
+        image_masks.append(torch.tensor([mask], dtype=torch.bool, device=device))
+
+    # Pad up to max_cameras with empty slots if the checkpoint declares fewer
+    # cameras than the model attends to.
+    while len(images) < config.max_cameras:
+        images.append(img_proc.make_empty_image().to(device=device))
+        image_masks.append(torch.tensor([False], dtype=torch.bool, device=device))
+
+    prompt = robot_obs.get("prompt", "") or ""
+    ids, attn = tokenize_prompt(tokenizer, prompt, config.tokenizer_max_length)
+    lang_tokens = torch.tensor([ids], dtype=torch.long, device=device)
+    lang_masks = torch.tensor([attn], dtype=torch.bool, device=device)
+
+    state = _build_state(robot_obs.get("state"), config.max_state_dim, device)
+
+    return images, image_masks, lang_tokens, lang_masks, state
+
+
+def _build_state(raw_state, max_state_dim: int, device: torch.device) -> torch.Tensor:
+    """Zero-pad / truncate the raw state to ``(1, max_state_dim)`` float32."""
+    if raw_state is None:
+        return torch.zeros(1, max_state_dim, dtype=torch.float32, device=device)
+    state = torch.as_tensor(raw_state, dtype=torch.float32, device=device)
+    if state.dim() == 1:
+        state = state[None, :]
+    if state.shape[-1] < max_state_dim:
+        state = F.pad(state, (0, max_state_dim - state.shape[-1]))
+    elif state.shape[-1] > max_state_dim:
+        state = state[..., :max_state_dim]
+    return state

vllm_omni/diffusion/registry.py

@@ -181,6 +181,11 @@ _DIFFUSION_MODELS = {
         "pipeline_gr00t",
         "Gr00tN1d7Pipeline",
     ),
+    "Pi0Pipeline": (
+        "pi0",
+        "pipeline_pi0",
+        "Pi0Pipeline",
+    ),
     "LongCatImageEditPipeline": (
         "longcat_image",
         "pipeline_longcat_image_edit",
@@ -358,6 +363,11 @@ _NO_CACHE_ACCELERATION = {
     # Pipelines that do not support cache acceleration (cache_dit / tea_cache).
     "NextStep11Pipeline",
     "AudioXPipeline",
+    # π0 is a flow-matching VLA with a self-contained sample_actions loop and no
+    # DiT-style ``.transformer`` block list, so cache_dit / tea_cache cannot apply
+    # to it; list it here so a stray cache_backend override disables gracefully
+    # instead of erroring.
+    "Pi0Pipeline",
     "LingBotWorldCausalDMDPipeline",
 }
 
@@ -560,6 +570,7 @@ _DIFFUSION_POST_PROCESS_FUNCS = {
     "LancePipeline": "get_lance_post_process_func",
     "MingImagePipeline": "get_ming_image_post_process_func",
     "InternVLAA1Pipeline": "get_internvla_a1_post_process_func",
+    "Pi0Pipeline": "get_pi0_post_process_func",
     "LongCatImageEditPipeline": "get_longcat_image_post_process_func",
     "StableDiffusion3Pipeline": "get_sd3_image_post_process_func",
     "FluxKontextPipeline": "get_flux_kontext_post_process_func",