docs: address architecture review feedback

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

.buildkite/cuda/test-nightly.yml

@@ -404,7 +404,7 @@ steps:
       - 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"
+          - pytest -s -v tests/e2e/online_serving/test_lingbot_video_moe.py -m "full_model and cuda" --run-level "full_model"
         mirror_hardwares: h100_1
 
       - label: ":full_moon: Diffusion X2V · Doc Test"

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

@@ -220,70 +220,65 @@ See `plan/voxcpm2_native_ar_design.md`.
 
 ### Steps
 
-1. **Write one adapter** under `vllm_omni/entrypoints/openai/tts_adapters/`.
-   `serving_speech.py` should not need an edit — detection, stage discovery and
-   dispatch are all derived from what the adapter declares.
+1. **Register in `serving_speech.py`** — add all 5 points in a **single commit**;
+   partial integration causes hard-to-debug failures. This file is modified by every
+   model PR and is the most common source of rebase conflicts — see conflict note below.
 
-   Create `vllm_omni/entrypoints/openai/tts_adapters/your_model.py`:
+   **Point 1** — stage constant (near the top, alongside the other `_*_TTS_MODEL_STAGES` sets):
    ```python
-   @register_tts_adapter
-   class YourModelAdapter(ARTTSAdapter):
-       name = "your_model"                              # registry key + log label
-       stage_keys = frozenset({"your_stage_key"})       # the deploy yaml's model_stage
-
-       def validate(self, request) -> str | None:
-           if not request.input or not request.input.strip():
-               return "Input text cannot be empty"
-           return None
-
-       async def build(self, request, sampling_params_list, has_inline_ref_audio):
-           params = {"text": [request.input]}
-           if request.voice is not None:
-               params["voice"] = [request.voice]
-           return PreparedRequest(
-               prompt={"prompt": request.input},
-               tts_params=params,
-               model_type=self.name,
-           )
+   _YOUR_MODEL_TTS_MODEL_STAGES = {"your_stage_key"}
    ```
 
-   Then add the module to the import block at the bottom of
-   `tts_adapters/__init__.py` so it registers. That import line is the only shared
-   file a new model touches — which is also why the old rebase-conflict hotspot is
-   gone.
+   **Point 2** — union into `_TTS_MODEL_STAGES`:
+   ```python
+   _TTS_MODEL_STAGES: set[str] = (
+       ...
+       | _YOUR_MODEL_TTS_MODEL_STAGES
+   )
+   ```
 
-   > **Pure-diffusion TTS does not go through adapters yet.** Under
-   > `for_diffusion()`, `create_speech()` routes straight to
-   > `_create_diffusion_speech()` and never calls `validate()`/`build()`.
-   > `DiffusionTTSAdapter` is scaffolding with no production subclass, so logic
-   > placed in one would silently never run. Diffusion-engine models follow the
-   > existing diffusion path; wiring it through adapters is open work (#4855).
+   **Point 3** — model type detection in `_detect_tts_model_type()`:
+   ```python
+   if model_stage in _YOUR_MODEL_TTS_MODEL_STAGES:
+       return "your_model"
+   ```
 
-   **If a stage key alone cannot identify the model**, declare
-   `model_archs = frozenset({"YourModelForConditionalGeneration"})`; add
-   `arch_identifies_entry_stage = True` when the model owns no stage key at all
-   (Ming dense). For a rule that is not set membership, override `matches()` (see
-   `covo_audio.py`). For a genuine overlap with another adapter, give one an
-   explicit `detect_priority` — `test_tts_detection.py` fails on an unordered
-   overlap. Models that only serve speech in some topologies override
-   `stage_serves_speech()` (see `audex.py`).
+   **Point 4** — validation dispatch in `_validate_tts_request()`:
+   ```python
+   if self._tts_model_type == "your_model":
+       return self._validate_your_model_request(request)
+   ```
 
-   **Reuse shared helpers via `self.ctx.server`** rather than reimplementing them:
-   `_resolve_ref_audio`, `_apply_uploaded_speaker`, `_validate_ref_audio_format`,
-   `_max_instructions_length`. Read a comparable adapter first — `fish_speech.py`
-   (voice cloning), `higgs_audio_v3.py` (parameter-heavy), `moss_tts.py` (family
-   sharing a base class).
+   **Point 5** — validation + parameter-builder methods:
+   ```python
+   def _validate_your_model_request(self, request) -> str | None:
+       if not request.input or not request.input.strip():
+           return "Input text cannot be empty"
+       return None
 
-   > **Do not add `self._tts_model_type == ...` branches to `serving_speech.py`.**
-   > Older models predate the adapter framework and still have them; they are being
-   > migrated out (RFC #4327, #4855). `tools/pre_commit/check_tts_adapter.py` is a
-   > ratchet on the remaining count and fails the commit if it grows. Behaviour that
-   > no adapter hook can express is a missing hook — propose it on the RFC.
+   def _build_your_model_params(self, request) -> dict:
+       params = {"text": [request.input]}
+       if request.voice is not None:
+           params["voice"] = [request.voice]
+       return params
+   ```
+   Wire `_build_your_model_params` into `_create_tts_request()` alongside the other
+   model-specific param builders.
 
-   > **Unused variable rule**: only extract fields in `build()` that are actually
-   > forwarded to the model. Unused extractions fail `ruff F841`. For voice-cloning
-   > fields (`ref_audio` -> `prompt_audio_path`, `ref_text` -> `prompt_text`), add
-   > them to the params and verify they reach the model call.
+   > **Two dispatch patterns coexist**: Fish Speech uses a `self._is_fish_speech` boolean
+   > instance attribute checked before `elif self._is_tts`, while all newer models
+   > (CosyVoice3, MOSS-TTS-Nano) use the `_tts_model_type` string returned by
+   > `_detect_tts_model_type()`. For new models, always use the `_tts_model_type` string
+   > pattern — do not add new `_is_*` flags.
+
+   > **Unused variable rule**: only extract fields in `_build_your_model_params` that
+   > are actually forwarded to the model. Unused extractions fail `ruff F841`.
+   > For voice-cloning fields (`ref_audio` → `prompt_audio_path`, `ref_text` →
+   > `prompt_text`), add them to the param builder and verify they reach the model call.
+
+   **Rebase conflict note**: when rebasing onto `main` after another model was merged,
+   `serving_speech.py` will conflict. Resolution: always keep *both* the upstream
+   model's additions and your own — never discard either side.
 
 2. **Handle model-specific parameters**:
    - Voice cloning: `ref_audio` encoding and prompt injection
@@ -300,7 +295,7 @@ import base64
 from pathlib import Path
 
 def build_voice_clone_prompt(ref_audio_path: str, text: str, codec) -> list:
-    """Build prompt with reference audio for voice cloning, called from the adapter."""
+    """Build prompt with reference audio for voice cloning in serving_speech.py."""
     audio_bytes = Path(ref_audio_path).read_bytes()
     codes = codec.encode(audio_bytes)  # Encode on CPU using model's codec (e.g., DAC)
     token_ids = [code + codec.vocab_offset for code in codes.flatten().tolist()]
@@ -326,7 +321,7 @@ Classify the model's **CI priority** first (high / medium / low). High-priority
 
 | Level | Location | Marker | CI pipeline | Notes |
 |-------|----------|--------|-------------|-------|
-| **L1** | `tests/model_executor/…`, `tests/entrypoints/openai_api/…`, stage-processor tests | `core_model` + `cpu` | `test-ready.yml` | Prompt assembly, async_chunk helpers, adapter validation — no GPU |
+| **L1** | `tests/model_executor/…`, `tests/entrypoints/openai_api/…`, stage-processor tests | `core_model` + `cpu` | `test-ready.yml` | Prompt assembly, async_chunk helpers, `serving_speech` validation — no GPU |
 | **L2** | `tests/e2e/online_serving/test_{slug}.py` | **`core_model` + `advanced_model`** (both on baseline smoke) + `tts` + `@hardware_test(...)` | `test-ready.yml` (`ready` label) | Default deploy smoke: single `/v1/audio/speech` or offline `OmniRunner` path |
 | **L3** | `tests/e2e/online_serving/test_{slug}.py` **and** `tests/e2e/offline_inference/test_{slug}.py` | Baseline smoke: **`core_model` + `advanced_model`**; heavier cases: `advanced_model` only (+ `tts`) | `test-merge.yml` **or** merged into nightly TTS function job | Streaming, voice clone, batch/queue, async_chunk |
 | **L4** | `tests/e2e/online_serving/test_{slug}_expansion.py`, optional offline expansion | `full_model` + `tts` | `test-nightly.yml` (`:full_moon: TTS · Function Test with L4`) | Feature matrix; perf → `tests/dfx/perf/tests/test_tts.json` |
@@ -371,7 +366,7 @@ See [vllm-omni-test skill](../vllm-omni-test/SKILL.md) § **Runtime send helpers
 
 ### Deliverables
 
-- One adapter file under `tts_adapters/` plus its line in the package import block
+- Updated `serving_speech.py` with all 5 integration points (single commit)
 - Client scripts and server launcher under `examples/online_serving/text_to_speech/<model>/`
 - Gradio demo with streaming and voice cloning UI in the same dir
 - E2E tests per **Test Case Writing (CI Levels)** above (priority tier determines L1–L4 scope)
@@ -519,8 +514,8 @@ Use this checklist when integrating a new TTS model:
 - [ ] README.md written
 
 ### Phase 3: Online Serving
-- [ ] Adapter written under `tts_adapters/` and registered in the import block
-- [ ] Only extract params in `build()` that are forwarded to the model call (ruff F841)
+- [ ] All 5 `serving_speech.py` integration points added in one commit
+- [ ] Only extract params in `_build_*_params` that are forwarded to the model call (ruff F841)
 - [ ] Prompt builder handles text input correctly
 - [ ] Voice cloning works (if supported)
 - [ ] All response formats work (wav, mp3, flac, pcm)

.claude/skills/readme.md

@@ -1,9 +1,9 @@
-# Repository Skills for vLLM-Omni
+# Claude Skills for vLLM-Omni
 
-This directory contains repository-scale skills maintained for `vllm-omni`.
-They capture repeatable workflows for common contributor and maintainer tasks
-such as model integration, CI-aligned testing, performance optimization, and
-pull request review.
+This directory contains Claude Code skills maintained for the `vllm-omni`
+repository. These skills capture repeatable workflows for common contributor
+tasks such as model integration, pull request review, and release note
+generation.
 
 ## Directory Structure
 
@@ -14,41 +14,31 @@ include:
 - `references/`: focused reference material used by the skill
 - `scripts/`: small helper scripts used by the skill
 
-## Using the Skills
-
-Coding agents that discover repository skills can invoke the relevant skill by
-name. For other agents, point them to the skill's `SKILL.md` and ask them to
-read it before changing code. Combine a domain skill with `vllm-omni-test` when
-tests or CI coverage change, then use `precheck-pr` for the contributor's final
-self-check.
-
 ## Available Skills
 
-- [`add-diffusion-model`](add-diffusion-model/SKILL.md): guides integration of
-  a new diffusion model into `vllm-omni`
-- [`add-tts-model`](add-tts-model/SKILL.md): covers integration of new TTS
-  models and related serving workflows
-- [`diffusion-perf-opt`](diffusion-perf-opt/SKILL.md): guides diffusion model
-  performance optimization, including profiling traces, parallel strategies,
-  stage timing analysis, and benchmark-driven tuning
-- [`precheck-pr`](precheck-pr/SKILL.md): self-checks a branch before creating a
-  PR by validating title format, dead code, accuracy and performance claims,
-  and merge readiness
-- [`quantization`](quantization/SKILL.md): guides quantization method selection,
-  model integration, checkpoint loading, and quality/performance validation
-  for vLLM-Omni
-- [`review-pr`](review-pr/SKILL.md): provides a frozen-snapshot, contract-aware
-  workflow for maintainers and reviewers; contributors should use
-  `precheck-pr` for self-review
-- [`vllm-omni-npu-model-runner-upgrade`](vllm-omni-npu-upgrade/SKILL.md):
-  upgrades NPU model runners to align with the latest vllm-ascend
-  `NPUModelRunner`
-- [`vllm-omni-test`](vllm-omni-test/SKILL.md): guides generation and execution
-  of CI-aligned tests (L1–L4), pytest marker selection (`core_model` /
-  `advanced_model` / `full_model`, `omni` / `tts` / `diffusion`), Buildkite
-  wiring (`test-ready.yml`, `test-merge.yml`, `test-nightly.yml`,
-  `test-weekly.yml`), and copy-paste local plus CI-like `pytest` commands; see
-  `references/test-routing.md` for level-to-command mapping
+- `add-diffusion-model`: guides integration of a new diffusion model into
+  `vllm-omni`
+- `diffusion-perf-opt`: guides diffusion model performance optimization,
+  including profiling traces, parallel strategies, stage timing analysis, and
+  benchmark-driven tuning
+- `quantization`: guides quantization method selection, model integration,
+  checkpoint loading, and quality/performance validation for vLLM-Omni
+- `add-omni-model`: covers addition of new omni-modality model support
+- `add-tts-model`: covers integration of new TTS models and related serving
+  workflows
+- `generate-release-note`: helps prepare release notes for repository changes
+- `precheck-pr`: self-check a branch before creating a PR — validates PR title
+  format, catches dead code, verifies accuracy/perf claims, and confirms merge
+  readiness
+- `vllm-omni-test`: guides generation and execution of CI-aligned tests (L1–L4),
+  pytest marker selection (`core_model` / `advanced_model` / `full_model`,
+  `omni` / `tts` / `diffusion`), Buildkite wiring (`test-ready.yml`,
+  `test-merge.yml`, `test-nightly.yml`, `test-weekly.yml`), and copy-paste
+  local plus CI-like `pytest` commands; see `references/test-routing.md` for
+  level-to-command mapping
+- `review-pr`: provides a structured workflow for reviewing pull requests
+- `vllm-omni-npu-model-runner-upgrade`: upgrades NPU model runners to align with the
+  latest vllm-ascend NPUModelRunner
 
 ## Maintenance Guidelines
 

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

@@ -1,231 +0,0 @@
----
-name: review-pr
-description: Review pull requests and local branches for vllm-project/vllm-omni with a frozen snapshot, module-design ownership, feature-design overlays, targeted validation, and concise evidence-backed findings. Use for default, detailed, or repeat maintainer reviews; checking correctness, compatibility, tests, benchmarks, model additions, distributed changes, or breaking behavior; and identifying or explicitly requesting the most relevant code-owner reviewers. Use precheck-pr instead for an author's pre-submit self-check.
----
-
-# Review vLLM-Omni Pull Requests
-
-Review like a maintainer: direct, selective, and focused on issues that CI does
-not prove. Prefer a few high-confidence findings over exhaustive commentary.
-Zero findings is a valid result.
-
-## Quality contract
-
-Make every finding:
-
-- **Correct:** prove a reachable failure, not a suspicion.
-- **Prioritized:** lead with merge blockers and high-impact defects.
-- **Actionable:** identify the smallest safe fix direction.
-- **Evidence-based:** cite code, tests, docs, CI, or measurements.
-- **Concise:** avoid review templates and repeated summaries.
-- **Calibrated:** match severity to user and maintainer impact.
-
-Do not report unrelated backlog, style already enforced by pre-commit, or a
-missing test that would not protect changed behavior.
-
-## Select the input and depth
-
-Use `vllm-project/vllm-omni` as the base repository. Accept its forks and local
-checkouts; use another skill for unrelated repositories.
-
-| Input | Review surface |
-| --- | --- |
-| PR number or URL | Frozen PR metadata, full diff, and relevant threads. |
-| Local branch/worktree | Frozen target-base SHA through committed, staged, unstaged, and in-scope untracked changes. |
-| Pre-filled context | Reuse supplied metadata; fetch only missing facts and the full diff. |
-
-Default to maintainer brevity. A detailed or audit request expands coverage and
-lists `path:line` findings, but keeps the same confidence and severity bar.
-
-## Reference guide
-
-Load references in review order: process, one primary module contract, matching
-feature designs, evidence checks, then delivery. Every file is linked directly
-below; do not load unrelated module references.
-
-Each concise reference links to the maintained
-[vLLM-Omni documentation](https://docs.vllm.ai/projects/vllm-omni/en/latest/).
-For branch-specific behavior, inspect the matching `docs/` file in the reviewed
-checkout first; use the published latest docs for current guidance and discovery.
-If docs and live code disagree, verify the code/tests and report the drift.
-
-### Review process
-
-| Reference | Read when |
-| --- | --- |
-| [review-execution.md](references/process/review-execution.md) | Every review; freeze inputs, inspect safely, and deliver against the same snapshot. |
-| [general-checks.md](references/process/general-checks.md) | Every review; apply repository-wide correctness and evidence rules. |
-| [design-contracts.md](references/process/design-contracts.md) | Every production review; resolve branch-local module and feature design status. |
-| [review-routing.md](references/process/review-routing.md) | After the diff census; select one primary module and conditional overlays. |
-
-### Primary module contract
-
-| Reference | Read when |
-| --- | --- |
-| [entrypoints.md](references/modules/entrypoints.md) | Offline/CLI/API ingress, validation, rendering, streaming, or sessions change. |
-| [configuration.md](references/modules/configuration.md) | Config construction, deploy/stage schema, defaults, registry, or topology changes. |
-| [input-output-modality.md](references/modules/input-output-modality.md) | Requests, messages, serialization, output types, accumulation, or completion change. |
-| [error-contracts.md](references/modules/error-contracts.md) | Error classification, fatality, propagation, sanitization, or rendering changes. |
-| [engine-orchestration.md](references/modules/engine-orchestration.md) | Cross-stage routing, request state, output ordering, RPC correlation, or terminal convergence changes. |
-| [stage-runtime.md](references/modules/stage-runtime.md) | Placement, startup, readiness, replica identity, affinity, membership, or shutdown changes. |
-| [omni-connector.md](references/modules/omni-connector.md) | Cross-stage/process/device/node transport or synchronization changes. |
-| [model-integration.md](references/modules/model-integration.md) | Registration, preprocessing, loading, runners, or model-specific execution changes. |
-| [ar-runtime.md](references/modules/ar-runtime.md) | AR scheduling, request/cache state, adapters, workers, or upstream vLLM semantics change. |
-| [diffusion.md](references/modules/diffusion.md) | Diffusion runtime, models, batching, parallelism, or offload changes. |
-| [execution-platforms.md](references/modules/execution-platforms.md) | Hardware selection, capabilities, vendor workers, kernels, or patches change. |
-| [cache-management.md](references/modules/cache-management.md) | Cache identity, reuse, validity, reset, eviction, or teardown changes. |
-| [quantization.md](references/modules/quantization.md) | Quantization selection, checkpoint metadata, layer mapping, precision, or constraints change. |
-| [observability.md](references/modules/observability.md) | Metrics, logs, units, labels, correlation, or lifecycle changes. |
-| [profiling.md](references/modules/profiling.md) | Profiling instrumentation, traces, start/stop lifecycle, or overhead changes. |
-| [benchmarking.md](references/modules/benchmarking.md) | Benchmark workload, metric calculation, CLI, or result metadata changes. |
-
-### Feature-design overlays
-
-| Reference | Read when |
-| --- | --- |
-| [runtime-stage-execution.md](references/features/runtime-stage-execution.md) | Disaggregated inference, async chunk/output/materialization, or prefix caching changes. |
-| [communication.md](references/features/communication.md) | A concrete OmniConnector backend or its deployment contract changes. |
-| [diffusion-acceleration.md](references/features/diffusion-acceleration.md) | Diffusion parallelism, attention, quantization, cache, batching, or offload changes. |
-| [infrastructure-performance.md](references/features/infrastructure-performance.md) | Metrics infrastructure or documented speech optimization stacks change. |
-
-### Evidence and quality checks
-
-| Reference | Read when |
-| --- | --- |
-| [model-addition-checklist.md](references/checks/model-addition-checklist.md) | A model, architecture, loader, processor, registry, or stage config is added. |
-| [perf-verification.md](references/checks/perf-verification.md) | The PR makes a latency, throughput, memory, or quality claim. |
-| [test-quality-evaluation.md](references/checks/test-quality-evaluation.md) | Tests change, are absent for risky code, or may not exercise production behavior. |
-| [tests-docs-checklist.md](references/checks/tests-docs-checklist.md) | Coverage, CI markers, examples, user docs, or PR evidence need review. |
-| [verification.md](references/checks/verification.md) | Hardware, a server, or a runnable affected path is available for active verification. |
-
-### Delivery and reviewer coordination
-
-| Reference | Read when |
-| --- | --- |
-| [maintainer-style-study.md](references/delivery/maintainer-style-study.md) | Findings are ready for concise maintainer-style delivery. |
-| [review-requests.md](references/delivery/review-requests.md) | The user asks to identify, suggest, request, or ping code-owner reviewers. |
-
-## Workflow
-
-### 1. Freeze and report the snapshot
-
-Pin the base and head before reading source or running validation. Within 60 seconds,
-report the pinned head, CI, mergeability, and preliminary findings in the host
-conversation. Do not wait for CI or post this update to GitHub.
-
-If the target changes while fetching, discard the evidence and retry once. If
-it changes again, report the churn and wait for a stable target.
-
-For a trusted PR head, materialize the pinned head in an isolated detached
-worktree. A worktree freezes identity but is not a security sandbox. Treat fork
-heads as untrusted unless the user and environment policy explicitly establish
-otherwise: execute them only in a disposable, secret-free sandbox with restricted
-filesystem, network, and resources; without one, use static SHA-addressed reads
-and CI evidence only. For a local review, freeze the committed, index, worktree,
-and NUL-safe in-scope untracked contents. Follow
-[review-execution.md](references/process/review-execution.md) for trust gates,
-state fingerprints, and byte-for-byte staleness checks.
-
-### 2. Build the diff census
-
-Group files into production code, tests, docs, configuration, build/CI, and
-generated artifacts. Map each changed production file and test group to the PR
-goal. Compare the title/body claims with the actual diff; use linked issues only
-when they define the contract or reproduction.
-
-Mark unrelated scope and unexplained generated artifacts. Do not infer behavior
-from the PR description without tracing the live code.
-
-### 3. Route from the live behavior
-
-Trace each claimed behavior through the changed producer to its live consumer,
-then use [design-contracts.md](references/process/design-contracts.md) and
-[review-routing.md](references/process/review-routing.md) to select one primary
-module contract, a second only for a real documented cross-boundary call path,
-and every matching feature-design and evidence overlay. Treat titles and paths
-as hints; live behavior and the frozen head's current design metadata are
-authoritative. For docs-, tests-, or CI-only changes, route to the production
-contract they protect or use only the applicable evidence checks.
-
-### 4. Run the blocker scan
-
-Apply every category in [general-checks.md](references/process/general-checks.md) before
-lower-priority comments.
-
-For each changed value or behavior, trace:
-
-```text
-public ingress -> validation/defaulting -> producer -> transformations
-  -> stage/worker/connector boundary -> final consumer -> terminal cleanup
-```
-
-Cover every applicable offline/online, streaming/non-streaming, sync/async,
-feature-on/off, topology, and compatibility path. Search bounded callers and
-sibling implementations rather than assuming the changed hunk is the only path.
-
-### 5. Apply module and feature contracts
-
-Apply the reference set selected in step 3 and any matching repo-local skill.
-Read the exact module and feature pages in the frozen head, including status,
-ownership boundary, dependencies, candidate invariants, safe-change guide, and
-promotion gate. Candidate or draft rules are questions, not blockers, unless
-current code, tests, or policy enforce them. Inspect both sides of any config,
-registry, serialization, connector, cache, or stage boundary.
-
-When a diff adds or expands a helper, class, fallback, compatibility branch, or
-public behavior, run a subtraction pass: remove out-of-scope behavior and check
-whether each new abstraction can be deleted, merged, moved, or inlined.
-
-### 6. Verify the changed path
-
-Before each validation group, verify the frozen SHA plus the tracked, index,
-untracked, and ignored-file fingerprint, or recreate a pristine snapshot. On a
-trusted head or inside the required sandbox, run an import/version preflight,
-then the narrowest relevant tests and low-cost static checks. Bind every result
-to the head SHA, snapshot fingerprint, and environment fingerprint. Never run
-imports, tests, builds, hooks, or repo-configurable tooling from an untrusted
-head on the reviewer host.
-
-- Treat CI as status evidence; inspect only the first overlapping failure.
-- For docs-only changes, use diff hygiene, links/build checks, and bounded live
-  contract verification instead of dependency setup or pytest.
-- For hardware-dependent paths, run available static/CPU checks and name the
-  exact GPU/NPU gap. Never simulate device evidence.
-- For performance or accuracy claims, require comparable base/head runs with
-  the same environment, workload, warmup, repetitions, and quality criteria.
-
-Stop when each changed semantic path has a supported finding or an explicit
-no-issue conclusion. Do not search further only to increase confidence.
-
-### 7. Consolidate and deliver
-
-Verify each finding against the current diff, deduplicate by root cause, and
-order by severity.
-
-Re-read the remote head and reverify or recreate the pristine validation
-snapshot immediately before delivery. If either changed, mark the review stale
-and restart from the new snapshot.
-
-Return findings first. Use
-[maintainer-style-study.md](references/delivery/maintainer-style-study.md) to keep them
-direct and brief. Each finding must include an exact `path:line`, trigger or
-call path, current behavior, impact, and smallest fix direction. If there are
-no findings, say so briefly and name material validation gaps.
-
-Keep the review read-only unless the user explicitly authorizes posting. Do not
-submit `APPROVE`, `COMMENT`, or `REQUEST_CHANGES`, add labels, edit code, or push
-commits as an implied part of review.
-
-### 8. Optionally request focused owner reviews
-
-Only when the user asks to identify or request reviewers, read
-[review-requests.md](references/delivery/review-requests.md). Rank path-matched
-CODEOWNERS with the frozen module page's owners or required reviewers and
-documented governance expertise; propose one to three focused reviewers with an
-explicit contract rationale.
-
-Identifying or suggesting reviewers is read-only. Requesting reviewers or
-posting `@mention` comments changes external state and requires explicit user
-authorization. When authorized, recheck the head, deduplicate existing
-requests, and post at most one consolidated comment. Do not infer this
-permission from a request to review the code.

.claude/skills/review-pr/agents/openai.yaml

@@ -1,4 +0,0 @@
-interface:
-  display_name: "vLLM-Omni PR Review"
-  short_description: "Review vLLM-Omni PRs and route owner requests"
-  default_prompt: "Use $review-pr to review this vLLM-Omni pull request with owner-specific rules and evidence-backed findings."

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

@@ -1,121 +0,0 @@
-# Model Addition Checklist
-
-Read this reference when a PR adds or expands a model architecture, pipeline,
-loader, processor, registry entry, or stage configuration.
-
-Official docs: [model contribution guides](https://docs.vllm.ai/projects/vllm-omni/en/latest/contributing/model/),
-[adding an omni model](https://docs.vllm.ai/projects/vllm-omni/en/latest/contributing/model/adding_omni_model/),
-[adding a diffusion model](https://docs.vllm.ai/projects/vllm-omni/en/latest/contributing/model/adding_diffusion_model/),
-[adding a TTS model](https://docs.vllm.ai/projects/vllm-omni/en/latest/contributing/model/adding_tts_model/),
-[supported models](https://docs.vllm.ai/projects/vllm-omni/en/latest/models/supported_models/),
-[diffusion features](https://docs.vllm.ai/projects/vllm-omni/en/latest/user_guide/diffusion_features/),
-and [feature compatibility](https://docs.vllm.ai/projects/vllm-omni/en/latest/user_guide/feature_compatibility/).
-
-## Match claims to the diff
-
-- Reconcile every claimed architecture, modality, variant, feature, and example
-  with changed files and registry entries.
-- Resolve config model types, architecture names, callable paths, exports, and
-  dependency extras against live symbols.
-- Check sibling generations sharing an architecture name are disambiguated by
-  a stable config predicate rather than import order or an accidental default.
-
-## Close the integration chain
-
-Trace:
-
-```text
-public model id -> config/pipeline selection -> registry -> loader/processor
-  -> stage inputs -> model execution -> stage/public output
-```
-
-Verify optional dependencies fail with an actionable message, weight names and
-dtypes map correctly, and each advertised serving mode reaches the production
-dispatcher. Confirm outputs are non-empty and valid for their modality, shape,
-sample rate, or response schema.
-
-## Keep support documentation truthful
-
-- Build an evidence-backed inventory of the new model's exact architecture and
-  checkpoint identifiers, modalities and tasks, hardware backends, serving
-  modes, and supported acceleration, parallelism, cache, offload, and
-  quantization features. Treat untested support as unknown, not supported.
-- Update `docs/models/supported_models.md` with the architecture, model family,
-  example checkpoints, and only the backends validated by the PR.
-- For a diffusion model, add it to the applicable ImageGen, VideoGen, or
-  AudioGen table in `docs/user_guide/diffusion_features.md`. Fill every feature
-  column according to its legend and document partial support or constraints.
-- Update `docs/user_guide/feature_compatibility.md` when the model changes valid
-  feature combinations, configuration constraints, examples, or limitations,
-  and update any other branch-local support table affected by the claims.
-- Cross-check every documented support mark against the registry, configuration,
-  production-path tests, and PR evidence; do not copy a sibling model's support
-  claims without validation.
-
-## Ship the model recipe
-
-- Require every new model PR to add or update one model-family recipe under
-  `recipes/<vendor>/` and its row in the `recipes/README.md` Available Recipes
-  table. Follow [`recipes/TEMPLATE.md`](../../../../../recipes/TEMPLATE.md) and
-  the layout rules in
-  [`recipes/README.md`](../../../../../recipes/README.md); the
-  external recipes repository is a structural reference, not a substitute.
-- Include only tested task, serving-mode, and hardware configurations. Keep the
-  exact model identifiers, flags, commands, verification, feature support,
-  constraints, and limitations consistent with code, tests, examples, and the
-  support documentation.
-- Cover each validated platform in its own hardware section. Use one recipe per
-  model family by default; justify a split when configurations cannot be kept
-  clear in one file, and do not add placeholder sections for untested platforms.
-
-## Compare the reference implementation
-
-Require every new model PR to include a head-versus-reference accuracy or
-quality and performance comparison:
-
-- Name the canonical implementation and pin its repository revision, package
-  version, configuration, and checkpoint. If it cannot run, explain why and use
-  the closest runnable reference; do not silently omit the comparison.
-- Match inputs or dataset, seed, output settings, precision or quantization,
-  hardware, software, warmup, repetitions, synchronization, and timing and
-  memory scope. Include exact commands and environment details for both sides.
-- Compare accuracy or output quality with a task-appropriate metric and explicit
-  tolerance. When no reliable metric exists, provide paired fixed-input outputs,
-  artifacts, and evaluation criteria, and explain expected numerical or
-  qualitative differences.
-- Report end-to-end latency, task-appropriate throughput, peak device memory,
-  run-to-run variability, and head/reference ratios at equivalent output quality.
-- Prefer a timing split for preprocessing or encoding, each model stage,
-  inter-stage transfer or communication, decoding or postprocessing, and output
-  assembly. Define the boundaries, account for overlap, reconcile the split with
-  end-to-end time, and explain any unavailable stage measurement.
-
-Use [perf-verification.md](perf-verification.md) for the comparison protocol and
-repository benchmark and profiling links.
-
-## Remove accidental surface
-
-Search bounded call sites for:
-
-- inference-dead training `forward()` paths, unused factories/wrappers, never-set
-  branch keys, and immediately discarded parameters;
-- duplicate payload strings, validation, or shape coercion across stages;
-- compatibility aliases in brand-new code without an existing caller;
-- private symbols re-exported as public API or module-level side effects.
-
-Keep code only when it has a distinct live caller, invariant, or compatibility
-contract. Prefer one typed producer-consumer schema to repeated string keys.
-
-## Require proportionate evidence
-
-- Run a representative production-path inference and assert output content, not
-  only process survival.
-- Require profiling or A/B tables when the PR makes performance, memory,
-  precision, or quality claims, or when a suspected hot-path/device bug needs
-  that evidence. Do not impose fixed utilization or regression thresholds that
-  the repository does not define.
-- Require focused registry/config/loading tests, a representative E2E path, and
-  user-facing model/usage documentation.
-
-Use [diffusion.md](../modules/diffusion.md) for diffusion models and
-[perf-verification.md](perf-verification.md) for quantitative claims.

.claude/skills/review-pr/references/checks/perf-verification.md

@@ -1,51 +0,0 @@
-# Performance and Accuracy Verification
-
-Activate this reference when the PR adds a model or claims or intentionally
-changes latency, throughput, memory, scaling, precision, or output quality.
-
-Official docs: [profiling](https://docs.vllm.ai/projects/vllm-omni/en/latest/contributing/profiling/),
-[serving benchmarks](https://docs.vllm.ai/projects/vllm-omni/en/latest/cli/bench/serve/),
-and [metrics](https://docs.vllm.ai/projects/vllm-omni/en/latest/contributing/metrics/).
-
-## Comparable A/B contract
-
-Compare frozen base and head for an existing path. For a new model absent from
-base, compare head with a pinned canonical reference implementation. Use the same:
-
-- hardware, driver, software versions, model/checkpoint, and dependencies;
-- input or dataset, seed, precision, batch/concurrency, topology, and feature
-  flags;
-- warmup, measured repetitions, synchronization, timing scope, and memory
-  collection method.
-
-Keep exact commands and report variability rather than a cherry-picked run.
-Use the repository's benchmark when it covers the claim; otherwise use the
-smallest reproducible workload that reaches the changed production path.
-
-## Evidence table
-
-| Dimension | Typical evidence |
-| --- | --- |
-| Latency | End-to-end latency and, when available, TTFT plus a defined per-stage and transfer-time split. |
-| Throughput/scaling | Requests, tokens, frames, or audio duration per second across the claimed concurrency/topology. |
-| Memory | Peak allocated/reserved device memory and OOM boundary if relevant. |
-| Quality/accuracy | Repository metric or known-good output comparison with an explicit tolerance; paired samples when no metric exists. |
-
-Every optimization needs correctness or quality evidence. A faster result on a
-different workload, precision, seed, or topology is not a valid comparison.
-
-## Reviewer verification ladder
-
-1. Run base/head A/B on suitable hardware when affordable.
-2. If only one side can run, verify the script and request a comparable pair.
-3. If hardware or assets are unavailable, audit methodology, code path, and
-   contributor evidence; name the exact unverified claim.
-
-Classify discrepancies before reporting: implementation regression, benchmark
-bug, environmental drift, noise, or unsupported claim. Explain material
-regressions against the PR's stated goal or repository contract; do not invent
-universal percentage thresholds.
-
-Report claimed and measured values together, bind them to the frozen SHAs and
-environment, and keep unavailable hardware as a validation gap rather than a
-fabricated pass.

.claude/skills/review-pr/references/checks/test-quality-evaluation.md

@@ -1,39 +0,0 @@
-# Test Quality Evaluation
-
-Read this reference when tests change, a risky production path has no test, or
-a test-only PR may not prove the intended behavior.
-
-Official docs: [test system overview](https://docs.vllm.ai/projects/vllm-omni/en/latest/contributing/ci/test_system_overview/)
-and [test writing guide](https://docs.vllm.ai/projects/vllm-omni/en/latest/contributing/ci/test_writing_guide/).
-
-## Static proof check
-
-For each changed semantic path, ask whether the test would fail if that behavior
-were reverted or broken. Check:
-
-- assertions pin the contract, not only non-null output, logs, or “no crash”;
-- the production dispatcher, registry, connector, or scheduler is reached
-  instead of the changed behavior being mocked away;
-- fakes preserve relevant types, MRO, shapes, devices, async behavior, and
-  lifecycle transitions;
-- seeds, ordering, timing, synchronization, external services, and numeric
-  tolerances are deterministic or explicitly controlled;
-- normal, invalid, boundary, feature-off, failure/cancellation, and regression
-  paths are covered where the diff changes them;
-- run-level and domain markers place hardware/model tests in the intended CI
-  lane without silently skipping the contract.
-
-Map source symbols to tests with bounded `rg` searches; do not assume the test
-tree mirrors production paths.
-
-## Runtime check
-
-Run the narrowest affected tests supported by the environment. Record skipped
-hardware/model cases as gaps. Classify failures as code, test, infrastructure,
-or flaky before turning them into findings; one passing rerun does not erase a
-flakiness signal.
-
-Keep grades or matrices internal. Report only concrete code bugs or the one or
-two test defects that leave changed behavior materially unprotected. Use
-[tests-docs-checklist.md](tests-docs-checklist.md) for CI and documentation
-coverage.

.claude/skills/review-pr/references/checks/tests-docs-checklist.md

@@ -1,48 +0,0 @@
-# Tests and Documentation Checklist
-
-Use this reference to connect changed behavior to coverage, CI selection,
-examples, documentation, and reproducible PR evidence.
-
-Official docs: [test writing guide](https://docs.vllm.ai/projects/vllm-omni/en/latest/contributing/ci/test_writing_guide/),
-[test execution guide](https://docs.vllm.ai/projects/vllm-omni/en/latest/contributing/ci/test_execution_guide/),
-and [documentation guide](https://docs.vllm.ai/projects/vllm-omni/en/latest/contributing/DOCS_GUIDE/).
-
-## Coverage packet
-
-For each high-risk change, record a compact internal packet:
-
-```text
-change and failure risk -> existing unit/E2E coverage -> uncovered boundary
-  -> smallest test and stable assertion that closes the gap
-```
-
-Prefer a regression test that fails on the frozen base for bug fixes. Check
-numeric tolerances, deterministic inputs, realistic mocks, hardware/domain
-markers, and Buildkite/CI wiring using the documentation present on the reviewed
-branch. A skipped test is not a pass.
-
-## Documentation sync
-
-Require user-facing updates when the diff changes a model, feature, CLI/API,
-config key, default, compatibility behavior, or supported platform. Verify:
-
-- the actual docs navigation and support tables used by the branch;
-- exact identifiers, defaults, constraints, known limitations, and migration;
-- a minimal runnable example for newly exposed behavior;
-- examples and docs agree on commands, parameters, output schema, and supported
-  modes;
-- links and the bounded live contract build or resolve correctly.
-
-Do not request docs for internal behavior with no user-visible contract.
-
-## PR evidence
-
-For contributor-run tests or benchmarks, require enough provenance to evaluate
-the claim: hardware, relevant software versions, exact commands, inputs/model,
-result, and approximate runtime. Performance or accuracy evidence must compare
-base and head under equivalent conditions; use
-[perf-verification.md](perf-verification.md).
-
-Turn a missing test or document into a finding only when it protects or explains
-changed behavior. Name the smallest useful addition rather than requesting a
-broad coverage expansion.

.claude/skills/review-pr/references/checks/verification.md

@@ -1,36 +0,0 @@
-# Active Verification
-
-Read this reference when a runnable affected path and suitable local or remote
-hardware are available. Never claim device evidence from static analysis or a
-simulated backend.
-
-Official docs: [installation](https://docs.vllm.ai/projects/vllm-omni/en/latest/getting_started/installation/)
-and [test execution guide](https://docs.vllm.ai/projects/vllm-omni/en/latest/contributing/ci/test_execution_guide/).
-
-## Choose the narrowest level
-
-| Available environment | Verification |
-| --- | --- |
-| Matching accelerator, model/assets, and server | Targeted unit/integration test plus one representative E2E request. |
-| Matching accelerator without serving setup | Targeted model/runtime test and output or metric inspection. |
-| CPU/static environment only | Import/version preflight, focused CPU tests, config/registry resolution, and static contract checks. |
-| Missing dependencies or inaccessible hardware | Record the exact gap and evaluate contributor/CI evidence. |
-
-## Execute safely
-
-1. Bind the run to the frozen head SHA and record Python, platform, dependency,
-   model, device, and relevant environment fingerprints.
-2. Run the smallest affected unit test before an E2E workload.
-3. Exercise the changed public or production-dispatch path with bounded inputs.
-4. Compare actual output, metrics, cleanup, and exit behavior with the PR claim.
-5. For base/head claims, use the equivalent A/B protocol in
-   [perf-verification.md](perf-verification.md).
-
-Avoid disturbing shared servers, ports, models, or accelerators without user
-authorization. Do not install large dependencies, download gated assets, or
-start remote workloads merely because hardware might exist.
-
-Report the command, environment, result, and any skipped path. Classify failures
-as change-induced, pre-existing, infrastructure, environment, or flaky before
-making a finding. A hardware gap lowers confidence; it is not automatic proof
-of a defect.

.claude/skills/review-pr/references/delivery/maintainer-style-study.md

@@ -1,105 +0,0 @@
-# Maintainer Review Style
-
-Use this reference only when turning proved findings into comments. The baseline
-maintainer samples favor short, direct comments and reserve long explanations
-for architecture or non-obvious failure paths.
-
-Official docs: [contributing and review process](https://docs.vllm.ai/projects/vllm-omni/en/latest/contributing/).
-
-## Evidence basis
-
-This calibration used 928 PRs merged from 2026-05-10 through 2026-08-08.
-Candidates were ranked by substantive human inline-review threads, then by
-distinct reviewers and review rounds. Author self-comments, bots and AI
-reviewers, and bodyless approvals were excluded. Counts selected the sample;
-the practices below came from reading root comments and follow-up replies.
-
-The main set contains the six leaders after filtering; three other high-ranked
-PRs add core-runtime and benchmark coverage. All were merged.
-
-| PR | Review boundary |
-| --- | --- |
-| [#2906](https://github.com/vllm-project/vllm-omni/pull/2906) | New TTS model, shared utilities, recipes, and request state. |
-| [#3907](https://github.com/vllm-project/vllm-omni/pull/3907) | Full-duplex API, session lifecycle, concurrency, and realtime evidence. |
-| [#2162](https://github.com/vllm-project/vllm-omni/pull/2162) | Diffusion world model, OpenPI serving, tests, examples, and docs. |
-| [#3454](https://github.com/vllm-project/vllm-omni/pull/3454) | Diffusion model, generic CLI surface, loaders, and configuration scope. |
-| [#4425](https://github.com/vllm-project/vllm-omni/pull/4425) | Structured configuration, typing, defaults, and compatibility. |
-| [#5397](https://github.com/vllm-project/vllm-omni/pull/5397) | Distributed offload, collectives, failure handling, and load semantics. |
-| [#4281](https://github.com/vllm-project/vllm-omni/pull/4281) | Composable parallelism, configuration contracts, and examples. |
-| [#3628](https://github.com/vllm-project/vllm-omni/pull/3628) | Stage metrics, benchmark definitions, opt-in API behavior, and cleanup. |
-| [#3855](https://github.com/vllm-project/vllm-omni/pull/3855) | Stage runtime, thread safety, startup, and compatibility. |
-
-Historical comments calibrate delivery; they do not create merge policy. Live
-code, tests, repository docs, and the current `CODEOWNERS` file remain
-authoritative. When refreshing this study, promote a practice only when it
-recurs across at least three PRs or is supported by current repository guidance.
-
-## Recurring review behavior
-
-- Start from a changed contract, not a style preference. State the exact
-  request, configuration, topology, or failure path that triggers the issue.
-- Protect repository boundaries. Keep model-specific behavior in its domain,
-  move genuinely reusable logic to a shared module, and reuse existing loaders,
-  protocols, definitions, and config sources instead of bypassing them.
-- Challenge unrelated scope and abstractions. Ask why a new flag, config, class,
-  helper, fallback, or file is needed when an existing path appears sufficient.
-- Trace concurrency and terminal paths: overlapping requests, disconnect,
-  cancellation, partial failure, stale replies, collective schedule mismatch,
-  cleanup, and retry. Prefer an explicit startup error for unsupported
-  combinations over silently running with incomplete state or wrong weights.
-- Preserve user-visible behavior across variants. An accepted CLI/API/config
-  field must be applied, explicitly rejected, or visibly warned about; compare
-  normal and headless, offline and online, and streaming and non-streaming paths.
-- Ask for evidence at the affected boundary: a regression test using the real
-  tensor or protocol shape, correct pytest markers so CI runs it, comparable A/B
-  performance results, or an SLO-oriented latency/quality measurement.
-- Keep interfaces explicit: avoid `Any` and opaque strings where a precise
-  union, enum, or typed envelope exists; remove dead code and duplicated
-  constants; validate requests before expensive execution.
-- Document module scope and non-obvious invariants. Update examples, recipes,
-  supported-model/feature docs, and hardware assumptions when users rely on
-  them.
-- When expertise crosses an ownership boundary, ask a named owner to inspect a
-  specific file or contract and explain why. Do not send a generic `PTAL` list.
-- Keep non-blocking cleanup in a linked follow-up only after the current PR is
-  safe. If a revision fixes only part of a failure chain, continue the existing
-  thread with the remaining trigger instead of opening a duplicate finding.
-
-## Write the finding
-
-- Lead with the issue or decisive question; omit a review preamble.
-- Name the concrete trigger and impact, then the smallest fix direction.
-- Keep obvious fixes to one sentence. Use more detail only when the call path or
-  ownership boundary is otherwise unclear.
-- Prefer one root-cause comment over repeated symptom comments.
-- Hedge only when evidence is genuinely incomplete; mark that item as a
-  question or validation gap rather than overstating severity.
-- Say briefly that no findings were found when the review is clean; zero
-  comments is a valid outcome when posting is authorized.
-
-Useful forms:
-
-```text
-This leaves <state> allocated when <failure/cancellation path>. Release it in
-the shared terminal cleanup path.
-```
-
-```text
-Does this producer also update <consumer>? It still reads the old field here.
-```
-
-```text
-With <configuration>, this bypasses <existing loader/validator>, so <impact>.
-Reuse that path or reject this combination before startup.
-```
-
-```text
-@<owner>, could you check <specific boundary>? This changes <contract> used by
-<consumer>.
-```
-
-Use a priority label only for a proved high-impact defect. Avoid generic praise,
-routine “Nit:” prefixes, decorative severity labels, audit templates, rule IDs,
-comment-count narration, and “I left a few comments.” Keep approvals, review
-events, reviewer requests, mentions, and GitHub replies subject to the user's
-explicit authorization.

.claude/skills/review-pr/references/delivery/review-requests.md

@@ -1,99 +0,0 @@
-# Focused Code-Owner Review Requests
-
-Read only when the user asks to identify, suggest, request, add, or ping
-reviewers. Finding candidates is read-only; GitHub review requests and
-`@mention` comments are external writes.
-
-Owner sources, in order:
-
-1. the reviewed head's
-   [CODEOWNERS](https://github.com/vllm-project/vllm-omni/blob/main/.github/CODEOWNERS)
-   for path ownership;
-2. the active module design page's `owners` and `required_reviewers` metadata
-   for contract ownership and mandatory design review; and
-3. documented
-   [governance expertise](https://docs.vllm.ai/projects/vllm-omni/en/latest/community/governance/)
-   for specialization and tie-breaking.
-
-Resolve all three from the reviewed head when available because ownership
-changes over time. Use published docs only to discover a page absent from an
-older checkout, never to override the frozen head, and do not copy a static
-owner list into review output. CODEOWNERS remains the path authority; module
-metadata identifies contract experts and required design reviewers.
-
-## Rank candidates
-
-1. Freeze the PR head and collect changed files, author, requested reviewers,
-   submitted reviews, and existing comments.
-2. Resolve the **last matching CODEOWNERS rule** for each changed file. Do not
-   union an earlier broad rule with a later specific rule.
-3. Resolve the primary module and feature overlays through
-   [design-contracts.md](../process/design-contracts.md). Read the active module
-   page's status, `owners`, and `required_reviewers`; do not treat owners on an
-   archived, superseded, or unrelated page as current authority.
-4. Group files by the selected primary owner and the exact module or feature
-   contract needing review. Weight production owners and live consumers above
-   tests/docs/CI owners and the repository-wide fallback. Include an applicable
-   `required_reviewers` entry even when it is not the path owner, and label why.
-5. Use documented governance specialties only to choose among matching owners:
-   for example serving versus configuration, diffusion model versus cache or
-   parallelism, TTS versus generic model execution, or CI versus tests.
-6. Exclude the PR author, bots, users who already completed the needed review,
-   and duplicate pending requests. Do not infer availability or invent
-   expertise from a username.
-7. Select one reviewer for a single-owner change and at most three for a real
-   cross-module change. If more are plausible, rank them but do not ping a
-   broad group; ask the user before exceeding three.
-
-If every specific owner is excluded, consider one repository-wide fallback
-owner and label that choice as a fallback rather than a path match. Never fan
-out to the full fallback list. Prefer a qualified owner who covers multiple
-critical path groups over separate reviewers for each file. If CODEOWNERS is
-missing or invalid, state that gap and use governance only as a labeled fallback.
-
-Recent file history may break a tie only when CODEOWNERS and governance remain
-ambiguous. Never use commit count alone to override an explicit current owner.
-
-## Preview before writing
-
-Show the user the exact candidate set and rationale:
-
-```text
-@owner — <owned paths or module role>; please verify <specific module or feature contract>.
-```
-
-Draft one consolidated comment tied to the frozen head:
-
-```text
-Requesting focused review for `<short-head-sha>`:
-
-- @owner1: could you check the <module contract> in `<path/group>`?
-- @owner2: could you check the <feature or cross-boundary contract> in `<path/group>`?
-
-Relevant validation: <one concise result or exact remaining gap>.
-```
-
-Mention only owners being requested. Keep the reason specific enough that each
-person knows what decision or invariant needs attention; do not post a generic
-“please review” or a generated ownership audit.
-
-## External-write gate
-
-- “Who should review?”, “suggest reviewers”, or “draft a request” authorizes
-  only the preview.
-- “Request/add these reviewers” authorizes the GitHub review-request mutation.
-- “Ping/comment/tag the owners” authorizes the consolidated comment.
-- When the user explicitly asks to request reviews **with an owner comment**,
-  both writes are authorized. Obey narrower wording when only one action is
-  requested.
-
-Immediately before an authorized write, verify the PR is still open, the head
-SHA is unchanged, and the candidate is not the author or already satisfied.
-Report draft/WIP state and avoid notifications unless the user explicitly wants
-reviewers requested before ready-for-review.
-
-Use the GitHub connector for reviewer requests and the single top-level comment;
-use `gh` only when connector coverage is unavailable. Do not submit an approval,
-request-changes event, label, assignment, or multiple comments as part of this
-action. After writing, reread the PR and report the reviewers requested and the
-comment URL. If the head changed, discard the draft and rerank before posting.

.claude/skills/review-pr/references/features/communication.md

@@ -1,24 +0,0 @@
-# Communication Features
-
-Use with the OmniConnector, stage-runtime, configuration, and I/O module
-contracts. Load the concrete backend design from the reviewed head:
-
-| Backend | Design |
-| --- | --- |
-| Mooncake store | [MooncakeStoreConnector](https://docs.vllm.ai/projects/vllm-omni/en/latest/design/feature/omni_connectors/mooncake_store_connector/) |
-| Mooncake transfer engine | [MooncakeTransferEngineConnector](https://docs.vllm.ai/projects/vllm-omni/en/latest/design/feature/omni_connectors/mooncake_transfer_engine_connector/) |
-| Mori transfer engine | [MoriTransferEngineConnector](https://docs.vllm.ai/projects/vllm-omni/en/latest/design/feature/omni_connectors/mori_transfer_engine_connector/) |
-| Shared memory | [SharedMemoryConnector](https://docs.vllm.ai/projects/vllm-omni/en/latest/design/feature/omni_connectors/shared_memory_connector/) |
-| Yuanrong store | [YuanrongConnector](https://docs.vllm.ai/projects/vllm-omni/en/latest/design/feature/omni_connectors/yuanrong_connector/) |
-| Yuanrong transfer engine | [YuanrongTransferEngineConnector](https://docs.vllm.ai/projects/vllm-omni/en/latest/design/feature/omni_connectors/yuanrong_transfer_engine_connector/) |
-
-## Feature checks
-
-- Verify dependency, service, device/network, port, memory-pool, and environment
-  prerequisites with actionable startup errors.
-- Match key/metadata construction, sender and receiver roles, allocation,
-  ownership, completion, and supported fast/serialized paths.
-- Exercise timeout, retry, stale sender/buffer reclamation, partial transfer,
-  cancellation, and connector close semantics.
-- Validate the smallest supported same-host or cross-node topology and document
-  backend limitations and operational diagnostics.

.claude/skills/review-pr/references/features/diffusion-acceleration.md

@@ -1,26 +0,0 @@
-# Diffusion Acceleration Features
-
-Use with the exact diffusion submodule plus platform, cache, quantization, or
-benchmarking contracts selected by the live path.
-
-| Feature family | Designs |
-| --- | --- |
-| Parallelism | [CFG](https://docs.vllm.ai/projects/vllm-omni/en/latest/design/feature/cfg_parallel/), [Expert](https://docs.vllm.ai/projects/vllm-omni/en/latest/design/feature/expert_parallel/), [HSDP](https://docs.vllm.ai/projects/vllm-omni/en/latest/design/feature/hsdp/), [Pipeline](https://docs.vllm.ai/projects/vllm-omni/en/latest/design/feature/pipeline_parallel/), [Sequence](https://docs.vllm.ai/projects/vllm-omni/en/latest/design/feature/sequence_parallel/), [Tensor](https://docs.vllm.ai/projects/vllm-omni/en/latest/design/feature/tensor_parallel/), [VAE patch](https://docs.vllm.ai/projects/vllm-omni/en/latest/design/feature/vae_parallel/) |
-| Attention | [Skip-Softmax](https://docs.vllm.ai/projects/vllm-omni/en/latest/design/feature/skip_softmax/) |
-| Quantization | [Quantization](https://docs.vllm.ai/projects/vllm-omni/en/latest/design/feature/quantization/) |
-| Cache | [Cache-DiT](https://docs.vllm.ai/projects/vllm-omni/en/latest/design/feature/cache_dit/), [TeaCache](https://docs.vllm.ai/projects/vllm-omni/en/latest/design/feature/teacache/) |
-| Batching | [Diffusion Continuous Batching](https://docs.vllm.ai/projects/vllm-omni/en/latest/design/feature/diffusion_continuous_batching/) |
-| Offload | [Distributed Layerwise Offload](https://docs.vllm.ai/projects/vllm-omni/en/latest/design/feature/distributed_layerwise_offload/) |
-
-## Feature checks
-
-- Verify feature selection, supported models/platforms/topologies, defaults,
-  dependencies, and every touched feature combination.
-- Preserve feature-off and single-rank correctness; reject unsupported
-  combinations before collectives, loading, or execution begin.
-- Trace shapes, shards, ranks, collectives, precision, cache validity, residency,
-  and per-request state through the selected runtime hooks.
-- Compare output quality or accuracy against the disabled/reference path before
-  accepting latency or memory improvements.
-- Require exact commands, topology, environment, warmup, repetitions, and
-  measurements for performance claims; update feature compatibility docs.

.claude/skills/review-pr/references/features/infrastructure-performance.md

@@ -1,22 +0,0 @@
-# Infrastructure and Performance Features
-
-Load the matching design from the reviewed head:
-
-- [Prometheus Metrics](https://docs.vllm.ai/projects/vllm-omni/en/latest/design/metrics/)
-  for logger wrapping, gating, lifecycle, stage transfer, and metric definitions.
-- [Speech Generation Performance Optimizations](https://docs.vllm.ai/projects/vllm-omni/en/latest/design/qwen3_omni_tts_performance_optimization/)
-  for stage batching, CUDA graphs, async chunk, streaming, re-prefill, compile,
-  and deployment evidence.
-
-## Feature checks
-
-- Route instrumentation changes through observability or profiling and result
-  methodology through benchmarking and the performance evidence check.
-- Preserve opt-in/default behavior, units, labels, aggregation, timing scope,
-  lifecycle resets, and bounded overhead.
-- For stacked optimizations, measure each increment against the same baseline
-  and explain interactions, quality guards, regressions, and limitations.
-- Require exact model, checkpoint, hardware, software, workload, topology,
-  concurrency, warmup, repetitions, and production commands.
-- Update the design page when metric meaning, supported optimization stack, or
-  deployment guidance changes.

.claude/skills/review-pr/references/features/runtime-stage-execution.md

@@ -1,24 +0,0 @@
-# Runtime and Stage Execution Features
-
-Load the matching design from the reviewed head:
-
-| Feature | Design | Primary module intersections |
-| --- | --- | --- |
-| Disaggregated inference | [Design](https://docs.vllm.ai/projects/vllm-omni/en/latest/design/feature/disaggregated_inference/) | Configuration, stage runtime, OmniConnector, I/O contracts |
-| Async chunk | [Design](https://docs.vllm.ai/projects/vllm-omni/en/latest/design/feature/async_chunk/) | Model integration, orchestration, stage runtime, connector, I/O |
-| Async diffusion output | [Design](https://docs.vllm.ai/projects/vllm-omni/en/latest/design/feature/async_diffusion_output/) | Diffusion runtime, I/O contracts, entrypoints |
-| Async Omni output materialization | [Design](https://docs.vllm.ai/projects/vllm-omni/en/latest/design/feature/omni_async_output_materialization/) | AR runtime, model integration, I/O contracts, orchestration |
-| Automatic prefix caching | [Design](https://docs.vllm.ai/projects/vllm-omni/en/latest/design/feature/prefix_caching/) | AR runtime, cache management, I/O contracts |
-
-## Feature checks
-
-- Confirm the feature's supported models, entrypoints, stage types, topology,
-  configuration, default, and compatibility limitations.
-- Trace enablement from public/config ingress through every participating module
-  and verify the advertised production dispatch actually selects it.
-- Preserve a correct disabled or fallback path and make unsupported combinations
-  fail or warn as documented.
-- Test overlap, ordering, identity, backpressure, cancellation, failure, final
-  partial output, and cleanup at each asynchronous boundary.
-- Update the feature page, support matrices, examples, and recipes when behavior
-  or coverage changes.

.claude/skills/review-pr/references/modules/ar-runtime.md

@@ -1,22 +0,0 @@
-# Autoregressive Runtime
-
-Primary design: [Autoregressive Runtime](https://docs.vllm.ai/projects/vllm-omni/en/latest/design/module/ar_runtime/).
-
-Use for AR request queues, scheduling state, token and memory budgets, KV/input
-readiness, workers, runners, and upstream vLLM request/cache transitions.
-
-## Contract checks
-
-- Preserve upstream request-state, scheduling, cache, and preemption semantics
-  unless an Omni-specific difference is documented and tested.
-- Convert modality-specific data through explicit input or output adapters; do
-  not inject it through unrelated scheduler state.
-- Keep workers and model runners focused on assigned execution rather than
-  admission or cross-stage routing.
-- Synchronize token, sequence, batch, and memory budgets with worker-visible
-  state and prevent starvation, double counting, or stale readiness.
-- Preserve legal transitions and one terminal cleanup path across completion,
-  abort, timeout, preemption, worker failure, and shutdown.
-
-Test request lifecycle, scheduling decisions, abort, cache state, adapters, and
-every affected worker execution mode against the pinned upstream contract.

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

@@ -1,24 +0,0 @@
-# Benchmarking
-
-Primary design: [Benchmarking](https://docs.vllm.ai/projects/vllm-omni/en/latest/design/module/benchmarking/).
-
-Use when benchmark code, workloads, metric calculations, CLI benchmark
-entrypoints, or result metadata change. Use the performance check separately
-when a PR uses benchmark results as evidence.
-
-## Contract checks
-
-- Record model and revision, code SHA, hardware, software, configuration,
-  workload, precision, topology, concurrency, warmup, repetitions, and
-  measurement window.
-- Validate successful and semantically acceptable outputs before accepting
-  timing, memory, or scaling results.
-- Define every metric's unit, population, aggregation, synchronization, and
-  timing scope.
-- Keep workload parsing and metric computation deterministic and reusable
-  across base and head.
-- Reject comparisons that change material workload or runtime settings without
-  isolating and explaining that difference.
-
-Test workload parsing and metric calculations with deterministic fixtures and
-publish exact commands plus variability for quantitative claims.

.claude/skills/review-pr/references/modules/cache-management.md

@@ -1,22 +0,0 @@
-# Cache Management
-
-Primary design: [Cache Management](https://docs.vllm.ai/projects/vllm-omni/en/latest/design/module/cache_management/).
-
-Use for AR or diffusion cache identity, reusable state, validity, lifecycle,
-reset, eviction, and teardown.
-
-## Contract checks
-
-- Include every model, request, input, timestep, precision, device, topology,
-  and feature property that can change a reused value in cache identity.
-- Preserve a correct disabled and miss path; never reuse after validity inputs
-  change.
-- Isolate request-scoped state across concurrent requests and define ownership
-  for model-scoped state.
-- Make reset, invalidation, eviction, capacity, and teardown explicit on
-  completion, cancellation, failure, model reload, and shutdown.
-- Keep cache optimizations behind the owning runtime's hooks rather than adding
-  a second request lifecycle.
-
-Test hit, miss, invalidation, disabled behavior, concurrency, capacity, reset,
-and teardown, with quality comparison when reuse is approximate.

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

@@ -1,24 +0,0 @@
-# vLLM-Omni Configuration
-
-Primary design: [vLLM-Omni Configuration](https://docs.vllm.ai/projects/vllm-omni/en/latest/design/module/vllm_omni_config/).
-
-Use for config construction, deploy YAML, registries, schema, defaults,
-aliases, CLI projection, stage config, and topology. Inspect the reviewed
-page's status: while its contract remains deferred or draft, current code and
-tests—not proposed precedence rules—are authoritative.
-
-## Contract checks
-
-- Resolve each supported structured, legacy, CLI, environment, default, alias,
-  and stage-override producer through its actual runtime consumer.
-- Reject unknown or owner-mismatched fields explicitly; keep process-local
-  runtime objects out of transportable configuration.
-- Verify default, explicit, and feature-off values reach every live factory and
-  consumer without silent reinterpretation.
-- Treat stage count, placement, connector, parallelism, device, and memory
-  fields as one topology contract and reject impossible combinations early.
-- Preserve supported construction-path parity until a documented migration
-  removes a path.
-
-Require normalization and schema tests, a live-consumer assertion, and docs for
-public keys, defaults, constraints, precedence, or migration behavior.

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

@@ -1,33 +0,0 @@
-# Diffusion Module Family
-
-Primary design: [Diffusion Modules](https://docs.vllm.ai/projects/vllm-omni/en/latest/design/module/diffusion/).
-
-Use for diffusion-stage behavior, then load the exact submodule page from the
-reviewed head:
-
-| Submodule | Signals | Design |
-| --- | --- | --- |
-| Runtime | Admission, scheduling, execution, progress, outputs, cancellation, hooks, cleanup | [Diffusion Runtime](https://docs.vllm.ai/projects/vllm-omni/en/latest/design/module/diffusion/diffusion_runtime/) |
-| Model Integration | Pipelines, registry, loader, adapters, shared layers, model-specific processing | [Diffusion Model Integration](https://docs.vllm.ai/projects/vllm-omni/en/latest/design/module/diffusion/diffusion_model_integration/) |
-| Continuous Batching | Compatibility, request/step batches, per-request progress, capacity | [Continuous Batching](https://docs.vllm.ai/projects/vllm-omni/en/latest/design/module/diffusion/continuous_batching/) |
-| Parallelism | Rank topology, process groups, sharding, collectives, distributed execution | [Parallelism](https://docs.vllm.ai/projects/vllm-omni/en/latest/design/module/diffusion/parallelism/) |
-| Offloader | Residency, transfers, memory accounting, prefetch, teardown | [Offloader](https://docs.vllm.ai/projects/vllm-omni/en/latest/design/module/diffusion/offloader/) |
-
-## Family-wide checks
-
-- Keep one scheduler-owned lifecycle from admission through completion,
-  cancellation, failure, and cleanup; optional features must use defined hooks.
-- Select pipelines through registry and loader contracts instead of scattered
-  model-name conditionals; keep genuine model differences in model directories.
-- Trace latent, conditioning, timestep, generator, shape, layout, dtype, device,
-  batch expansion, and output conversion through the actual consumer.
-- Make batch compatibility and per-request isolation explicit; never associate
-  output by unstable batch position.
-- Derive topology from validated configuration, define every sharding boundary,
-  keep collectives symmetric, and preserve a supported single-rank path.
-- Track one residency owner per offloaded component, wait for transfer readiness,
-  preserve model state, bound retained copies, and clean up deterministically.
-
-Load matching feature designs for cache, quantization, parallelism, continuous
-batching, or offload. Require feature-off correctness, representative inference,
-terminal-path tests, and quality evidence for optimizations.

.claude/skills/review-pr/references/modules/engine-orchestration.md

@@ -1,23 +0,0 @@
-# Engine Orchestration
-
-Primary design: [Engine Orchestration](https://docs.vllm.ai/projects/vllm-omni/en/latest/design/module/engine_orchestration/).
-
-Use for request-state creation, cross-stage routing, output ordering, companion
-tracking, RPC correlation, cancellation propagation, and terminal convergence.
-Orchestration does not own replica placement, stage-process startup, public
-rendering, connector transport, or payload and error schemas.
-
-## Contract checks
-
-- Keep one owner for cross-stage routing; entrypoints, models, and stage clients
-  must not independently select the next logical stage.
-- Preserve request identity and output order across concurrent stages and
-  correlate every control or RPC result to the originating request and wave.
-- Make terminal state monotonic: no new forwarding after success, abort,
-  request failure, or fatal engine failure.
-- Propagate cancellation and failure to every active stage and release
-  orchestrator-owned tasks, queues, companions, and request state.
-- Preserve shutdown ordering across the orchestrator and stage runtime.
-
-Test representative multi-stage routing, ordering, correlation, cancellation,
-failure, and repeated shutdown.

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

@@ -1,25 +0,0 @@
-# Entrypoints and Serving Boundaries
-
-Primary design: [Entrypoints and Serving Boundaries](https://docs.vllm.ai/projects/vllm-omni/en/latest/design/module/entrypoints/).
-
-Use for offline APIs, CLI and server composition, OpenAI-compatible routes,
-request validation, response conversion, streaming, sessions, and engine
-handoff. Entrypoints adapt public contracts; they do not own cross-stage
-routing, stage lifecycle, configuration precedence, payload schemas, or error
-classification.
-
-## Contract checks
-
-- Normalize and validate public values once before engine submission.
-- Preserve request identity, modality, ordering, and exactly one terminal event
-  across streaming, disconnect, cancellation, and failure.
-- Translate internal outputs and errors explicitly for each advertised offline,
-  HTTP, SSE, WebSocket, or asynchronous-job path.
-- Keep model-specific behavior behind a common adapter or processor instead of
-  adding model-name policy to generic entrypoints.
-- Require API or CLI changes to explain necessity, alternatives, compatibility,
-  migration, and the supported route or transport matrix.
-
-Test the affected public protocol, normalization, engine handoff, rendering,
-and terminal outcomes. Route orchestration, I/O schema, and semantic error
-changes to their module contracts.

.claude/skills/review-pr/references/modules/error-contracts.md

@@ -1,24 +0,0 @@
-# Error Classification, Propagation, and Rendering
-
-Primary design: [Error Classification, Propagation, and Rendering](https://docs.vllm.ai/projects/vllm-omni/en/latest/design/module/error_contracts/).
-
-Use when a change classifies failures, crosses process boundaries, decides
-fatal versus request-scoped impact, sanitizes context, or renders offline,
-HTTP, SSE, WebSocket, or asynchronous-job errors. The I/O module owns the wire
-schema; this module owns the meaning of its error fields.
-
-## Contract checks
-
-- Keep request-scoped failure isolated unless the underlying engine is no
-  longer usable.
-- Preserve stable classification and request context across process and
-  protocol boundaries without exposing prompts, outputs, secrets, or internal
-  stack details.
-- Keep cancellation distinguishable from internal failure.
-- Terminate each public transport exactly once with consistent status, body,
-  event, finish reason, and metrics classification.
-- Maintain compatibility across mixed or older producers only when the
-  reviewed contract defines that behavior.
-
-Test table-driven classification, wire round trips, fatal/request-scoped
-propagation, cancellation, sanitization, and every affected public renderer.

.claude/skills/review-pr/references/modules/execution-platforms.md

@@ -1,23 +0,0 @@
-# Execution Platforms
-
-Primary design: [Execution Platforms](https://docs.vllm.ai/projects/vllm-omni/en/latest/design/module/execution_platforms/).
-
-Use for hardware capability detection, worker selection, patches, kernels,
-attention backends, and vendor-specific configuration or connectors.
-
-## Contract checks
-
-- Guard hardware behavior through platform selection or capability detection
-  and fail clearly when unsupported.
-- Keep portable modules free of direct vendor imports when a common selector
-  exists; verify portable imports without the target accelerator.
-- Override only behavior that differs from common or upstream implementations,
-  and compare sibling worker hooks when inheritance does not provide parity.
-- Preserve dtype, layout, rank, stream, synchronization, workspace,
-  graph-capture, and resource-lifetime contracts.
-- Do not let simplified CPU doubles hide platform MRO, initialization, or
-  dispatch failures.
-
-Run portable checks first, then the smallest matching-device test in a fresh
-environment. Report unavailable hardware as a validation gap, never simulated
-device evidence.

.claude/skills/review-pr/references/modules/input-output-modality.md

@@ -1,23 +0,0 @@
-# Input, Output, and Modality Contracts
-
-Primary design: [Input, Output, and Modality Contracts](https://docs.vllm.ai/projects/vllm-omni/en/latest/design/module/input_output_modality_contracts/).
-
-Use for requests, prompt and modality types, message schemas, serialization,
-output types, accumulation, completion, and compatibility shims crossing
-entrypoint, orchestration, stage, connector, or model boundaries.
-
-## Contract checks
-
-- Give every boundary value an explicit modality and validated typed schema.
-- Preserve request and stage identity across conversion, serialization,
-  streaming updates, batching, cancellation, and errors.
-- Distinguish partial, final, empty, and failed output states for every modality.
-- Verify producer and consumer agreement on shape, layout, dtype, device,
-  ownership, metadata, ordering, and optional or unknown fields.
-- Keep internal objects out of public protocols; translate them at the owning
-  entrypoint.
-- Preserve wire compatibility deliberately and give deprecated shims a bounded
-  migration path.
-
-Require construction, round-trip, accumulation, completion, rejection, and
-compatibility tests at each changed producer-consumer boundary.

.claude/skills/review-pr/references/modules/model-integration.md

@@ -1,25 +0,0 @@
-# Model Integration
-
-Primary design: [Model Integration](https://docs.vllm.ai/projects/vllm-omni/en/latest/design/module/model_integration/).
-
-Use for model registration, configuration adapters, preprocessing, checkpoint
-loading, stage inputs, model-specific hooks, shared layers, runners, and model
-execution. Use the diffusion family reference for diffusion models.
-
-## Contract checks
-
-- Declare explicitly how the model class, loader, input processor, stage
-  configuration, and runtime capabilities are selected.
-- Preserve weight-name mapping, dtype/device placement, optional-dependency
-  errors, and checkpoint-specific loading semantics.
-- Trace each typed input and output through preprocessing, runner state, model
-  call, and its actual consumer.
-- Reuse upstream vLLM behavior when sufficient; document and test the precise
-  difference for every override or patch.
-- Keep model code out of admission, cross-stage routing, public policy, and
-  terminal lifecycle ownership.
-- Place reusable behavior in shared modules and test it with more than one
-  integration.
-
-Require focused registry, loader, processor, runner, and representative model
-execution tests. Apply the model-addition check for new or expanded support.

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

@@ -1,21 +0,0 @@
-# Observability
-
-Primary design: [Observability](https://docs.vllm.ai/projects/vllm-omni/en/latest/design/module/observability/).
-
-Use for metrics, logs, correlation fields, and cross-stage request visibility.
-
-## Contract checks
-
-- Preserve stable request and stage identity across entrypoints, orchestration,
-  stages, workers, errors, and terminal cleanup.
-- Treat metric names, units, labels, aggregation, and lifecycle as public
-  contracts; document any semantic change.
-- Keep labels bounded: never use request IDs, prompts, outputs, paths, or other
-  unbounded user values.
-- Register and reset metrics exactly once across repeated startup and shutdown,
-  and keep disabled instrumentation inexpensive.
-- Align emitted values with their actual timing and lifecycle owner rather than
-  reconstructing them from ambiguous client strings.
-
-Test registration, label sets, units, resets, error classification, and
-representative multi-stage emission.

.claude/skills/review-pr/references/modules/omni-connector.md

@@ -1,24 +0,0 @@
-# OmniConnector
-
-Primary design: [OmniConnector](https://docs.vllm.ai/projects/vllm-omni/en/latest/design/module/omni_connector/).
-
-Use for connector contracts and model-agnostic data transfer across stages,
-processes, devices, or nodes. Connector implementations transport and
-synchronize data; they do not choose the next stage or implement model policy.
-
-## Contract checks
-
-- Prove producer and consumer agreement on request/stage identity, shape,
-  layout, dtype, placement, ownership, ordering, and completion.
-- Keep backend selection and metadata/key construction explicit; do not silently
-  reinterpret or drop payloads across SHM, store, network, or vendor paths.
-- Bound connect, put/get, retry, synchronization, and readiness waits with
-  backpressure, cancellation, and actionable errors.
-- Prevent route, endpoint, port, key, and buffer collisions across repeated
-  startup, concurrent requests, and replica replacement.
-- Release connections, handles, buffers, listener work, and stale sender state
-  on success, cancellation, failure, and shutdown.
-
-Load the matching communication feature design for a concrete backend. Require
-focused producer-consumer tests and the smallest runnable multi-process or
-multi-node path.

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

@@ -1,21 +0,0 @@
-# Profiling
-
-Primary design: [Profiling](https://docs.vllm.ai/projects/vllm-omni/en/latest/design/module/profiling/).
-
-Use for opt-in instrumentation and trace collection across supported profiler
-backends.
-
-## Contract checks
-
-- Keep expensive collection disabled by default and bounded by explicit start,
-  stop, timeout, and cleanup behavior.
-- Preserve scheduling, generated outputs, resource ownership, and concurrency
-  semantics apart from documented synchronization overhead.
-- Include stage, rank, worker, operation, and request correlation where useful
-  without embedding user payloads or secrets.
-- Make repeated start/stop and partial failure safe, including multi-rank trace
-  naming and artifact ownership.
-- Measure disabled and enabled overhead before making performance claims.
-
-Test the disabled path, repeated lifecycle, failure cleanup, trace context, and
-each affected backend.

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

@@ -1,22 +0,0 @@
-# Quantization
-
-Primary design: [Quantization](https://docs.vllm.ai/projects/vllm-omni/en/latest/design/module/quantization/).
-
-Use for method registration, checkpoint interpretation, layer replacement,
-reduced precision, platform capability, and runtime constraints.
-
-## Contract checks
-
-- Validate checkpoint metadata, selected method, supported layers, device
-  capability, required dependencies, and parallel layout before loading.
-- Preserve loader and weight-name mapping semantics, dtype, scale/granularity,
-  zero point, padding, and buffer handling through the real kernel consumer.
-- Fail clearly for unsupported combinations; do not silently choose a different
-  method or precision.
-- Keep online and pre-quantized checkpoint paths explicit and ensure patches or
-  fallbacks do not bypass model-owned loaders.
-- Require numerical comparison against a defined reference with explicit
-  tolerance before treating performance results as valid.
-
-Test registration, loading, representative layers, metadata, platform guards,
-parallel combinations, and numerical correctness separately from performance.

.claude/skills/review-pr/references/modules/stage-runtime.md

@@ -1,23 +0,0 @@
-# Stage Runtime and Replica Lifecycle
-
-Primary design: [Stage Runtime and Replica Lifecycle](https://docs.vllm.ai/projects/vllm-omni/en/latest/design/module/stage_runtime/).
-
-Use for local or distributed placement, startup, readiness, stage pools,
-replica identity and membership, selection, affinity, liveness, draining, and
-stage-client or process lifecycle. Cross-stage request policy belongs to engine
-orchestration; model scheduling belongs to the owning runtime.
-
-## Contract checks
-
-- Acquire, replace, and retire replicas through one stage-runtime lifecycle.
-- Preserve stable replica identity and request affinity until termination or a
-  declared replica loss.
-- Make readiness explicit before admission; surface partial-startup and
-  membership failures without leaving half-initialized capacity.
-- Keep local and distributed semantics aligned for supported modes.
-- Make drain, shutdown, and cleanup idempotent across repeated signals and
-  process failure.
-
-Test startup, readiness, selection, affinity, membership changes, abort,
-process loss, draining, and repeated shutdown on affected local/distributed
-paths.

.claude/skills/review-pr/references/process/design-contracts.md

@@ -1,34 +0,0 @@
-# Resolve Design Contracts
-
-Use after freezing the review head and before selecting module or feature
-references. The maintained [design index](https://docs.vllm.ai/projects/vllm-omni/en/latest/design/)
-defines two routing axes:
-
-- **Module designs** own code boundaries and invariants. Select one primary
-  module and a second only when the live producer-consumer path crosses it.
-- **Feature designs** describe behavior spanning modules. Load every feature
-  page whose implementation, default, support, or contract changes.
-
-## Resolution order
-
-1. Open `docs/design/index.md` in the reviewed head, not the reviewer's checkout
-   or published latest tree.
-2. Match changed behavior against each module page's `primary_code_paths`,
-   `primary_path_exceptions`, ownership boundary, and `depends_on` metadata.
-3. Read `status`, `architecture_state`, `last_verified_commit`, and any linked
-   in-flight work before treating prose as current behavior.
-4. Use current code and tests when a page is draft, deferred, stale, absent, or
-   conflicts with the frozen implementation. Report material documentation
-   drift separately.
-5. Treat candidate invariants and promotion gates as review questions until
-   repository policy, current code/tests, or owner-approved normative text
-   makes them enforceable. Do not turn a draft identifier into an automatic
-   blocker.
-6. Use published latest docs only for discovery when the reviewed head lacks a
-   page; never import a newer contract into an older branch silently.
-
-When behavior changes an active module or feature contract, require the same PR
-to update that page. Require a new page only when repository policy, an accepted
-RFC/issue, or explicit maintainer acceptance criteria do so; otherwise treat it
-as a non-blocking design suggestion. Do not demand a second page when an existing
-design already owns the exact contract.

.claude/skills/review-pr/references/process/general-checks.md

@@ -1,129 +0,0 @@
-# General Review Checks
-
-Apply these checks to every PR after freezing the review surface. A pattern is
-not a finding until the diff introduces or exposes a reachable trigger, impact,
-and smallest safe fix. Do not report unrelated backlog.
-
-Official docs: [contributing guide](https://docs.vllm.ai/projects/vllm-omni/en/latest/contributing/)
-[feature compatibility](https://docs.vllm.ai/projects/vllm-omni/en/latest/user_guide/feature_compatibility/),
-and [design documents](https://docs.vllm.ai/projects/vllm-omni/en/latest/design/).
-
-## Contract and scope
-
-- Match the title/body claims to every changed production path, test, config,
-  dependency, generated artifact, and user-visible document.
-- Trace each changed value through public ingress, validation/defaulting,
-  producer, transformations, process or device boundaries, final consumer, and
-  terminal cleanup.
-- Check every applicable sync/async, offline/online, streaming/non-streaming,
-  feature-off, topology, and compatibility path without demanding unsupported
-  modes merely for symmetry.
-
-## Module, API, and class design
-
-Apply these rules to new or materially changed production code; do not require
-unrelated cleanup from untouched files.
-
-1. Start each source module with a concise module docstring that defines its
-   responsibility and boundaries and identifies its primary classes and key
-   functions. Place it after any required license header and before imports.
-2. Give every added or materially changed class and function a clear docstring
-   covering its responsibility, parameters, return value, raised exceptions,
-   and non-obvious invariants. Use explicit typed parameters at owned API
-   boundaries; avoid opaque `**kwargs` and `Any`. Allow them only when an
-   upstream or compatibility interface requires them, and document and validate
-   the accepted keys and concrete types.
-3. Move stable helpers shared by multiple features into an appropriately scoped
-   utility module. Do not let feature modules accumulate large collections of
-   unrelated private utility functions; keep a private helper local only when
-   it is single-purpose and tightly coupled to that feature.
-4. Keep every method on a key class within that class's stated responsibility,
-   and document non-obvious design decisions and invariants. Before adding a
-   method, prefer reuse, consolidation, or extension of an existing abstraction;
-   split the class when new behavior creates a separate responsibility or makes
-   the class unnecessarily long.
-5. Require changes to user-facing behavior to update `docs/`. When an existing
-   active module or feature design page owns the changed contract, update that
-   page. Require a new design page only when the reviewed branch's repository
-   policy, an accepted RFC/issue, or explicit maintainer acceptance criteria do
-   so; otherwise suggest it as non-blocking. For a major architectural change,
-   apply the branch's contributing policy for a design RFC or issue; the current
-   policy uses more than 500 changed production-code lines, excluding kernel,
-   data, config, and test changes. Resolve page status and authority through
-   [design-contracts.md](design-contracts.md), and do not demand a duplicate page
-   when an existing design owns the exact contract. When required, cover scope,
-   data flow, public contracts, failure behavior, alternatives, validation,
-   limitations, and rollout.
-6. Require every performance PR to provide reproducible base-versus-head A/B
-   results under the same hardware, software, model, workload, precision,
-   topology, warmup, repetitions, and measurement scope. Include exact commands,
-   variability, and a correctness or quality guard; one-sided or non-comparable
-   numbers are incomplete evidence. For a new model absent from the frozen base,
-   compare head with its pinned canonical reference implementation under rule 12
-   instead. Follow
-   [perf-verification.md](../checks/perf-verification.md) for the full protocol.
-7. Require every addition, removal, default change, or semantic change to a
-   user-facing inference API or CLI to explain why it is necessary, which
-   alternatives were considered, and why the chosen interface is preferable.
-   Record compatibility, deprecation or migration behavior, and the rationale
-   in the PR. Update the corresponding module and feature design documents when
-   they define the changed interface or workflow, or when policy requires one.
-8. Require every bug-fix PR to add an automated regression test that reproduces
-   the defect on the frozen base and passes on the fixed head. Exercise the
-   production path or nearest stable boundary and assert the corrected behavior,
-   not only process survival. Record the exact base/head command and result, and
-   place hardware- or model-specific coverage in the correct CI lane. A manual
-   reproduction supplements but does not replace the regression test.
-9. Require every new or materially changed recipe to follow
-   [`recipes/TEMPLATE.md`](../../../../../recipes/TEMPLATE.md) and the naming and
-   layout conventions in
-   [`recipes/README.md`](../../../../../recipes/README.md).
-   Complete every applicable summary, use-case, reference, hardware,
-   environment, exact-command, verification, and notes section with tested,
-   reproducible details; explain any intentional deviation in the PR.
-10. Require every new model to verify its supported modalities, platforms,
-    execution modes, and features with production-path evidence. Update
-    `docs/models/supported_models.md` and every applicable feature support or
-    compatibility table; do not claim support that the PR does not validate.
-    Follow
-    [model-addition-checklist.md](../checks/model-addition-checklist.md) for the
-    model-specific documentation checks.
-11. Require every new model PR to add or update its in-repository model-family
-    recipe under `recipes/` and its `recipes/README.md` index entry. Apply rule
-    9, and keep the recipe's model identifiers, tasks, serving modes, hardware,
-    commands, supported features, and limitations aligned with validated PR
-    evidence. An external recipe link does not replace the in-repository recipe.
-12. Require every new model to compare accuracy or output quality and performance
-    against a pinned baseline or canonical reference implementation. Match the
-    checkpoint, inputs, precision, hardware, workload, and measurement method;
-    provide exact commands, results, and explanations for material differences.
-    Prefer a detailed timing split for each stage and explain when it is
-    unavailable. Follow
-    [model-addition-checklist.md](../checks/model-addition-checklist.md) and
-    [perf-verification.md](../checks/perf-verification.md) for the evidence
-    contract.
-
-## Blocking risk scan
-
-| Risk | Prove before reporting |
-| --- | --- |
-| Correctness | A live input reaches a wrong output, exception, silent drop, shape/dtype/device mismatch, or partially initialized state. |
-| Compatibility | A supported API, config key, default, serialization schema, model identifier, or caller breaks without validation, migration, or deprecation. |
-| Lifecycle | Allocation or startup lacks cleanup on partial failure, timeout, cancellation, shutdown, or repeated requests. |
-| Concurrency/distribution | Ordering, identity, rank, world size, completion, timeout, backpressure, or collective assumptions fail on a supported path. |
-| Cache/shared state | Identity omits a correctness-affecting input, or isolation, invalidation, disabled behavior, eviction, or cleanup is incomplete. |
-| Async behavior | Blocking I/O, sleep, a lock across `await`, or unnecessary serialization harms a reachable async path. |
-| Security/data handling | Secrets, user payloads, unsafe deserialization/eval/shell, unvalidated paths, or unbounded metric labels are reachable. |
-| Validation evidence | Tests bypass the production dispatcher, mock away the change, use unrealistic types/MRO, or cannot fail when the defect returns. |
-| User contract | User-visible model, feature, API, CLI, config, default, or compatibility behavior changes without accurate docs or examples. |
-
-Search bounded callers and sibling implementations before reporting dead code,
-duplication, or a missed consumer. Keep new abstractions only when they have a
-distinct live caller, invariant, or compatibility need.
-
-## Finding bar
-
-Anchor each finding to a changed `path:line`; name the trigger or call path,
-current behavior, impact, and smallest fix direction. Treat pending CI, missing
-hardware, or unsupported measurements as validation gaps unless the repository
-contract makes that evidence a merge requirement.

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

@@ -1,191 +0,0 @@
-# Review Execution and Delivery
-
-Use this reference for every review. It defines snapshot collection, gates,
-validation records, delivery permissions, maintainer tone, and re-review.
-
-Official docs: [contributing guide](https://docs.vllm.ai/projects/vllm-omni/en/latest/contributing/)
-and [CI failure triage](https://docs.vllm.ai/projects/vllm-omni/en/latest/contributing/ci/failures/).
-
-## Contents
-
-- [Freeze the review surface](#freeze-the-review-surface)
-- [Report status before analysis](#report-status-before-analysis)
-- [Apply review gates](#apply-review-gates)
-- [Run bounded validation](#run-bounded-validation)
-- [Deliver maintainer-style findings](#deliver-maintainer-style-findings)
-- [Re-review safely](#re-review-safely)
-
-## Freeze the review surface
-
-For a GitHub PR, read the base/head SHA before and after fetching metadata and
-the diff:
-
-```bash
-gh api "repos/vllm-project/vllm-omni/pulls/<PR>" \
-  --jq '{base_sha: .base.sha, head_sha: .head.sha}'
-REVIEW_FIELDS="number,url,title,body,isDraft,baseRefName,headRefName,mergeable,mergeStateStatus,statusCheckRollup,files"
-gh pr view <PR> --repo vllm-project/vllm-omni \
-  --json "${REVIEW_FIELDS}"
-gh pr diff <PR> --repo vllm-project/vllm-omni
-gh api "repos/vllm-project/vllm-omni/pulls/<PR>" \
-  --jq '{base_sha: .base.sha, head_sha: .head.sha}'
-```
-
-Discard the snapshot if either SHA changed. Do not mix comments or validation
-from different heads.
-
-After the SHAs stabilize, fetch and materialize a trusted `head_sha` in an
-isolated detached worktree. Run every source read, `rg` search, import, and test
-from that snapshot, not the caller's checkout. A detached worktree provides
-snapshot isolation, not a security boundary.
-
-Treat a fork head as untrusted unless the user and environment policy explicitly
-establish trust. Never run its imports, tests, builds, hooks, package setup, or
-repo-configurable linters/plugins on the reviewer host. Execute them only in a
-disposable sandbox or VM with no credentials or inherited secrets, no host agent
-or service sockets, minimal read-only host mounts, disabled or explicitly
-allowlisted network, resource/time limits, and destruction after the run. If
-that boundary is unavailable, limit the review to the remote diff, SHA-addressed
-reads such as `git show <head_sha>:<path>`, and existing CI evidence; report all
-executable validation as a gap.
-
-Before the first source read, record a pristine snapshot fingerprint outside
-the reviewed worktree. Include `HEAD`, the NUL-delimited index entries and blob
-IDs, and a NUL-safe manifest of every worktree entry except Git metadata,
-including tracked, untracked, and ignored paths with file type, mode, symlink
-target, and content hash. Before each validation group and delivery, recompute
-and byte-compare the fingerprint as well as asserting `HEAD == head_sha`. If a
-tool changes or creates any entry, discard affected evidence and recreate the
-snapshot before another group; do not rely on `HEAD` alone or clean an unknown
-worktree in place. If an exact snapshot cannot be created, use SHA-addressed
-reads and report filesystem-dependent validation as a gap.
-
-For a local branch/worktree, determine the target ref from the user, current PR,
-or configured upstream; never infer it from the branch name. Resolve the target
-once and include all task-owned worktree state:
-
-```bash
-git status --porcelain=v2 -z
-git rev-parse HEAD
-git rev-parse <target-ref>
-git merge-base <target-base-sha> HEAD
-git diff --stat <comparison-commit>
-git diff --name-status <comparison-commit>
-git diff --binary <comparison-commit>
-git diff --cached --binary <comparison-commit>
-git diff --binary
-git ls-files --others --exclude-standard -z
-```
-
-Freeze the exact bytes of every in-scope untracked file from the NUL-delimited
-list, plus a NUL-safe path, file-type, mode, and content-hash manifest. Also
-fingerprint `HEAD`, the target and merge base, porcelain status, and both binary
-index and worktree patches. Recording only untracked names is insufficient.
-Keep these snapshots in the evidence packet and do not mutate the reviewed
-checkout during a read-only review.
-
-For local changes without PR metadata, mark the title/body as synthetic. Base
-them on the user request and commit messages without inventing validation claims.
-
-If no PR, branch, or usable worktree is identifiable, ask for a PR URL/number or
-explicit base and head rather than guessing.
-
-## Report status before analysis
-
-Within 60 seconds of starting, and before source searches or tests, send this
-host update:
-
-```text
-Pinned head: <SHA>
-Base/comparison: <ref and SHA>
-CI: <pass/fail/pending/not applicable>
-Mergeability: <state/not applicable>
-Preliminary findings: <brief finding or none yet>
-```
-
-Mark early findings as preliminary and continue. This is not a GitHub comment.
-
-## Apply review gates
-
-- Report draft/WIP state. Continue a local review when explicitly requested,
-  but do not publish review events without separate authorization.
-- Record DCO, pre-commit, required CI, and mergeability. Pending or unknown gates
-  do not block source review.
-- Treat a failed gate as its own evidence. Do not restate its formatting/lint
-  output as a new code-review finding.
-- Open CI logs only when the first failing step overlaps the frozen diff or
-  blocks the verdict. Start with the first error, not the last cascade.
-- Treat the PR body as navigation. Commands, benchmark tables, and claimed tests
-  become evidence only after their provenance and relevance are checked.
-
-## Run bounded validation
-
-Keep one evidence packet for files read, bounded searches, callers, tests, CI,
-hardware, routes, and findings. Reuse it rather than refetching facts.
-
-Before pytest, run a short import/version compatibility check. Record each
-validation result with:
-
-```text
-repo, head SHA, command, result, Python/platform, dependency or lock fingerprint
-```
-
-After preflight, run targeted tests and low-cost static checks alongside source
-inspection when possible. Imports, tests, builds, and linters may create caches
-or rewrite files, so isolate each potentially mutating group in a disposable
-snapshot or recreate the pinned snapshot before continuing. Map source symbols
-to tests with bounded `rg` searches rather than assuming the test directory
-mirrors production paths.
-
-Classify failures as code, test, infrastructure, or flaky before reporting.
-Skipped hardware tests are gaps, not passes. For available hardware, verify the
-smallest representative unit/E2E path and compare actual output with PR claims.
-
-## Deliver maintainer-style findings
-
-Default to roughly 1-5 short comments, but treat that as calibration rather than
-a quota: report every real blocker and report no finding when there is no issue.
-
-Write each finding as:
-
-```text
-[P1] Short imperative title — path/to/file.py:<line>
-<Trigger or call path>. <Current behavior and impact>. <Smallest fix direction>.
-```
-
-Use [maintainer-style-study.md](../delivery/maintainer-style-study.md) for
-comment tone.
-Keep rule IDs, grades, and audit matrices internal unless the user asks for the
-complete audit.
-
-Verify every inline `path:line` against the frozen diff before delivery. For a
-PR, re-read the remote head and byte-compare the detached snapshot fingerprint,
-or recreate it from `head_sha`, before asserting current lines. For a local
-review, recompute and byte-compare the frozen `HEAD`, target, merge base, status,
-index patch, worktree patch, and untracked-content manifest. Any mismatch makes
-the review stale; discard the affected evidence and restart. Prefer one
-root-cause comment to several symptom comments.
-
-Local presentation is the default. Only explicit authorization permits GitHub
-posting. When authorized, publish one consolidated final review after
-validation; do not post preliminary or incremental comments. Submit a review
-event (`APPROVE`, `COMMENT`, or `REQUEST_CHANGES`) only when the user explicitly
-chooses that event.
-
-Reviewer requests and owner `@mention` comments are separate external writes;
-follow [review-requests.md](../delivery/review-requests.md) when the user asks
-for them.
-
-## Re-review safely
-
-Freeze the new head and compare it with the previously reviewed SHA. Then:
-
-1. inspect the delta and every file affected by conflict resolution or rebase;
-2. revalidate earlier findings against current lines and behavior;
-3. read unresolved, non-outdated threads and the author's evidence;
-4. rerun only checks invalidated by the delta;
-5. look for new regressions introduced by the fix;
-6. do not repeat resolved or outdated comments.
-
-If the target head changes during re-review, discard stale validation and start
-again from the new snapshot.

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

@@ -1,76 +0,0 @@
-# Module and Feature Review Routing
-
-Read [design-contracts.md](design-contracts.md) first. Route from the frozen
-head's live producer-consumer behavior, then confirm the result against its
-`docs/design/` metadata. Titles and paths are evidence, not the deciding rule.
-
-## Primary module contract
-
-Choose one primary module and a second only when the live call path crosses a
-documented dependency or primary-path exception.
-
-| Module | Live behavior signals | Read |
-| --- | --- | --- |
-| Entrypoints | Offline/CLI/API ingress, validation, rendering, streaming, sessions | [entrypoints.md](../modules/entrypoints.md) |
-| Configuration | Schema, defaults, deploy/stage construction, registry, topology | [configuration.md](../modules/configuration.md) |
-| I/O and Modality | Requests, messages, serialization, output types, accumulation, completion | [input-output-modality.md](../modules/input-output-modality.md) |
-| Error Contracts | Classification, fatality, propagation, sanitization, public rendering | [error-contracts.md](../modules/error-contracts.md) |
-| Engine Orchestration | Cross-stage routing, request state, ordering, RPC correlation, terminal convergence | [engine-orchestration.md](../modules/engine-orchestration.md) |
-| Stage Runtime | Placement, startup, readiness, replicas, affinity, membership, shutdown | [stage-runtime.md](../modules/stage-runtime.md) |
-| OmniConnector | Cross-stage/process/device/node transport and synchronization | [omni-connector.md](../modules/omni-connector.md) |
-| Model Integration | Registration, preprocessing, loading, runners, model-specific execution | [model-integration.md](../modules/model-integration.md) |
-| AR Runtime | Scheduling, request/cache state, adapters, workers, upstream vLLM semantics | [ar-runtime.md](../modules/ar-runtime.md) |
-| Diffusion Family | Diffusion runtime, models, batching, parallelism, or offload | [diffusion.md](../modules/diffusion.md) |
-| Execution Platforms | Hardware selection, capabilities, vendor workers, kernels, patches | [execution-platforms.md](../modules/execution-platforms.md) |
-| Cache Management | Cache identity, validity, reuse, eviction, reset, teardown | [cache-management.md](../modules/cache-management.md) |
-| Quantization | Method/metadata selection, layer mapping, reduced precision, constraints | [quantization.md](../modules/quantization.md) |
-| Observability | Metrics, logs, labels, units, correlation, lifecycle | [observability.md](../modules/observability.md) |
-| Profiling | Opt-in instrumentation, traces, start/stop lifecycle, overhead | [profiling.md](../modules/profiling.md) |
-| Benchmarking | Workloads, metric computation, benchmark CLI, result metadata | [benchmarking.md](../modules/benchmarking.md) |
-
-For tests-, docs-, CI-, or recipe-only changes, route to the production module
-whose contract they protect. When none exists, use the applicable evidence
-check without inventing a production owner.
-
-## Feature-design overlays
-
-Load every category whose documented behavior, enablement, default, support, or
-compatibility changes.
-
-| Feature-design section | Read |
-| --- | --- |
-| Runtime and stage execution | [runtime-stage-execution.md](../features/runtime-stage-execution.md) |
-| Communication and concrete connectors | [communication.md](../features/communication.md) |
-| Diffusion acceleration | [diffusion-acceleration.md](../features/diffusion-acceleration.md) |
-| Infrastructure and performance | [infrastructure-performance.md](../features/infrastructure-performance.md) |
-
-## Evidence and change overlays
-
-| Signal | Read | Optional repo-local skill |
-| --- | --- | --- |
-| New or expanded model, loader, processor, registry, or stage config | [model-addition-checklist.md](../checks/model-addition-checklist.md) | [`add-tts-model`](../../../add-tts-model/SKILL.md) or [`add-diffusion-model`](../../../add-diffusion-model/SKILL.md) |
-| Latency, throughput, memory, scaling, precision, or quality claim | [perf-verification.md](../checks/perf-verification.md) | [`diffusion-perf-opt`](../../../diffusion-perf-opt/SKILL.md) for diffusion |
-| Tests changed, absent for risky behavior, or test-only | [test-quality-evaluation.md](../checks/test-quality-evaluation.md) | [`vllm-omni-test`](../../../vllm-omni-test/SKILL.md) |
-| CI, examples, docs, public behavior, or contributor evidence | [tests-docs-checklist.md](../checks/tests-docs-checklist.md) | None |
-| Suitable hardware/server and runnable affected path | [verification.md](../checks/verification.md) | None |
-| User asks who should review or requests owner notification | [review-requests.md](../delivery/review-requests.md) | None |
-
-Use [`quantization`](../../../quantization/SKILL.md) for a quantization module
-change and [`$vllm-omni-npu-model-runner-upgrade`](../../../vllm-omni-npu-upgrade/SKILL.md)
-for an NPU execution-platform change when those skills are available.
-
-For a bug fix, require a reachable reproduction and regression test. For a
-refactor, prove parity and remove obsolete paths. For a feature, verify the
-public/config contract, module integration, compatibility/default behavior,
-production dispatch, feature design, and docs.
-
-## Calibrate findings
-
-- **P0:** security exposure, data corruption, or broad project unusability.
-- **P1:** reachable runtime failure, wrong output, compatibility break, or
-  unsafe lifecycle in the changed behavior.
-- **P2:** real non-blocking defect with a concrete future failure mode.
-
-Treat draft candidate invariants, missing hardware, and unsupported claims as
-questions or validation gaps unless current code, tests, or policy makes them a
-merge requirement.

.github/CODEOWNERS

@@ -1,11 +1,6 @@
 # CODEOWNERS is used to automatically assign PR reviewers
 # Format: path pattern @username or @team
 # More info: https://docs.github.com/en/repositories/managing-your-repositorys-settings-and-features/customizing-your-repository/about-code-owners
-#
-# The vllm_omni/** source-tree rules below are derived from the `owners` and
-# `primary_code_paths` front matter in docs/design/module/*.md. When updating
-# ownership, update the design doc first, then regenerate the matching rule here.
-# Last-match-wins: more specific rules must come AFTER broader ones.
 
 # Default owners (fallback reviewers for all files)
 * @Gaohan123 @hsliuustc0106 @ywang96
@@ -26,10 +21,25 @@
 # CI
 /.buildkite/ @yenuo26 @congw729 @NickCao
 
-# ComfyUI / external apps
-/apps/ @fhfuih @alex-jw-brooks
+# Engine and model_executor
+/vllm_omni/core/ @tzhouam @yinpeiqi @fake0fan @Sy0307
+/vllm_omni/engine/ @tzhouam @yinpeiqi @fake0fan @Sy0307
+/vllm_omni/entrypoints/ @tzhouam @yinpeiqi @fake0fan @alex-jw-brooks @NickCao
+/vllm_omni/model_executor/ @tzhouam @gcanlin
+/vllm_omni/worker/ @tzhouam @gcanlin
 
-# Benchmarks (out-of-tree)
+# Diffusion
+/vllm_omni/diffusion/ @Isotr0py @princepride @SamitHuang @wtomin @ZJY0516 @RuixiangMa @david6666666 @xuechendi
+/vllm_omni/lora/ @princepride @SamitHuang @wtomin @ZJY0516 @RuixiangMa @yuanheng-zhao
+
+# Diffusion CPU offloading
+/vllm_omni/diffusion/offloader/ @Isotr0py @princepride @SamitHuang @wtomin @ZJY0516 @RuixiangMa @yuanheng-zhao @xuechendi
+
+# Omni and TTS support
+/vllm_omni/transformers_utils/ @tzhouam @yuanheng-zhao
+/vllm_omni/model_executor/models/ @linyueqian @ZeldaHuang @princepride @yuanheng-zhao @amy-why-3459 @Bounty-hunter @Sy0307 @gcanlin @NickCao
+
+# Benchmarks
 /benchmarks/accuracy/ @david6666666
 /benchmarks/build_dataset/ @linyueqian @yuanheng-zhao
 /benchmarks/diffusion/ @Isotr0py @princepride @SamitHuang @wtomin @ZJY0516 @RuixiangMa @xuechendi
@@ -37,112 +47,20 @@
 /benchmarks/glm_image/ @princepride @JaredforReal
 /benchmarks/tts/ @linyueqian @ZeldaHuang @princepride @Sy0307 @gcanlin
 
-# =====================================================================
-# Module design ownership (docs/design/module/*.md)
-# Each section header cites the source design doc.
-# =====================================================================
-
-# Autoregressive runtime — docs/design/module/ar_runtime.md
-/vllm_omni/core/ @tzhouam @yinpeiqi @fake0fan @Sy0307 @Gaohan123
-/vllm_omni/worker/ @tzhouam @yinpeiqi @fake0fan @Sy0307 @Gaohan123
-
-# Engine orchestration & stage runtime — docs/design/module/engine_orchestration.md, stage_runtime.md
-/vllm_omni/engine/ @tzhouam @fake0fan
-# File-level overrides: input/output modality contracts own these engine files
-# — docs/design/module/input_output_modality_contracts.md
-/vllm_omni/engine/messages.py @Sy0307 @amy-why-3459 @Gaohan123 @alex-jw-brooks
-/vllm_omni/engine/serialization.py @Sy0307 @amy-why-3459 @Gaohan123 @alex-jw-brooks
-/vllm_omni/engine/mm_outputs.py @Sy0307 @amy-why-3459 @Gaohan123 @alex-jw-brooks
-/vllm_omni/engine/output_modality.py @Sy0307 @amy-why-3459 @Gaohan123 @alex-jw-brooks
-/vllm_omni/engine/output_processor.py @Sy0307 @amy-why-3459 @Gaohan123 @alex-jw-brooks
-
-# Input/output modality contracts — docs/design/module/input_output_modality_contracts.md
-/vllm_omni/inputs/ @Sy0307 @amy-why-3459 @Gaohan123 @alex-jw-brooks
-/vllm_omni/outputs/ @Sy0307 @amy-why-3459 @Gaohan123 @alex-jw-brooks
-/vllm_omni/request.py @Sy0307 @amy-why-3459 @Gaohan123 @alex-jw-brooks
-/vllm_omni/data_entry_keys.py @Sy0307 @amy-why-3459 @Gaohan123 @alex-jw-brooks
-
-# Entrypoints and serving boundaries — docs/design/module/entrypoints.md
-/vllm_omni/entrypoints/ @alex-jw-brooks @linyueqian @NickCao
-# File-level overrides within entrypoints/
-/vllm_omni/entrypoints/openai/errors.py @alex-jw-brooks @NickCao
-/vllm_omni/entrypoints/cli/benchmark/ @alex-jw-brooks @Bounty-hunter
-
-# Error contracts — docs/design/module/error_contracts.md
-/vllm_omni/errors.py @alex-jw-brooks @NickCao
-
-# OmniConnector (inter-stage transport) — docs/design/module/omni_connector.md
-/vllm_omni/distributed/ @princepride @yuanheng-zhao @xuechendi @natureofnature @fake0fan
-
-# Model integration — docs/design/module/model_integration.md
-/vllm_omni/model_executor/ @tzhouam @gcanlin
-/vllm_omni/model_executor/models/ @tzhouam @gcanlin @Sy0307 @amy-why-3459 @linyueqian
-/vllm_omni/model_extras/ @tzhouam @gcanlin @princepride
-/vllm_omni/plugins/ @gcanlin @tjtanaa @xuechendi
-# Config override within model_executor — docs/design/module/vllm_omni_config.md
-/vllm_omni/model_executor/stage_configs/ @lishunyang12 @alex-jw-brooks
-
-# Execution platforms (hardware backends) — docs/design/module/execution_platforms.md
-/vllm_omni/platforms/ @gcanlin @tjtanaa @xuechendi
-/vllm_omni/attention/ @gcanlin @tjtanaa @xuechendi @david6666666 @lishunyang12
-
-# Configuration & deploy — docs/design/module/vllm_omni_config.md
-/vllm_omni/config/ @lishunyang12 @alex-jw-brooks
-/vllm_omni/deploy/ @lishunyang12 @alex-jw-brooks
-
-# Quantization — docs/design/module/quantization.md
-/vllm_omni/quantization/ @david6666666 @Isotr0py @lishunyang12
-
-# Observability — docs/design/module/observability.md
-/vllm_omni/metrics/ @lishunyang12 @vraiti
-
-# Profiling — docs/design/module/profiling.md
+# Usability and Observability
+/vllm_omni/deploy/ @tzhouam @lishunyang12
+/vllm_omni/metrics/ @lishunyang12
 /vllm_omni/profiler/ @gcanlin
 
-# Benchmarking (in-tree) — docs/design/module/benchmarking.md
-/vllm_omni/benchmarks/ @alex-jw-brooks @Bounty-hunter
+# ComfyUI
+/apps/ @fhfuih @alex-jw-brooks
 
-# LoRA (no dedicated design doc; ownership intentionally narrowed)
-/vllm_omni/lora/ @SamitHuang @wtomin
+# Large Scale Serving
+/vllm_omni/distributed/ @princepride @yuanheng-zhao @xuechendi @natureofnature @fake0fan
 
-# Transformers utils (related path only in design docs; preserves prior ownership)
-/vllm_omni/transformers_utils/ @tzhouam @yuanheng-zhao
+# Quantization and configuration
+/vllm_omni/config/ @lishunyang12 @alex-jw-brooks
+/vllm_omni/quantization/ @david6666666 @Isotr0py @lishunyang12
 
-# =====================================================================
-# Diffusion module family — docs/design/module/diffusion/
-# Umbrella rule first; sub-module rules after it win via last-match.
-# =====================================================================
-
-# Diffusion umbrella (top-level files: data.py, forward_context.py, etc.)
-# — docs/design/module/diffusion/index.md
-/vllm_omni/diffusion/ @Isotr0py @princepride @SamitHuang @wtomin @ZJY0516 @RuixiangMa @david6666666 @xuechendi
-
-# Cache management — docs/design/module/cache_management.md
-/vllm_omni/diffusion/cache/ @Isotr0py @princepride @SamitHuang
-
-# Diffusion quantization — docs/design/module/quantization.md
-/vllm_omni/diffusion/quantization/ @david6666666 @Isotr0py @lishunyang12
-
-# Diffusion profiling — docs/design/module/profiling.md
-/vllm_omni/diffusion/profiler/ @gcanlin
-
-# Diffusion runtime (scheduler, executor, worker)
-# — docs/design/module/diffusion/diffusion_runtime.md, continuous_batching.md
-/vllm_omni/diffusion/sched/ @Isotr0py @princepride @SamitHuang @fhfuih
-/vllm_omni/diffusion/executor/ @Isotr0py @princepride @SamitHuang @fhfuih
-/vllm_omni/diffusion/worker/ @Isotr0py @princepride @SamitHuang @fhfuih
-
-# Diffusion model integration (pipelines, registration, checkpoint loading)
-# — docs/design/module/diffusion/diffusion_model_integration.md
-/vllm_omni/diffusion/models/ @fhfuih @Bounty-hunter @wtomin
-/vllm_omni/diffusion/model_loader/ @fhfuih @Bounty-hunter @wtomin @RuixiangMa
-/vllm_omni/diffusion/lora/ @fhfuih @Bounty-hunter @wtomin @RuixiangMa
-
-# Diffusion parallelism (rank topology, TP/SP/PP/VAE patch)
-# — docs/design/module/diffusion/parallelism.md
-/vllm_omni/diffusion/distributed/ @princepride @yuanheng-zhao @xuechendi @Bounty-hunter
-/vllm_omni/diffusion/attention/parallel/ @princepride @yuanheng-zhao @xuechendi @Bounty-hunter
-
-# Diffusion offloader (CPU offloading, DLO)
-# — docs/design/module/diffusion/offloader.md
-/vllm_omni/diffusion/offloader/ @yuanheng-zhao @david6666666 @lishunyang12
+# Hardware plugins and accelerators
+/vllm_omni/platforms/ @gcanlin @tjtanaa @xuechendi

.pre-commit-config.yaml

@@ -61,16 +61,6 @@ repos:
       types: [python]
       files: ^tests/
 
-    - id: check-tts-adapter-migration
-      name: Ratchet TTS per-model branches out of serving_speech.py
-      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)$
-      # 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
-
     # Keep `suggestion` last
     - id: suggestion
       name: Suggestion

docs/.nav.yml

@@ -16,21 +16,31 @@ nav:
   - Examples:
     - examples/README.md
     - Offline Inference:
+      - GLM-Image Multistage End-to-End Inference: user_guide/examples/offline_inference/glm_image.md
+      - HunyuanImage-3.0 Image-to-Text Inference: user_guide/examples/offline_inference/hunyuan_image3.md
       - Image-To-Image: user_guide/examples/offline_inference/image_to_image.md
       - Image-To-Video: user_guide/examples/offline_inference/image_to_video.md
-      - Speech-To-Video: user_guide/examples/offline_inference/speech_to_video.md
+      - InternVLA-A1: user_guide/examples/offline_inference/internvla_a1.md
+      - MiMo-Audio: user_guide/examples/offline_inference/mimo_audio.md
+      - Qwen2.5-Omni: user_guide/examples/offline_inference/qwen2_5_omni.md
+      - Qwen3-Omni: user_guide/examples/offline_inference/qwen3_omni.md
       - Text-To-Audio: user_guide/examples/offline_inference/text_to_audio.md
       - Text-To-Image: user_guide/examples/offline_inference/text_to_image.md
       - Text-To-Speech: user_guide/examples/offline_inference/text_to_speech.md
       - Text-To-Video: user_guide/examples/offline_inference/text_to_video.md
-      - X-To-Text: user_guide/examples/offline_inference/x_to_text.md
       - X-To-Video-Audio: user_guide/examples/offline_inference/x_to_video_audio.md
     - Online Serving:
+      - BAGEL-7B-MoT: user_guide/examples/online_serving/bagel.md
+      - vLLM-Omni Helm Chart: user_guide/examples/online_serving/chart-helm.md
+      - Diffusers Backend Adapter: user_guide/examples/online_serving/diffusers_pipeline_adapter.md
+      - GLM-Image Online Serving: user_guide/examples/online_serving/glm_image.md
       - Image-To-Image: user_guide/examples/online_serving/image_to_image.md
       - Image-To-Video: user_guide/examples/online_serving/image_to_video.md
-      - Speech-To-Video: user_guide/examples/online_serving/speech_to_video.md
-      - Text-To-Image: user_guide/examples/online_serving/text_to_image.md
+      - MiMo-Audio: user_guide/examples/online_serving/mimo_audio.md
+      - Qwen2.5-Omni: user_guide/examples/online_serving/qwen2_5_omni.md
+      - Qwen3-Omni: user_guide/examples/online_serving/qwen3_omni.md
       - Text-To-Speech: user_guide/examples/online_serving/text_to_speech.md
+      - Text-To-Image: user_guide/examples/online_serving/text_to_image.md
       - Text-To-Video: user_guide/examples/online_serving/text_to_video.md
   - General:
     - usage/*

docs/contributing/README.md

@@ -76,47 +76,6 @@ Please refer to the [test instructions](./ci/test_execution_guide.md) for detail
 !!! warning
     Currently, not all unit tests pass when run on CPU platforms. If you don't have access to a GPU platform to run unit tests locally, rely on the continuous integration system to run the tests for now.
 
-### Using repository skills with coding agents
-
-vLLM-Omni maintains [repository-scale skills](https://github.com/vllm-project/vllm-omni/tree/main/.claude/skills) for common coding, testing, performance, and review workflows. These skills capture repository-specific structure, validation requirements, and evidence standards that a general coding agent may not know.
-
-If your coding agent discovers repository skills automatically, ask it to use the relevant skill by name. Otherwise, point it to the linked `SKILL.md` and ask it to read the file before changing code. Skills can be combined: use a domain skill for the implementation, `vllm-omni-test` for test and CI decisions, and `precheck-pr` before opening the pull request.
-
-| Task | Repository skill |
-| --- | --- |
-| Add or extend a diffusion model | [`add-diffusion-model`](https://github.com/vllm-project/vllm-omni/blob/main/.claude/skills/add-diffusion-model/SKILL.md) |
-| Add or extend a text-to-speech model | [`add-tts-model`](https://github.com/vllm-project/vllm-omni/blob/main/.claude/skills/add-tts-model/SKILL.md) |
-| Add, select, or debug quantization | [`quantization`](https://github.com/vllm-project/vllm-omni/blob/main/.claude/skills/quantization/SKILL.md) |
-| Diagnose or optimize diffusion performance | [`diffusion-perf-opt`](https://github.com/vllm-project/vllm-omni/blob/main/.claude/skills/diffusion-perf-opt/SKILL.md) |
-| Upgrade an NPU model runner | [`vllm-omni-npu-model-runner-upgrade`](https://github.com/vllm-project/vllm-omni/blob/main/.claude/skills/vllm-omni-npu-upgrade/SKILL.md) |
-| Add a regression test, choose L1-L4 coverage, or wire Buildkite | [`vllm-omni-test`](https://github.com/vllm-project/vllm-omni/blob/main/.claude/skills/vllm-omni-test/SKILL.md) |
-| Self-check a branch before opening a pull request | [`precheck-pr`](https://github.com/vllm-project/vllm-omni/blob/main/.claude/skills/precheck-pr/SKILL.md) |
-| Perform a maintainer or reviewer review of an existing pull request or local branch | [`review-pr`](https://github.com/vllm-project/vllm-omni/blob/main/.claude/skills/review-pr/SKILL.md) |
-
-Use the following workflow when collaborating with a coding agent:
-
-1. **Ground the task.** Provide the issue, RFC, or design document that defines the expected behavior. Ask the agent to state its assumptions and identify the affected module or feature contracts before editing code.
-2. **Select the narrowest applicable skills.** Start with the domain skill that owns the change. Add `vllm-omni-test` whenever production behavior or tests change. For a bug fix, ask for the smallest reproducer and a regression test tied to the original symptom.
-3. **Require executable evidence.** Ask the agent to run the narrowest relevant checks and report the exact commands and results. It must identify tests it could not run because of unavailable hardware, model weights, credentials, or dependencies; an unrun check is not a pass.
-4. **Inspect the resulting diff.** Confirm that the implementation stays within the issue or RFC, does not overwrite unrelated work, and includes the required tests, documentation, and benchmark or accuracy evidence.
-5. **Run the pre-submit workflow.** Use `precheck-pr`, then run the applicable local tests and pre-commit hooks before opening the pull request.
-
-For example:
-
-```text
-Use add-diffusion-model and vllm-omni-test to implement the linked issue.
-Read the relevant design documents first, keep the change within the issue,
-and report every test command, result, and hardware validation gap.
-```
-
-```text
-Use vllm-omni-test to turn this bug reproducer into the smallest deterministic
-regression test. Prefer L1 CPU coverage; if the failure requires real weights
-or serving, explain the required L2/L3 environment and provide the exact command.
-```
-
-Repository skills guide the agent, but they do not replace contributor judgment, the accepted issue or RFC, required test evidence, or maintainer review. Review all generated changes before submitting them.
-
 ## Issues
 
 If you encounter a bug or have a feature request, please search existing issues first to see if it has already been reported. If not, please file a new issue, providing as much relevant information as possible.

docs/contributing/model/adding_tts_model.md

@@ -672,147 +672,100 @@ down and spawn a new one mid-module. A few rules that save real CI debugging tim
 
 ## Online Serving Integration
 
-To expose your model through the `/v1/audio/speech` OpenAI-compatible endpoint, write
-one adapter under `vllm_omni/entrypoints/openai/tts_adapters/`. The adapter declares
-how your model is recognized and owns its request handling; `serving_speech.py` should
-not need an edit.
+To expose your model through the `/v1/audio/speech` OpenAI-compatible endpoint, add
+**all five** of the following integration points to
+`vllm_omni/entrypoints/openai/serving_speech.py` in a **single commit**. Adding them
+piecemeal causes partial-integration failures that are hard to debug.
 
-### 1. Write the adapter
+### 1. Stage constant
 
-Create `vllm_omni/entrypoints/openai/tts_adapters/your_model.py`:
+Near the top of the file, alongside the other `_*_TTS_MODEL_STAGES` constants:
 
 ```python
-# SPDX-License-Identifier: Apache-2.0
-"""YourModel serving adapter."""
-
-from typing import TYPE_CHECKING
-
-from vllm_omni.entrypoints.openai.tts_adapters import register_tts_adapter
-from vllm_omni.entrypoints.openai.tts_adapters.base import ARTTSAdapter, PreparedRequest
-
-if TYPE_CHECKING:
-    from vllm_omni.entrypoints.openai.protocol.audio import OpenAICreateSpeechRequest
-
-
-@register_tts_adapter
-class YourModelAdapter(ARTTSAdapter):
-    # The registry key, and the model-type string used in logs.
-    name = "your_model"
-    # The engine ``model_stage`` value(s) your deployment yaml sets. Detection,
-    # stage discovery and `/v1/audio/speech` routing all derive from this.
-    stage_keys = frozenset({"your_model_stage_key"})
-
-    def validate(self, request: "OpenAICreateSpeechRequest") -> str | None:
-        """Return an error string, or None if the request is valid."""
-        if not request.input or not request.input.strip():
-            return "Input text cannot be empty"
-        return None
-
-    async def build(
-        self,
-        request: "OpenAICreateSpeechRequest",
-        sampling_params_list: list,
-        has_inline_ref_audio: bool,
-    ) -> PreparedRequest:
-        """Build the engine prompt and per-model parameters."""
-        params: dict[str, list] = {"text": [request.input]}
-        if request.voice is not None:
-            params["voice"] = [request.voice]
-        return PreparedRequest(
-            prompt={"prompt": request.input},
-            tts_params=params,
-            model_type=self.name,
-        )
+_YOUR_MODEL_TTS_MODEL_STAGES = {"your_model_stage_key"}
 ```
 
-Then add it to the import block at the bottom of
-`vllm_omni/entrypoints/openai/tts_adapters/__init__.py` so it registers on import.
-That import line is the only shared file a new model has to touch.
+### 2. Union into `_TTS_MODEL_STAGES`
 
-> **Pure-diffusion TTS does not go through adapters yet.** When the server is built
-> by `OmniOpenAIServingSpeech.for_diffusion()`, `create_speech()` routes straight to
-> `_create_diffusion_speech()` and never calls `validate()` or `build()`.
-> `DiffusionTTSAdapter` is scaffolding for that migration and has no production
-> subclass today, so validation or prompt construction placed in one would silently
-> never run. If your model is served by the diffusion engine rather than
-> `engine_client`, follow the existing diffusion path and say so on #4855 — wiring
-> that path through the adapter layer is open work, not something to do inside a
-> new-model PR.
-
-### 2. If a stage key alone cannot identify your model
-
-Two escape hatches, in order of preference:
+Add to the `_TTS_MODEL_STAGES` set union:
 
 ```python
-# The architecture is authoritative (e.g. your model has no dedicated
-# model_stage value, or shares one with another model).
-model_archs = frozenset({"YourModelForConditionalGeneration"})
-
-# Set this too if your model owns NO stage key, so stage discovery has to find
-# your AR entry stage by architecture.
-arch_identifies_entry_stage = True
+_TTS_MODEL_STAGES: set[str] = (
+    ...
+    | _YOUR_MODEL_TTS_MODEL_STAGES
+)
 ```
 
-If your rule is not a set-membership test, override `matches()` — see
-`covo_audio.py`, which shares the generic `fused_thinker_talker` stage key with
-non-CoVo deployments and confirms with the architecture:
+### 3. Model type detection
+
+In `_detect_tts_model_type()`, add before the final `return None`:
 
 ```python
-@classmethod
-def matches(cls, model_stage: str | None, model_arch: str | None) -> bool:
-    return super().matches(model_stage, model_arch) and bool(model_arch) and "CovoAudio" in model_arch
+if model_stage in _YOUR_MODEL_TTS_MODEL_STAGES:
+    return "your_model"
 ```
 
-If your model can match the same deployment as another adapter, give one of them an
-explicit `detect_priority` (lower runs first). `tests/entrypoints/openai_api/
-test_tts_detection.py` fails if two same-priority detectors can both match one input,
-so an unordered overlap cannot be merged.
+### 4. Request validation dispatch
 
-Models that are speech-capable only in some deployment topologies override
-`stage_serves_speech()` — see `audex.py`, whose omni thinker is text-final unless the
-speech decoder is deployed alongside it.
+In `_validate_tts_request()`, add before the fallback `return`:
 
-### 3. Reuse the shared helpers
+```python
+if self._tts_model_type == "your_model":
+    return self._validate_your_model_request(request)
+```
 
-Adapters reach shared serving helpers through `self.ctx.server`: reference-audio
-resolution (`_resolve_ref_audio`), uploaded-speaker handling
-(`_apply_uploaded_speaker`), format validation (`_validate_ref_audio_format`), and
-prompt-length limits (`_max_instructions_length`). Read a comparable adapter before
-writing your own — `fish_speech.py` for voice cloning, `higgs_audio_v3.py` for
-parameter-heavy models, `moss_tts.py` for a family sharing one base class.
+### 5. Validation and parameter-builder methods
 
-### 4. Test it
+Add two new methods:
 
-`tests/entrypoints/openai_api/test_tts_adapter.py` covers registry invariants and
-`test_tts_detection.py` covers detection. Add your model to
-`EXPECTED_MODEL_TYPES` in the former, and a case to the latter if you added a
-`model_archs` or `matches()` rule.
+```python
+def _validate_your_model_request(
+    self, request: OpenAICreateSpeechRequest
+) -> str | None:
+    """Validate YourModel request. Returns an error string or None."""
+    if not request.input or not request.input.strip():
+        return "Input text cannot be empty"
+    return None
 
-### Do not add branches to `serving_speech.py`
+def _build_your_model_params(
+    self, request: OpenAICreateSpeechRequest
+) -> dict[str, Any]:
+    """Build additional_information dict for YourModel."""
+    params: dict[str, Any] = {"text": [request.input]}
+    if request.voice is not None:
+        params["voice"] = [request.voice]
+    # Add any other model-specific fields here
+    return params
+```
 
-Older models predate the adapter framework and still have per-model branches in
-`serving_speech.py`; they are being migrated out (RFC #4327, #4855). Do not copy
-that pattern. `tools/pre_commit/check_tts_adapter.py` is a ratchet on the remaining
-`self._tts_model_type == ...` comparisons and fails the commit if the count grows.
+Then wire `_build_your_model_params` into the request-dispatch block in
+`_create_tts_request()` (search for the equivalent `_build_*_params` call for an
+existing model to find the right location). If the model supports voice cloning
+(`ref_audio` → `prompt_audio_path`, `ref_text` → `prompt_text`), add those mappings
+here too — follow any existing `_build_<model>_params` in `serving_speech.py` (e.g.
+`_build_moss_tts_params` for the voice-cloning variant) for the pattern.
 
-If your model genuinely needs behaviour that no adapter hook can express, that is a
-missing hook — propose it on the RFC rather than adding a branch.
+> **Two dispatch patterns coexist:** Fish Speech uses a `self._is_fish_speech` boolean
+> checked *before* `elif self._is_tts`. All newer models use the `_tts_model_type`
+> string pattern shown above. For new models, always use the string pattern — do not
+> add new `_is_*` boolean flags.
+
+> **Note on unused variables:** Only extract parameters in `_build_your_model_params`
+> that you actually pass to the model's generate / `inference_stream` call. Extracting
+> a variable without forwarding it will trigger a `ruff F841` pre-commit failure.
 
 ### Merge conflicts
 
-Your adapter is a new file, so it cannot conflict. The one shared line is your entry
-in the import block at the bottom of `tts_adapters/__init__.py`, which conflicts only
-when another model lands at the same time. The resolution is always to **keep both**
-imports — never discard either side. After resolving:
+`serving_speech.py` is modified by every new model PR and is the most common source of
+rebase conflicts. When rebasing onto `main` and a conflict appears here, the resolution
+is always to **keep both** the upstream model's additions and your own — never discard
+either side. After resolving:
 
 ```bash
-git add vllm_omni/entrypoints/openai/tts_adapters/__init__.py
+git add vllm_omni/entrypoints/openai/serving_speech.py
 git rebase --continue
 ```
 
-If you find yourself resolving a conflict inside `serving_speech.py`, something in
-your model is still going through the legacy path; see the note above.
-
 ## Single-Stage Models
 
 Some TTS models (e.g. MOSS-TTS-Nano) do not use a two-stage pipeline. Instead the

docs/design/module/ar_runtime.md

@@ -7,7 +7,6 @@ owners:
   - "@yinpeiqi"
   - "@fake0fan"
   - "@Sy0307"
-  - "@Gaohan123"
 primary_code_paths:
   - vllm_omni/core/**
   - vllm_omni/worker/**

docs/design/module/benchmarking.md

@@ -4,7 +4,6 @@ kind: module
 status: draft
 owners:
   - "@alex-jw-brooks"
-  - "@Bounty-hunter"
 primary_code_paths:
   - vllm_omni/benchmarks/**
   - vllm_omni/entrypoints/cli/benchmark/**

docs/design/module/cache_management.md

@@ -5,7 +5,6 @@ status: draft
 owners:
   - "@Isotr0py"
   - "@princepride"
-  - "@SamitHuang"
 primary_code_paths:
   - vllm_omni/diffusion/cache/**
 related_code_paths:

docs/design/module/diffusion/diffusion_model_integration.md

@@ -3,10 +3,9 @@ title: Diffusion Model Integration
 kind: module
 status: draft
 owners:
-  - "@fhfuih"
-  - "@Bounty-hunter"
+  - "@Isotr0py"
   - "@wtomin"
-  - "@RuixiangMa"
+  - "@ZJY0516"
 primary_code_paths:
   - vllm_omni/diffusion/models/**
   - vllm_omni/diffusion/model_loader/**

docs/design/module/diffusion/diffusion_runtime.md

@@ -6,7 +6,6 @@ owners:
   - "@Isotr0py"
   - "@princepride"
   - "@SamitHuang"
-  - "@fhfuih"
 primary_code_paths:
   - vllm_omni/diffusion/sched/**
   - vllm_omni/diffusion/executor/**

docs/design/module/diffusion/offloader.md

@@ -4,8 +4,7 @@ kind: module
 status: draft
 owners:
   - "@yuanheng-zhao"
-  - "@david6666666"
-  - "@lishunyang12"
+  - "@Isotr0py"
 primary_code_paths:
   - vllm_omni/diffusion/offloader/**
 related_code_paths:

docs/design/module/diffusion/parallelism.md

@@ -6,7 +6,6 @@ owners:
   - "@princepride"
   - "@yuanheng-zhao"
   - "@xuechendi"
-  - "@Bounty-hunter"
 primary_code_paths:
   - vllm_omni/diffusion/distributed/**
   - vllm_omni/diffusion/attention/parallel/**

docs/design/module/entrypoints.md

@@ -6,7 +6,6 @@ architecture_state: current-plus-in-flight
 owners:
   - "@alex-jw-brooks"
   - "@linyueqian"
-  - "@NickCao"
 document_stewards:
   - "@hsliuustc0106"
   - "@Gaohan123"

docs/design/module/execution_platforms.md

@@ -8,15 +8,8 @@ owners:
   - "@xuechendi"
 primary_code_paths:
   - vllm_omni/platforms/**
+related_code_paths:
   - vllm_omni/attention/**
-primary_code_path_owners:
-  # Additional owners scoped to attention kernels (quantized/custom attention)
-  - paths:
-      - vllm_omni/attention/**
-    owners:
-      - "@david6666666"
-      - "@lishunyang12"
-related_code_paths: []
 depends_on:
   - ar_runtime.md
   - diffusion/index.md

docs/design/module/input_output_modality_contracts.md

@@ -6,8 +6,6 @@ architecture_state: current
 owners:
   - "@Sy0307"
   - "@amy-why-3459"
-  - "@Gaohan123"
-  - "@alex-jw-brooks"
 document_stewards:
   - "@hsliuustc0106"
   - "@Gaohan123"

docs/design/module/model_integration.md

@@ -9,14 +9,6 @@ primary_code_paths:
   - vllm_omni/model_executor/**
   - vllm_omni/model_extras/**
   - vllm_omni/plugins/**
-primary_code_path_owners:
-  # Additional owners scoped to a sub-path of model_executor/**
-  - paths:
-      - vllm_omni/model_executor/models/**
-    owners:
-      - "@Sy0307"
-      - "@amy-why-3459"
-      - "@linyueqian"
 related_code_paths:
   - vllm_omni/transformers_utils/**
   - vllm_omni/tokenizers/**

docs/design/module/observability.md

@@ -4,7 +4,6 @@ kind: module
 status: draft
 owners:
   - "@lishunyang12"
-  - "@vraiti"
 primary_code_paths:
   - vllm_omni/metrics/**
 related_code_paths:

docs/features/session_state_manager.md

@@ -20,12 +20,11 @@ extracts it into a shared, typed contract:
   (matching the bespoke caches it replaces), and reports per-device byte usage
   via `stats()` for observability.
 
-Autoregressive generation KV is *not* managed here: for DreamZero it is owned
-by the AR-Diffusion engine's paged KV pool (PR
-[#4534](https://github.com/vllm-project/vllm-omni/pull/4534)). Cosmos3's fixed,
-encode-once UND conditioning K/V is different and is covered by the dense
-Cosmos3 adapter described below. Integrating the engine's per-session paged KV
-handle into the same byte accounting is RFC #4480 Phase 1.
+Attention KV is *not* managed here: for DreamZero it is owned by the
+AR-Diffusion engine's paged KV pool (PR
+[#4534](https://github.com/vllm-project/vllm-omni/pull/4534)). The manager
+covers the model's non-KV session state; integrating the engine's per-session
+KV handle into the same byte accounting is RFC #4480 Phase 1.
 
 ## Enabling
 
@@ -159,62 +158,3 @@ of `num_frame_per_block` frames instead: identical output, bounded memory, and
 the per-step quantisation no longer scales with session length. Asking for more
 frames than the ring retains raises rather than silently returning a shorter
 window. The bespoke path is unchanged.
-
-### Cosmos3
-
-When `enable_session_state_manager` is enabled, the Cosmos3 adapter stores the
-dense, encode-once UND text K/V for every transformer layer and active CFG
-branch, together with that branch's GEN M-RoPE cosine/sine tensors. These are
-GPU-resident tensors and can contribute to CUDA OOM. A useful description of
-the retained storage on one rank is:
-
-```text
-sum_over_branches(
-  sum_over_layers(storage_bytes(K) + storage_bytes(V))
-  + storage_bytes(freqs_gen_cos) + storage_bytes(freqs_gen_sin)
-)
-```
-
-This is not a peak-memory upper bound: construction of the next branch can
-temporarily overlap the already retained branch with UND activations, the new
-full K/V result, RoPE tensors, and generator activations. K/V slices also keep
-their underlying allocation alive, so physical storage can follow the padded
-text length rather than only the exposed trimmed length. The manager's byte
-accounting deliberately measures storage for this reason.
-
-Branch placement changes the per-rank cost:
-
-- no CFG retains one conditional branch;
-- sequential CFG can retain both conditional and unconditional branches on the
-  same GPU;
-- CFG-parallel retains one branch on each CFG rank;
-- UND K/V is replicated across sequence-parallel ranks rather than divided by
-  the SP degree.
-
-Consequently, risk increases with text batch/sequence length, media token count
-(which sizes `freqs_gen`), sequential CFG, dtype width, and concurrent live
-requests. Phase 0 records `nbytes:cuda:*` but does not enforce `byte_budget`, so
-the manager cannot prevent an allocation from exhausting the device. The
-count-based session limit is not an OOM guarantee.
-
-The adapter transfers ownership by tensor reference; it does not clone K/V or
-RoPE tensors. For a normal serial request this should not materially increase
-tensor storage relative to Cosmos3's existing bespoke cond/uncond cache, which
-retains the same branch values. It does add small CPU-side session/object
-metadata. This claim is based on the reference flow in the implementation;
-there is not yet a current-head GPU peak-memory A/B result for this PR.
-
-Request-finally cleanup drops the manager-owned session, so branches do not
-accumulate with completed request IDs. The transformer handoff field still
-references the last loaded branch until `reset_cache()` runs at the start of
-the next generation (or the pipeline is destroyed). Thus this is bounded
-post-request residency, not growth per completed request, but manager
-`stats()` after `drop_session()` no longer includes that last transformer-held
-alias. A profiler that only reads manager statistics can therefore under-report
-live process memory.
-
-For OOM validation, measure complete cache-off and true flag-on generations in
-otherwise identical fresh processes. Synchronize CUDA and report process-level
-peak memory plus PyTorch peak allocated and peak reserved memory. Include the
-period between requests when checking cleanup; do not infer safety from the
-manager byte total, a single denoising step, or an operator-only profile.

docs/mkdocs/hooks/generate_argparse.py

@@ -123,7 +123,6 @@ def extract_omni_serve_subparser_init():
                         "TrackingNamespace": None,  # type annotation only
                         "make_arg_parser": lambda parser: parser,  # no-op for doc
                         "_ensure_vllm_platform": lambda: None,  # no-op for doc
-                        "_nonneg_finite_float": float,
                         "VLLM_SUBCMD_PARSER_EPILOG": "",
                         "logger": logger,
                         "DummySubparsers": DummySubparsers,

docs/mkdocs/hooks/generate_examples.py

@@ -24,23 +24,6 @@ SERVING_MODE_TITLES = {
     "online_serving": "Online serving",
 }
 
-# Keep the Examples navigation focused on reusable modality/task entry points.
-# Model-specific scripts remain available under ``examples/`` and are surfaced
-# from these shared pages instead of getting one page per model.
-GENERAL_EXAMPLE_SLUGS = frozenset(
-    {
-        "image_to_image",
-        "image_to_video",
-        "speech_to_video",
-        "text_to_audio",
-        "text_to_image",
-        "text_to_speech",
-        "text_to_video",
-        "x_to_text",
-        "x_to_video_audio",
-    }
-)
-
 
 def load_model_display_names() -> dict[str, str]:
     try:
@@ -63,13 +46,6 @@ def load_model_display_names() -> dict[str, str]:
 MODEL_DISPLAY_NAMES = load_model_display_names()
 
 
-def is_general_example(example: "Example") -> bool:
-    """Return whether an example should get a generated serving page."""
-    if example.category not in SERVING_MODE_TITLES:
-        return True
-    return example.path.stem in GENERAL_EXAMPLE_SLUGS
-
-
 def fix_case(text: str) -> str:
     subs = {
         "api": "API",
@@ -364,21 +340,22 @@ def update_nav_file(examples: list[Example]):
             examples_by_category[category] = []
         examples_by_category[category].append(example)
 
-    def category_items(category: str) -> list[dict[str, str]]:
-        items = []
-        for example in sorted(examples_by_category[category], key=lambda e: e.path.stem):
+    # Build the new Examples section - start with preserved items
+    examples_section = preserved_items.copy()
+
+    # Add examples grouped by category, sorted by category name
+    for category in sorted(examples_by_category.keys()):
+        category_examples = sorted(examples_by_category[category], key=lambda e: e.path.stem)
+        category_items = []
+        for example in category_examples:
             doc_path = EXAMPLE_DOC_DIR / example.category / f"{example.path.stem}.md"
             rel_path = doc_path.relative_to(ROOT_DIR / "docs")
-            items.append({example.nav_title: rel_path.as_posix()})
-        return items
+            category_items.append({example.nav_title: rel_path.as_posix()})
 
-    # Build the new Examples section - start with preserved items.
-    examples_section = preserved_items.copy()
-    for category in sorted(examples_by_category.keys()):
-        items = category_items(category)
-        if items:
+        if category_items:
+            # Format category name (e.g., "offline_inference" -> "Offline Inference")
             category_title = fix_case(category.replace("_", " ").title())
-            examples_section.append({category_title: items})
+            examples_section.append({category_title: category_items})
 
     # Update the nav structure
     nav_list[user_guide_idx]["User Guide"][examples_idx]["Examples"] = examples_section
@@ -413,8 +390,6 @@ def on_startup(command: Literal["build", "gh-deploy", "serve"], dirty: bool):
         for path in category.glob("*/*.md"):
             examples.append(Example(path.parent, category.stem))
 
-    examples = [example for example in examples if is_general_example(example)]
-
     # Generate the example documentation
     for example in sorted(examples, key=lambda e: e.path.stem):
         example_name = f"{example.path.stem}.md"

docs/models/supported_models.md

@@ -17,99 +17,83 @@ th {
 }
 </style>
 
-| Architecture | Models | Example HF Models | NVIDIA GPU | AMD GPU | Ascend NPU | Intel GPU | Recipe |
-|--------------|--------|-------------------|------------|---------|-----|-----------|--------|
-| `Qwen3OmniMoeForConditionalGeneration` | Qwen3-Omni | `Qwen/Qwen3-Omni-30B-A3B-Instruct` | ✅︎ | | | | [Repository](https://github.com/vllm-project/vllm-omni/blob/main/recipes/Qwen/Qwen3-Omni.md) |
-| `Qwen2_5OmniForConditionalGeneration` | Qwen2.5-Omni | `Qwen/Qwen2.5-Omni-7B`, `Qwen/Qwen2.5-Omni-3B` | ✅︎ | ✅︎ | ✅︎ | ✅︎ | — |
-| `MingFlashOmniForConditionalGeneration` + `MingImagePipeline` | Ming-flash-omni-2.0 (omni-speech + imagegen<sup>1</sup>) | `Jonathan1909/Ming-flash-omni-2.0` | ✅︎ |   |   |   | — |
-| `BagelForConditionalGeneration` | BAGEL (DiT-only) | `ByteDance-Seed/BAGEL-7B-MoT` | ✅︎ | ✅︎ | | ✅︎ | — |
-| `InternVLAA1Pipeline` | InternVLA-A1 | `InternRobotics/InternVLA-A1-3B` | ✅︎ | ✅︎ | | | — |
-| `Gr00tN1d7Pipeline` | GR00T N1.7 | `nvidia/GR00T-N1.7-3B` | ✅︎ | | | | — |
-| `HunyuanImage3ForCausalMM` | HunyuanImage3.0 (DiT-only) | `tencent/HunyuanImage-3.0`, `tencent/HunyuanImage-3.0-Instruct` | ✅︎ | ✅︎ | ✅︎ | ✅︎ | — |
-| `QwenImagePipeline` | Qwen-Image | `Qwen/Qwen-Image` | ✅︎ | | | | [Published](https://recipes.vllm.ai/Qwen/Qwen-Image) |
-| `QwenImagePipeline` | Qwen-Image-2512 | `Qwen/Qwen-Image-2512` | ✅︎ | | | | [Repository](https://github.com/vllm-project/vllm-omni/blob/main/recipes/Qwen/Qwen-Image-2512.md) |
-| `QwenImageEditPipeline` | Qwen-Image-Edit | `Qwen/Qwen-Image-Edit` | ✅︎ | ✅︎ | ✅︎ | ✅︎ | — |
-| `QwenImageEditPlusPipeline` | Qwen-Image-Edit-2509 | `Qwen/Qwen-Image-Edit-2509` | ✅︎ | ✅︎ | ✅︎ | ✅︎ | — |
-| `QwenImageLayeredPipeline` | Qwen-Image-Layered | `Qwen/Qwen-Image-Layered` | ✅︎ | ✅︎ | ✅︎ | ✅︎ | — |
-| `QwenImageEditPlusPipeline` | Qwen-Image-Edit-2511 | `Qwen/Qwen-Image-Edit-2511` | ✅︎ | ✅︎ | ✅︎ | ✅︎ | — |
-| `GlmImagePipeline` | GLM-Image | `zai-org/GLM-Image` | ✅︎ | ✅︎ | | | — |
-| `ZImagePipeline` | Z-Image | `Tongyi-MAI/Z-Image-Turbo` | ✅︎ | ✅︎ | ✅︎ | ✅︎ | — |
-| `Krea2Pipeline` | Krea 2 (Raw + Turbo) | `krea/Krea-2-Raw`, `krea/Krea-2-Turbo` | ✅︎ | | | | — |
-| `WanPipeline` | Wan2.1-T2V, Wan2.2-T2V, Wan2.2-TI2V | `Wan-AI/Wan2.1-T2V-1.3B-Diffusers`, `Wan-AI/Wan2.1-T2V-14B-Diffusers`, `Wan-AI/Wan2.2-T2V-A14B-Diffusers`, `Wan-AI/Wan2.2-TI2V-5B-Diffusers` | ✅︎ | ✅︎ | ✅︎ | ✅︎ | — |
-| `WanImageToVideoPipeline` | Wan2.2-I2V | `Wan-AI/Wan2.2-I2V-A14B-Diffusers` | ✅︎ | ✅︎ | ✅︎ | ✅︎ | — |
-| `Cosmos3OmniDiffusersPipeline` | Cosmos3 T2I, T2V, I2V, V2V, T2V with sound, action policy | `nvidia/Cosmos3-Nano`, `nvidia/Cosmos3-Super` | ✅︎ | ✅︎ | ✅︎ | ✅︎ | — |
-| `Wan22S2VPipeline` | Wan2.2-S2V | `Wan-AI/Wan2.2-S2V-14B` | ✅︎ | ✅︎ | ✅︎ | ✅︎ | — |
-| `Wan22VACEPipeline` | Wan2.1-VACE | `Wan-AI/Wan2.1-VACE-1.3B-diffusers`, `Wan-AI/Wan2.1-VACE-14B-diffusers` | ✅︎ | ✅︎ | ✅︎ | ✅︎ | — |
-| `Wan22VACEPipeline` | Wan2.2-VACE | `Pyros13/Wan2.2-VACE-Fun-A14B-Diffusers` | ✅︎ |   |   |   | — |
-| `LTX2Pipeline` | LTX-2 / LTX-2.3 one-stage T2V and I2V | `Lightricks/LTX-2`, `diffusers/LTX-2.3-Diffusers` | ✅︎ | ✅︎ | | | — |
-| `LTX2DistilledPipeline` | LTX-2 distilled two-stage T2V and I2V | `rootonchair/LTX-2-19b-distilled` | ✅︎ | ✅︎ | | | — |
-| `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` | ✅︎ | ✅︎ | ✅︎ | ✅︎ | — |
-| `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` | ✅︎ | ✅︎ | | ✅︎ | — |
-| `LongcatImagePipeline` | LongCat-Image | `meituan-longcat/LongCat-Image` | ✅︎ | ✅︎ | ✅︎ | ✅︎ | — |
-| `LongCatImageEditPipeline` | LongCat-Image-Edit | `meituan-longcat/LongCat-Image-Edit` | ✅︎ | ✅︎ | ✅︎ | ✅︎ | — |
-| `BooguImagePipeline` | Boogu-Image | `Boogu/Boogu-Image-0.1-Base`, `Boogu/Boogu-Image-0.1-Edit` | ✅︎ | | | | — |
-| `StableDiffusionXLPipeline` | Stable-Diffusion-XL | `stabilityai/stable-diffusion-xl-base-1.0` | ✅︎ | ✅︎ | ✅︎ | ✅︎ | — |
-| `StableDiffusion3Pipeline` | Stable-Diffusion-3 | `stabilityai/stable-diffusion-3.5-medium` | ✅︎ | ✅︎ | | ✅︎ | — |
-| `CosyVoice3Model` | CosyVoice3 | `FunAudioLLM/Fun-CosyVoice3-0.5B-2512` | ✅︎ | ✅︎ | | ✅︎ | — |
-| `OmniVoiceModel` | OmniVoice | `k2-fsa/OmniVoice` | ✅︎ | | | | — |
-| `VoxCPM2TalkerForConditionalGeneration` | VoxCPM2 | `openbmb/VoxCPM2` | ✅︎ | | | | — |
-| `MammothModa2ForConditionalGeneration` | MammothModa2-Preview | `bytedance-research/MammothModa2-Preview` | ✅︎ | ✅︎ | | | — |
-| `MammothModa2ForConditionalGeneration` | MammothModa2-Dev (AR-only image understanding) | `bytedance-research/MammothModa2-Dev` | ✅︎ | | | | — |
-| `Flux2KleinPipeline` | FLUX.2-klein | `black-forest-labs/FLUX.2-klein-4B`, `black-forest-labs/FLUX.2-klein-9B` | ✅︎ | ✅︎ | ✅︎ | ✅︎ | — |
-| `FluxKontextPipeline` | FLUX.1-Kontext-dev | `black-forest-labs/FLUX.1-Kontext-dev` | ✅︎ | ✅︎ | | | — |
-| `FluxPipeline` | FLUX.1-dev | `black-forest-labs/FLUX.1-dev` | ✅︎ | ✅︎ | | ✅︎ | — |
-| `FluxPipeline` | FLUX.1-schnell | `black-forest-labs/FLUX.1-schnell` | ✅︎ | ✅︎ | | ✅︎ | — |
-| `OmniGen2Pipeline` | OmniGen2 | `OmniGen2/OmniGen2` | ✅︎ | ✅︎ | | ✅︎ | — |
-| `StableAudioPipeline` | Stable-Audio-Open | `stabilityai/stable-audio-open-1.0` | ✅︎ | ✅︎ | | ✅︎ | — |
-| `SoulXSingerPipeline` | SoulX-Singer (SVS) | `Soul-AILab/SoulX-Singer` | ✅︎ | | | | — |
-| `SoulXSingerSVCPipeline` | SoulX-Singer-SVC | `Soul-AILab/SoulX-Singer` (`model-svc.pt`) | ✅︎ | | | | — |
-| `AudioXPipeline` | AudioX | `zhangj1an/AudioX` | ✅︎ | ✅︎ | | | — |
-| `Qwen3TTSForConditionalGeneration` | Qwen3-TTS-12Hz-1.7B-CustomVoice | `Qwen/Qwen3-TTS-12Hz-1.7B-CustomVoice` | ✅︎ | | | | [Published](https://recipes.vllm.ai/Qwen/Qwen3-TTS-12Hz-1.7B-CustomVoice) |
-| `Qwen3TTSForConditionalGeneration` | Qwen3-TTS-12Hz-1.7B-VoiceDesign | `Qwen/Qwen3-TTS-12Hz-1.7B-VoiceDesign` | ✅︎ | ✅︎ | ✅︎ | ✅︎ | — |
-| `Qwen3TTSForConditionalGeneration` | Qwen3-TTS-12Hz-1.7B-Base | `Qwen/Qwen3-TTS-12Hz-1.7B-Base` | ✅︎ | ✅︎ | ✅︎ | ✅︎ | — |
-| `MingTTSForConditionalGeneration` | Ming-omni-tts dense 0.5B | `inclusionAI/Ming-omni-tts-0.5B` | ✅︎ | | | | — |
-| `GLMTTSForConditionalGeneration` | GLM-TTS | `zai-org/GLM-TTS` | ✅︎ | | | | — |
-| `MossTTSNanoForCausalLM` | MOSS-TTS-Nano | `OpenMOSS-Team/MOSS-TTS-Nano` | ✅︎ | | | | — |
-| `MossTTSDelayModel` | MOSS-TTS, MOSS-TTSD, MOSS-SoundEffect, MOSS-VoiceGenerator | `OpenMOSS-Team/MOSS-VoiceGenerator` | ✅︎ | | | | — |
-| `MossTTSRealtime` | MOSS-TTS-Realtime | `OpenMOSS-Team/MOSS-TTS-Realtime` | ✅︎ | | | | — |
-| `HiggsAudioV2ForConditionalGeneration` | Higgs-Audio v2 | `bosonai/higgs-audio-v2-generation-3B-base` | ✅︎ | | | | — |
-| `HiggsMultimodalQwen3ForConditionalGeneration` | Higgs-Audio v3 (TTS) | `bosonai/higgs-audio-v3-tts-4b` | ✅︎ | | | | [Published](https://recipes.vllm.ai/bosonai/higgs-audio-v3-tts-4b) |
-| `IndexTTS2TalkerForConditionalGeneration` | IndexTTS-2 | `IndexTeam/IndexTTS-2` | ✅︎ | | | | — |
-| `NextStep11Pipeline` | NextStep-1.1 | `stepfun-ai/NextStep-1.1` | ✅︎ | ✅︎ | | ✅︎ | — |
-| `MiMoAudioModel` | MiMo-Audio-7B-Instruct | `XiaomiMiMo/MiMo-Audio-7B-Instruct` | ✅︎ | ✅︎ | | | — |
-| `MiMoV2ASRForCausalLM` | MiMo-V2.5-ASR | `XiaomiMiMo/MiMo-V2.5-ASR` | ✅︎ | ✅︎ | | | — |
-| `Flux2Pipeline` | FLUX.2-dev | `black-forest-labs/FLUX.2-dev` | ✅︎ | ✅︎ | | | — |
-| `FishSpeechSlowARForConditionalGeneration` | Fish Speech S2 Pro | `fishaudio/s2-pro` | ✅︎ | ✅︎ | | ✅︎ | — |
-| `DreamIDOmniPipeline` | DreamID-Omni | `XuGuo699/DreamID-Omni` | ✅︎ | ✅︎ | | | — |
-| `SenseNovaU1Pipeline` | SenseNova-U1 (DiT-only) | `SenseNova/SenseNova-U1-8B-MoT` | ✅︎ | | | | — |
-| `LancePipeline` | Lance | `bytedance-research/Lance` | ✅︎ | | | | — |
-| `HunyuanVideo15Pipeline` | HunyuanVideo-1.5-T2V | `hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-480p_t2v`, `hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-720p_t2v` | ✅︎ | ✅︎ | | | — |
-| `HunyuanVideo15ImageToVideoPipeline` | HunyuanVideo-1.5-I2V | `hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-480p_i2v`, `hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-720p_i2v` | ✅︎ | ✅︎ | | | — |
-| `VoxtralTTSForConditionalGeneration` | Voxtral TTS | `mistralai/Voxtral-4B-TTS-2603` | ✅︎ | ✅︎ | | | — |
-| `CovoAudioForConditionalGeneration` | Covo-Audio-Chat | `tencent/Covo-Audio-Chat` | ✅︎ | | | | — |
-|`DyninOmniForConditionalGeneration` | Dynin-Omni | `snu-aidas/Dynin-Omni` | ✅︎ | | | | — |
-| `MiniCPMO45OmniForConditionalGeneration` | MiniCPM-o 4.5 | `openbmb/MiniCPM-o-4_5` | ✅︎ | | ✅︎ | | — |
-| `ErnieImagePipeline` | ERNIE-Image | `baidu/ERNIE-Image`, `baidu/ERNIE-Image-Turbo` | ✅︎ | ✅︎ | ✅︎ | ✅︎ | — |
-|`HiDreamImagePipeline` | HiDream-I1-Full | `HiDream-ai/HiDream-I1-Full` | ✅︎ | ✅︎ | | | — |
-
-The `Recipe` column links the corresponding deployment recipe. `Published` is
-preferred when a corresponding `recipes.vllm.ai` page exists; otherwise the
-link points to the repository recipe. `—` means that no recipe link has been
-added for that model row yet.
-
-For rows with an audited recipe link, the hardware checkmarks reflect hardware
-explicitly documented in that recipe. Other rows retain the implementation
-support metadata until their recipe is audited.
+| Architecture | Models | Example HF Models | NVIDIA GPU | AMD GPU | Ascend NPU | Intel GPU |
+|--------------|--------|-------------------|------------|---------|-----|-----------|
+| `Qwen3OmniMoeForConditionalGeneration` | Qwen3-Omni | `Qwen/Qwen3-Omni-30B-A3B-Instruct` | ✅︎ | ✅︎ | ✅︎ | ✅︎ |
+| `Qwen2_5OmniForConditionalGeneration` | Qwen2.5-Omni | `Qwen/Qwen2.5-Omni-7B`, `Qwen/Qwen2.5-Omni-3B` | ✅︎ | ✅︎ | ✅︎ | ✅︎ |
+| `MingFlashOmniForConditionalGeneration` + `MingImagePipeline` | Ming-flash-omni-2.0 (omni-speech + imagegen<sup>1</sup>) | `Jonathan1909/Ming-flash-omni-2.0` | ✅︎ |   |   |   |
+| `BagelForConditionalGeneration` | BAGEL (DiT-only) | `ByteDance-Seed/BAGEL-7B-MoT` | ✅︎ | ✅︎ | | ✅︎ |
+| `InternVLAA1Pipeline` | InternVLA-A1 | `InternRobotics/InternVLA-A1-3B` | ✅︎ | ✅︎ | | |
+| `Gr00tN1d7Pipeline` | GR00T N1.7 | `nvidia/GR00T-N1.7-3B` | ✅︎ | | | |
+| `HunyuanImage3ForCausalMM` | HunyuanImage3.0 (DiT-only) | `tencent/HunyuanImage-3.0`, `tencent/HunyuanImage-3.0-Instruct` | ✅︎ | ✅︎ | ✅︎ | ✅︎ |
+| `QwenImagePipeline` | Qwen-Image | `Qwen/Qwen-Image` | ✅︎ | ✅︎ | ✅︎ | ✅︎ |
+| `QwenImagePipeline` | Qwen-Image-2512 | `Qwen/Qwen-Image-2512` | ✅︎ | ✅︎ | ✅︎ | ✅︎ |
+| `QwenImageEditPipeline` | Qwen-Image-Edit | `Qwen/Qwen-Image-Edit` | ✅︎ | ✅︎ | ✅︎ | ✅︎ |
+| `QwenImageEditPlusPipeline` | Qwen-Image-Edit-2509 | `Qwen/Qwen-Image-Edit-2509` | ✅︎ | ✅︎ | ✅︎ | ✅︎ |
+| `QwenImageLayeredPipeline` | Qwen-Image-Layered | `Qwen/Qwen-Image-Layered` | ✅︎ | ✅︎ | ✅︎ | ✅︎ |
+| `QwenImageEditPlusPipeline` | Qwen-Image-Edit-2511 | `Qwen/Qwen-Image-Edit-2511` | ✅︎ | ✅︎ | ✅︎ | ✅︎ |
+| `GlmImagePipeline` | GLM-Image | `zai-org/GLM-Image` | ✅︎ | ✅︎ | | |
+| `ZImagePipeline` | Z-Image | `Tongyi-MAI/Z-Image-Turbo` | ✅︎ | ✅︎ | ✅︎ | ✅︎ |
+| `Krea2Pipeline` | Krea 2 (Raw + Turbo) | `krea/Krea-2-Raw`, `krea/Krea-2-Turbo` | ✅︎ | | | |
+| `WanPipeline` | Wan2.1-T2V, Wan2.2-T2V, Wan2.2-TI2V | `Wan-AI/Wan2.1-T2V-1.3B-Diffusers`, `Wan-AI/Wan2.1-T2V-14B-Diffusers`, `Wan-AI/Wan2.2-T2V-A14B-Diffusers`, `Wan-AI/Wan2.2-TI2V-5B-Diffusers` | ✅︎ | ✅︎ | ✅︎ | ✅︎ |
+| `WanImageToVideoPipeline` | Wan2.2-I2V | `Wan-AI/Wan2.2-I2V-A14B-Diffusers` | ✅︎ | ✅︎ | ✅︎ | ✅︎ |
+| `Cosmos3OmniDiffusersPipeline` | Cosmos3 T2I, T2V, I2V, V2V, T2V with sound, action policy | `nvidia/Cosmos3-Nano`, `nvidia/Cosmos3-Super` | ✅︎ | ✅︎ | ✅︎ | ✅︎ |
+| `Wan22S2VPipeline` | Wan2.2-S2V | `Wan-AI/Wan2.2-S2V-14B` | ✅︎ | ✅︎ | ✅︎ | ✅︎ |
+| `Wan22VACEPipeline` | Wan2.1-VACE | `Wan-AI/Wan2.1-VACE-1.3B-diffusers`, `Wan-AI/Wan2.1-VACE-14B-diffusers` | ✅︎ | ✅︎ | ✅︎ | ✅︎ |
+| `Wan22VACEPipeline` | Wan2.2-VACE | `Pyros13/Wan2.2-VACE-Fun-A14B-Diffusers` | ✅︎ |   |   |   |
+| `LTX2Pipeline` | LTX-2 / LTX-2.3 one-stage T2V and I2V | `Lightricks/LTX-2`, `diffusers/LTX-2.3-Diffusers` | ✅︎ | ✅︎ | | |
+| `LTX2DistilledPipeline` | LTX-2 distilled two-stage T2V and I2V | `rootonchair/LTX-2-19b-distilled` | ✅︎ | ✅︎ | | |
+| `LingBotVideoPipeline` | LingBot-Video dense and MoE T2V | `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` | ✅︎ | | | |
+| `DreamZeroPipeline` | DreamZero-DROID | `GEAR-Dreams/DreamZero-DROID` | ✅︎ | ✅︎ | ✅︎ | ✅︎ |
+| `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` | ✅︎ | ✅︎ | | ✅︎ |
+| `LongcatImagePipeline` | LongCat-Image | `meituan-longcat/LongCat-Image` | ✅︎ | ✅︎ | ✅︎ | ✅︎ |
+| `LongCatImageEditPipeline` | LongCat-Image-Edit | `meituan-longcat/LongCat-Image-Edit` | ✅︎ | ✅︎ | ✅︎ | ✅︎ |
+| `BooguImagePipeline` | Boogu-Image | `Boogu/Boogu-Image-0.1-Base`, `Boogu/Boogu-Image-0.1-Edit` | ✅︎ | | | |
+| `StableDiffusionXLPipeline` | Stable-Diffusion-XL | `stabilityai/stable-diffusion-xl-base-1.0` | ✅︎ | ✅︎ | ✅︎ | ✅︎ |
+| `StableDiffusion3Pipeline` | Stable-Diffusion-3 | `stabilityai/stable-diffusion-3.5-medium` | ✅︎ | ✅︎ | | ✅︎ |
+| `CosyVoice3Model` | CosyVoice3 | `FunAudioLLM/Fun-CosyVoice3-0.5B-2512` | ✅︎ | ✅︎ | | ✅︎ |
+| `OmniVoiceModel` | OmniVoice | `k2-fsa/OmniVoice` | ✅︎ | | | |
+| `VoxCPM2TalkerForConditionalGeneration` | VoxCPM2 | `openbmb/VoxCPM2` | ✅︎ | | | |
+| `MammothModa2ForConditionalGeneration` | MammothModa2-Preview | `bytedance-research/MammothModa2-Preview` | ✅︎ | ✅︎ | | |
+| `MammothModa2ForConditionalGeneration` | MammothModa2-Dev (AR-only image understanding) | `bytedance-research/MammothModa2-Dev` | ✅︎ | | | |
+| `Flux2KleinPipeline` | FLUX.2-klein | `black-forest-labs/FLUX.2-klein-4B`, `black-forest-labs/FLUX.2-klein-9B` | ✅︎ | ✅︎ | ✅︎ | ✅︎ |
+| `FluxKontextPipeline` | FLUX.1-Kontext-dev | `black-forest-labs/FLUX.1-Kontext-dev` | ✅︎ | ✅︎ | | |
+| `FluxPipeline` | FLUX.1-dev | `black-forest-labs/FLUX.1-dev` | ✅︎ | ✅︎ | | ✅︎ |
+| `FluxPipeline` | FLUX.1-schnell | `black-forest-labs/FLUX.1-schnell` | ✅︎ | ✅︎ | | ✅︎ |
+| `OmniGen2Pipeline` | OmniGen2 | `OmniGen2/OmniGen2` | ✅︎ | ✅︎ | | ✅︎ |
+| `StableAudioPipeline` | Stable-Audio-Open | `stabilityai/stable-audio-open-1.0` | ✅︎ | ✅︎ | | ✅︎ |
+| `SoulXSingerPipeline` | SoulX-Singer (SVS) | `Soul-AILab/SoulX-Singer` | ✅︎ | | | |
+| `SoulXSingerSVCPipeline` | SoulX-Singer-SVC | `Soul-AILab/SoulX-Singer` (`model-svc.pt`) | ✅︎ | | | |
+| `AudioXPipeline` | AudioX | `zhangj1an/AudioX` | ✅︎ | ✅︎ | | |
+| `Qwen3TTSForConditionalGeneration` | Qwen3-TTS-12Hz-1.7B-CustomVoice | `Qwen/Qwen3-TTS-12Hz-1.7B-CustomVoice` | ✅︎ | ✅︎ | ✅︎ | ✅︎ |
+| `Qwen3TTSForConditionalGeneration` | Qwen3-TTS-12Hz-1.7B-VoiceDesign | `Qwen/Qwen3-TTS-12Hz-1.7B-VoiceDesign` | ✅︎ | ✅︎ | ✅︎ | ✅︎ |
+| `Qwen3TTSForConditionalGeneration` | Qwen3-TTS-12Hz-1.7B-Base | `Qwen/Qwen3-TTS-12Hz-1.7B-Base` | ✅︎ | ✅︎ | ✅︎ | ✅︎ |
+| `MingTTSForConditionalGeneration` | Ming-omni-tts dense 0.5B | `inclusionAI/Ming-omni-tts-0.5B` | ✅︎ | | | |
+| `GLMTTSForConditionalGeneration` | GLM-TTS | `zai-org/GLM-TTS` | ✅︎ | | | |
+| `MossTTSNanoForCausalLM` | MOSS-TTS-Nano | `OpenMOSS-Team/MOSS-TTS-Nano` | ✅︎ | | | |
+| `MossTTSDelayModel` | MOSS-TTS, MOSS-TTSD, MOSS-SoundEffect, MOSS-VoiceGenerator | `OpenMOSS-Team/MOSS-VoiceGenerator` | ✅︎ | | | |
+| `MossTTSRealtime` | MOSS-TTS-Realtime | `OpenMOSS-Team/MOSS-TTS-Realtime` | ✅︎ | | | |
+| `HiggsAudioV2ForConditionalGeneration` | Higgs-Audio v2 | `bosonai/higgs-audio-v2-generation-3B-base` | ✅︎ | | | |
+| `HiggsMultimodalQwen3ForConditionalGeneration` | Higgs-Audio v3 (TTS) | `bosonai/higgs-audio-v3-tts-4b` | ✅︎ | | | |
+| `IndexTTS2TalkerForConditionalGeneration` | IndexTTS-2 | `IndexTeam/IndexTTS-2` | ✅︎ | | | |
+| `NextStep11Pipeline` | NextStep-1.1 | `stepfun-ai/NextStep-1.1` | ✅︎ | ✅︎ | | ✅︎ |
+| `MiMoAudioModel` | MiMo-Audio-7B-Instruct | `XiaomiMiMo/MiMo-Audio-7B-Instruct` | ✅︎ | ✅︎ | | |
+| `MiMoV2ASRForCausalLM` | MiMo-V2.5-ASR | `XiaomiMiMo/MiMo-V2.5-ASR` | ✅︎ | ✅︎ | | |
+| `Flux2Pipeline` | FLUX.2-dev | `black-forest-labs/FLUX.2-dev` | ✅︎ | ✅︎ | | |
+| `FishSpeechSlowARForConditionalGeneration` | Fish Speech S2 Pro | `fishaudio/s2-pro` | ✅︎ | ✅︎ | | ✅︎ |
+| `DreamIDOmniPipeline` | DreamID-Omni | `XuGuo699/DreamID-Omni` | ✅︎ | ✅︎ | | |
+| `SenseNovaU1Pipeline` | SenseNova-U1 (DiT-only) | `SenseNova/SenseNova-U1-8B-MoT` | ✅︎ | | | |
+| `LancePipeline` | Lance | `bytedance-research/Lance` | ✅︎ | | | |
+| `HunyuanVideo15Pipeline` | HunyuanVideo-1.5-T2V | `hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-480p_t2v`, `hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-720p_t2v` | ✅︎ | ✅︎ | | |
+| `HunyuanVideo15ImageToVideoPipeline` | HunyuanVideo-1.5-I2V | `hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-480p_i2v`, `hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-720p_i2v` | ✅︎ | ✅︎ | | |
+| `VoxtralTTSForConditionalGeneration` | Voxtral TTS | `mistralai/Voxtral-4B-TTS-2603` | ✅︎ | ✅︎ | | |
+| `CovoAudioForConditionalGeneration` | Covo-Audio-Chat | `tencent/Covo-Audio-Chat` | ✅︎ | | | |
+|`DyninOmniForConditionalGeneration` | Dynin-Omni | `snu-aidas/Dynin-Omni` | ✅︎ | | | |
+| `MiniCPMO45OmniForConditionalGeneration` | MiniCPM-o 4.5 | `openbmb/MiniCPM-o-4_5` | ✅︎ | | ✅︎ | |
+| `ErnieImagePipeline` | ERNIE-Image | `baidu/ERNIE-Image`, `baidu/ERNIE-Image-Turbo` | ✅︎ | ✅︎ | ✅︎ | ✅︎ |
+|`HiDreamImagePipeline` | HiDream-I1-Full | `HiDream-ai/HiDream-I1-Full` | ✅︎ | ✅︎ | | |
 
 ✅︎ indicates the model is supported on that backend. Empty cells mean not listed as supported on that backend.
-
-<sup>H3</sup> MiniMax H3 AMD GPU support is validated on gfx942 (MI300X) and
-gfx950 (MI350) in BF16, using the AITER `FLASH_ATTN` diffusion attention backend.
-Other AMD SKUs (e.g. MI325X) are not listed until their
-own validation evidence is added. See the
-[MiniMax H3 recipe](https://recipes.vllm.ai/MiniMaxAI/MiniMax-H3) for
-per-architecture serving commands.

docs/user_guide/diffusion/attention_backends.md

@@ -283,20 +283,23 @@ blocks — use Ulysses SP (`ring_degree=1`).
 
 ### Which geometries run sparse
 
-MindIE-SD rf_v2 handles arbitrary video grids by spatially rearranging video first and, when
-necessary, promoting a real-video suffix to its always-kept prefix segment. It never pads: rf_v2
-does not consume an attention mask for padded keys. The remaining video rows stay sparse, while the
-promoted rows remain visible to every query and key.
+Sparsity is only applied when the video segment is a **multiple of 128 rows**, where the row count
+is `latent_t × (height / 32) × (width / 32)`. Otherwise `rf_v2`'s block mask and the kernel's own
+tiling disagree on the block count, and the block straddling the seam mixes video and prefix rows
+that selection may then drop. Any resolution still runs — an unaligned geometry falls back to dense
+attention and logs `RAINFUSION_ATTN staying dense` with the row count it computed — but it gets no
+speedup, so pick an aligned geometry when you want one.
 
-For example, MiniMax-H3 at 1344x768 has grid `(62, 24, 42)`: its 62496 video rows leave a 32-row
-128-block residue, and its two non-8-wide latent columns form a 2928-row rearranged suffix.
-MindIE-SD promotes 2976 rows, leaving 59520 sparse rows (`465 × 128`). This keeps the block mask
-and kernel tiling aligned, at the cost of retaining more blocks.
+Alignment is necessary, not sufficient, for good quality. `rf_v2` groups video positions into 8x8
+spatial tiles (two tiles fill one 128-row block), which is what makes a selected block a compact
+patch of the frame. When the latent `h` or `w` is not a multiple of 8, the leftover rows or columns
+are peeled off and appended as a flat run instead of being tiled, so they get pooled with spatially
+distant positions and selection can no longer rank them meaningfully. This is silent, and invisible
+to a `sparsity=0` check, because a fully populated mask does not care how blocks are grouped.
 
-8x8-aligned spatial grids remain preferable for performance. A latent `h` or `w` not divisible by 8
-creates an always-kept residual suffix and lowers realized sparsity. "Multiple of 8" on the latent
-grid means **width and height that are multiples of 256**. This path requires a MindIE-SD release
-that implements protected video tails for rf_v2.
+"Multiple of 8" on the latent grid means **width and height that are multiples of 256**. Aligned
+resolutions off that grid still run sparse; they just lose more fidelity at the same `sparsity`,
+which you can buy back with `start_step`.
 
 ## End-to-End Benchmark (BF16, sm_120 RTX Pro 6000 Blackwell)
 

docs/user_guide/diffusion_features.md

@@ -159,7 +159,7 @@ The following tables show which models support each feature:
 | **HunyuanVideo-1.5 T2V I2V** |     ❌     |     ✅      |           ✅           |       ✅        |         ✅         |         ❌         |   ✅    |             ✅             |  ✅ (encode/decode)   |       ✅        |        ❌         |
 | **DreamID-Omni**             |     ❌     |     ❌      |           ❌           |       ✅        |         ❌         |         ❌         |   ✅    |             ✅             |          ❌           |       ❌        |        ❌         |
 | **Cosmos3**                  |     ❌     |     ✅      |           ✅           |       ✅        |         ✅         |         ❌         |   ✅    |             ✅             |  ✅ (encode/decode)   |       ✅        |        ❌         |
-| **MiniMax-H3**               | ✅ (FL2VA) |     ✅      |           ✅           |       ❌        |       ✅ (DiT/TE)  |         ❌         |   ✅    |             ✅             |       ✅ (tile)       |      ✅ (DiT)      |        ❌         |
+| **MiniMax-H3**               |     ❌     |     ✅      |           ✅           |       ❌        |       ✅ (DiT/TE)  |         ❌         |   ✅    |             ✅             |       ✅ (tile)       |      ✅ (DiT)      |        ❌         |
 
 **Frame Interpolation Support**
 

docs/user_guide/examples/online_serving/diffusers_pipeline_adapter.md

@@ -99,7 +99,7 @@ a native vLLM-Omni pipeline for those cases.
 ### Attention Backends
 
 The diffusers backend converts
-[vLLM-Omni standard of attention backend setting](../../diffusion/attention_backends.md)
+[vLLM-Omni standard of attention backend setting](../../../docs/user_guide/diffusion/attention_backends.md)
 to [diffusers standard](https://huggingface.co/docs/diffusers/optimization/attention_backends#available-backends).
 
 Specifically for `FLASH_ATTN`, it will first attempt to use FlashAttention-3 and then FlashAttention-2.

examples/offline_inference/image_to_video/README.md

@@ -33,25 +33,6 @@ This folder provides a unified CLI script for image-to-video generation using vL
 
 Default model: `Wan-AI/Wan2.2-I2V-A14B-Diffusers`.
 
-### LingBot-Video TI2V
-
-LingBot-Video accepts exactly one first-frame image and uses the same dense or
-MoE checkpoint as its T2I and T2V modes:
-
-```bash
-python examples/offline_inference/image_to_video/image_to_video.py \
-  --model robbyant/lingbot-video-dense-1.3b \
-  --model-class-name LingBotVideoPipeline \
-  --image /path/to/input.png \
-  --prompt "the fox looks toward the camera" \
-  --height 192 --width 320 --num-frames 9 --num-inference-steps 2 \
-  --guidance-scale 3.0 --flow-shift 3.0 --fps 24 \
-  --output lingbot_ti2v.mp4
-```
-
-The input image keeps its original geometry until the LingBot pipeline applies
-its model-specific resize and center crop.
-
 ## Prerequisites
 
 Download the example image used in the snippets below:

examples/offline_inference/text_to_image/README.md

@@ -43,23 +43,6 @@ This folder provides several entrypoints for experimenting with text-to-image di
 
 Default model: `Qwen/Qwen-Image`
 
-### LingBot-Video
-
-LingBot-Video uses the same checkpoint for image and video generation. Run the
-shared T2I entry point with:
-
-```bash
-python examples/offline_inference/text_to_image/text_to_image.py \
-  --model robbyant/lingbot-video-dense-1.3b \
-  --prompt "a red fox standing in fresh snow" \
-  --height 192 --width 320 --num-inference-steps 2 \
-  --guidance-scale 3.0 --extra-body '{"flow_shift":3.0}' \
-  --output lingbot_t2i.png
-```
-
-The LingBot prompt builder selects the `image` output modality and requests one
-frame. Passing an input image is not supported in LingBot T2I mode.
-
 ## Quick Start
 
 ### Python API

examples/offline_inference/text_to_video/text_to_video.md

@@ -55,21 +55,6 @@ python text_to_video.py \
   --output vace_t2v_output.mp4
 ```
 
-### LingBot-Video
-
-The dedicated LingBot example explicitly selects the video output modality:
-
-```bash
-python examples/offline_inference/text_to_video/text_to_video_lingbot.py \
-  --model robbyant/lingbot-video-dense-1.3b \
-  --height 192 --width 320 --num-frames 9 --num-inference-steps 2 \
-  --output lingbot_t2v.mp4
-```
-
-Use the same command with `robbyant/lingbot-video-moe-30b-a3b` for the MoE
-checkpoint. Requested frame counts are rounded upward to the causal
-VAE's `4n+1` grid.
-
 ### LTX-2
 
 ```bash

examples/offline_inference/text_to_video/text_to_video_lingbot.py

@@ -20,9 +20,9 @@ from vllm_omni.platforms import current_omni_platform
 
 def parse_args() -> argparse.Namespace:
     parser = argparse.ArgumentParser(description="Generate text-to-video with a LingBot-Video checkpoint.")
-    parser.add_argument("--model", default="robbyant/lingbot-video-dense-1.3b")
+    parser.add_argument("--model", default="/home/models/lingbot-video-dense-1.3b")
     parser.add_argument("--prompt", default="a robotic arm picks up a red block")
-    parser.add_argument("--negative-prompt", default=None)
+    parser.add_argument("--negative-prompt", default="")
     parser.add_argument("--output", default="lingbot_video_output.mp4")
     parser.add_argument("--height", type=int, default=192)
     parser.add_argument("--width", type=int, default=320)
@@ -58,12 +58,9 @@ def _to_frame_array(video: Any) -> np.ndarray:
     if isinstance(video, torch.Tensor):
         tensor = video.detach().cpu()
         if tensor.dim() == 5:
-            tensor = tensor[0]
-        if tensor.dim() == 4 and tensor.shape[-1] not in (3, 4):
-            if tensor.shape[0] in (3, 4):
-                tensor = tensor.permute(1, 2, 3, 0)
-            elif tensor.shape[1] in (3, 4):
-                tensor = tensor.permute(0, 2, 3, 1)
+            tensor = tensor[0] if tensor.shape[1] not in (3, 4) else tensor[0].permute(1, 2, 3, 0)
+        elif tensor.dim() == 4 and tensor.shape[0] in (3, 4):
+            tensor = tensor.permute(1, 2, 3, 0)
         if tensor.is_floating_point():
             if float(tensor.min()) < 0.0:
                 tensor = tensor.clamp(-1, 1) * 0.5 + 0.5
@@ -90,12 +87,8 @@ def main() -> None:
         parallel_config=DiffusionParallelConfig(),
     )
 
-    prompt: dict[str, Any] = {
-        "prompt": args.prompt,
-        "modalities": ["video"],
-        "num_frames": args.num_frames,
-    }
-    if args.negative_prompt is not None:
+    prompt: dict[str, str] = {"prompt": args.prompt}
+    if args.negative_prompt:
         prompt["negative_prompt"] = args.negative_prompt
 
     sampling_params = OmniDiffusionSamplingParams(

examples/online_serving/lingbot_video/README.md

@@ -1,74 +0,0 @@
-# LingBot-Video
-
-LingBot-Video uses one `LingBotVideoPipeline` for text-to-image (T2I),
-text-to-video (T2V), and text-image-to-video (TI2V) generation. Both the dense
-and MoE checkpoints use the same request format.
-
-## Start the server
-
-```bash
-MODEL=robbyant/lingbot-video-dense-1.3b bash run_server.sh
-```
-
-The MoE checkpoint uses the same server and request scripts, but requires
-substantially more GPU memory:
-
-```bash
-MODEL=robbyant/lingbot-video-moe-30b-a3b bash run_server.sh
-```
-
-## Text to image
-
-The image endpoint selects T2I mode and always generates one frame:
-
-```bash
-bash run_curl_text_to_image.sh
-```
-
-The script sends a `320x192`, two-step smoke request and writes
-`lingbot_t2i.png`.
-
-## Text or text-image to video
-
-Run the video script without an image to select T2V mode:
-
-```bash
-bash run_curl_text_image_to_video.sh
-```
-
-Pass a first-frame image to the same script to select TI2V mode:
-
-```bash
-INPUT_IMAGE=/path/to/input.png bash run_curl_text_image_to_video.sh
-```
-
-The client scripts omit the optional `model` request field, so they target
-whichever dense or MoE checkpoint the server loaded. The video example uses the
-lightweight `320x192`, 9-frame, two-step configuration.
-
-Until the shared `/v1/videos` reference-image resizing is removed, TI2V target
-dimensions must be sent through `extra_params`, for example
-`{"size":"320x192"}`. Do not use the top-level `size`, `width`, or `height`
-fields for TI2V because the serving layer currently applies those dimensions
-to the reference image before the model receives it. T2V requests can continue
-to use the top-level dimension fields.
-
-LingBot video frame counts use the causal VAE `4n+1` grid. The pipeline rounds
-any requested frame count upward to the next valid value. An explicit
-`num_frames` takes precedence over `seconds`; otherwise, the server first
-resolves `seconds * fps` and the pipeline applies the same alignment.
-
-Official `resolution`/`ratio` presets can be sent through `extra_params`, for
-example `{"resolution":"720p","ratio":"16:9"}`. The `2k` and `4k` entries
-only define output dimensions; whether they run successfully depends on the
-checkpoint, GPU memory, and memory optimizations available in the deployment.
-
-For `/v1/images/generations`, the server resolves these aliases to their final
-pixel dimensions before applying `--max-generated-image-size`. Requests above
-the configured limit return HTTP 400 before engine dispatch. LingBot produces
-one output per prompt; image requests with `n>1` are also rejected with HTTP
-400.
-
-LingBot TI2V accepts exactly one image reference. Image editing, video
-references, audio references, batching, and Refiner execution are not supported
-by this pipeline mode.

examples/online_serving/lingbot_video/run_curl_text_image_to_video.sh

@@ -1,67 +0,0 @@
-#!/bin/bash
-
-set -euo pipefail
-
-if [ -n "${INPUT_IMAGE:-}" ] && [ ! -f "${INPUT_IMAGE}" ]; then
-    echo "INPUT_IMAGE does not exist: ${INPUT_IMAGE}"
-    exit 1
-fi
-
-BASE_URL="${BASE_URL:-http://localhost:8099}"
-POLL_INTERVAL="${POLL_INTERVAL:-2}"
-
-if [ -n "${INPUT_IMAGE:-}" ]; then
-    default_output="lingbot_ti2v.mp4"
-else
-    default_output="lingbot_t2v.mp4"
-fi
-OUTPUT_PATH="${OUTPUT_PATH:-${default_output}}"
-
-curl_args=(
-    -sS -X POST "${BASE_URL}/v1/videos"
-    -H "Accept: application/json"
-    -F "prompt=the subject turns toward the camera with smooth natural motion"
-    -F "num_frames=9"
-    -F "fps=24"
-    -F "num_inference_steps=2"
-    -F "guidance_scale=3.0"
-    -F "flow_shift=3.0"
-    -F "seed=42"
-)
-if [ -n "${INPUT_IMAGE:-}" ]; then
-    curl_args+=(
-        -F "input_reference=@${INPUT_IMAGE}"
-        -F 'extra_params={"size":"320x192"}'
-    )
-else
-    curl_args+=(-F "width=320" -F "height=192")
-fi
-
-create_response="$(curl "${curl_args[@]}")"
-
-video_id="$(echo "${create_response}" | jq -er '.id')"
-echo "Created video job ${video_id}"
-
-while true; do
-    status_response="$(curl -sS "${BASE_URL}/v1/videos/${video_id}")"
-    status="$(echo "${status_response}" | jq -er '.status')"
-    case "${status}" in
-        queued|in_progress)
-            sleep "${POLL_INTERVAL}"
-            ;;
-        completed)
-            break
-            ;;
-        failed)
-            echo "${status_response}" | jq .
-            exit 1
-            ;;
-        *)
-            echo "Unexpected video job status: ${status}"
-            exit 1
-            ;;
-    esac
-done
-
-curl -sS -L "${BASE_URL}/v1/videos/${video_id}/content" -o "${OUTPUT_PATH}"
-echo "Saved video to ${OUTPUT_PATH}"

examples/online_serving/lingbot_video/run_curl_text_to_image.sh

@@ -1,20 +0,0 @@
-#!/bin/bash
-
-set -euo pipefail
-
-BASE_URL="${BASE_URL:-http://localhost:8099}"
-OUTPUT_PATH="${OUTPUT_PATH:-lingbot_t2i.png}"
-
-curl -sS -X POST "${BASE_URL}/v1/images/generations" \
-    -H "Content-Type: application/json" \
-    -d "{
-        \"prompt\": \"a red fox standing in fresh snow\",
-        \"size\": \"320x192\",
-        \"num_inference_steps\": 2,
-        \"guidance_scale\": 3.0,
-        \"flow_shift\": 3.0,
-        \"seed\": 42,
-        \"response_format\": \"b64_json\"
-    }" | jq -er '.data[0].b64_json' | base64 -d > "${OUTPUT_PATH}"
-
-echo "Saved image to ${OUTPUT_PATH}"

examples/online_serving/lingbot_video/run_server.sh

@@ -1,10 +0,0 @@
-#!/bin/bash
-
-set -euo pipefail
-
-MODEL="${MODEL:-robbyant/lingbot-video-dense-1.3b}"
-PORT="${PORT:-8099}"
-
-vllm serve "${MODEL}" --omni \
-    --model-class-name LingBotVideoPipeline \
-    --port "${PORT}"

examples/quantization/check_modelopt_fp8_export.py

@@ -0,0 +1,247 @@
+#!/usr/bin/env python3
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""Verify a ModelOpt FP8 diffusers checkpoint exported by
+quantize_wan2_2_modelopt_fp8.py / quantize_hunyuanvideo_15_modelopt_fp8.py.
+
+Three checks:
+  A. transformer/config.json has a sane quantization_config block.
+  B. transformer/*.safetensors contains FP8 (float8_e4m3fn) quantized tensors.
+  C. transformer disk size is materially smaller than a BF16 baseline.
+
+Example:
+    python examples/quantization/check_modelopt_fp8_export.py \\
+        --output ./hv15-480p-modelopt-fp8
+
+    # Optional: compare disk size against a local or HF BF16 baseline.
+    python examples/quantization/check_modelopt_fp8_export.py \\
+        --output ./hv15-480p-modelopt-fp8 \\
+        --baseline hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-480p_t2v
+"""
+
+from __future__ import annotations
+
+import argparse
+import json
+import sys
+from collections import Counter
+from pathlib import Path
+
+SUPPORTED_ALGOS = ("FP8",)
+
+
+def _check_config(transformer_dir: Path) -> tuple[int, str | None]:
+    """Returns (status, quant_algo). status: 0 pass, 1 fail, 2 warn."""
+    cfg_path = transformer_dir / "config.json"
+    if not cfg_path.exists():
+        print(f"[FAIL] {cfg_path} missing.")
+        return 1, None
+
+    with cfg_path.open(encoding="utf-8") as f:
+        cfg = json.load(f)
+
+    qc = cfg.get("quantization_config")
+    if not isinstance(qc, dict):
+        print(f"[FAIL] No `quantization_config` block in {cfg_path}.")
+        return 1, None
+
+    print(f"[A] quantization_config from {cfg_path}:")
+    print(json.dumps(qc, indent=2))
+
+    quant_algo = qc.get("quant_algo")
+    issues = []
+    if qc.get("quant_method") != "modelopt":
+        issues.append(f"quant_method={qc.get('quant_method')!r} (expected 'modelopt')")
+    if quant_algo not in SUPPORTED_ALGOS:
+        issues.append(
+            f"quant_algo={quant_algo!r} (expected one of {SUPPORTED_ALGOS} — vllm-omni adapter may not auto-detect)"
+        )
+
+    if issues:
+        print("[A] WARN — config looks incomplete:")
+        for issue in issues:
+            print(f"    - {issue}")
+        return 2, quant_algo
+    print(f"[A] PASS — config looks correct (quant_algo={quant_algo}).")
+    return 0, quant_algo
+
+
+def _read_safetensors_header(path: Path) -> dict:
+    """Read the JSON header of a safetensors file. Bypass-safe — doesn't materialize tensors.
+
+    Returns {tensor_name: {'dtype': 'F8_E4M3', 'shape': [...], 'data_offsets': [...]}}.
+    Header dtype strings: F8_E4M3, F8_E5M2, BF16, F16, F32, F64, I8, I16, I32, I64, BOOL, U8, ...
+    """
+    import struct
+
+    with open(path, "rb") as f:
+        header_len = struct.unpack("<Q", f.read(8))[0]
+        header = json.loads(f.read(header_len))
+    header.pop("__metadata__", None)
+    return header
+
+
+def _classify_weight_scale_granularity(weight_scale_shapes: list[list[int]]) -> str:
+    """Infer per-tensor vs per-channel vs per-block from sample weight_scale shapes.
+
+    ModelOpt block-wise produces shapes like `[16, 1, 16, 1]` (broadcasting dims of 1
+    interleaved with block-count dims). We count "meaningful" dims — ones with size > 1 —
+    and classify: 0 meaningful dims = per-tensor (scalar), 1 = per-channel, 2+ = per-block.
+    """
+    if not weight_scale_shapes:
+        return "no weight_scale tensors found"
+
+    def meaningful_dims(shape: list[int]) -> int:
+        return sum(1 for d in shape if d > 1)
+
+    per_tensor = sum(1 for s in weight_scale_shapes if meaningful_dims(s) == 0)
+    per_channel = sum(1 for s in weight_scale_shapes if meaningful_dims(s) == 1)
+    per_block = sum(1 for s in weight_scale_shapes if meaningful_dims(s) >= 2)
+    total = len(weight_scale_shapes)
+    if per_tensor == total:
+        return "per-tensor (all scalar scales)"
+    if per_channel == total:
+        return "per-channel (1 meaningful dim)"
+    if per_block == total:
+        return "per-block (2+ meaningful dims — e.g. [M//bm, 1, N//bn, 1] for tiles)"
+    return f"mixed: per-tensor={per_tensor}, per-channel={per_channel}, per-block={per_block} of {total}"
+
+
+def _check_safetensors(transformer_dir: Path) -> int:
+    """Returns 0 on pass, 1 on fail. Reads on-disk dtype from the safetensors header."""
+    files = sorted(transformer_dir.glob("*.safetensors"))
+    if not files:
+        print(f"[FAIL] No *.safetensors in {transformer_dir}.")
+        return 1
+
+    header_dtype_counts: Counter[str] = Counter()
+    sample_quant_weight_keys: list[str] = []
+    sample_scale_keys: list[str] = []
+    weight_scale_shapes: list[list[int]] = []
+    sample_weight_scale_entries: list[tuple[str, list[int]]] = []
+
+    for f in files:
+        try:
+            header = _read_safetensors_header(f)
+        except Exception as exc:
+            print(f"[B] WARN — could not parse header of {f}: {exc}")
+            continue
+        for k, info in header.items():
+            dtype = info.get("dtype", "?")
+            header_dtype_counts[dtype] += 1
+            # FP8 stores weights as F8_E4M3 directly.
+            if dtype.startswith("F8") and k.endswith(".weight") and len(sample_quant_weight_keys) < 5:
+                sample_quant_weight_keys.append(k)
+            if k.endswith(("_scale", ".weight_scale", ".input_scale", "_scale_inv")) and len(sample_scale_keys) < 5:
+                sample_scale_keys.append(k)
+            if k.endswith(".weight_scale"):
+                weight_scale_shapes.append(info.get("shape", []))
+                if len(sample_weight_scale_entries) < 5:
+                    sample_weight_scale_entries.append((k, info.get("shape", [])))
+
+    print(f"\n[B] On-disk dtype counts across {len(files)} safetensors file(s) (from header, not get_tensor):")
+    for dtype, count in sorted(header_dtype_counts.items(), key=lambda kv: -kv[1]):
+        marker = "  <-- FP8" if dtype.startswith("F8") else ""
+        print(f"    {dtype:10s} {count:>6d}{marker}")
+
+    quant_count = sum(c for d, c in header_dtype_counts.items() if d.startswith("F8"))
+    if quant_count == 0:
+        print("[B] FAIL — no FP8 tensors on disk. Calibration likely did not actually quantize the weights.")
+        return 1
+
+    print(f"[B] PASS — {quant_count} FP8 tensors stored on disk.")
+    if sample_quant_weight_keys:
+        print(f"    sample quantized weight tensors: {sample_quant_weight_keys[:3]}")
+    if sample_scale_keys:
+        print(f"    sample scale tensors:            {sample_scale_keys[:3]}")
+    print("    (Note: torch's get_tensor() may return these as bf16 views on some versions —")
+    print("     irrelevant; vLLM's loader uses native FP8 ops.)")
+
+    # Weight-scale granularity — per-tensor (scalar) vs per-channel (1-D) vs per-block (N-D).
+    print(f"\n    weight_scale granularity: {_classify_weight_scale_granularity(weight_scale_shapes)}")
+    for key, shape in sample_weight_scale_entries[:3]:
+        print(f"      {key}: shape {shape}")
+    return 0
+
+
+def _disk_size_gib(p: Path) -> float:
+    return sum(f.stat().st_size for f in p.rglob("*") if f.is_file()) / (1024**3)
+
+
+def _check_size_vs_baseline(transformer_dir: Path, baseline: str | None) -> int:
+    """Returns 0 always (informational only)."""
+    quant_size = _disk_size_gib(transformer_dir)
+    print(f"\n[C] FP8 transformer disk size: {quant_size:.2f} GiB")
+
+    if baseline is None:
+        print("[C] SKIP — pass --baseline <path or HF id> to compare against BF16.")
+        return 0
+
+    baseline_path = Path(baseline)
+    if not baseline_path.exists():
+        # Try HF download.
+        try:
+            from huggingface_hub import snapshot_download
+        except ImportError:
+            print("[C] SKIP — huggingface_hub not installed and baseline not a local path.")
+            return 0
+        print(f"    Downloading baseline transformer from HF: {baseline}")
+        baseline_path = Path(snapshot_download(baseline, allow_patterns=["transformer/*"]))
+
+    bf16_dir = baseline_path / "transformer" if (baseline_path / "transformer").exists() else baseline_path
+    bf16_size = _disk_size_gib(bf16_dir)
+    if bf16_size == 0:
+        print(f"[C] WARN — baseline transformer dir empty: {bf16_dir}")
+        return 0
+
+    # Expected reduction for FP8: ~50%.
+    min_reduction = 30
+    reduction = (1 - quant_size / bf16_size) * 100
+    print(f"[C] BF16 baseline transformer disk size: {bf16_size:.2f} GiB ({bf16_dir})")
+    print(f"[C] Disk reduction: {reduction:.1f}%  (FP8 transformer is {quant_size / bf16_size:.0%} of BF16)")
+    if reduction < min_reduction:
+        print(
+            f"[C] WARN — FP8 should typically reduce disk by ~40-50%; <{min_reduction}% suggests partial quantization."
+        )
+    return 0
+
+
+def main() -> None:
+    p = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter)
+    p.add_argument("--output", required=True, help="Path to the exported ModelOpt FP8 checkpoint root.")
+    p.add_argument(
+        "--baseline",
+        default=None,
+        help="Optional BF16 baseline (local diffusers dir or HF id) for disk-size comparison.",
+    )
+    args = p.parse_args()
+
+    out_root = Path(args.output).expanduser().resolve()
+    transformer_dir = out_root / "transformer"
+    if not transformer_dir.exists():
+        print(f"[FAIL] {transformer_dir} does not exist.")
+        sys.exit(1)
+
+    print(f"Checking: {out_root}\n")
+
+    fail = 0
+    config_status, _quant_algo = _check_config(transformer_dir)
+    fail |= config_status
+    fail |= _check_safetensors(transformer_dir)
+    _check_size_vs_baseline(transformer_dir, args.baseline)
+
+    print()
+    if fail == 0:
+        print("=" * 60)
+        print("ALL CHECKS PASSED — checkpoint looks ready for vllm-omni serving.")
+    elif fail == 1:
+        print("=" * 60)
+        print("FAILURES detected — calibration may need to be re-run.")
+        sys.exit(1)
+    else:
+        print("=" * 60)
+        print("WARNINGS only — checkpoint may serve but with caveats. See [A] above.")
+
+
+if __name__ == "__main__":
+    main()

examples/quantization/quantize_hunyuanvideo_15_modelopt_fp8.py

@@ -0,0 +1,448 @@
+#!/usr/bin/env python3
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""Quantize HunyuanVideo-1.5 (480p T2V) to a ModelOpt FP8 Hugging Face checkpoint.
+
+Calibrates the DiT transformer using a small video prompt set and exports a
+diffusers-style directory whose transformer carries ModelOpt FP8 metadata.
+The exported checkpoint is consumable by vllm-omni's ModelOpt FP8 adapter
+(see vllm_omni/diffusion/model_loader/checkpoint_adapters/modelopt.py).
+
+Layers kept full precision match the #2728 / #2795 pattern: modulation,
+AdaLayerNorm, entry/exit projections, embeddings, the token refiner path,
+and final proj_out. MHA quantizers are off by default; HV-1.5 self-attention
+empirically degrades under FP8 (see #2920 ablation).
+
+Example:
+    python examples/quantization/quantize_hunyuanvideo_15_modelopt_fp8.py \\
+        --model hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-480p_t2v \\
+        --output ./hv15-480p-modelopt-fp8 \\
+        --overwrite
+"""
+
+from __future__ import annotations
+
+import argparse
+import copy
+import json
+import re
+import shutil
+import sys
+from pathlib import Path
+from typing import Any
+
+import torch
+from diffusers import DiffusionPipeline
+
+DEFAULT_PROMPTS = [
+    "A dog running across a field of golden wheat.",
+    "An astronaut riding a horse across the surface of Mars, red dust swirling, cinematic wide shot.",
+    "A hummingbird hovering in front of a vibrant red flower, slow motion, macro shot.",
+    "A crackling campfire at night under a starry sky, sparks rising into the dark.",
+    "An underwater shot of a coral reef with tropical fish swimming by, sun rays piercing the water.",
+    "A close-up of a blooming rose covered in morning dew, soft natural light.",
+    "A peaceful mountain village at dawn, mist rolling over the rooftops, cinematic establishing shot.",
+    "A skateboarder doing a kickflip in an urban plaza, slow motion, golden hour lighting.",
+]
+
+
+def _build_parser() -> argparse.ArgumentParser:
+    p = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter)
+    p.add_argument("--model", required=True, help="Input HV-1.5 diffusers directory or HF id.")
+    p.add_argument("--output", required=True, help="Output directory for the ModelOpt FP8 checkpoint.")
+    p.add_argument("--dtype", choices=("bfloat16", "float16"), default="bfloat16")
+    p.add_argument("--height", type=int, default=480)
+    p.add_argument("--width", type=int, default=832)
+    p.add_argument(
+        "--num-frames",
+        type=int,
+        default=33,
+        help="Frames per calibration sample. 33 matches the typical short benchmark.",
+    )
+    p.add_argument("--guidance-scale", type=float, default=6.0)
+    p.add_argument(
+        "--calib-steps",
+        type=int,
+        default=10,
+        help="Denoising steps per calibration prompt (10 is enough for amax statistics).",
+    )
+    p.add_argument("--calib-size", type=int, default=8, help="How many prompts to use for calibration.")
+    p.add_argument("--seed", type=int, default=42)
+    p.add_argument(
+        "--prompt",
+        action="append",
+        default=[],
+        help="Custom calibration prompt. Repeat to provide multiple.",
+    )
+    p.add_argument(
+        "--quantize-mha",
+        action="store_true",
+        help="Enable FP8 attention K/V/softmax quantizers. Off by default — empirically degrades HV-1.5 video output.",
+    )
+    p.add_argument(
+        "--weight-block-size",
+        type=str,
+        default=None,
+        help="Per-block weight quantization as 'M,N' (e.g. '128,128' for 128x128 tiles). "
+        "Default: per-tensor (one scale per linear). Block-wise typically gives tighter quality at "
+        "negligible memory cost. Static FP8 is exempt from upstream vLLM's online block-wise gate.",
+    )
+    p.add_argument("--overwrite", action="store_true", help="Replace an existing output directory.")
+    return p
+
+
+def _parse_block_size(spec: str | None) -> list[int] | None:
+    if spec is None:
+        return None
+    parts = [int(x) for x in spec.split(",") if x.strip()]
+    if len(parts) != 2:
+        raise SystemExit(f"--weight-block-size must be 'M,N' (2 ints), got {spec!r}")
+    return parts
+
+
+def _require_modelopt() -> Any:
+    try:
+        import modelopt.torch.quantization as mtq
+    except ModuleNotFoundError as exc:
+        raise SystemExit(
+            "NVIDIA ModelOpt is not installed. Install with:\n"
+            "  pip install 'nvidia-modelopt[all]'\n"
+            f"Original error: {exc}"
+        ) from exc
+    return mtq
+
+
+def _ensure_paths(args: argparse.Namespace) -> tuple[str, Path]:
+    model_path = args.model
+    output_dir = Path(args.output).expanduser().resolve()
+    if output_dir.exists():
+        if not args.overwrite:
+            raise SystemExit(f"Output directory already exists: {output_dir}\nPass --overwrite to replace it.")
+        shutil.rmtree(output_dir)
+    return model_path, output_dir
+
+
+def _select_dtype(name: str) -> torch.dtype:
+    return {"bfloat16": torch.bfloat16, "float16": torch.float16}[name]
+
+
+def _build_prompts(args: argparse.Namespace) -> list[str]:
+    prompts = args.prompt or DEFAULT_PROMPTS
+    if args.calib_size <= 0:
+        raise SystemExit("--calib-size must be positive.")
+    if len(prompts) < args.calib_size:
+        repeats = (args.calib_size + len(prompts) - 1) // len(prompts)
+        prompts = (prompts * repeats)[: args.calib_size]
+    return prompts[: args.calib_size]
+
+
+# Layers to KEEP at full precision (mirror of the #2920 wiring + #2728/#2795 skip pattern).
+# - x_embedder, image_embedder, context_embedder*, time_embed*, cond_type_embed: entry/embedding
+# - norm_out, norm1*.linear, norm1_context*.linear, norm2*, norm2_context*: AdaLayerNorm modulation
+# - proj_out: final output projection
+# - token_refiner*: text-encoder refinement uses diffusers raw nn.Linear
+def _filter_func_hv15(name: str) -> bool:
+    pattern = re.compile(
+        r"(proj_out.*|"
+        r".*(x_embedder|image_embedder|context_embedder|context_embedder_2|"
+        r"time_embed|cond_type_embed|"
+        r"norm_out|norm1\.linear|norm1_context\.linear|norm2|norm2_context|"
+        r"token_refiner).*)"
+    )
+    return pattern.match(name) is not None
+
+
+def _mha_filter_func(name: str) -> bool:
+    pattern = re.compile(
+        r".*(q_bmm_quantizer|k_bmm_quantizer|v_bmm_quantizer|softmax_quantizer|bmm2_output_quantizer).*"
+    )
+    return pattern.match(name) is not None
+
+
+def _disable_known_problematic_quantizers(mtq: Any, backbone: torch.nn.Module, *, quantize_mha: bool) -> None:
+    if not hasattr(mtq, "disable_quantizer"):
+        return
+    mtq.disable_quantizer(backbone, _filter_func_hv15)
+    if not quantize_mha:
+        mtq.disable_quantizer(backbone, _mha_filter_func)
+
+
+def _load_pipeline(model_path: str, dtype: torch.dtype) -> DiffusionPipeline:
+    pipe = DiffusionPipeline.from_pretrained(model_path, torch_dtype=dtype)
+    if hasattr(pipe, "set_progress_bar_config"):
+        pipe.set_progress_bar_config(disable=True)
+    pipe.to("cuda")
+    return pipe
+
+
+def _build_forward_loop(pipe: DiffusionPipeline, args: argparse.Namespace, prompts: list[str]):
+    generator = torch.Generator(device="cuda")
+
+    # Try to set guidance on the pipeline's guider object up front (modern
+    # diffusers HV-1.5 uses a Guider abstraction, not a per-call kwarg). Falls
+    # back silently — calibration uses whatever default the pipeline ships with.
+    guider = getattr(pipe, "guider", None)
+    if guider is not None and hasattr(guider, "guidance_scale"):
+        try:
+            guider.guidance_scale = args.guidance_scale
+        except Exception:
+            pass
+
+    base_kwargs = dict(
+        height=args.height,
+        width=args.width,
+        num_frames=args.num_frames,
+        num_inference_steps=args.calib_steps,
+        output_type="latent",
+    )
+
+    def forward_loop(*_unused_args, **_unused_kwargs) -> None:
+        with torch.inference_mode():
+            for idx, prompt in enumerate(prompts):
+                generator.manual_seed(args.seed + idx)
+                # Try with guidance_scale first; fall back without on TypeError
+                # for pipelines (like HV-1.5) that take CFG via guider config.
+                try:
+                    pipe(prompt=prompt, generator=generator, guidance_scale=args.guidance_scale, **base_kwargs)
+                except TypeError as exc:
+                    if "guidance_scale" not in str(exc):
+                        raise
+                    pipe(prompt=prompt, generator=generator, **base_kwargs)
+
+    return forward_loop
+
+
+def _summarize_export(output_dir: Path) -> None:
+    cfg_path = output_dir / "transformer" / "config.json"
+    if not cfg_path.exists():
+        print(f"[warn] {cfg_path} missing.", file=sys.stderr)
+        return
+    with cfg_path.open(encoding="utf-8") as f:
+        cfg = json.load(f)
+    qc = cfg.get("quantization_config")
+    if not isinstance(qc, dict):
+        print("[warn] No quantization_config in transformer/config.json.", file=sys.stderr)
+        return
+    print("Export summary:")
+    print(f"  quant_method: {qc.get('quant_method')}")
+    print(f"  quant_algo:   {qc.get('quant_algo')}")
+    producer = qc.get("producer")
+    if isinstance(producer, dict):
+        print(f"  producer:     {producer.get('name')} {producer.get('version')}")
+    print(f"  config path:  {cfg_path}")
+
+
+def _force_export_quantized_weights(backbone: torch.nn.Module, dtype: torch.dtype) -> int:
+    """Convert in-memory weights of quantized modules to actual FP8 storage.
+
+    `export_hf_checkpoint` skips this step for unknown model types (HV-1.5 isn't
+    in ModelOpt's recognized-model registry), so we must call the per-weight
+    export helper ourselves. Same workaround as the HunyuanImage-3 calibration
+    helper.
+    """
+    from modelopt.torch.export.quant_utils import (
+        QUANTIZATION_NONE,
+        get_quantization_format,
+        quantizer_attr_names,
+        weight_attr_names,
+    )
+    from modelopt.torch.export.unified_export_hf import _export_quantized_weight
+
+    exported = 0
+    for name, module in backbone.named_modules():
+        try:
+            quantization_format = get_quantization_format(module)
+        except Exception as exc:
+            print(f"[warn] Could not inspect quantization format for {name}: {exc}", file=sys.stderr)
+            continue
+        if quantization_format == QUANTIZATION_NONE:
+            continue
+        for weight_name in weight_attr_names(module):
+            quantizer_attrs = quantizer_attr_names(weight_name)
+            weight_quantizer = getattr(module, quantizer_attrs.weight_quantizer, None)
+            if weight_quantizer is None or not getattr(weight_quantizer, "is_enabled", False):
+                continue
+            _export_quantized_weight(module, dtype, weight_name)
+            exported += 1
+    return exported
+
+
+def _hv15_quant_config_block(weight_block_size: list[int] | None = None) -> dict:
+    """Mirror ModelOpt FP8 metadata expected by vllm-omni's adapter (#2913).
+
+    Same shape as the HunyuanImage-3 author's _hunyuan_quant_config(). When
+    `weight_block_size` is given, advertise block-wise weight quantization in
+    the saved metadata (so consumers know to expect multi-element scale tensors).
+    """
+    weights_cfg: dict = {"dynamic": False, "num_bits": 8, "type": "float"}
+    if weight_block_size is not None:
+        weights_cfg["strategy"] = "block"
+        weights_cfg["block_structure"] = f"{weight_block_size[0]}x{weight_block_size[1]}"
+    return {
+        "config_groups": {
+            "group_0": {
+                "input_activations": {"dynamic": False, "num_bits": 8, "type": "float"},
+                "weights": weights_cfg,
+                "targets": ["Linear"],
+            }
+        },
+        "ignore": [
+            "context_embedder*",
+            "context_embedder_2*",
+            "cond_type_embed*",
+            "image_embedder*",
+            "norm1.linear*",
+            "norm1_context.linear*",
+            "norm2*",
+            "norm2_context*",
+            "norm_out*",
+            "proj_out*",
+            "time_embed*",
+            "token_refiner*",
+            "x_embedder*",
+        ],
+        "producer": {"name": "modelopt"},
+        "quant_algo": "FP8",
+        "quant_method": "modelopt",
+    }
+
+
+def _patch_quant_config(output_dir: Path, weight_block_size: list[int] | None = None) -> None:
+    """Inject quant_algo: FP8 + config_groups into transformer/config.json so
+    vllm-omni's adapter (#2913) recognises the checkpoint as ModelOpt FP8."""
+    cfg_path = output_dir / "transformer" / "config.json"
+    with cfg_path.open(encoding="utf-8") as f:
+        cfg = json.load(f)
+
+    new_qc = _hv15_quant_config_block(weight_block_size=weight_block_size)
+    existing = cfg.get("quantization_config")
+    if isinstance(existing, dict):
+        producer = existing.get("producer")
+        if isinstance(producer, dict):
+            new_qc["producer"] = producer
+
+    cfg["quantization_config"] = new_qc
+    with cfg_path.open("w", encoding="utf-8") as f:
+        json.dump(cfg, f, indent=2)
+
+
+def _save_pipeline_with_fp8_transformer(
+    pipe: DiffusionPipeline,
+    model_path: str,
+    output_dir: Path,
+    max_shard_size: str = "5GB",
+) -> None:
+    """Save the pipeline with the (now FP8) transformer.
+
+    Copies the source directory verbatim except for `transformer/`, then
+    saves the transformer with quantizers hidden so the state dict contains
+    only the FP8 weights + scale tensors.
+    """
+    from modelopt.torch.export.diffusers_utils import hide_quantizers_from_state_dict
+
+    src = Path(model_path)
+    if not src.exists():
+        from huggingface_hub import snapshot_download
+
+        src = Path(snapshot_download(model_path))
+
+    if output_dir.exists():
+        shutil.rmtree(output_dir)
+    shutil.copytree(src, output_dir, ignore=shutil.ignore_patterns("transformer"))
+
+    transformer_out = output_dir / "transformer"
+    # `hide_quantizers_from_state_dict` walks named_modules(); pass the actual
+    # nn.Module (transformer), not the diffusers Pipeline wrapper.
+    with hide_quantizers_from_state_dict(pipe.transformer):
+        pipe.transformer.save_pretrained(
+            str(transformer_out),
+            safe_serialization=True,
+            max_shard_size=max_shard_size,
+        )
+
+
+def main() -> None:
+    args = _build_parser().parse_args()
+    if not torch.cuda.is_available():
+        raise SystemExit("CUDA is required for ModelOpt FP8 quantization.")
+
+    mtq = _require_modelopt()
+    model_path, output_dir = _ensure_paths(args)
+    dtype = _select_dtype(args.dtype)
+    prompts = _build_prompts(args)
+
+    print("Quantization plan:")
+    weight_block_size = _parse_block_size(args.weight_block_size)
+
+    print(f"  input:           {args.model}")
+    print(f"  output:          {output_dir}")
+    print(f"  dtype:           {dtype}")
+    print(f"  height/width:    {args.height}x{args.width}")
+    print(f"  num_frames:      {args.num_frames}")
+    print(f"  calib_size:      {len(prompts)}")
+    print(f"  calib_steps:     {args.calib_steps}")
+    print(f"  quantize_mha:    {args.quantize_mha}")
+    print(
+        f"  weight strategy: {'block-wise ' + str(weight_block_size) if weight_block_size else 'per-tensor (default)'}"
+    )
+
+    pipe = _load_pipeline(model_path, dtype)
+    backbone = pipe.transformer
+
+    quant_config = copy.deepcopy(mtq.FP8_DEFAULT_CFG)
+    if weight_block_size is not None:
+        # Switch from per-tensor (default) to block-wise weight quantization.
+        # ModelOpt's wildcard "*weight_quantizer" matches every linear's weight quantizer.
+        quant_config["quant_cfg"]["*weight_quantizer"] = {
+            "num_bits": (4, 3),  # E4M3 (FP8 weights, same as default)
+            "block_sizes": {-1: weight_block_size[1], -2: weight_block_size[0]},
+        }
+        print(
+            f"  -> overriding weight quantizer with block_sizes={weight_block_size} "
+            f"({weight_block_size[0]}x{weight_block_size[1]} tiles)"
+        )
+
+    forward_loop = _build_forward_loop(pipe, args, prompts)
+    quantized = mtq.quantize(backbone, quant_config, forward_loop)
+    if quantized is not None:
+        pipe.transformer = quantized
+        backbone = quantized
+
+    _disable_known_problematic_quantizers(mtq, backbone, quantize_mha=args.quantize_mha)
+
+    print("\nForcing FP8 weight serialization (HV-1.5 isn't in ModelOpt's recognized-model registry,")
+    print("so we have to call the per-weight export helper ourselves)...")
+    exported = _force_export_quantized_weights(backbone, dtype)
+    print(f"  -> {exported} weights converted to FP8 in memory")
+    if exported == 0:
+        raise SystemExit(
+            "No quantized weights were exported. Calibration may have skipped every layer "
+            "(check the disable_quantizer regex) or `mtq.quantize` did not actually wrap any "
+            "weight quantizers."
+        )
+
+    print("\nSaving pipeline with FP8 transformer...")
+    _save_pipeline_with_fp8_transformer(pipe, model_path, output_dir)
+    _patch_quant_config(output_dir, weight_block_size=weight_block_size)
+    print(f"Saved to: {output_dir}")
+    _summarize_export(output_dir)
+
+    print("\nNext: validate the checkpoint with vllm-omni:")
+    print(
+        "  python examples/offline_inference/text_to_video/text_to_video.py \\\n"
+        f"    --model {output_dir} \\\n"
+        "    --quantization fp8 \\\n"
+        "    --prompt 'A dog running across a field of golden wheat.' \\\n"
+        f"    --height {args.height} --width {args.width} --num-frames {args.num_frames} \\\n"
+        "    --num-inference-steps 30 --guidance-scale 6.0 --seed 42 \\\n"
+        "    --output outputs/hv15_modelopt_fp8.mp4 \\\n"
+        "    --enforce-eager"
+    )
+    print(
+        "\n  (--quantization fp8 is auto-upgraded to ModelOpt FP8 at runtime because the "
+        "checkpoint's config.json has modelopt metadata.)"
+    )
+
+
+if __name__ == "__main__":
+    main()

examples/quantization/quantize_wan2_2_modelopt_fp8.py

@@ -0,0 +1,471 @@
+#!/usr/bin/env python3
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""Quantize Wan2.2 (TI2V-5B, 704x1280 T2V) to a ModelOpt FP8 Hugging Face checkpoint.
+
+Calibrates the DiT transformer using a small video prompt set and exports a
+diffusers-style directory whose transformer carries ModelOpt FP8 metadata.
+The exported checkpoint is consumable by vllm-omni's ModelOpt FP8 adapter
+(see vllm_omni/diffusion/model_loader/checkpoint_adapters/modelopt.py).
+
+Layers kept full precision match the #2728 / #2795 pattern: condition embedder
+(time/text/image), patch embedding, modulation (scale_shift_table), final
+norm + proj_out, and sequence-parallel helpers. All attention + FFN linears
+are quantized — static calibration handles the numerics that online FP8
+couldn't (see #2920 ablation).
+
+Default target is `Wan-AI/Wan2.2-TI2V-5B-Diffusers`, the dense 5B variant that
+fits 80GB BF16. The A14B MoE variants need 2+ GPUs and are out of scope here.
+
+Example:
+    python examples/quantization/quantize_wan2_2_modelopt_fp8.py \\
+        --model Wan-AI/Wan2.2-TI2V-5B-Diffusers \\
+        --output ./wan22-ti2v-modelopt-fp8 \\
+        --overwrite
+"""
+
+from __future__ import annotations
+
+import argparse
+import copy
+import json
+import re
+import shutil
+import sys
+from pathlib import Path
+from typing import Any
+
+import torch
+from diffusers import DiffusionPipeline
+
+DEFAULT_PROMPTS = [
+    "A dog running across a field of golden wheat.",
+    "An astronaut riding a horse across the surface of Mars, red dust swirling, cinematic wide shot.",
+    "A hummingbird hovering in front of a vibrant red flower, slow motion, macro shot.",
+    "A crackling campfire at night under a starry sky, sparks rising into the dark.",
+    "An underwater shot of a coral reef with tropical fish swimming by, sun rays piercing the water.",
+    "A close-up of a blooming rose covered in morning dew, soft natural light.",
+    "A peaceful mountain village at dawn, mist rolling over the rooftops, cinematic establishing shot.",
+    "A skateboarder doing a kickflip in an urban plaza, slow motion, golden hour lighting.",
+]
+
+
+def _build_parser() -> argparse.ArgumentParser:
+    p = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter)
+    p.add_argument("--model", required=True, help="Input Wan2.2 diffusers directory or HF id.")
+    p.add_argument("--output", required=True, help="Output directory for the ModelOpt FP8 checkpoint.")
+    p.add_argument("--dtype", choices=("bfloat16", "float16"), default="bfloat16")
+    p.add_argument("--height", type=int, default=704, help="Calibration video height (Wan2.2 TI2V-5B native: 704).")
+    p.add_argument("--width", type=int, default=1280, help="Calibration video width (Wan2.2 TI2V-5B native: 1280).")
+    p.add_argument(
+        "--num-frames",
+        type=int,
+        default=49,
+        help="Frames per calibration sample. 49 matches the typical short benchmark; "
+        "use 17 to reduce memory pressure during calibration.",
+    )
+    p.add_argument("--guidance-scale", type=float, default=5.0)
+    p.add_argument(
+        "--calib-steps",
+        type=int,
+        default=10,
+        help="Denoising steps per calibration prompt (10 is enough for amax statistics).",
+    )
+    p.add_argument("--calib-size", type=int, default=8, help="How many prompts to use for calibration.")
+    p.add_argument("--seed", type=int, default=42)
+    p.add_argument(
+        "--prompt",
+        action="append",
+        default=[],
+        help="Custom calibration prompt. Repeat to provide multiple.",
+    )
+    p.add_argument(
+        "--quantize-mha",
+        action="store_true",
+        help="Enable FP8 attention K/V/softmax quantizers. Off by default — Wan2.2's long attention "
+        "sequences amplified FP8 drift in the online ablation (see #2920).",
+    )
+    p.add_argument(
+        "--weight-block-size",
+        type=str,
+        default=None,
+        help="Per-block weight quantization as 'M,N' (e.g. '128,128'). Default per-tensor. "
+        "Note: vllm-omni's ModelOpt adapter may not yet dispatch block-wise scales — check #2924 "
+        "for the HV-1.5 investigation status before relying on this.",
+    )
+    p.add_argument("--overwrite", action="store_true", help="Replace an existing output directory.")
+    return p
+
+
+def _parse_block_size(spec: str | None) -> list[int] | None:
+    if spec is None:
+        return None
+    parts = [int(x) for x in spec.split(",") if x.strip()]
+    if len(parts) != 2:
+        raise SystemExit(f"--weight-block-size must be 'M,N' (2 ints), got {spec!r}")
+    return parts
+
+
+def _require_modelopt() -> Any:
+    try:
+        import modelopt.torch.quantization as mtq
+    except ModuleNotFoundError as exc:
+        raise SystemExit(
+            "NVIDIA ModelOpt is not installed. Install with:\n"
+            "  pip install 'nvidia-modelopt[all]'\n"
+            f"Original error: {exc}"
+        ) from exc
+    return mtq
+
+
+def _ensure_paths(args: argparse.Namespace) -> tuple[str, Path]:
+    model_path = args.model
+    output_dir = Path(args.output).expanduser().resolve()
+    if output_dir.exists():
+        if not args.overwrite:
+            raise SystemExit(f"Output directory already exists: {output_dir}\nPass --overwrite to replace it.")
+        shutil.rmtree(output_dir)
+    return model_path, output_dir
+
+
+def _select_dtype(name: str) -> torch.dtype:
+    return {"bfloat16": torch.bfloat16, "float16": torch.float16}[name]
+
+
+def _build_prompts(args: argparse.Namespace) -> list[str]:
+    prompts = args.prompt or DEFAULT_PROMPTS
+    if args.calib_size <= 0:
+        raise SystemExit("--calib-size must be positive.")
+    if len(prompts) < args.calib_size:
+        repeats = (args.calib_size + len(prompts) - 1) // len(prompts)
+        prompts = (prompts * repeats)[: args.calib_size]
+    return prompts[: args.calib_size]
+
+
+# Layers to KEEP at full precision. Wan2.2's module naming:
+# - condition_embedder: time_embedder, time_proj, text_embedder, image_embedder (I2V)
+# - patch_embedding: Conv3dLayer (already not Linear, belt-and-suspenders skip)
+# - scale_shift_table: nn.Parameter modulation (not Linear, but pattern guard)
+# - norm_out: AdaLayerNorm final
+# - proj_out: final nn.Linear
+# - timestep_proj_prepare / output_scale_shift_prepare: SP helpers
+def _filter_func_wan22(name: str) -> bool:
+    pattern = re.compile(
+        r"(proj_out.*|"
+        r".*(condition_embedder|patch_embedding|"
+        r"norm_out|scale_shift_table|"
+        r"timestep_proj_prepare|output_scale_shift_prepare).*)"
+    )
+    return pattern.match(name) is not None
+
+
+def _mha_filter_func(name: str) -> bool:
+    pattern = re.compile(
+        r".*(q_bmm_quantizer|k_bmm_quantizer|v_bmm_quantizer|softmax_quantizer|bmm2_output_quantizer).*"
+    )
+    return pattern.match(name) is not None
+
+
+def _disable_known_problematic_quantizers(mtq: Any, backbone: torch.nn.Module, *, quantize_mha: bool) -> None:
+    if not hasattr(mtq, "disable_quantizer"):
+        return
+    mtq.disable_quantizer(backbone, _filter_func_wan22)
+    if not quantize_mha:
+        mtq.disable_quantizer(backbone, _mha_filter_func)
+
+
+def _load_pipeline(model_path: str, dtype: torch.dtype) -> DiffusionPipeline:
+    pipe = DiffusionPipeline.from_pretrained(model_path, torch_dtype=dtype)
+    if hasattr(pipe, "set_progress_bar_config"):
+        pipe.set_progress_bar_config(disable=True)
+    pipe.to("cuda")
+    return pipe
+
+
+def _build_forward_loop(pipe: DiffusionPipeline, args: argparse.Namespace, prompts: list[str]):
+    generator = torch.Generator(device="cuda")
+
+    # Try setting guidance on the pipeline's guider if present (newer diffusers APIs).
+    guider = getattr(pipe, "guider", None)
+    if guider is not None and hasattr(guider, "guidance_scale"):
+        try:
+            guider.guidance_scale = args.guidance_scale
+        except Exception:
+            pass
+
+    base_kwargs = dict(
+        height=args.height,
+        width=args.width,
+        num_frames=args.num_frames,
+        num_inference_steps=args.calib_steps,
+        output_type="latent",
+    )
+
+    def forward_loop(*_unused_args, **_unused_kwargs) -> None:
+        with torch.inference_mode():
+            for idx, prompt in enumerate(prompts):
+                generator.manual_seed(args.seed + idx)
+                # Try with guidance_scale first; fall back without on TypeError
+                # for pipelines that take CFG via guider config only.
+                try:
+                    pipe(prompt=prompt, generator=generator, guidance_scale=args.guidance_scale, **base_kwargs)
+                except TypeError as exc:
+                    if "guidance_scale" not in str(exc):
+                        raise
+                    pipe(prompt=prompt, generator=generator, **base_kwargs)
+
+    return forward_loop
+
+
+def _summarize_export(output_dir: Path) -> None:
+    cfg_path = output_dir / "transformer" / "config.json"
+    if not cfg_path.exists():
+        print(f"[warn] {cfg_path} missing.", file=sys.stderr)
+        return
+    with cfg_path.open(encoding="utf-8") as f:
+        cfg = json.load(f)
+    qc = cfg.get("quantization_config")
+    if not isinstance(qc, dict):
+        print("[warn] No quantization_config in transformer/config.json.", file=sys.stderr)
+        return
+    print("Export summary:")
+    print(f"  quant_method: {qc.get('quant_method')}")
+    print(f"  quant_algo:   {qc.get('quant_algo')}")
+    producer = qc.get("producer")
+    if isinstance(producer, dict):
+        print(f"  producer:     {producer.get('name')} {producer.get('version')}")
+    print(f"  config path:  {cfg_path}")
+
+
+def _force_export_quantized_weights(backbone: torch.nn.Module, dtype: torch.dtype) -> int:
+    """Convert in-memory weights of quantized modules to actual FP8 storage.
+
+    `export_hf_checkpoint` skips this step for unknown model types (Wan2.2 isn't
+    in ModelOpt's recognized-model registry), so we must call the per-weight
+    export helper ourselves. Same workaround as the HunyuanVideo-1.5 / HunyuanImage-3
+    calibration helpers.
+    """
+    from modelopt.torch.export.quant_utils import (
+        QUANTIZATION_NONE,
+        get_quantization_format,
+        quantizer_attr_names,
+        weight_attr_names,
+    )
+    from modelopt.torch.export.unified_export_hf import _export_quantized_weight
+
+    exported = 0
+    for name, module in backbone.named_modules():
+        try:
+            quantization_format = get_quantization_format(module)
+        except Exception as exc:
+            print(f"[warn] Could not inspect quantization format for {name}: {exc}", file=sys.stderr)
+            continue
+        if quantization_format == QUANTIZATION_NONE:
+            continue
+        for weight_name in weight_attr_names(module):
+            quantizer_attrs = quantizer_attr_names(weight_name)
+            weight_quantizer = getattr(module, quantizer_attrs.weight_quantizer, None)
+            if weight_quantizer is None or not getattr(weight_quantizer, "is_enabled", False):
+                continue
+            _export_quantized_weight(module, dtype, weight_name)
+            exported += 1
+    return exported
+
+
+def _wan22_quant_config_block(weight_block_size: list[int] | None = None) -> dict:
+    """Mirror ModelOpt FP8 metadata expected by vllm-omni's adapter (#2913)."""
+    weights_cfg: dict = {"dynamic": False, "num_bits": 8, "type": "float"}
+    if weight_block_size is not None:
+        weights_cfg["strategy"] = "block"
+        weights_cfg["block_structure"] = f"{weight_block_size[0]}x{weight_block_size[1]}"
+    return {
+        "config_groups": {
+            "group_0": {
+                "input_activations": {"dynamic": False, "num_bits": 8, "type": "float"},
+                "weights": weights_cfg,
+                "targets": ["Linear"],
+            }
+        },
+        "ignore": [
+            "condition_embedder*",
+            "norm_out*",
+            "output_scale_shift_prepare*",
+            "patch_embedding*",
+            "proj_out*",
+            "scale_shift_table*",
+            "timestep_proj_prepare*",
+        ],
+        "producer": {"name": "modelopt"},
+        "quant_algo": "FP8",
+        "quant_method": "modelopt",
+    }
+
+
+def _patch_quant_config(output_dir: Path, weight_block_size: list[int] | None = None) -> None:
+    """Inject quant_algo: FP8 + config_groups into each transformer's config.json
+    so vllm-omni's adapter (#2913) recognises the checkpoint as ModelOpt FP8.
+
+    Patches both ``transformer`` and ``transformer_2`` (A14B) when present."""
+    for sub in ("transformer", "transformer_2"):
+        cfg_path = output_dir / sub / "config.json"
+        if not cfg_path.exists():
+            continue
+        with cfg_path.open(encoding="utf-8") as f:
+            cfg = json.load(f)
+
+        new_qc = _wan22_quant_config_block(weight_block_size=weight_block_size)
+        existing = cfg.get("quantization_config")
+        if isinstance(existing, dict):
+            producer = existing.get("producer")
+            if isinstance(producer, dict):
+                new_qc["producer"] = producer
+
+        cfg["quantization_config"] = new_qc
+        with cfg_path.open("w", encoding="utf-8") as f:
+            json.dump(cfg, f, indent=2)
+
+
+def _list_transformers(pipe: DiffusionPipeline) -> list[tuple[str, torch.nn.Module]]:
+    """Return [(attr_name, module)] for every DiT on the pipeline.
+
+    Wan2.2 A14B has both ``transformer`` (low-noise) and ``transformer_2``
+    (high-noise). TI2V-5B has only ``transformer``.
+    """
+    out = []
+    for attr in ("transformer", "transformer_2"):
+        mod = getattr(pipe, attr, None)
+        if mod is not None:
+            out.append((attr, mod))
+    return out
+
+
+def _save_pipeline_with_fp8_transformer(
+    pipe: DiffusionPipeline,
+    model_path: str,
+    output_dir: Path,
+    max_shard_size: str = "5GB",
+) -> None:
+    """Copy source dir verbatim minus transformer dirs, then save each quantized DiT.
+
+    Handles A14B's dual transformer (``transformer`` + ``transformer_2``) as well
+    as the single-DiT TI2V-5B.
+    """
+    from modelopt.torch.export.diffusers_utils import hide_quantizers_from_state_dict
+
+    src = Path(model_path)
+    if not src.exists():
+        from huggingface_hub import snapshot_download
+
+        src = Path(snapshot_download(model_path))
+
+    if output_dir.exists():
+        shutil.rmtree(output_dir)
+    shutil.copytree(src, output_dir, ignore=shutil.ignore_patterns("transformer", "transformer_2"))
+
+    for attr, backbone in _list_transformers(pipe):
+        transformer_out = output_dir / attr
+        # Pass the nn.Module (transformer), not the Pipeline wrapper.
+        with hide_quantizers_from_state_dict(backbone):
+            backbone.save_pretrained(
+                str(transformer_out),
+                safe_serialization=True,
+                max_shard_size=max_shard_size,
+            )
+
+
+def main() -> None:
+    args = _build_parser().parse_args()
+    if not torch.cuda.is_available():
+        raise SystemExit("CUDA is required for ModelOpt FP8 quantization.")
+
+    mtq = _require_modelopt()
+    model_path, output_dir = _ensure_paths(args)
+    dtype = _select_dtype(args.dtype)
+    prompts = _build_prompts(args)
+    weight_block_size = _parse_block_size(args.weight_block_size)
+
+    print("Quantization plan:")
+    print(f"  input:           {args.model}")
+    print(f"  output:          {output_dir}")
+    print(f"  dtype:           {dtype}")
+    print(f"  height/width:    {args.height}x{args.width}")
+    print(f"  num_frames:      {args.num_frames}")
+    print(f"  calib_size:      {len(prompts)}")
+    print(f"  calib_steps:     {args.calib_steps}")
+    print(f"  quantize_mha:    {args.quantize_mha}")
+    print(
+        f"  weight strategy: {'block-wise ' + str(weight_block_size) if weight_block_size else 'per-tensor (default)'}"
+    )
+
+    pipe = _load_pipeline(model_path, dtype)
+    transformers = _list_transformers(pipe)
+    if not transformers:
+        raise SystemExit("Pipeline has no transformer or transformer_2 attribute.")
+    print(f"  found {len(transformers)} transformer(s): {', '.join(a for a, _ in transformers)}")
+
+    quant_config = copy.deepcopy(mtq.FP8_DEFAULT_CFG)
+    if weight_block_size is not None:
+        quant_config["quant_cfg"]["*weight_quantizer"] = {
+            "num_bits": (4, 3),
+            "block_sizes": {-1: weight_block_size[1], -2: weight_block_size[0]},
+        }
+        print(
+            f"  -> overriding weight quantizer with block_sizes={weight_block_size} "
+            f"({weight_block_size[0]}x{weight_block_size[1]} tiles)"
+        )
+
+    forward_loop = _build_forward_loop(pipe, args, prompts)
+
+    # Quantize each DiT. The forward_loop runs the full pipeline, so quantizers
+    # on every attached transformer (A14B: transformer + transformer_2) get
+    # calibrated during the same passes.
+    for attr, backbone in transformers:
+        print(f"\nQuantizing {attr}...")
+        quantized = mtq.quantize(backbone, quant_config, forward_loop)
+        if quantized is not None:
+            setattr(pipe, attr, quantized)
+        _disable_known_problematic_quantizers(mtq, getattr(pipe, attr), quantize_mha=args.quantize_mha)
+
+    # Export weights to FP8 only AFTER all transformers are calibrated. Exporting
+    # inside the loop would convert transformer's weights to real FP8, and the
+    # next transformer's forward_loop (full pipeline) would then fake-quantize an
+    # already-FP8 weight -> "fake_e4m3fy not implemented for Float8_e4m3fn".
+    print("\nForcing FP8 weight serialization (Wan2.2 isn't in ModelOpt's recognized-model registry,")
+    print("so we have to call the per-weight export helper ourselves)...")
+    total_exported = 0
+    for attr, backbone in _list_transformers(pipe):
+        exported = _force_export_quantized_weights(backbone, dtype)
+        print(f"  -> {exported} weights converted to FP8 in {attr}")
+        total_exported += exported
+
+    if total_exported == 0:
+        raise SystemExit(
+            "No quantized weights were exported. Calibration may have skipped every layer "
+            "(check the disable_quantizer regex) or `mtq.quantize` did not actually wrap any "
+            "weight quantizers."
+        )
+
+    print("\nSaving pipeline with FP8 transformer(s)...")
+    _save_pipeline_with_fp8_transformer(pipe, model_path, output_dir)
+    _patch_quant_config(output_dir, weight_block_size=weight_block_size)
+    print(f"Saved to: {output_dir}")
+    _summarize_export(output_dir)
+
+    print("\nNext: validate the checkpoint with vllm-omni:")
+    print(
+        "  python examples/offline_inference/text_to_video/text_to_video.py \\\n"
+        f"    --model {output_dir} \\\n"
+        "    --quantization fp8 \\\n"
+        "    --prompt 'A dog running across a field of golden wheat.' \\\n"
+        f"    --height {args.height} --width {args.width} --num-frames {args.num_frames} \\\n"
+        "    --num-inference-steps 30 --guidance-scale 5.0 --seed 42 \\\n"
+        "    --output outputs/wan22_modelopt_fp8.mp4"
+    )
+    print(
+        "\n  (--quantization fp8 is auto-upgraded to ModelOpt FP8 at runtime because the "
+        "checkpoint's config.json has modelopt metadata.)"
+    )
+
+
+if __name__ == "__main__":
+    main()

recipes/MiniMaxAI/MiniMax-H3-NPU.md

@@ -79,8 +79,7 @@ sharing the tokenizer, processor, text encoder, and VAEs from `FL2VA`.
 ### Multi-NPU: 768P combined-service configuration
 
 Use Ulysses sequence parallelism degree 8, text-encoder tensor parallelism
-degree 8, native tiled VAE patch parallelism degree 8, and distributed
-layerwise offload:
+degree 8, native tiled VAE patch parallelism degree 8, and layerwise offload:
 
 ```bash
 export ASCEND_RT_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
@@ -99,7 +98,7 @@ vllm serve "${MODEL}" \
   --usp 8 \
   --ring 1 \
   --text-encoder-tp-size 8 \
-  --enable-distributed-layerwise-offload \
+  --enable-layerwise-offload \
   --vae-parallel-mode tile \
   --vae-use-tiling \
   --vae-patch-parallel-size 8 \
@@ -113,11 +112,6 @@ H3 is CFG-distilled, so `--cfg-parallel-size` must remain 1.
 The same endpoint accepts `task=t2va`, `task=fl2va`, and `task=ref2va`; no
 partition restart is required. Layerwise offload applies to both DiTs.
 
-On Atlas 800I A3 (64 GB HBM per device) the combined service does not fit at
-768P without offloading or sharding: use distributed layerwise offload (as
-above) or HSDP — see
-[§ Memory and attention optimizations](#memory-and-attention-optimizations-a3).
-
 ### Optional optimizations
 
 Two independent optimizations may be enabled on top of the configuration
@@ -140,63 +134,6 @@ Keep `--ring 1` when using RainFusion: the `rf_v2` kernel ranks key blocks
 over the whole sequence, so ring parallelism would split away the keys it
 needs. Scale with `--usp` instead.
 
-## Memory and attention optimizations (A3)
-
-### Fitting 768P into 64 GB HBM: distributed layerwise offload or HSDP
-
-Each Atlas 800I A3 NPU has 64 GB of HBM, which is not enough for the
-combined MiniMax-H3 service at 768P. Enable **one** of the following at
-server startup:
-
-**Distributed layerwise offload** — DiT layers are offloaded with parameters
-gathered across the parallel group instead of replicated per rank:
-
-```bash
-  --enable-distributed-layerwise-offload
-```
-
-Measured peak NPU memory is about 45 GB per device for Ref2VA with a 13.88 s
-input video generating a 15 s 768P (1344x768) output video. Host (CPU) memory
-usage is high with this option.
-
-**HSDP** — hybrid sharded data parallelism for the DiT parameters. The
-multi-stream memory-reuse knob is mandatory with this flag:
-
-```bash
-export MULTI_STREAM_MEMORY_REUSE=2
-```
-
-```bash
-  --use-hsdp
-```
-
-Host memory usage is small, but large shapes may still OOM; HBM usage
-optimizations for this path are ongoing.
-
-### FLASH_ATTN backend with MindIE-SD
-
-Keep `--diffusion-attention-backend FLASH_ATTN` and install MindIE-SD (see
-[§ Environment](#environment)). On NPU this backend carries most of the
-memory-reduction and performance work: a mask-free packed varlen path that
-never materializes the quadratic `full_qk` padding mask, and K/V prefix
-slicing, both driven by the packed `cu_seqlens` metadata emitted by the H3
-transformer.
-
-### Optional: LaserAttention fused kernel
-
-For an additional attention speedup, select the Ascend LaserAttention fused
-kernel before starting the server:
-
-```bash
-export MINDIE_SD_FA_TYPE="ascend_laser_attention"
-```
-
-This requires the FLASH_ATTN backend and MindIE-SD. H3 automatically applies
-exact power-of-two input pre-scaling (`laser_input_scale=256`) so the
-kernel's fp16 workspace cannot overflow on outlier activations. Measured
-speedup numbers will be added here.
-
-
 ## HTTP API examples
 
 The request format is identical to the GPU recipe; see
@@ -217,8 +154,8 @@ configuration above:
 
 | Workload | Configuration |
 |----------|---------------|
-| T2VA, 209 frames, 1344x768 | TE TP8, distributed layerwise offload, Ulysses 8, VPP8 tile, regional compile |
-| Ref2VA (prompt + video), 124 frames, 1344x768 | TE TP8, distributed layerwise offload, Ulysses 8, VPP8 tile, regional compile |
+| T2VA, 209 frames, 1344x768 | TE TP8, layerwise offload, Ulysses 8, VPP8 tile, regional compile |
+| Ref2VA (prompt + video), 124 frames, 1344x768 | TE TP8, layerwise offload, Ulysses 8, VPP8 tile, regional compile |
 
 These measurements describe the validated shapes rather than a general
 throughput guarantee.

recipes/MiniMaxAI/MiniMax-H3-RTX-PRO-6000.md

@@ -1,346 +0,0 @@
-# MiniMax-H3 on RTX PRO 6000 Blackwell GPUs
-
-This recipe runs MiniMax-H3 in BF16 on 96 GiB RTX PRO 6000 Blackwell GPUs.
-The additional HBM over the RTX PRO 5000 profile removes the four-GPU
-minimum: two GPUs are enough to keep the model resident with TP2. Four and
-eight GPUs raise the Ulysses degree to shard the attention sequence further.
-CPU offload and distributed layerwise offload are not required in any of
-these configurations.
-
-Validated on:
-- Host: YLX Y762
-- GPUs: 8 × RTX PRO 6000 Blackwell (96 GiB)
-- Device order: default (`CUDA_VISIBLE_DEVICES` not set)
-- Driver / CUDA: 580.105.08 / 13.0
-- Container image: `vllm/vllm-omni:minimax-h3`
-- Workload: T2VA, 1344×768, `duration=5.0`, 50 steps, `flow_shift=12`, `seed=1101`
-
-## Capacity requirements
-
-| Resource | Two GPUs | Four GPUs | Eight GPUs |
-| --- | ---: | ---: | ---: |
-| GPU HBM | 96 GiB per GPU | 96 GiB per GPU | 96 GiB per GPU |
-| Checkpoint storage | 135 GiB per partition | 135 GiB per partition | 135 GiB per partition |
-| Available system RAM | 200 GiB minimum | 200 GiB minimum | 200 GiB minimum |
-| Recommended system RAM | 384 GiB | 384 GiB | 384 GiB |
-| Measured peak HBM per GPU | 77.49 GiB | 66.44 GiB | 61.07 GiB |
-
-`FL2VA` and `Ref2VA` are separate checkpoint partitions. Start one server at
-a time on a host sized for the minimum system-memory requirement, or pass
-`--task-type fl2va` or `--task-type ref2va` to download and load a single
-partition. The two-server layout described in the eight-GPU section doubles
-both the storage and the host-memory requirement.
-
-## Parallelism summary
-
-| Configuration | TP | Ulysses | Text-encoder TP | VAE patch parallel |
-| --- | ---: | ---: | ---: | ---: |
-| Two GPUs | 2 | 1 | 2 | 2 |
-| Four GPUs | 2 | 2 | 4 | 4 |
-| Eight GPUs | 2 | 4 | 8 | 8 |
-| Eight GPUs, headroom variant | 4 | 2 | 8 | 8 |
-
-Per-GPU weight residency is set by the tensor-parallel degree alone. Raising
-the Ulysses degree lowers activation memory and per-step latency but leaves
-weight residency unchanged. Ring parallelism stays at 1, and H3 is
-CFG-distilled, so `--cfg-parallel-size` must also remain 1.
-
-## PCIe topology and GPU order
-
-RTX PRO 6000 Blackwell does not provide NVLink. Before starting the server,
-inspect the host topology:
-
-```bash
-nvidia-smi topo -m
-nvidia-smi nvlink -s
-```
-
-Tensor-parallel groups are consecutive ranks. Ulysses groups are strided by
-the tensor-parallel degree. Confirm this grouping against the server log
-before tuning the device order on a new host.
-
-For the two-GPU configuration, pick a single `PXB` pair on one NUMA node.
-For the four-GPU configuration, pick two `PXB` pairs on the same node and
-order them so that each Ulysses group lands inside one pair. If physical
-GPUs `(0,1)` and `(2,3)` are the two local pairs, the order is
-`CUDA_VISIBLE_DEVICES=0,2,1,3`. Do not copy these IDs blindly: reproduce the
-same relationship on the target host.
-
-Device-order pinning and NUMA binding are tuning knobs, not prerequisites.
-The measurements in this recipe were taken without either, so any gain from
-them is additional to the numbers reported below and is host-specific.
-
-## Two-GPU serving configuration
-
-Two 96 GiB GPUs hold the BF16 model with TP2 alone. There is no Ulysses
-group, so the DiT attention sequence is not sharded and activation memory is
-higher than in the four-GPU profile at the same output shape. Re-measure
-peak memory before increasing the output shape, the reference-input length,
-or concurrency.
-
-```bash
-export MODEL_ROOT=/path/to/MiniMax-H3
-export MODEL="${MODEL_ROOT}/FL2VA"
-export PORT=8000
-
-VLLM_OMNI_VIDEO_SYNC_TIMEOUT=1800 \
-vllm serve "${MODEL}" \
-  --omni \
-  --host 0.0.0.0 \
-  --port "${PORT}" \
-  --trust-remote-code \
-  --num-gpus 2 \
-  --tensor-parallel-size 2 \
-  --usp 1 \
-  --ring 1 \
-  --text-encoder-tp-size 2 \
-  --vae-patch-parallel-size 2 \
-  --vae-parallel-mode tile \
-  --vae-use-tiling \
-  --diffusion-attention-backend CUDNN_ATTN
-```
-
-## Four-GPU serving configuration
-
-Four GPUs keep TP2 for the weight shard and add Ulysses2, which shards the
-attention sequence and lowers per-GPU activation memory. Text-encoder TP4
-and VAE patch parallelism 4 spread the Qwen3-VL encoder and tiled decode
-across all four GPUs. TP1 is not an option on this card: an unsharded BF16
-partition does not fit in 96 GiB.
-
-```bash
-export MODEL_ROOT=/path/to/MiniMax-H3
-export MODEL="${MODEL_ROOT}/FL2VA"
-export PORT=8000
-
-VLLM_OMNI_VIDEO_SYNC_TIMEOUT=1800 \
-vllm serve "${MODEL}" \
-  --omni \
-  --host 0.0.0.0 \
-  --port "${PORT}" \
-  --trust-remote-code \
-  --num-gpus 4 \
-  --tensor-parallel-size 2 \
-  --usp 2 \
-  --ring 1 \
-  --text-encoder-tp-size 4 \
-  --vae-patch-parallel-size 4 \
-  --vae-parallel-mode tile \
-  --vae-use-tiling \
-  --diffusion-attention-backend CUDNN_ATTN
-```
-
-## Eight-GPU serving configuration
-
-Eight GPUs extend the Ulysses degree to 4 while keeping TP2. This does not
-lower per-GPU weight residency relative to the four-GPU profile; it lowers
-activation memory and per-step latency by sharding the attention sequence
-four ways.
-
-```bash
-export MODEL_ROOT=/path/to/MiniMax-H3
-export MODEL="${MODEL_ROOT}/FL2VA"
-export PORT=8000
-
-VLLM_OMNI_VIDEO_SYNC_TIMEOUT=1800 \
-vllm serve "${MODEL}" \
-  --omni \
-  --host 0.0.0.0 \
-  --port "${PORT}" \
-  --trust-remote-code \
-  --num-gpus 8 \
-  --tensor-parallel-size 2 \
-  --usp 4 \
-  --ring 1 \
-  --text-encoder-tp-size 8 \
-  --vae-patch-parallel-size 8 \
-  --vae-parallel-mode tile \
-  --vae-use-tiling \
-  --diffusion-attention-backend CUDNN_ATTN
-```
-
-### Rank ordering across two sockets
-
-With TP2 and Ulysses4 the two Ulysses groups are ranks `(0,2,4,6)` and
-`(1,3,5,7)`, and the four tensor-parallel pairs are `(0,1)`, `(2,3)`,
-`(4,5)`, and `(6,7)`. On a dual-socket host these two collectives cannot
-both be socket-local. If you choose to pin the device order, the trade-off
-is:
-
-| Device order | Socket-local collective | Crosses sockets |
-| --- | --- | --- |
-| `0,4,1,5,2,6,3,7` | Ulysses all-to-all | TP all-reduce |
-| `0,1,2,3,4,5,6,7` | TP all-reduce | Ulysses all-to-all |
-
-The first order assumes physical GPUs `0-3` are on NUMA node 0 and `4-7` on
-node 1. Reproduce that relationship on the target host rather than copying
-the IDs. The reported measurements use the default order and no NUMA
-binding; the two orders were not compared on this host.
-
-### Headroom variant: TP4 with Ulysses2
-
-Raising the tensor-parallel degree to 4 halves per-GPU weight residency and
-leaves room for long Ref2VA references, larger output shapes, or
-concurrency greater than one. It also adds a four-way all-reduce per DiT
-block over a link without NVLink. Use it when the TP2 profile runs out of
-headroom, not as the default. Replace the parallel flags above with:
-
-```bash
-  --tensor-parallel-size 4 \
-  --usp 2 \
-```
-
-### Two independent four-GPU servers
-
-An eight-GPU host can instead run one FL2VA server and one Ref2VA server
-side by side, each pinned to its own NUMA node with the four-GPU
-configuration. This removes every cross-socket collective and lifts the
-one-server-at-a-time restriction, at the cost of doubling the requirements
-to 400 GiB available system RAM and 270 GiB of model storage.
-
-Use `CUDA_VISIBLE_DEVICES=0,2,1,3`, `--cpunodebind=0 --membind=0`, and
-`--port 8091` for the FL2VA server. Use `CUDA_VISIBLE_DEVICES=4,6,5,7`,
-`--cpunodebind=1 --membind=1`, and `--port 8092` for the Ref2VA server.
-This layout is described for completeness and was not part of the measured
-runs.
-
-## Shared serving notes
-
-Do not add `--enforce-eager` for a performance run. Warm the server before
-measuring so regional compilation is outside the measured request. On this
-host the first request after startup ran 19% slower than the steady state
-(107.58 s versus 90.48 s on eight GPUs); two warmup requests were enough to
-converge.
-
-For Ref2VA on a single-server layout, stop the FL2VA server and restart the
-same command with `MODEL="${MODEL_ROOT}/Ref2VA"`.
-
-## Attention backend
-
-RTX PRO 6000 Blackwell is an SM120 part, so the datacenter-Blackwell
-`TRTLLM_ATTN` default does not apply here. Every configuration above selects
-cuDNN BF16 attention explicitly. This keeps the recipe independent of
-platform-default backend changes and does not depend on the experimental
-SM120 kernel.
-
-## Optional: online FP8
-
-`--quantization fp8` quantizes eligible DiT linears at load time and is
-compatible with tensor parallelism and VAE tiling. Use it when the resident
-BF16 peak leaves too little headroom for long Ref2VA references, a larger
-output shape, or concurrency greater than one. It cannot be combined with
-layerwise offload.
-
-## Target-hardware validation
-
-T2VA, 1344×768, `duration=5.0`, `fps=24`, 50 steps, `flow_shift=12`,
-`seed=1101`, BF16, `CUDNN_ATTN`, tiled VAE, one request at a time. Servers
-were started with the commands above — default device order, no NUMA
-binding — plus `--enable-diffusion-pipeline-profiler`. Two warmup requests
-preceded each measured request.
-
-| Measurement | Two GPUs | Four GPUs | Eight GPUs |
-| --- | ---: | ---: | ---: |
-| Topology | TP2 × USP1 | TP2 × USP2 | TP2 × USP4 |
-| Text encode | 0.040 s | 0.044 s | 0.035 s |
-| Denoise, 50 steps | 278.55 s | 168.73 s | 87.90 s |
-| Per step | 5.571 s | 3.375 s | 1.758 s |
-| VAE decode | 5.396 s | 2.791 s | 1.798 s |
-| Client E2E | 284.76 s | 172.32 s | 90.48 s |
-| Peak HBM per GPU | 77.49 GiB | 66.44 GiB | 61.07 GiB |
-| Headroom below 96 GiB | 18.5 GiB | 29.6 GiB | 34.9 GiB |
-
-Stage times are read from the `X-Stage-Durations` response header of
-`/v1/videos/sync`. Peak memory is the maximum of `nvidia-smi
---query-gpu=memory.used` sampled at 1 Hz across every device for the
-duration of the measured request. Per step is denoise wall time divided by
-the 50 requested steps. Stage times sum to roughly 0.75 s less than
-end-to-end in all three configurations; queueing, result transfer, and MP4
-muxing sit outside the profiled stages.
-
-### Scaling behaviour
-
-Denoise speedup is not uniform across the sweep: 1.65× from two to four
-GPUs (83% efficiency) but 1.92× from four to eight (96%). Two GPUs run
-Ulysses1, so there is no sequence parallelism at all; going to four GPUs
-pays the one-time cost of introducing the all-to-all, while going from four
-to eight only widens an all-to-all that already exists. Expect the second
-doubling to pay off better than the first on this topology.
-
-VAE decode behaves in the opposite direction — 1.93× from two to four GPUs
-but only 1.55× from four to eight — as patch parallelism produces smaller
-tiles and tile-boundary overhead grows.
-
-Text encoding is 0.04 s in every configuration and is not worth optimising;
-denoise is 96-98% of end-to-end throughout.
-
-### Per-GPU memory model
-
-Peak memory falls by 11.05 GiB from two to four GPUs and by a further
-5.37 GiB from four to eight — halving each time the Ulysses degree doubles,
-because all three configurations shard the DiT with TP2 and hold identical
-weights per GPU. Fitting the first two points gives:
-
-```
-peak HBM per GPU ≈ 55.4 GiB + 22.1 GiB / ulysses_degree
-```
-
-That fit predicts 60.9 GiB at Ulysses4 against 61.07 GiB measured, a 0.25%
-error. The practical consequence is a floor near 55 GiB per GPU: adding
-GPUs past eight will not reduce the per-GPU requirement further. To serve
-on smaller cards, raise the tensor-parallel degree instead.
-
-Re-measure memory for longer reference inputs, concurrency greater than
-one, or a different output shape.
-
-### Not yet measured
-
-Ref2VA latency and memory have not been measured on this host. Ref2VA
-drives the Qwen3-VL presentation from tens of tokens for text-only prompts
-to several thousand with image or video references, so T2VA numbers do not
-transfer to it. The eight-GPU device-order comparison in the section above
-was also not run.
-
-## T2VA request example
-
-`t2va` requires an explicit `aspect_ratio` even when `width` and `height`
-are supplied.
-
-```bash
-export API_URL="http://127.0.0.1:${PORT}/v1/videos/sync"
-
-curl -sS --max-time 1800 -X POST "${API_URL}" \
-  -F 'prompt=At night, three cats march into a bedroom playing tiny brass instruments, then abruptly file out, with synchronized room ambience.' \
-  -F 'width=1344' \
-  -F 'height=768' \
-  -F 'aspect_ratio=16:9' \
-  -F 'fps=24' \
-  -F 'num_inference_steps=50' \
-  -F 'flow_shift=12' \
-  -F 'seed=1101' \
-  -F 'extra_params={"task":"t2va","duration":5.0,"audio_flow_shift":3.0}' \
-  -o t2va.mp4
-```
-
-To collect the stage breakdown and peak memory for a request, add
-`--enable-diffusion-pipeline-profiler` to the server command and `-D
-headers.txt` to the curl invocation, then read `X-Stage-Durations` and
-`X-Peak-Memory-MB` from the saved headers.
-
-## Ref2VA request example
-
-`ref2va` defaults to a 16:9 aspect ratio, so `aspect_ratio` is optional
-here.
-
-```bash
-curl -X POST "http://127.0.0.1:${PORT}/v1/videos/sync" \
-  --fail-with-body -w '\nHTTP %{http_code}\n' \
-  --max-time 1200 \
-  -F "input_reference=@/root/hand.jpg;type=image/jpeg" \
-  -F "audio_reference=</root/audio_ref.json" \
-  -F "prompt=2D动画融合在一起的影像。夕阳余晖残留在窗边，生活感十足的小厨房里有旧木桌、洗到一半的马克杯、起雾的玻璃瓶、悬挂的抹布。画面带有智能手机单手拍摄的手抖、近距离对焦的犹豫、逆光曝光波动。要像在家中慌忙拍下某个不可思议事件的自然质感，不要广告影像的精心整理。声音只用厨房环境声与手绘生物柔和的电子音、小小的叫声。" \
-  -F 'width=1344' -F 'height=768' -F 'fps=24' \
-  -F 'num_inference_steps=60' -F 'flow_shift=12' -F 'seed=1101' \
-  -F 'extra_params={"task":"ref2va","duration":8,"audio_flow_shift":3.0}' \
-  -o /root/out_ref2va.mp4
-```

recipes/MiniMaxAI/MiniMax-H3-Spark-GB10.md

@@ -1,262 +0,0 @@
-# MiniMax-H3 on DGX Spark (GB10)
-
-This recipe uses online FP8 weight quantization, tiled VAE decode, and a single
-resident partition. GB10 is a unified-memory platform, so unlike the discrete-GPU
-recipes it uses **no offload of any kind** — see the capacity note below.
-
-Validated on:
-
-- Host: DGX Spark (GB10), aarch64
-- GPUs: 1 (unified memory)
-- Driver: <FILL_DRIVER_VERSION>
-- vLLM: 0.26.0
-- vLLM-Omni: `main` at `e1aa6eae75c460cd1893bc320546e81e66973831`
-- Workloads: T2VA and Ref2VA, 960x576, `duration=8.0`, 50 steps, `flow_shift=12`,
-  `seed=1101`
-
-
-## Capacity requirements
-
-| Resource | DGX Spark (GB10) |
-| --- | ---: |
-| Unified memory | 128 GB LPDDR5X (~121 GiB usable) |
-| GPUs | 1 |
-| Checkpoint storage | 135 GiB per partition |
-| Measured peak allocator high-water, T2VA (FP8) | 97.7 GiB |
-| Measured peak allocator high-water, Ref2VA (FP8) | 102.8 GiB |
-
-`FL2VA` and `Ref2VA` are separate 135 GiB checkpoint partitions. Start one server
-at a time.
-
-On GB10 the CPU and GPU share one physical memory pool, which changes the capacity
-math relative to the RTX and datacenter recipes:
-
-- **Do not use `--enable-distributed-layerwise-offload`.** DLO stages rank-local
-  weights into pinned host memory, which on GB10 is the same pool the weights are
-  already in. Peak usage roughly doubles and the process is killed by the OOM
-  killer (`Exit code: -9`) shortly after `Enabling offloader backend`.
-- **Do not use `--enable-cpu-offload`.** Moving weights from "VRAM" to "host RAM"
-  frees nothing here.
-- **`--quantization fp8` is mandatory.** A BF16 partition is 135 GiB and does not
-  fit in 121 GiB. Online FP8 quantizes the DiT only (62 GiB to ~31 GiB); the
-  Qwen3-VL text encoder and both VAEs stay BF16.
-
-With ~97.7 GiB of the pool held by the allocator at peak, T2VA leaves roughly
-23 GiB of headroom for the OS, page cache, and any other process on the box.
-Ref2VA is tighter on both counts. The allocator high-water rises to 102.8 GiB,
-and the allocator figure understates true pool usage.
-
-Sampling `/proc/meminfo` at 1 Hz across a 50-step Ref2VA request on a host with
-`MemTotal: 127600524 kB` (121.7 GiB), `MemAvailable` started at 8.1 GiB, fell to
-a trough of **7.4 GiB**, and recovered to 8.9 GiB. At the trough the pool held
-roughly **114.3 GiB** — 11.4 GiB more than the 102.8 GiB the allocator reports,
-which is the CUDA context, non-PyTorch allocations, and page cache the header
-does not count.
-
-Treat this platform as strictly single-tenant for Ref2VA. There is no room for a
-second process, and adding reference images, reference videos, or a larger output
-shape will run into the OOM killer.
-
-## One GB10: 960x576, 8 seconds
-
-```bash
-CUDA_VISIBLE_DEVICES=0 \
-FLASHINFER_DISABLE_VERSION_CHECK=1 \
-VLLM_WORKER_MULTIPROC_METHOD=spawn \
-VLLM_OMNI_VIDEO_SYNC_TIMEOUT=7200 \
-vllm serve /path/to/MiniMax-H3/FL2VA \
-  --omni --trust-remote-code --host 0.0.0.0 --port 8000 \
-  --init-timeout 3600 \
-  --num-gpus 1 --tensor-parallel-size 1 --text-encoder-tp-size 1 \
-  --usp 1 --ring 1 --vae-patch-parallel-size 1 \
-  --vae-parallel-mode tile --vae-use-tiling \
-  --quantization fp8 --enforce-eager \
-  --diffusion-attention-backend CUDNN_ATTN
-```
-
-Wait for `Application startup complete`, then run a short smoke test before
-committing to a full 50-step request.
-
-`t2va` requires an explicit `aspect_ratio` even when `width` and `height` are
-supplied; omitting it fails the request with
-`OmniClientError: t2va requires an explicit aspect_ratio`.
-
-```bash
-curl --fail-with-body -sS -X POST http://127.0.0.1:8000/v1/videos/sync \
-  -F 'prompt=At night, three cats march into a bedroom playing tiny brass instruments, then abruptly file out, with synchronized room ambience.' \
-  -F 'width=960' \
-  -F 'height=576' \
-  -F 'aspect_ratio=16:9' \
-  -F 'fps=24' \
-  -F 'num_inference_steps=10' \
-  -F 'flow_shift=12' \
-  -F 'seed=1101' \
-  -F 'extra_params={"task":"t2va","duration":4.0,"audio_flow_shift":3.0}' \
-  -o h3-gb10-smoke.mp4
-```
-
-The full-quality request uses 50 steps:
-
-```bash
-curl --fail-with-body -sS -X POST http://127.0.0.1:8000/v1/videos/sync \
-  -F 'prompt=At night, three cats march into a bedroom playing tiny brass instruments, then abruptly file out, with synchronized room ambience.' \
-  -F 'width=960' \
-  -F 'height=576' \
-  -F 'aspect_ratio=16:9' \
-  -F 'fps=24' \
-  -F 'num_inference_steps=50' \
-  -F 'flow_shift=12' \
-  -F 'seed=1101' \
-  -F 'extra_params={"task":"t2va","duration":8.0,"audio_flow_shift":3.0}' \
-  -o h3-gb10-t2va.mp4
-
-ffprobe -v error -show_entries \
-  stream=index,codec_name,width,height,r_frame_rate,sample_rate,channels \
-  -of json h3-gb10-t2va.mp4
-```
-
-Both requests return `200 OK` with an MP4 body: H.264 video at the requested
-geometry plus a 32 kHz stereo AAC track.
-
-## One GB10: Ref2VA, 960x576, 8 seconds
-
-`FL2VA` and `Ref2VA` are separate 135 GiB partitions and only one fits at a time.
-Stop the FL2VA server before starting this one.
-
-```bash
-CUDA_VISIBLE_DEVICES=0 \
-FLASHINFER_DISABLE_VERSION_CHECK=1 \
-VLLM_WORKER_MULTIPROC_METHOD=spawn \
-VLLM_OMNI_VIDEO_SYNC_TIMEOUT=14400 \
-vllm serve /path/to/MiniMax-H3/Ref2VA \
-  --omni --trust-remote-code --host 0.0.0.0 --port 8000 \
-  --init-timeout 3600 \
-  --num-gpus 1 --tensor-parallel-size 1 --text-encoder-tp-size 1 \
-  --usp 1 --ring 1 --vae-patch-parallel-size 1 \
-  --vae-parallel-mode tile --vae-use-tiling \
-  --quantization fp8 --enforce-eager \
-  --diffusion-attention-backend CUDNN_ATTN
-```
-
-The 7200 s timeout used for T2VA is too short here: a 50-step Ref2VA request
-takes over an hour on this platform. Use 14400 s.
-
-Unlike `t2va`, `ref2va` defaults to a 16:9 output ratio, so `aspect_ratio` can be
-omitted when `width` and `height` are supplied.
-
-```bash
-curl --fail-with-body -sS -X POST http://127.0.0.1:8000/v1/videos/sync \
-  -D ref2va-headers.txt \
-  -w 'client_e2e_s=%{time_total}\n' \
-  --max-time 14400 \
-  -F 'input_reference=@/path/to/reference.jpg;type=image/jpeg' \
-  -F 'prompt=A hand rests on a wooden table in warm evening light, then slowly opens and turns toward the camera, with quiet room ambience.' \
-  -F 'width=960' \
-  -F 'height=576' \
-  -F 'fps=24' \
-  -F 'num_inference_steps=50' \
-  -F 'flow_shift=12' \
-  -F 'seed=1101' \
-  -F 'extra_params={"task":"ref2va","duration":8.0,"audio_flow_shift":3.0}' \
-  -o h3-gb10-ref2va.mp4
-```
-
-The response is `200 OK` with an MP4 body: H.264 at the requested geometry plus a
-32 kHz stereo AAC track. `ffprobe` reports 8.032 s for `duration=8.0`.
-
-## Measured latency
-
-Single GB10, FP8, eager, cuDNN attention, tiled VAE, one request at a time:
-
-| Workload | Steps | Text encode | Denoise | Per step | VAE decode | MP4 mux | Client E2E | Peak memory |
-| --- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: |
-| T2VA, 4 s, smoke shape | 10 | — | — | 8.68 s | — | 0.49 s | 95.9 s | — |
-| T2VA, 8 s, 960x576 | 50 | 0.25 s | 2088 s | 42.61 s | 70 s | 4.84 s | 2169.4 s (36 min 9 s) | 97.7 GiB |
-| Ref2VA, 8 s, 960x576, one image (3 runs) | 10 | 8.43-10.94 s | 665.5-748.8 s | 66.55-74.88 s | 67.9-69.8 s | 3.63 s | 770.3-861.1 s | 102.8 GiB |
-| Ref2VA, 8 s, 960x576, one image | 50 | 9.66 s | 4039.6 s | 80.79 s | 69.4 s | 3.63 s | 4157.0 s (69 min 17 s) | 102.8 GiB |
-
-How these were measured:
-
-- Stage times come from `--enable-diffusion-pipeline-profiler`, read from the
-  `X-Stage-Durations` response header of `/v1/videos/sync`. The keys are
-  `MiniMaxH3Pipeline.encode_prompt` (text encode),
-  `MiniMaxH3Pipeline.diffuse` (denoise), and `MiniMaxH3Pipeline.decode`
-  (VAE decode).
-- MP4 mux is not a header field. It is the
-  `Video response encoding (MP4 bytes)` line in the server log.
-- Peak memory is the `X-Peak-Memory-MB` header, which reports the CUDA allocator
-  high-water mark (`torch.cuda.max_memory_reserved`) for the request. It excludes
-  the CUDA context and non-PyTorch allocations, so true pool usage is somewhat
-  higher. `nvidia-smi --query-gpu=memory.used` is not usable on GB10 — unified
-  memory has no discrete VRAM to report and the field returns `[N/A]`. See the
-  capacity section for a `/proc/meminfo` cross-check.
-- For T2VA, text encode and VAE decode were measured on a separate 10-step run at
-  the same 960x576 / 8 s geometry. Both are independent of step count, so they
-  transfer to the 50-step row unchanged. The Ref2VA MP4 mux figure transfers the
-  same way, from the 10-step run at identical output geometry and duration.
-- Ref2VA peak memory is byte-identical between the 10-step and 50-step runs
-  (`X-Peak-Memory-MB: 105318.000` in both), confirming that peak is set by output
-  geometry and reference input rather than step count.
-- Stage times do not sum exactly to end-to-end. Queueing, result transfer, and
-  engine bookkeeping sit outside the profiled stages.
-
-Denoise dominates at 96% of end-to-end, so step count and geometry are the only
-knobs that materially change the wall clock on this platform.
-
-Ref2VA costs 1.92x the wall clock of T2VA at the same geometry and step count:
-4157.0 s versus 2169.4 s. Text encoding shows the most dramatic ratio — 9.66 s
-versus 0.25 s, 39x — but at 0.2% of end-to-end it is not what makes Ref2VA slow.
-The cost is in denoise. A single reference image expands the Qwen3-VL
-presentation to 6883 tokens (logged as `MiniMax H3 ref2va Qwen presentation:
-6883 tokens, 1 reference images`), and those tokens sit in the attention context
-of every denoise step. Per-step time rises from 42.61 s to 80.79 s, and that
-1.90x multiplies across all 50 steps. VAE decode is unchanged at ~69 s, as
-expected for an identical output shape.
-
-Repeated identical 10-step T2VA requests varied by up to 23% in denoise time
-(397 s to 490 s) on a machine that had already been under sustained load. Treat
-these as single-run measurements on one machine, not a throughput guarantee, and
-expect the slower end of that range for back-to-back jobs.
-
-Ref2VA per-step time is load-dependent in the same way. On an idle machine the
-first 10-step request ran at 66.55 s per step. The 50-step request that followed
-occupied the box for 69 consecutive minutes and averaged 80.79 s per step. Two
-further 10-step requests taken immediately afterwards settled at 74.88 s and
-74.31 s — 12.5% slower than the idle run, and within 0.8% of each other, so this
-is a stable thermal state rather than measurement noise.
-
-Plan for 81 s per step when sizing a full 50-step Ref2VA job; 66 s per step is
-only reachable on a cold machine and does not hold for the length of one request.
-
-Ref2VA text encode converged downward across the four runs (10.94 s, 9.66 s,
-8.52 s, 8.43 s) as first-touch costs fell away; treat ~8.5 s as steady state.
-VAE decode was stable at 67.9-69.8 s throughout.
-
-Set `VLLM_OMNI_VIDEO_SYNC_TIMEOUT` well above the expected request time — the
-default 1800 s is shorter than a 50-step run on this platform, and 7200 s is
-shorter than a 50-step Ref2VA run.
-
-## Known limitations
-
-- One GPU means no Ulysses, ring, TP, or VAE patch parallelism. Pass
-  `--usp 1 --ring 1 --vae-patch-parallel-size 1` explicitly if a profile or
-  script would otherwise supply higher degrees.
-- `--enforce-eager` is used because regional compile adds startup time without a
-  parallelism win on a single rank.
-- `t2va` rejects requests without an explicit `aspect_ratio`. When `width` and
-  `height` are both supplied they take precedence and `aspect_ratio` only has to
-  name one of the supported ratios.
-- Sustained load degrades throughput. See the variance notes above.
-- Only one partition is resident. For Ref2VA, stop the FL2VA server and restart
-  with `/path/to/MiniMax-H3/Ref2VA`. Ref2VA with one reference image is measured
-  above; reference videos and additional images push the token count higher and
-  were not measured. With `MemAvailable` bottoming out at 7.4 GiB during a
-  single-image request, there is very little room to grow — re-measure before
-  adding references or raising the output shape.
-- Ref2VA requires a vLLM-Omni build newer than `v0.26.0`. See the version note
-  under **Validated on**: FP8 weight loading and image-only Ref2VA are both
-  broken on `release/v0.26.0`.suggestion is  `v0.26.1`.
-- Online FP8 is incompatible with layerwise offload — the offload path produces a
-  weight stride the Cutlass FP8 kernel rejects. This is not a practical
-  restriction here since offload is unusable on GB10 anyway.
-```

recipes/MiniMaxAI/MiniMax-H3.md

@@ -58,10 +58,6 @@ FlashAttention-4 extra:
 uv pip install -e '.[fa4]'
 ```
 
-On AMD ROCm, install without the `[fa4]` extra (FA4 is CUDA-only) and use
-`--diffusion-attention-backend FLASH_ATTN`; see
-[AMD ROCm (gfx942 / gfx950)](#amd-rocm-gfx942--gfx950).
-
 `ffmpeg` and `ffprobe` must be available on `PATH`. They are used for
 reference-video preparation and MP4 output.
 
@@ -391,101 +387,6 @@ incompatible with H3 layerwise offload because the offload path produces a
 weight stride rejected by the Cutlass FP8 kernel. Use resident FP8 with tensor
 parallelism and VAE tiling instead.
 
-## AMD ROCm (gfx942 / gfx950)
-
-The sections above describe the NVIDIA CUDA path. This section covers the AMD
-ROCm path; use it instead of the CUDA commands when running on AMD Instinct
-GPUs.
-
-MiniMax H3 runs on AMD Instinct GPUs (gfx942 / gfx950) in BF16. Use
-`--diffusion-attention-backend FLASH_ATTN`, which resolves to AITER packed varlen
-attention on both architectures.
-
-Select the device with `HIP_VISIBLE_DEVICES` (not `CUDA_VISIBLE_DEVICES`), drop the
-CUDA-only `FLASHINFER_DISABLE_VERSION_CHECK`, and install without the `[fa4]` extra
-(FA4 is CUDA-only). The VAE uses AITER GroupNorm on ROCm.
-
-Install (ROCm wheel + source vLLM-Omni):
-
-```bash
-pip install "vllm==0.26.0+rocm723" \
-  --extra-index-url https://wheels.vllm.ai/rocm/0.26.0/rocm723
-VLLM_OMNI_TARGET_DEVICE=rocm pip install -e . --no-build-isolation
-```
-
-Prebuilt image: `vllm/vllm-omni-rocm:minimax-h3`. All tasks work out of the box:
-the image bundles TorchCodec (for image+audio Ref2VA) and `ffmpeg` (for
-video-reference Ref2VA).
-
-### ROCm single GPU
-
-Single GPU with model-level CPU offload keeps the Qwen3-VL encoder and DiT from
-being co-resident:
-
-```bash
-export MODEL="${MODEL_ROOT}/FL2VA"
-export PORT=8091
-
-HIP_VISIBLE_DEVICES=0 \
-VLLM_WORKER_MULTIPROC_METHOD=spawn \
-VLLM_OMNI_VIDEO_SYNC_TIMEOUT=1800 \
-vllm serve "${MODEL}" \
-  --omni --host 0.0.0.0 --port "${PORT}" --trust-remote-code \
-  --num-gpus 1 --enable-cpu-offload \
-  --diffusion-attention-backend FLASH_ATTN
-```
-
-### ROCm four GPUs
-
-The best-practice CUDA four-GPU configuration works on ROCm with the changes above.
-It mirrors the CUDA command including Ulysses sequence parallelism, VAE patch
-parallelism, and text-encoder tensor parallelism, with no CPU offload when the model
-shards across the GPUs:
-
-```bash
-export MODEL="${MODEL_ROOT}/FL2VA"
-export PORT=8091
-
-HIP_VISIBLE_DEVICES=0,1,2,3 \
-VLLM_WORKER_MULTIPROC_METHOD=spawn \
-VLLM_OMNI_VIDEO_SYNC_TIMEOUT=1800 \
-vllm serve "${MODEL}" \
-  --omni \
-  --host 0.0.0.0 \
-  --port "${PORT}" \
-  --trust-remote-code \
-  --num-gpus 4 \
-  --usp 4 \
-  --ring 1 \
-  --text-encoder-tp-size 4 \
-  --vae-patch-parallel-size 4 \
-  --vae-parallel-mode tile \
-  --vae-use-tiling \
-  --diffusion-attention-backend FLASH_ATTN
-```
-
-As on CUDA, H3 is CFG-distilled, so keep `--cfg-parallel-size` at 1, and the H3 VAE
-supports only its native `tile` mode. `--text-encoder-tp-size` is validated on
-gfx942; on gfx950 it was not exercised.
-
-### Validated ROCm evidence
-
-vLLM-Omni with MiniMax H3 support, BF16. gfx942 rows measured with the
-`vllm/vllm-omni-rocm:minimax-h3` image; gfx950 rows measured with the
-`0.26.0+rocm723` wheel (HIP 7.2).
-
-| Workload | Configuration | Observed result |
-|----------|---------------|-----------------|
-| T2VA, 1344x768, 209 frames, 50 steps | 4x gfx942 (MI300X), FLASH_ATTN, USP4, text-enc TP4, VAE PP4 tile | encode 0.09 s, denoise 244.04 s, decode 4.15 s, 267.42 s client E2E; H.264 24 FPS + 32 kHz stereo AAC |
-| FL2VA, 1344x768, 209 frames, 50 steps | 4x gfx942 (MI300X), FLASH_ATTN, USP4, text-enc TP4, VAE PP4 tile | encode 13.98 s, denoise 257.58 s, decode 4.11 s, 287.07 s client E2E; H.264 24 FPS + 32 kHz stereo AAC |
-| T2VA, 832x480, ~4 s, 40 steps | 1x gfx950 (MI350), FLASH_ATTN, CPU offload | valid MP4 (H.264 + synced audio); ~0.73 s/denoise-step (~1.37 it/s), ~55 s client E2E incl. warmup |
-
-gfx942 figures are the mean of three requests after one excluded warmup
-(external evidence: vllm-project/recipes#732). gfx950 figures are
-functional-correctness validations, not tuned throughput; the first request
-includes lazy regional compilation. MI325X (gfx942) and other MI355X SKUs are not
-listed until their own evidence is added.
-
 ## HTTP API examples
 
 The following requests use the synchronous endpoint so the returned body can
@@ -750,98 +651,19 @@ audio spectral cosine 0.9589 (minimum 0.80), and audio RMS ratio 0.9342. The
 resident per-GPU peak was 68.52 GiB for BF16 and 53.51 GiB for FP8, a 22%
 reduction.
 
-## TeaCache acceleration
-
-TeaCache reuses DiT block residuals across denoising steps when consecutive
-timestep embeddings are similar. MiniMax-H3 TeaCache is currently calibrated
-only for the FL2VA partition. In combined serving, FL2VA requests use TeaCache
-while Ref2VA requests run uncached; Ref2VA-only serving rejects TeaCache.
-
-TeaCache and Cache-DiT are mutually exclusive; pick one cache backend per server.
-
-The model-specific default and examples use `rel_l1_thresh=0.17`, which
-provided the best conservative speed/quality balance in the validated 107-frame
-T2VA workload. Lower values
-may produce few or no cache hits, while higher values can improve performance
-at the cost of output quality. Validate the threshold on representative
-prompts and generation settings before changing it.
-
-### Offline (Python API)
-
-Export the directory containing the `FL2VA` and `Ref2VA` subdirectories before
-running the Python example, for example `export MODEL_ROOT=/models/MiniMax-H3`.
-
-```python
-import os
-
-from vllm_omni import Omni
-from vllm_omni.inputs.data import OmniDiffusionSamplingParams
-
-omni = Omni(
-    model=os.path.join(os.environ["MODEL_ROOT"], "FL2VA"),
-    cache_backend="tea_cache",
-    cache_config={"rel_l1_thresh": 0.17},
-    trust_remote_code=True,
-    enable_cpu_offload=True,
-)
-outputs = omni.generate(
-    "A quiet cinematic night scene with matching ambient sound.",
-    OmniDiffusionSamplingParams(
-        height=256,
-        width=448,
-        num_frames=29,
-        fps=24,
-        num_inference_steps=50,
-        seed=42,
-        extra_args={
-            "task": "t2va",
-            "duration": 4.0,
-            "aspect_ratio": "16:9",
-            "flow_shift": 12.0,
-            "audio_flow_shift": 3.0,
-        },
-    ),
-)
-```
-
-### Online serving
-
-```bash
-vllm serve "${MODEL_ROOT}/FL2VA" \
-  --omni \
-  --trust-remote-code \
-  --cache-backend tea_cache \
-  --cache-config '{"rel_l1_thresh":0.17}'
-```
-
 ## Known limitations
 
-- TeaCache is calibrated for FL2VA only; Ref2VA requests run uncached.
 - Combined serving requires sibling `FL2VA` and `Ref2VA` directories, loads
   both task-specific DiTs, and loads shared components once from `FL2VA`.
 - H3 currently executes one generation request per diffusion batch.
 - The first regional-compile request is a warmup and should not be included in
   steady-state performance measurements.
-- The serving path accepts fewer references than the model supports. H3 documents up
-  to 9 images, 3 video clips, and 3 audio clips (12 files) per Omni Reference
-  request; the current vLLM-Omni path takes exactly one image plus one audio
-  reference, or one or more videos with no separate `audio_reference` (it uses the
-  source soundtracks).
-- The 768 px short-edge mode is available for T2VA and FL2VA; 1344x768 is the
-  documented 16:9 request shape.
-- `--cfg-parallel-size > 1` is rejected by design (CFG-distilled, no negative branch).
-- VAE patch parallelism requires size 1 or the full DiT group size and supports the
-  H3 native `tile` mode only.
-- A U2 x Ring2 hybrid currently fails with an attention-mask length mismatch; use
-  pure Ulysses.
 - Online FP8 is not compatible with layerwise offload.
-- TeaCache and Cache-DiT cannot be enabled on the same server.
 - Image+audio Ref2VA accepts exactly one image and one audio reference.
 - Video Ref2VA accepts one or more video files, but not an additional standalone
   audio reference.
-- Pure Ulysses still replicates the full DiT on every rank, so smaller-memory GPUs
-  cannot use `--usp N --tp 1` as a resident capacity path. Use DiT tensor parallelism
-  or model-level CPU offload; text-encoder TP alone is not sufficient.
+- VAE patch parallelism requires size 1 or the full DiT group size and supports
+  the H3 native `tile` mode only.
 
 ## Additional resources
 

recipes/README.md

@@ -19,11 +19,6 @@ add files for this repo. Use one Markdown file per model family by default.
 > Individual pages resolve at `https://recipes.vllm.ai/<provider>/<model-id>`,
 > e.g. [`bosonai/higgs-audio-v3-tts-4b`](https://recipes.vllm.ai/bosonai/higgs-audio-v3-tts-4b).
 
-The `Recipe` column in the Supported Models documentation links to the
-published recipe when available and otherwise to the corresponding recipe in
-this directory. The recipe files remain the source of truth for commands and
-validation details.
-
 Example layout:
 
 ```text

tests/config/test_config_factory.py

@@ -1805,7 +1805,6 @@ class TestQwen2_5OmniPipeline:
         assert s.owns_tokenizer is True
         assert s.engine_output_type == "latent"
         assert s.requires_multimodal_data is True
-        assert s.hf_config_name == "thinker_config"
 
     def test_talker(self):
         p = resolve_pipeline_config("qwen2_5_omni")
@@ -1814,7 +1813,6 @@ class TestQwen2_5OmniPipeline:
         s = p.get_stage(1)
         assert isinstance(s, StagePipelineConfig)
         assert s.input_sources == (0,)
-        assert s.hf_config_name == "talker_config"
         assert s.sampling_constraints["stop_token_ids"] == [8294]
         # thinker2talker was removed: qwen2_5_omni has no async_chunk support,
         # so sync_process_input_func always wins and custom_process_input_func
@@ -1831,7 +1829,6 @@ class TestQwen2_5OmniPipeline:
         assert s.execution_type == StageExecutionType.LLM_GENERATION
         assert s.final_output_type == "audio"
         assert s.engine_output_type == "audio"
-        assert s.hf_config_name == "thinker_config"
 
 
 class TestQwen3TTSPipeline:

tests/config/test_omni_config.py

@@ -39,7 +39,6 @@ from vllm_omni.config.stage_config import (
     load_deploy_config,
     merge_pipeline_deploy,
 )
-from vllm_omni.diffusion.diffusion_kv.config import DiffusionKVCacheMode
 from vllm_omni.engine.stage_init_utils import build_legacy_engine_args_dict
 
 pytestmark = [pytest.mark.core_model, pytest.mark.cpu]
@@ -967,24 +966,20 @@ def test_diffusion_config_from_kwargs_reuses_legacy_normalization(monkeypatch):
 
     cfg = omni_config_module._DiffusionConfigProjection.from_kwargs(
         diffusion_attention_backend="flash_attn",
-        fa_deterministic=True,
         kv_cache_dtype="fp8",
         kv_cache_skip_steps="0-1",
         kv_cache_skip_layers=[2],
         static_lora_scale=0.25,
-        diffusion_kv_mode="paged_scheduler",
         diffusers_load_kwargs=None,
         diffusers_call_kwargs=None,
     )
 
     assert cfg.diffusion_attention_config.default.backend == "flash_attn"
-    assert cfg.fa_deterministic is True
     assert cfg.diffusion_kv_cache_dtype == "fp8"
     assert cfg.diffusion_kv_cache_skip_step_indices == {0, 1}
     assert cfg.diffusion_kv_cache_skip_layer_indices == {2}
     assert cfg.lora_scale == 0.25
     assert cfg.cache_backend == "tea_cache"
-    assert cfg.diffusion_kv_mode is DiffusionKVCacheMode.PAGED_SCHEDULER
     assert cfg.diffusers_load_kwargs == {}
     assert cfg.diffusers_call_kwargs == {}
 
@@ -999,8 +994,6 @@ def test_from_pipeline_config_normalizes_diffusion_config_aliases_from_engine_ar
                 "stages:",
                 "  - stage_id: 0",
                 "    diffusion_attention_backend: flash_attn",
-                "    fa_deterministic: true",
-                "    diffusion_kv_mode: paged_scheduler",
             ]
         )
     )
@@ -1012,29 +1005,6 @@ def test_from_pipeline_config_normalizes_diffusion_config_aliases_from_engine_ar
 
     assert isinstance(stage, VllmOmniDiffusionStageConfig)
     assert stage.diffusion_config.diffusion_attention_config.default.backend == "flash_attn"
-    assert stage.diffusion_config.fa_deterministic is True
-    assert stage.diffusion_config.diffusion_kv_mode is DiffusionKVCacheMode.PAGED_SCHEDULER
-
-
-def test_from_pipeline_config_rejects_reserved_diffusion_kv_mode(tmp_path):
-    deploy_path = tmp_path / "dreamzero_reserved_diffusion_kv.yaml"
-    deploy_path.write_text(
-        "\n".join(
-            [
-                "pipeline: dreamzero",
-                "async_chunk: false",
-                "stages:",
-                "  - stage_id: 0",
-                "    diffusion_kv_mode: paged_worker_local",
-            ]
-        )
-    )
-
-    with pytest.raises(ValueError, match="reserved but not implemented"):
-        _from_pipeline_key(
-            "dreamzero",
-            deploy_config_path=str(deploy_path),
-        )
 
 
 def test_diffusion_config_field_classification_covers_current_fields():
@@ -1055,7 +1025,6 @@ def test_diffusion_config_field_classification_covers_current_fields():
         "enable_session_state_manager",
         "prompt_embed_cache_size",
         "diffusion_kv_cache_dtype",
-        "diffusion_kv_mode",
     } <= omni_config_module._DIFFUSION_ONLY_CONFIG_FIELDS
     assert {
         "revision",

tests/cosmos3/test_session_memory_equivalence.py

@@ -1,192 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-"""Equivalence and session-isolation tests for Cosmos3StateAdapter (RFC #4480).
-
-These drive the adapter directly with tiny CPU tensors -- no model, no GPU --
-and assert it stores/returns the per-branch UND K/V exactly and isolates
-durable state between sessions. They do not cover the transformer's mutable
-load-to-forward handoff.
-"""
-
-from __future__ import annotations
-
-import pytest
-import torch
-
-from vllm_omni.experimental.world_models.adapters.state_cosmos3_adapter import (
-    Cosmos3StateAdapter,
-)
-from vllm_omni.experimental.world_models.session_state import SessionStateManager
-
-pytestmark = [pytest.mark.core_model, pytest.mark.cpu]
-
-LAYERS = 3
-SEQ, DIM = 3, 4
-
-
-def _fake_cached_kv(seed: int) -> list[tuple[torch.Tensor, torch.Tensor]]:
-    """A list of per-layer (K, V); contents seeded so branches/sessions differ."""
-    g = torch.Generator().manual_seed(seed)
-    return [(torch.randn(1, SEQ, DIM, generator=g), torch.randn(1, SEQ, DIM, generator=g)) for _ in range(LAYERS)]
-
-
-def _adapter(session_id: str, manager: SessionStateManager | None = None) -> Cosmos3StateAdapter:
-    # `manager is not None`, not `manager or ...`: an empty SessionStateManager is
-    # falsy (it defines __len__), so `or` would silently swap in a fresh manager.
-    return Cosmos3StateAdapter(session_id, manager if manager is not None else SessionStateManager())
-
-
-def _assert_kv_equal(got: list | None, expected: list) -> None:
-    assert got is not None and len(got) == len(expected) == LAYERS
-    for (k, v), (ke, ve) in zip(got, expected):
-        torch.testing.assert_close(k, ke)
-        torch.testing.assert_close(v, ve)
-
-
-class _FakeTransformer:
-    cached_kv = None
-    cached_freqs_gen = None
-
-
-# ----------------------------- equivalence -----------------------------
-
-
-def test_branch_starts_uninitialized() -> None:
-    a = _adapter("s0")
-    for is_neg in (False, True):
-        assert a.is_branch_initialized(is_neg) is False
-        assert a.get_branch_kv(is_neg) is None
-
-
-def test_set_then_get_roundtrip() -> None:
-    a = _adapter("s0")
-    kv = _fake_cached_kv(1)
-    a.set_branch_kv(False, kv)
-    assert a.is_branch_initialized(False) is True
-    _assert_kv_equal(a.get_branch_kv(False), kv)
-
-
-def test_cond_uncond_branches_isolated() -> None:
-    a = _adapter("s0")
-    kv_pos, kv_neg = _fake_cached_kv(1), _fake_cached_kv(2)
-    a.set_branch_kv(False, kv_pos)
-    a.set_branch_kv(True, kv_neg)
-    _assert_kv_equal(a.get_branch_kv(False), kv_pos)
-    _assert_kv_equal(a.get_branch_kv(True), kv_neg)
-
-
-def test_encode_once_capture_is_noop_when_initialized() -> None:
-    a = _adapter("s0")
-    kv = _fake_cached_kv(1)
-    a.set_branch_kv(False, kv)
-    tr = _FakeTransformer()
-    tr.cached_kv = _fake_cached_kv(999)  # different content
-    a.capture_from_transformer(tr, False)
-    _assert_kv_equal(a.get_branch_kv(False), kv)  # still the first one
-
-
-def test_capture_writes_when_uninitialized() -> None:
-    a = _adapter("s0")
-    tr = _FakeTransformer()
-    tr.cached_kv = _fake_cached_kv(5)
-    tr.cached_freqs_gen = (torch.ones(2), torch.zeros(2))
-    a.capture_from_transformer(tr, False)
-    _assert_kv_equal(a.get_branch_kv(False), _fake_cached_kv(5))
-
-
-def test_capture_without_freqs_does_not_partially_initialize() -> None:
-    a = _adapter("s0")
-    tr = _FakeTransformer()
-    tr.cached_kv = _fake_cached_kv(5)
-    tr.cached_freqs_gen = None
-    with pytest.raises(RuntimeError, match="did not produce GEN RoPE frequencies"):
-        a.capture_from_transformer(tr, False)
-    assert a.is_branch_initialized(False) is False
-
-
-def test_load_into_transformer_without_freqs_sets_kv() -> None:
-    a = _adapter("s0")
-    kv = _fake_cached_kv(1)
-    a.set_branch_kv(False, kv)
-    tr = _FakeTransformer()
-    a.load_into_transformer(tr, False)
-    _assert_kv_equal(tr.cached_kv, kv)
-    assert tr.cached_freqs_gen is None
-
-
-def test_set_then_get_preserves_per_layer_values() -> None:
-    # The stored value must reflect the tensors passed in (per-layer).
-    a = _adapter("s0")
-    kv = _fake_cached_kv(3)
-    a.set_branch_kv(False, kv)
-    _assert_kv_equal(a.get_branch_kv(False), kv)
-
-
-def test_load_into_transformer_restores_kv_and_freqs_gen() -> None:
-    a = _adapter("s0")
-    tr = _FakeTransformer()
-    tr.cached_kv = _fake_cached_kv(3)
-    tr.cached_freqs_gen = (torch.ones(2), torch.zeros(2))
-    a.capture_from_transformer(tr, False)
-    tr.cached_kv = None
-    tr.cached_freqs_gen = None
-    a.load_into_transformer(tr, False)
-    _assert_kv_equal(tr.cached_kv, _fake_cached_kv(3))
-    assert tr.cached_freqs_gen is not None
-    torch.testing.assert_close(tr.cached_freqs_gen[0], torch.ones(2))
-
-
-def test_partial_branch_raises() -> None:
-    a = _adapter("s0")
-    a.set_branch_kv(False, _fake_cached_kv(1))
-    a._session.get("und_kv/1").reset()  # type: ignore[union-attr]
-    with pytest.raises(RuntimeError, match="partially initialized"):
-        a.get_branch_kv(False)
-
-
-# ----------------------------- reset -----------------------------
-
-
-def test_reset_clears_both_branches() -> None:
-    a = _adapter("s0")
-    a.set_branch_kv(False, _fake_cached_kv(1))
-    a.set_branch_kv(True, _fake_cached_kv(2))
-    a.reset()
-    for is_neg in (False, True):
-        assert a.is_branch_initialized(is_neg) is False
-        assert a.get_branch_kv(is_neg) is None
-
-
-# ----------------------- concurrency isolation (core) -----------------------
-
-
-def test_two_sessions_do_not_clobber() -> None:
-    manager = SessionStateManager()
-    a, b = _adapter("A", manager), _adapter("B", manager)
-    kv_a, kv_b = _fake_cached_kv(10), _fake_cached_kv(20)
-    a.set_branch_kv(False, kv_a)
-    assert b.is_branch_initialized(False) is False  # B not seeing A's state
-    b.set_branch_kv(False, kv_b)
-    _assert_kv_equal(a.get_branch_kv(False), kv_a)
-    _assert_kv_equal(b.get_branch_kv(False), kv_b)
-
-
-def test_fresh_adapter_same_session_sees_prior_state() -> None:
-    manager = SessionStateManager()
-    kv = _fake_cached_kv(7)
-    _adapter("s", manager).set_branch_kv(False, kv)
-    again = Cosmos3StateAdapter("s", manager)
-    _assert_kv_equal(again.get_branch_kv(False), kv)
-
-
-def test_adapter_survives_session_lru_eviction() -> None:
-    # An in-use adapter keeps its state even after the manager evicts its
-    # session id from the lookup table (LRU bounded by max_sessions).
-    manager = SessionStateManager(max_sessions=2)
-    a = _adapter("active", manager)
-    kv = _fake_cached_kv(11)
-    a.set_branch_kv(False, kv)
-    for i in range(3):
-        _adapter(f"other{i}", manager)
-    assert "active" not in manager
-    _assert_kv_equal(a.get_branch_kv(False), kv)

tests/dfx/perf/tests/test_wan22_i2v_vllm_omni.json

@@ -43,9 +43,9 @@
         ],
         "baseline": {
           "H100": {
-            "throughput_qps": 0.0361,
-            "latency_mean": 27.6206,
-            "peak_memory_mb_mean": 80548.0
+            "throughput_qps": 0.0434,
+            "latency_mean": 23.0654,
+            "peak_memory_mb_mean": 76360.0
           }
         }
       }
@@ -100,9 +100,9 @@
         ],
         "baseline": {
           "H100": {
-            "throughput_qps": 0.05263,
-            "latency_mean": 18.9415,
-            "peak_memory_mb_mean": 50053.7
+            "throughput_qps": 0.0606,
+            "latency_mean": 16.4928,
+            "peak_memory_mb_mean": 47164.8
           }
         }
       },
@@ -127,9 +127,9 @@
         ],
         "baseline": {
           "H100": {
-            "throughput_qps": 0.009595,
-            "latency_mean": 104.1058,
-            "peak_memory_mb_mean": 59718.0
+            "throughput_qps": 0.0124,
+            "latency_mean": 80.7784,
+            "peak_memory_mb_mean": 55374.3429
           }
         }
       }

tests/dfx/reliability/test_reliability_qwen3_omni.py

@@ -315,32 +315,11 @@ def _assert_post_fault_health_terminal(host: str, port: int, *, scenario: str) -
     pytest.fail(f"[{scenario} health] no terminal post-fault health observed: {last_observation}")
 
 
-def _assert_server_alive_after_fault(host: str, port: int, *, scenario: str) -> None:
-    """#4285: a single-stage engine death must NOT take down the API server.
-
-    The process must stay up and keep answering /health (503 is acceptable —
-    errored but listening). A connection-refused means the server process
-    exited, which is the bug this fix closes.
-    """
-    deadline = time.monotonic() + 15.0
-    responded = False
-    while time.monotonic() < deadline:
-        try:
-            status, _ = get_health_raw(host, port, timeout_sec=5)
-            responded = True
-            assert status in (200, 503), f"[{scenario} alive] unexpected /health status={status}"
-        except Exception as exc:  # noqa: BLE001
-            if _looks_like_server_unreachable(exc):
-                pytest.fail(f"[{scenario} alive] server exited after single-stage OOM (#4285): {exc!r}")
-            raise
-        time.sleep(0.5)
-    assert responded, f"[{scenario} alive] /health never responded after fault"
-
-
 QWEN_PARAMS = create_reliability_omni_server_params(RELIABILITY_SCENARIOS, DEPLOY_CONFIGS_DIR)
 
 
 @pytest.mark.slow
+@pytest.mark.skip(reason="issue#4285")
 @hardware_test(res={"cuda": "H100"}, num_cards=2)
 @pytest.mark.parametrize("omni_server_function", QWEN_PARAMS, indirect=True)
 def test_reliability_fault_gpu_oom_error_contract_consistent_chat_speech(
@@ -407,11 +386,9 @@ def test_reliability_fault_gpu_oom_error_contract_consistent_chat_speech(
     assert "code" in chat_error, f"chat error lacks code field: {chat_error!r}"
     assert "code" in speech_error, f"speech error lacks code field: {speech_error!r}"
 
-    # #4285: a single dead stage must not take the whole API server down.
-    _assert_server_alive_after_fault(host, port, scenario="oom_contract")
-
 
 @pytest.mark.slow
+@pytest.mark.skip(reason="issue#4285")
 @hardware_test(res={"cuda": "H100"}, num_cards=2)
 @pytest.mark.parametrize("omni_server_function", QWEN_PARAMS, indirect=True)
 def test_reliability_fault_gpu_oom_chat_large_payload_failure(omni_server_function, openai_client_function) -> None:
@@ -454,6 +431,7 @@ def test_reliability_fault_gpu_oom_chat_large_payload_failure(omni_server_functi
 
 
 @pytest.mark.slow
+@pytest.mark.skip(reason="issue#4285")
 @hardware_test(res={"cuda": "H100"}, num_cards=2)
 @pytest.mark.parametrize("omni_server_function", QWEN_PARAMS, indirect=True)
 def test_reliability_fault_gpu_oom_concurrent_pressure_failure(omni_server_function, openai_client_function) -> None:
@@ -796,11 +774,7 @@ def test_reliability_fault_process_kill_tree_with_load_fast_fail_and_cleanup(
 
 
 @pytest.mark.slow
-@pytest.mark.skip(
-    reason="needs request-scoped OOM recovery: under transient pressure the stage "
-    "EngineCore still dies and is evicted (not respawned), so the engine does not "
-    "recover after the hog stops. Out of scope for the #4285 fault-isolation fix."
-)
+@pytest.mark.skip(reason="issue#4285")
 @hardware_test(res={"cuda": "H100"}, num_cards=2)
 @pytest.mark.parametrize("omni_server_function", QWEN_PARAMS, indirect=True)
 def test_reliability_fault_gpu_oom_state_converges_after_fault_removed(

tests/diffusion/attention/test_flash_attn.py

@@ -9,10 +9,6 @@ This script tests two main scenarios:
 2. Case 2: Comparing FlashAttention and SDPA backends for batch_size=2 with padding
 """
 
-import sys
-from types import SimpleNamespace
-from unittest.mock import Mock
-
 import pytest
 import torch
 
@@ -375,264 +371,6 @@ def test_packed_varlen_metadata_must_be_complete(monkeypatch):
         impl.forward_cuda(query, query, query, metadata)
 
 
-# ---------------------------------------------------------------------------
-# NPU mask-free packed attention (PR #5891).
-#
-# These tests cover the backend-side NPU branches that ``forward_fa_npu`` adds:
-#   - boundary resolution in ``_resolve_packed_seq_npu`` (pure Python, no device
-#     sync, no MindIE-SD import);
-#   - env dispatch in ``forward_fa_npu`` (which op a packed forward takes);
-#   - the laser input pre-scaling math in ``_forward_prefix_kv_slice_npu``.
-#
-# They run on CPU without a real NPU by injecting a fake ``mindiesd`` module
-# into ``sys.modules`` (same pattern as ``test_paged_attention`` /
-# ``benchmarks/conftest``). No production code is exercised for real kernels.
-
-
-def _npu_impl(causal: bool = False) -> FlashAttentionImpl:
-    return FlashAttentionImpl(num_heads=2, head_size=4, softmax_scale=0.5, causal=causal)
-
-
-def _fake_mindiesd(monkeypatch, *, attention_forward=None, attention_forward_varlen=None):
-    """Install a stub ``mindiesd`` so local ``from mindiesd import ...`` works.
-
-    Restored automatically by monkeypatch. Defaults are no-op callables so the
-    top-level import in ``forward_fa_npu`` succeeds even when the test mocks the
-    instance methods that would otherwise call them.
-    """
-    fake = SimpleNamespace(
-        attention_forward=attention_forward or Mock(return_value=torch.zeros(1)),
-        attention_forward_varlen=attention_forward_varlen or Mock(return_value=torch.zeros(1)),
-    )
-    monkeypatch.setitem(sys.modules, "mindiesd", fake)
-    return fake
-
-
-# --- Test group A: boundary resolution (_resolve_packed_seq_npu) -------------
-
-
-def test_resolve_packed_seq_accepts_real_plus_pad_contract():
-    impl = _npu_impl()
-    q = torch.randn(1, 8, 2, 4)
-    cu = torch.tensor([0, 5, 8], dtype=torch.int32)
-    extra = {"cu_seqlens_q": cu, "cu_seqlens_k": cu, "max_seqlen_q": 5, "max_seqlen_k": 5}
-
-    assert impl._resolve_packed_seq_npu(q, q, extra) == ([5, 8], [5, 8])
-
-
-def test_resolve_packed_seq_single_document_no_padding():
-    impl = _npu_impl()
-    q = torch.randn(1, 8, 2, 4)
-    cu = torch.tensor([0, 8], dtype=torch.int32)
-    extra = {"cu_seqlens_q": cu, "cu_seqlens_k": cu, "max_seqlen_q": 8, "max_seqlen_k": 8}
-
-    assert impl._resolve_packed_seq_npu(q, q, extra) == ([8], [8])
-
-
-@pytest.mark.parametrize(
-    "case",
-    [
-        "causal",
-        "missing_key",
-        "batch_ne_1",
-        "cu_too_long",
-        "cu_qk_mismatch",
-        "max_seqlen_not_int",
-        "used_gt_total",
-        "used_zero",
-        "pad_longer_than_real",
-    ],
-)
-def test_resolve_packed_seq_rejects_malformed_metadata(case):
-    """Any metadata outside the exact [real, pad] contract returns None so the
-    caller falls back to the masked path."""
-    cu3 = torch.tensor([0, 5, 8], dtype=torch.int32)
-    cu2 = torch.tensor([0, 8], dtype=torch.int32)
-    cu4 = torch.tensor([0, 3, 5, 8], dtype=torch.int32)
-    cu_short = torch.tensor([0, 3, 8], dtype=torch.int32)
-    base = {"cu_seqlens_q": cu3, "cu_seqlens_k": cu3, "max_seqlen_q": 5, "max_seqlen_k": 5}
-    q = torch.randn(1, 8, 2, 4)
-
-    impl = _npu_impl(causal=(case == "causal"))
-    if case == "missing_key":
-        extra = {"cu_seqlens_q": cu3, "max_seqlen_q": 5}  # 3 of 4 keys absent
-    elif case == "batch_ne_1":
-        q = torch.randn(2, 8, 2, 4)
-        extra = base
-    elif case == "cu_too_long":
-        extra = {**base, "cu_seqlens_q": cu4, "cu_seqlens_k": cu4}
-    elif case == "cu_qk_mismatch":
-        extra = {**base, "cu_seqlens_q": cu3, "cu_seqlens_k": cu2}
-    elif case == "max_seqlen_not_int":
-        extra = {**base, "max_seqlen_q": 5.0}
-    elif case == "used_gt_total":
-        extra = {**base, "max_seqlen_q": 9}  # 9 > total length 8
-    elif case == "used_zero":
-        extra = {**base, "max_seqlen_q": 0, "max_seqlen_k": 0}
-    elif case == "pad_longer_than_real":
-        # real=3, pad=5: the real document must be the longer one.
-        extra = {
-            "cu_seqlens_q": cu_short,
-            "cu_seqlens_k": cu_short,
-            "max_seqlen_q": 3,
-            "max_seqlen_k": 3,
-        }
-    else:
-        extra = base
-
-    assert impl._resolve_packed_seq_npu(q, q, extra) is None
-
-
-# --- Test group B: env dispatch in forward_fa_npu (current behavior) --------
-
-
-def _npu_impl_with_mocked_paths() -> tuple[FlashAttentionImpl, torch.Tensor]:
-    """FlashAttentionImpl whose two packed paths are Mocks returning a sentinel.
-
-    Lets a test assert which branch ``forward_fa_npu`` dispatched to without
-    running any real kernel.
-    """
-    impl = _npu_impl()
-    sentinel = torch.tensor([1.0])
-    impl._forward_varlen_packed_npu = Mock(return_value=sentinel)
-    impl._forward_prefix_kv_slice_npu = Mock(return_value=sentinel)
-    return impl, sentinel
-
-
-def test_npu_env_unset_uses_varlen(monkeypatch):
-    monkeypatch.delenv("MINDIE_SD_FA_TYPE", raising=False)
-    _fake_mindiesd(monkeypatch)
-    impl, _ = _npu_impl_with_mocked_paths()
-    metadata = AttentionMetadata(extra={"npu_attn_varlen": True})
-
-    impl.forward_fa_npu(torch.randn(1, 8, 2, 4), *[torch.randn(1, 8, 2, 4)] * 2, metadata)
-
-    impl._forward_varlen_packed_npu.assert_called_once()
-    impl._forward_prefix_kv_slice_npu.assert_not_called()
-
-
-def test_npu_env_laser_uses_slice(monkeypatch):
-    monkeypatch.setenv("MINDIE_SD_FA_TYPE", "ascend_laser_attention")
-    _fake_mindiesd(monkeypatch)
-    impl, _ = _npu_impl_with_mocked_paths()
-    metadata = AttentionMetadata(extra={"npu_attn_varlen": True})
-
-    impl.forward_fa_npu(torch.randn(1, 8, 2, 4), *[torch.randn(1, 8, 2, 4)] * 2, metadata)
-
-    impl._forward_prefix_kv_slice_npu.assert_called_once()
-    impl._forward_varlen_packed_npu.assert_not_called()
-
-
-@pytest.mark.parametrize("fa_type", ["prompt_flash_attn", "fused_attn_score", "ascend_laser_attenton", "garbage"])
-def test_npu_env_non_laser_value_falls_back_to_varlen(monkeypatch, fa_type):
-    """Locks in the CURRENT dispatch behavior (PR #5891).
-
-    Any value other than ``ascend_laser_attention`` — including the valid
-    ``prompt_flash_attn``/``fused_attn_score`` and typos — is silently routed to
-    the TND varlen op, which cannot honor ``MINDIE_SD_FA_TYPE``. This is review
-    comment #1 on the PR and is intentionally captured here as a regression
-    net: update this test when the dispatch starts honoring/rejecting these.
-    """
-    monkeypatch.setenv("MINDIE_SD_FA_TYPE", fa_type)
-    _fake_mindiesd(monkeypatch)
-    impl, _ = _npu_impl_with_mocked_paths()
-    metadata = AttentionMetadata(extra={"npu_attn_varlen": True})
-
-    impl.forward_fa_npu(torch.randn(1, 8, 2, 4), *[torch.randn(1, 8, 2, 4)] * 2, metadata)
-
-    impl._forward_varlen_packed_npu.assert_called_once()
-    impl._forward_prefix_kv_slice_npu.assert_not_called()
-
-
-def test_npu_varlen_opt_in_unset_takes_mask_path(monkeypatch):
-    """Models that do not set ``npu_attn_varlen`` (e.g. Wan/Cosmos) keep using
-    the existing masked attention_forward path; the new branches are skipped."""
-    fake_forward = Mock(return_value=torch.zeros(1, 8, 2, 4))
-    _fake_mindiesd(monkeypatch, attention_forward=fake_forward)
-    impl, _ = _npu_impl_with_mocked_paths()
-    metadata = AttentionMetadata(
-        attn_mask=torch.tensor([[True, True, True, True, True, False, False, False]]),
-        extra={},  # no npu_attn_varlen opt-in
-    )
-    q = torch.randn(1, 8, 2, 4)
-
-    out = impl.forward_fa_npu(q, q, q, metadata)
-
-    impl._forward_varlen_packed_npu.assert_not_called()
-    impl._forward_prefix_kv_slice_npu.assert_not_called()
-    fake_forward.assert_called_once()
-    assert out is fake_forward.return_value
-
-
-# --- Test group C: laser input pre-scaling in _forward_prefix_kv_slice_npu --
-
-
-def test_prefix_kv_slice_applies_laser_input_scaling(monkeypatch):
-    monkeypatch.setenv("MINDIE_SD_FA_TYPE", "ascend_laser_attention")
-    captured: dict = {}
-
-    def fake_attention_forward(query, key, value, **kwargs):
-        captured["query"] = query
-        captured["key"] = key
-        captured["value"] = value
-        captured.update(kwargs)
-        return torch.ones_like(query)  # deterministic stand-in kernel output
-
-    _fake_mindiesd(monkeypatch, attention_forward=fake_attention_forward)
-    impl = _npu_impl()  # softmax_scale == 0.5
-    q = torch.randn(1, 8, 2, 4)
-    cu = torch.tensor([0, 5, 8], dtype=torch.int32)
-    extra = {
-        "cu_seqlens_q": cu,
-        "cu_seqlens_k": cu,
-        "max_seqlen_q": 5,
-        "max_seqlen_k": 5,
-        "laser_input_scale": 256.0,
-    }
-
-    out = impl._forward_prefix_kv_slice_npu(q, q, q, extra)
-
-    # K/V sliced to the valid prefix (used_k == 5); q keeps full length.
-    assert captured["key"].shape == (1, 5, 2, 4)
-    assert captured["value"].shape == (1, 5, 2, 4)
-    assert captured["query"].shape == (1, 8, 2, 4)
-    assert captured["attn_mask"] is None
-    # Pre-divide q/k/v by the factor and compensate the kernel scale by its
-    # square (exact for power-of-two); output scaled back by the factor.
-    torch.testing.assert_close(captured["query"], q / 256.0)
-    torch.testing.assert_close(captured["key"], q[:, :5] / 256.0)
-    assert captured["scale"] == pytest.approx(0.5 * 256.0**2)
-    torch.testing.assert_close(out, torch.ones(1, 8, 2, 4) * 256.0)
-
-
-def test_prefix_kv_slice_no_scaling_without_factor(monkeypatch):
-    monkeypatch.setenv("MINDIE_SD_FA_TYPE", "ascend_laser_attention")
-    captured: dict = {}
-
-    def fake_attention_forward(query, key, value, **kwargs):
-        captured["query"] = query
-        captured.update(kwargs)
-        return torch.ones_like(query)
-
-    _fake_mindiesd(monkeypatch, attention_forward=fake_attention_forward)
-    impl = _npu_impl()
-    q = torch.randn(1, 8, 2, 4)
-    cu = torch.tensor([0, 5, 8], dtype=torch.int32)
-    extra = {
-        "cu_seqlens_q": cu,
-        "cu_seqlens_k": cu,
-        "max_seqlen_q": 5,
-        "max_seqlen_k": 5,
-        # no laser_input_scale → no pre-scaling
-    }
-
-    out = impl._forward_prefix_kv_slice_npu(q, q, q, extra)
-
-    torch.testing.assert_close(captured["query"], q)
-    assert captured["scale"] == pytest.approx(0.5)
-    torch.testing.assert_close(out, torch.ones(1, 8, 2, 4))
-
-
 if __name__ == "__main__":
     print("Running FlashAttention Padding Tests...")
     print("=" * 60)

tests/diffusion/attention/test_rainfusion_plan.py

@@ -5,9 +5,10 @@
 
 The grids here are the ones MiniMax-H3 FL2VA actually produces for an 8.7s clip:
 1280x768 gives a 62x24x40 latent grid whose 59520 video rows land on the 128-row
-kernel block, and 1344x768 gives 62x24x42 whose 62496 rows do not. Both are
-handed to MindIE-SD: its rf_v2 preprocessing promotes an irregular video tail
-to the always-kept prefix segment before generating the block mask.
+kernel block, and 1344x768 gives 62x24x42 whose 62496 rows straddle it. Only the
+first can run sparse: rf_v2 tiles the sequence as a whole but pools its block
+mask from the video and prefix segments separately, so the two only agree on the
+block count when the video segment ends on a boundary.
 """
 
 import dataclasses
@@ -56,13 +57,10 @@ def test_block_aligned_video_segment_runs_sparse():
     assert plan.latent_shape == list(ALIGNED_GRID)
 
 
-def test_misaligned_video_segment_runs_sparse_with_updated_mindiesd():
-    plan = make_impl()._resolve_plan(make_metadata(MISALIGNED_GRID))
-
-    assert plan is not None
-    assert plan.prefix_len == PREFIX_ROWS
-    assert plan.used_len == PREFIX_ROWS + 62496
-    assert plan.latent_shape == list(MISALIGNED_GRID)
+def test_misaligned_video_segment_stays_dense():
+    # Realigning the sequence would mean padding it, and rf_v2 ignores attn_mask:
+    # pad keys would take a share of every real query's softmax denominator.
+    assert make_impl()._resolve_plan(make_metadata(MISALIGNED_GRID)) is None
 
 
 @pytest.mark.parametrize("prefix_len", [1, 127, _BLOCK_SIZE, 710, 900])
@@ -157,28 +155,3 @@ def test_fully_populated_mask_reproduces_dense_attention():
     ).transpose(1, 2)
     error = (out.float() - reference.float()).abs().mean() / reference.float().abs().mean()
     assert error < 2e-3, f"mean relative error {error:.4%} against dense attention"
-
-
-@pytest.mark.skipif(not current_omni_platform.is_npu(), reason="rf_v2 runs on Ascend NPU only.")
-def test_irregular_video_tail_reproduces_dense_attention_with_updated_mindiesd():
-    """vLLM forwards the full irregular grid; MindIE-SD owns its tail handling."""
-    torch.manual_seed(1)
-    prefix_len, grid = 384, (4, 16, 10)
-    video_len = grid[0] * grid[1] * grid[2]
-    used = prefix_len + video_len
-    heads, head_dim = 4, 128
-    q, k, v = (torch.randn(1, used, heads, head_dim, dtype=torch.bfloat16, device="npu") for _ in range(3))
-
-    impl = make_impl()
-    impl.rainfusion = dataclasses.replace(impl.rainfusion, sparsity=0.0)
-    plan = RainFusionPlan(prefix_len=prefix_len, used_len=used, latent_shape=list(grid))
-    out = impl._forward_sparse_npu(q, k, v, plan)
-
-    reference = torch.nn.functional.scaled_dot_product_attention(
-        q.transpose(1, 2),
-        k.transpose(1, 2),
-        v.transpose(1, 2),
-        scale=impl.softmax_scale,
-    ).transpose(1, 2)
-    error = (out.float() - reference.float()).abs().mean() / reference.float().abs().mean()
-    assert error < 2e-3, f"mean relative error {error:.4%} against dense attention"

tests/diffusion/cache/test_cache_backends.py

@@ -471,66 +471,6 @@ class TestTeaCacheBackend:
         assert backend.enabled is True
         mock_apply_hook.assert_called_once()
 
-    @patch("vllm_omni.diffusion.cache.teacache.backend.apply_teacache_hook")
-    def test_enable_uses_generic_default_threshold(self, mock_apply_hook):
-        pipeline = Mock()
-        pipeline.__class__.__name__ = "QwenImagePipeline"
-        pipeline.transformer = Mock()
-        pipeline.transformer.__class__.__name__ = "QwenImageTransformer2DModel"
-
-        TeaCacheBackend(DiffusionCacheConfig()).enable(pipeline)
-        assert mock_apply_hook.call_args.args[1].rel_l1_thresh == 0.2
-
-    @pytest.mark.parametrize("partition", ["fl2va", "combined"])
-    @pytest.mark.parametrize(
-        ("configured_threshold", "expected_threshold"),
-        [(None, 0.17), (0.2, 0.2)],
-    )
-    @patch("vllm_omni.diffusion.cache.teacache.backend.apply_teacache_hook")
-    def test_minimax_h3_only_enables_fl2va_teacache(
-        self,
-        mock_apply_hook,
-        partition,
-        configured_threshold,
-        expected_threshold,
-    ):
-        pipeline = Mock()
-        pipeline.__class__.__name__ = "MiniMaxH3Pipeline"
-        pipeline.partition = partition
-        pipeline.transformer = Mock()
-        pipeline.transformer.__class__.__name__ = "MiniMaxH3DiTModel"
-        pipeline.transformers_ref = Mock()
-
-        config = (
-            DiffusionCacheConfig()
-            if configured_threshold is None
-            else DiffusionCacheConfig(rel_l1_thresh=configured_threshold)
-        )
-        backend = TeaCacheBackend(config)
-        backend.enable(pipeline)
-
-        mock_apply_hook.assert_called_once()
-        transformer, teacache_config = mock_apply_hook.call_args.args
-        assert transformer is pipeline.transformer
-        assert transformer is not pipeline.transformers_ref
-        assert teacache_config.rel_l1_thresh == expected_threshold
-
-    @patch("vllm_omni.diffusion.cache.teacache.backend.apply_teacache_hook")
-    def test_minimax_h3_rejects_ref2va_teacache(self, mock_apply_hook):
-        pipeline = Mock()
-        pipeline.__class__.__name__ = "MiniMaxH3Pipeline"
-        pipeline.partition = "ref2va"
-        pipeline.transformer = Mock()
-        pipeline.transformer.__class__.__name__ = "MiniMaxH3DiTModel"
-
-        backend = TeaCacheBackend(DiffusionCacheConfig())
-
-        with pytest.raises(ValueError, match="only supports the MiniMax-H3 FL2VA partition"):
-            backend.enable(pipeline)
-
-        assert backend.enabled is False
-        mock_apply_hook.assert_not_called()
-
     @patch("vllm_omni.diffusion.cache.teacache.backend.apply_teacache_hook")
     def test_enable_with_coefficients(self, mock_apply_hook):
         """Test enabling TeaCache with custom coefficients."""

tests/diffusion/cache/test_teacache_extractors.py

@@ -15,23 +15,19 @@ Currently implemented:
 - TestFlux2KleinExtractor: Flux2Klein model extractor
 - TestFlux2Extractor: Flux2 model extractor
 - TestFluxExtractor: Flux model extractor
-- TestMiniMaxH3Extractor: MiniMaxH3DiTModel extractor
 """
 
 from abc import ABC, abstractmethod
-from types import SimpleNamespace
 from unittest.mock import MagicMock, Mock, patch
 
 import pytest
 import torch
-import torch.nn as nn
 
 from tests.helpers.mark import hardware_test
 from vllm_omni.diffusion.cache.teacache.extractors import (
     extract_flux2_context,
     extract_flux2_klein_context,
     extract_flux_context,
-    extract_minimax_h3_context,
 )
 from vllm_omni.diffusion.models.flux.flux_transformer import FluxTransformer2DModel
 from vllm_omni.diffusion.models.flux2_klein.flux2_klein_transformer import (
@@ -427,288 +423,3 @@ class TestFluxExtractor(BaseExtractorTest):
                 img_ids=torch.randint(0, 64, (1, 16, 3)),
                 txt_ids=torch.randint(0, 64, (1, 8, 3)),
             )
-
-
-class _MiniMaxH3FakeLinear(nn.Module):
-    def __init__(
-        self,
-        in_features,
-        out_features,
-        *,
-        bias=False,
-        params_dtype=None,
-        quant_config=None,
-        prefix="",
-        total_num_heads=None,
-        total_num_kv_heads=None,
-        **kwargs,
-    ):
-        del kwargs
-        super().__init__()
-        self.prefix = prefix
-        self.quant_config = quant_config
-        self.weight = nn.Parameter(torch.randn(out_features, in_features, dtype=params_dtype or torch.float32))
-        if bias:
-            self.bias = nn.Parameter(torch.zeros(out_features, dtype=params_dtype or torch.float32))
-        else:
-            self.register_parameter("bias", None)
-        self.num_heads = total_num_heads
-        self.num_kv_heads = total_num_kv_heads
-
-    def forward(self, x):
-        return torch.nn.functional.linear(x, self.weight, self.bias), None
-
-
-class _MiniMaxH3FakeMergedLinear(_MiniMaxH3FakeLinear):
-    def __init__(self, in_features, out_features, **kwargs):
-        super().__init__(in_features, sum(out_features), **kwargs)
-
-
-class _MiniMaxH3FakeQKVLinear(_MiniMaxH3FakeLinear):
-    def __init__(
-        self,
-        *,
-        hidden_size,
-        head_size,
-        total_num_heads,
-        total_num_kv_heads,
-        **kwargs,
-    ):
-        qkv_features = (total_num_heads + 2 * total_num_kv_heads) * head_size
-        super().__init__(
-            hidden_size,
-            qkv_features,
-            total_num_heads=total_num_heads,
-            total_num_kv_heads=total_num_kv_heads,
-            **kwargs,
-        )
-
-
-class _MiniMaxH3FakeAttention(nn.Module):
-    class FakeBackend:
-        supports_prefix_kv_slicing = False
-
-        # Mirror AttentionBackend.supports_packed_mask_free: H3's packed
-        # attention path calls attn_backend.supports_packed_mask_free() to decide
-        # whether to build the padding mask. Return False so the fake exercises
-        # the masked path (the abstract backend default).
-        @classmethod
-        def supports_packed_mask_free(cls) -> bool:
-            return False
-
-    def __init__(self, **kwargs):
-        del kwargs
-        super().__init__()
-        self.attn_backend = self.FakeBackend
-        self.use_ring = False
-
-    def forward(self, q, k, v, metadata=None):
-        del k, v, metadata
-        return q
-
-
-def _minimax_h3_small_od_config():
-    arch = {
-        "num_layers": 1,
-        "token_refiner_num_layers": 1,
-        "hidden_size": 8,
-        "num_attention_heads": 1,
-        "attention_head_dim": 8,
-        "ffn_hidden_size": 16,
-        "latents_dim": 2,
-        "audio_latents_dim": 2,
-        "patch_size": (1, 2, 2),
-        "text_dim": 6,
-        "timestep_input_dim": 4,
-        "time_embed_hidden_size": 8,
-        "time_embed_dim": 4,
-        "adaln_out_features": 18 * 8,
-        "final_adaln_out_features": 2 * 8,
-        "rope_inv_freq_len": 1,
-    }
-    return SimpleNamespace(
-        tf_model_config=arch,
-        parallel_config=SimpleNamespace(ulysses_degree=1),
-    )
-
-
-def _minimax_h3_sample_kwargs(*, seq_len: int = 8, device: torch.device | str = "cpu") -> dict:
-    video_row_dim = 2 * 1 * 2 * 2
-    audio_row_dim = 2
-    text_len = 2
-    img_rows = 2
-
-    img_pos = torch.tensor([2, 3], dtype=torch.long, device=device)
-    audio_pos = torch.tensor([4, 5], dtype=torch.long, device=device)
-    text_pos = torch.tensor([0, 1], dtype=torch.long, device=device)
-    infer_out_pos = img_pos.clone()
-
-    token_tags = torch.full((seq_len,), -1, dtype=torch.long, device=device)
-    token_tags[text_pos] = 1
-    token_tags[img_pos] = 0
-    token_tags[audio_pos] = 2
-
-    inverse_indices = torch.zeros(seq_len, dtype=torch.long, device=device)
-
-    return {
-        "x": torch.randn(1, seq_len, video_row_dim, device=device),
-        "audio_x": torch.randn(1, seq_len, audio_row_dim, device=device),
-        "img_position_ids": torch.zeros(1, seq_len, 3, dtype=torch.float64, device=device),
-        "unique_timesteps": torch.tensor([0.5], dtype=torch.float32, device=device),
-        "inverse_indices": inverse_indices,
-        "update_mask": torch.ones(img_rows, dtype=torch.bool, device=device),
-        "token_tags": token_tags,
-        "prompt_embeds": torch.randn(text_len, 6, device=device),
-        "img_pos_info": {"position_ids": img_pos},
-        "audio_pos_info": {"position_ids": audio_pos},
-        "text_pos_info": {"position_ids": text_pos},
-        "img_pos_for_infer_output_info": {"position_ids": infer_out_pos},
-        "packed_seq_params": {
-            "cu_seqlens_q": torch.tensor([0, seq_len], dtype=torch.int32, device=device),
-            "max_seqlen_q": seq_len,
-        },
-        "refiner_packed_seq_params": {
-            "cu_seqlens_q": torch.tensor([0, text_len], dtype=torch.int32, device=device),
-            "max_seqlen_q": text_len,
-        },
-    }
-
-
-@pytest.mark.cpu
-class TestMiniMaxH3Extractor(BaseExtractorTest):
-    """Test extract_minimax_h3_context function."""
-
-    def get_extractor(self):
-        return extract_minimax_h3_context
-
-    @pytest.fixture
-    def minimax_h3_module(self, monkeypatch):
-        """Create a minimal MiniMaxH3DiTModel with fake parallel linears for CPU tests."""
-        from vllm_omni.diffusion.models.minimax_h3 import minimax_h3_transformer as h3
-
-        monkeypatch.setattr(h3, "ColumnParallelLinear", _MiniMaxH3FakeLinear)
-        monkeypatch.setattr(h3, "MergedColumnParallelLinear", _MiniMaxH3FakeMergedLinear)
-        monkeypatch.setattr(h3, "QKVParallelLinear", _MiniMaxH3FakeQKVLinear)
-        monkeypatch.setattr(h3, "RowParallelLinear", _MiniMaxH3FakeLinear)
-        monkeypatch.setattr(h3, "Attention", _MiniMaxH3FakeAttention)
-        monkeypatch.setattr(h3, "get_tensor_model_parallel_world_size", lambda: 1)
-
-        model = h3.MiniMaxH3DiTModel(_minimax_h3_small_od_config(), quant_config=None)
-        for submodule in model.modules():
-            if isinstance(submodule, h3.MiniMaxH3Attention):
-                submodule.rope._forward_method = submodule.rope.forward_native
-        model.eval()
-        return model
-
-    def get_module(self, minimax_h3_module):
-        return minimax_h3_module
-
-    @pytest.fixture
-    def sample_inputs(self):
-        """Create sample packed forward kwargs for MiniMaxH3DiTModel."""
-        return _minimax_h3_sample_kwargs(seq_len=8)
-
-    def get_sample_inputs(self, sample_inputs):
-        return sample_inputs
-
-    def test_modulated_input_shape(self, minimax_h3_module, sample_inputs):
-        """Test that modulated_input matches packed sequence shape [T, H]."""
-        context = extract_minimax_h3_context(minimax_h3_module, **sample_inputs)
-
-        seq_len = sample_inputs["x"].shape[1]
-        assert context.modulated_input.shape == (seq_len, minimax_h3_module.hidden_size)
-
-    def test_run_transformer_blocks_callable(self, minimax_h3_module, sample_inputs):
-        """Test that run_transformer_blocks is callable."""
-        context = extract_minimax_h3_context(minimax_h3_module, **sample_inputs)
-        assert callable(context.run_transformer_blocks)
-
-    def test_postprocess_callable(self, minimax_h3_module, sample_inputs):
-        """Test that postprocess is callable."""
-        context = extract_minimax_h3_context(minimax_h3_module, **sample_inputs)
-        assert callable(context.postprocess)
-
-    def test_postprocess_output_shape(self, minimax_h3_module, sample_inputs):
-        """Test that postprocess returns video/audio logits for infer rows."""
-        context = extract_minimax_h3_context(minimax_h3_module, **sample_inputs)
-        (hidden,) = context.run_transformer_blocks()
-        video_logits, audio_logits = context.postprocess(hidden)
-
-        num_video_rows = sample_inputs["img_pos_info"]["position_ids"].shape[0]
-        num_audio_rows = sample_inputs["audio_pos_info"]["position_ids"].shape[0]
-        assert video_logits.shape[0] == num_video_rows
-        assert audio_logits.shape[0] == num_audio_rows
-
-    def test_extractor_matches_native_forward(self, minimax_h3_module, sample_inputs):
-        """The uncached extractor path must preserve the model's forward result."""
-        expected_video, expected_audio = minimax_h3_module(**sample_inputs)
-
-        context = extract_minimax_h3_context(minimax_h3_module, **sample_inputs)
-        (hidden,) = context.run_transformer_blocks()
-        actual_video, actual_audio = context.postprocess(hidden)
-
-        torch.testing.assert_close(actual_video, expected_video)
-        torch.testing.assert_close(actual_audio, expected_audio)
-
-    def test_run_transformer_blocks_forwards_packed_layout(
-        self,
-        minimax_h3_module,
-        sample_inputs,
-        monkeypatch,
-    ):
-        """The extractor block call must mirror the native packed contract."""
-        from vllm_omni.diffusion.attention.backends.abstract import VideoTokenLayout
-
-        video_layout = VideoTokenLayout(prefix_len=2, latent_grid=(1, 1, 2))
-        inputs = {**sample_inputs, "video_token_layout": video_layout}
-        block = minimax_h3_module.blocks[0]
-        original_forward = block.forward
-        received: list[tuple[int, VideoTokenLayout | None]] = []
-
-        def capture_forward(*args, **kwargs):
-            received.append((kwargs["packed_total"], kwargs["video_layout"]))
-            return original_forward(*args, **kwargs)
-
-        monkeypatch.setattr(block, "forward", capture_forward)
-        context = extract_minimax_h3_context(minimax_h3_module, **inputs)
-        context.run_transformer_blocks()
-
-        assert received == [(sample_inputs["x"].shape[1], video_layout)]
-
-    def test_teacache_reuses_h3_block_residual(self, minimax_h3_module, sample_inputs, monkeypatch):
-        """A guaranteed cache hit must skip the H3 transformer block."""
-        from vllm_omni.diffusion.cache.teacache.config import TeaCacheConfig
-        from vllm_omni.diffusion.cache.teacache.hook import TeaCacheHook
-
-        block = minimax_h3_module.blocks[0]
-        original_forward = block.forward
-        block_calls = 0
-
-        def counted_forward(*args, **kwargs):
-            nonlocal block_calls
-            block_calls += 1
-            return original_forward(*args, **kwargs)
-
-        monkeypatch.setattr(block, "forward", counted_forward)
-        hook = TeaCacheHook(
-            TeaCacheConfig(
-                transformer_type="MiniMaxH3DiTModel",
-                rel_l1_thresh=0.3,
-            )
-        )
-        hook.initialize_hook(minimax_h3_module)
-
-        hook.new_forward(minimax_h3_module, **sample_inputs)
-        video_logits, audio_logits = hook.new_forward(minimax_h3_module, **sample_inputs)
-
-        assert block_calls == 1
-        assert video_logits.shape[0] == sample_inputs["img_pos_info"]["position_ids"].shape[0]
-        assert audio_logits.shape[0] == sample_inputs["audio_pos_info"]["position_ids"].shape[0]
-
-    def test_invalid_module_raises_error(self):
-        """Test that invalid module without blocks raises ValueError."""
-        invalid_module = Mock()
-        invalid_module.blocks = []
-
-        with pytest.raises(ValueError, match="Module must have blocks"):
-            extract_minimax_h3_context(invalid_module, **_minimax_h3_sample_kwargs())

tests/diffusion/diffusion_kv/test_config.py

@@ -1,52 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-
-import pytest
-
-from vllm_omni.diffusion.data import OmniDiffusionConfig
-from vllm_omni.diffusion.diffusion_kv.config import DiffusionKVCacheMode
-
-pytestmark = [pytest.mark.core_model, pytest.mark.cpu, pytest.mark.diffusion]
-
-
-@pytest.fixture(autouse=True)
-def _fixed_master_port(monkeypatch) -> None:
-    monkeypatch.setattr(OmniDiffusionConfig, "_resolve_master_port", lambda _self: 29500)
-
-
-def test_dense_legacy_is_default() -> None:
-    config = OmniDiffusionConfig.from_kwargs()
-
-    assert config.diffusion_kv_mode is DiffusionKVCacheMode.DENSE_LEGACY
-
-
-def test_paged_worker_local_is_rejected_until_implemented() -> None:
-    with pytest.raises(ValueError, match="reserved but not implemented"):
-        OmniDiffusionConfig.from_kwargs(diffusion_kv_mode="paged_worker_local")
-
-
-def test_unknown_cache_mode_is_rejected() -> None:
-    with pytest.raises(ValueError, match="Unsupported Diffusion KV diffusion_kv_mode"):
-        OmniDiffusionConfig.from_kwargs(diffusion_kv_mode="unknown")
-
-
-def test_non_mapping_omni_kv_config_is_rejected() -> None:
-    with pytest.raises(TypeError, match="omni_kv_config must be a mapping"):
-        OmniDiffusionConfig.from_kwargs(omni_kv_config="paged_scheduler")
-
-
-def test_paged_scheduler_rejects_dense_legacy_kv_receive() -> None:
-    with pytest.raises(ValueError, match="does not support imported AR KV"):
-        OmniDiffusionConfig.from_kwargs(
-            diffusion_kv_mode="paged_scheduler",
-            omni_kv_config={"need_recv_cache": True},
-        )
-
-
-def test_paged_scheduler_does_not_depend_on_model_registry() -> None:
-    config = OmniDiffusionConfig.from_kwargs(
-        model_class_name="FutureDiffusionModel",
-        diffusion_kv_mode="paged_scheduler",
-    )
-
-    assert config.diffusion_kv_mode is DiffusionKVCacheMode.PAGED_SCHEDULER

tests/diffusion/diffusion_kv/test_request.py

@@ -1,200 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-
-import pytest
-import torch
-import vllm.v1.core.single_type_kv_cache_manager as native_kv_managers
-from vllm.v1.core.kv_cache_manager import KVCacheManager
-from vllm.v1.core.kv_cache_utils import BlockHash
-from vllm.v1.kv_cache_interface import (
-    FullAttentionSpec,
-    KVCacheConfig,
-    KVCacheGroupSpec,
-    KVCacheTensor,
-)
-from vllm.v1.request import RequestStatus
-
-from vllm_omni.diffusion.diffusion_kv.request import DiffusionKVContext, DiffusionKVRequest
-
-pytestmark = [pytest.mark.core_model, pytest.mark.cpu, pytest.mark.diffusion]
-
-BLOCK_SIZE = 4
-
-
-def _request(**overrides) -> DiffusionKVRequest:
-    values = dict(
-        request_id="req/diffusion-kv/0",
-        sequence_id=0,
-        prefix_len=4,
-        target_len=4,
-        seq_len=8,
-    )
-    values.update(overrides)
-    return DiffusionKVRequest(**values)
-
-
-def test_request_exposes_native_request_and_diffusion_semantics() -> None:
-    request = _request()
-
-    assert request.sequence_id == 0
-    assert request.prefix_len == 4
-    assert request.target_len == 4
-    assert request.num_tokens == request.seq_len == 8
-    assert request.num_prompt_tokens == 4
-    assert request.num_computed_tokens == 0
-    assert request.block_hashes == []
-    assert request.kv_contexts == ()
-    assert request.skip_reading_prefix_cache is True
-    assert request.shared_prefix_boundary == 0
-    assert request.status is RequestStatus.WAITING
-
-
-@pytest.mark.parametrize(
-    ("overrides", "message"),
-    [
-        ({"sequence_id": -1}, "sequence_id"),
-        ({"prefix_len": -1}, "prefix_len"),
-        ({"target_len": 0}, "target_len"),
-        ({"seq_len": 0}, "seq_len"),
-        ({"prefix_len": 5, "target_len": 4, "seq_len": 8}, "must not exceed"),
-    ],
-)
-def test_request_rejects_invalid_lengths(overrides, message: str) -> None:
-    with pytest.raises(ValueError, match=message):
-        _request(**overrides)
-
-
-@pytest.mark.parametrize(
-    ("overrides", "message"),
-    [
-        ({"context_id": ""}, "context_id"),
-        ({"cache_role": ""}, "cache_role"),
-        ({"num_tokens": 0}, "num_tokens"),
-    ],
-)
-def test_context_rejects_invalid_identity_or_length(overrides, message: str) -> None:
-    values = dict(context_id="text", cache_role="cross.text", num_tokens=8)
-    values.update(overrides)
-
-    with pytest.raises(ValueError, match=message):
-        DiffusionKVContext(**values)
-
-
-def test_request_owns_independent_kv_contexts() -> None:
-    text = DiffusionKVContext(context_id="text", cache_role="cross.text", num_tokens=8)
-    image = DiffusionKVContext(context_id="image", cache_role="cross.image", num_tokens=4)
-
-    request = _request(kv_contexts=(text, image))
-
-    assert request.kv_contexts == (text, image)
-
-
-def test_request_rejects_duplicate_context_ids() -> None:
-    contexts = (
-        DiffusionKVContext(context_id="condition", cache_role="cross.text", num_tokens=8),
-        DiffusionKVContext(context_id="condition", cache_role="cross.image", num_tokens=4),
-    )
-
-    with pytest.raises(ValueError, match="unique context_id"):
-        _request(kv_contexts=contexts)
-
-
-def test_request_rejects_non_context_values() -> None:
-    with pytest.raises(TypeError, match="DiffusionKVContext"):
-        _request(kv_contexts=(object(),))
-
-
-def _manager(*, enable_caching: bool) -> KVCacheManager:
-    # vLLM 0.26 lazily registers its built-in KV specs. Another test may have
-    # already registered an out-of-tree spec, making the registry non-empty
-    # before FullAttentionSpec is registered. Initialize the native mappings
-    # explicitly; vLLM 0.25 has no registration hook and needs no setup.
-    register_all = getattr(native_kv_managers, "register_all_kvcache_specs", None)
-    if register_all is not None:
-        register_all(None)
-
-    spec = FullAttentionSpec(
-        block_size=BLOCK_SIZE,
-        num_kv_heads=2,
-        head_size=8,
-        dtype=torch.bfloat16,
-    )
-    group = KVCacheGroupSpec(layer_names=["layer0"], kv_cache_spec=spec)
-    config = KVCacheConfig(
-        num_blocks=8,
-        kv_cache_tensors=[KVCacheTensor(size=spec.page_size_bytes * 8, shared_by=["layer0"])],
-        kv_cache_groups=[group],
-    )
-    return KVCacheManager(
-        config,
-        max_model_len=64,
-        scheduler_block_size=BLOCK_SIZE,
-        hash_block_size=BLOCK_SIZE,
-        enable_caching=enable_caching,
-    )
-
-
-def test_empty_hash_request_conforms_to_native_request_local_allocation() -> None:
-    manager = _manager(enable_caching=True)
-    free_before = manager.block_pool.get_num_free_blocks()
-    request = _request()
-
-    # vLLM 0.26 adds a shared-prefix-boundary return value.
-    computed_blocks, num_computed_tokens, *_ = manager.get_computed_blocks(request)
-    assert num_computed_tokens == 0
-    blocks = manager.allocate_slots(
-        request,
-        num_new_tokens=request.num_tokens,
-        new_computed_blocks=computed_blocks,
-        delay_cache_blocks=True,
-        full_sequence_must_fit=True,
-    )
-
-    assert blocks is not None
-    assert len(manager.get_block_ids(request.request_id)[0]) == 2
-    # Empty hashes intentionally mean request-local residency only. Calling
-    # native cache_blocks() would require a canonical hash for the full prefix
-    # block and is therefore outside this request's contract.
-    manager.free(request)
-    assert manager.block_pool.get_num_free_blocks() == free_before
-
-
-def test_hashed_request_conforms_to_native_delayed_prefix_commit() -> None:
-    manager = _manager(enable_caching=True)
-    prefix_hash = BlockHash(b"prefix".ljust(32, b"\0"))
-    target_hash = BlockHash(b"target".ljust(32, b"\0"))
-    request = _request(block_hashes=[prefix_hash, target_hash])
-
-    computed_blocks, num_computed_tokens, *_ = manager.get_computed_blocks(request)
-    assert num_computed_tokens == 0
-    blocks = manager.allocate_slots(
-        request,
-        num_new_tokens=request.num_tokens,
-        new_computed_blocks=computed_blocks,
-        delay_cache_blocks=True,
-        full_sequence_must_fit=True,
-    )
-
-    assert blocks is not None
-    manager.cache_blocks(request, request.prefix_len)
-    manager.free(request)
-
-    cached_request = _request(
-        request_id="req/diffusion-kv/cached",
-        block_hashes=[prefix_hash, target_hash],
-    )
-    cached_blocks, num_computed_tokens, *_ = manager.get_computed_blocks(cached_request)
-
-    # Only the stable prefix was published. The dynamic target remains a miss.
-    assert num_computed_tokens == cached_request.prefix_len
-    assert len(cached_blocks.blocks[0]) == 1
-    cached_allocation = manager.allocate_slots(
-        cached_request,
-        num_new_tokens=cached_request.num_tokens - num_computed_tokens,
-        num_new_computed_tokens=num_computed_tokens,
-        new_computed_blocks=cached_blocks,
-        delay_cache_blocks=True,
-        full_sequence_must_fit=True,
-    )
-    assert cached_allocation is not None
-    manager.free(cached_request)

tests/diffusion/layers/test_rope_broadcast.py

@@ -244,7 +244,7 @@ def test_mindie_full_dim_rope_keeps_h3_cos_sin_layout(monkeypatch: pytest.Monkey
     captured: dict[str, object] = {}
 
     def rotary_position_embedding(x, cos, sin, **kwargs):
-        captured.update(cos=cos, sin=sin, kwargs=kwargs)
+        captured.update(x=x, cos=cos, sin=sin, kwargs=kwargs)
         return x
 
     monkeypatch.setitem(
@@ -256,8 +256,10 @@ def test_mindie_full_dim_rope_keeps_h3_cos_sin_layout(monkeypatch: pytest.Monkey
     cos = torch.randn(11, 96, dtype=torch.bfloat16)
     sin = torch.randn(11, 96, dtype=torch.bfloat16)
 
-    apply_rotary_emb_mindiesd(x, cos, sin, interleaved=False, half_head_dim=False)
+    actual = apply_rotary_emb_mindiesd(x, cos, sin, interleaved=False, half_head_dim=False)
 
+    assert actual is x
+    assert captured["x"] is x
     assert captured["cos"] is cos
     assert captured["sin"] is sin
     assert captured["kwargs"] == {
@@ -265,31 +267,3 @@ def test_mindie_full_dim_rope_keeps_h3_cos_sin_layout(monkeypatch: pytest.Monkey
         "head_first": False,
         "fused": True,
     }
-
-
-def test_mindie_rope_restores_3d_layout(monkeypatch: pytest.MonkeyPatch) -> None:
-    """mindiesd rotary_position_embedding only accepts 4D ``x``."""
-    captured: dict[str, torch.Tensor] = {}
-
-    def rotary_position_embedding(x, cos, sin, **kwargs):
-        # Mirror mindiesd's own input guard.
-        assert x.dim() == 4, f"mindiesd requires 4D x, got {x.dim()}"
-        captured.update(x=x)
-        return x
-
-    monkeypatch.setitem(
-        sys.modules,
-        "mindiesd",
-        types.SimpleNamespace(rotary_position_embedding=rotary_position_embedding),
-    )
-
-    S, H, D = 11, 3, 96
-    x = torch.randn(S, H, D, dtype=torch.bfloat16)
-    cos = torch.randn(S, D, dtype=torch.bfloat16)
-    sin = torch.randn(S, D, dtype=torch.bfloat16)
-
-    actual = apply_rotary_emb_mindiesd(x, cos, sin, interleaved=False, half_head_dim=False)
-
-    assert captured["x"].shape == (1, S, H, D)
-    assert actual.shape == (S, H, D)
-    torch.testing.assert_close(actual, x, atol=0, rtol=0)

tests/diffusion/models/cosmos3/test_cosmos3_pipeline.py

@@ -16,7 +16,6 @@ from PIL import Image
 from torch import nn
 
 from vllm_omni.diffusion.worker.request_batch import DiffusionRequestBatch
-from vllm_omni.experimental.world_models.session_state import SessionStateManager
 
 pytestmark = [pytest.mark.core_model, pytest.mark.cpu, pytest.mark.diffusion]
 
@@ -426,29 +425,6 @@ def test_distilled_generation_rejects_robolab_policy(make_cosmos3_pipeline) -> N
         pipeline._forward_robolab_policy(make_sampling_params(), None, 0.0)
 
 
-def test_forward_threads_request_id_to_robolab(make_cosmos3_pipeline) -> None:
-    pipeline = make_cosmos3_pipeline()
-    robolab_inputs = object()
-    captured: dict[str, Any] = {}
-    expected = object()
-    pipeline._build_robolab_policy_inputs = lambda sp, prompt, request_id: robolab_inputs
-
-    def fake_forward_robolab(sp, inputs, pipeline_start, session_id=None):
-        del sp, inputs, pipeline_start
-        captured["session_id"] = session_id
-        return expected
-
-    pipeline._forward_robolab_policy = fake_forward_robolab
-    request = SimpleNamespace(
-        prompts=["policy"],
-        sampling_params=make_sampling_params(),
-        request_id="robolab-request-7",
-    )
-
-    assert pipeline.forward(request) is expected
-    assert captured["session_id"] == "robolab-request-7"
-
-
 @pytest.mark.parametrize(
     ("prompt", "sampling_params", "message"),
     [
@@ -619,7 +595,6 @@ def _make_od_config(
     return SimpleNamespace(
         enable_cpu_offload=False,
         enable_diffusion_pipeline_profiler=False,
-        enable_session_state_manager=False,
         model="/nonexistent/model/path",
         dtype=torch.float32,
         flow_shift=None,
@@ -1698,32 +1673,6 @@ def test_diffuse_covers_cfg_i2v_and_multimodal_steps(make_cosmos3_pipeline) -> N
     torch.testing.assert_close(action_result, torch.full((), 44.0).expand_as(action_result))
 
 
-def test_diffuse_drops_session_when_progress_iteration_fails(make_cosmos3_pipeline) -> None:
-    pipeline = make_cosmos3_pipeline()
-    pipeline._use_session_state = True
-    pipeline._memory_manager = SessionStateManager()
-
-    class FailingProgress:
-        def __iter__(self):
-            raise RuntimeError("progress failed before first step")
-
-    pipeline.progress_bar = lambda timesteps: FailingProgress()
-    with pytest.raises(RuntimeError, match="progress failed"):
-        pipeline.diffuse(
-            latents=torch.zeros(1, 2, 1, 1, 1),
-            timesteps=torch.tensor([7]),
-            cond_ids=_ids(2),
-            cond_mask=_mask(),
-            uncond_ids=_ids(1),
-            uncond_mask=_mask(),
-            guidance_scale=1.0,
-            shared_kwargs={"video_shape": (1, 1, 1), "fps": 24.0},
-            session_id="request-that-fails",
-        )
-
-    assert "request-that-fails" not in pipeline._memory_manager
-
-
 def test_diffuse_transfer_applies_control_cfg(make_cosmos3_pipeline, sequential_cfg_parallel) -> None:
     pipeline = make_cosmos3_pipeline()
     latents = torch.zeros(1, 2, 1, 1, 1)
@@ -1753,28 +1702,6 @@ def test_diffuse_transfer_applies_control_cfg(make_cosmos3_pipeline, sequential_
     torch.testing.assert_close(result, torch.full_like(latents, 254.0))
 
 
-def test_diffuse_transfer_rejects_session_state_manager(make_cosmos3_pipeline) -> None:
-    pipeline = make_cosmos3_pipeline()
-    pipeline._use_session_state = True
-
-    with pytest.raises(NotImplementedError, match="richer cache keys"):
-        pipeline.diffuse_transfer(
-            latents=torch.zeros(1, 2, 1, 1, 1),
-            timesteps=torch.tensor([7]),
-            cond_ids=_ids(2),
-            cond_mask=_mask(),
-            uncond_ids=_ids(1),
-            uncond_mask=_mask(),
-            guidance_scale=1.0,
-            control_guidance=1.0,
-            control_guidance_interval=None,
-            control_latents=[],
-            shared_kwargs={},
-            velocity_mask=torch.ones(1, 1, 1, 1, 1),
-            condition_latents=torch.zeros(1, 2, 1, 1, 1),
-        )
-
-
 def test_diffuse_transfer_skips_idle_cfg_branches(make_cosmos3_pipeline, sequential_cfg_parallel) -> None:
     latents = torch.zeros(1, 2, 1, 1, 1)
     velocity_mask = torch.ones(1, 1, 1, 1, 1)

tests/diffusion/models/hunyuan_image3/test_diffusion_kv_request.py

@@ -1,447 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-
-from types import SimpleNamespace
-
-import pytest
-import torch
-from transformers.utils.generic import ModelOutput
-
-import vllm_omni.diffusion.models.hunyuan_image3.pipeline_hunyuan_image3 as pipeline_module
-from vllm_omni.diffusion.diffusion_kv.config import DiffusionKVCacheMode
-from vllm_omni.diffusion.diffusion_kv.request import DiffusionKVRequest
-from vllm_omni.diffusion.models.hunyuan_image3.hunyuan_image3_tokenizer import TokenizerEncodeOutput
-from vllm_omni.diffusion.models.hunyuan_image3.hunyuan_image3_transformer import (
-    ImageInfo,
-    JointImageInfo,
-)
-from vllm_omni.diffusion.models.hunyuan_image3.pipeline_hunyuan_image3 import (
-    HunyuanImage3Pipeline,
-    get_hunyuan_image_3_pre_process_func,
-)
-from vllm_omni.diffusion.models.hunyuan_image3.request_layout import (
-    HunyuanPreparedLayout,
-    build_hunyuan_diffusion_kv_requests,
-    extract_hunyuan_prompt_inputs,
-    hunyuan_num_image_tokens,
-    normalize_hunyuan_cot_text,
-    prepare_hunyuan_layout,
-)
-from vllm_omni.diffusion.request import OmniDiffusionRequest
-from vllm_omni.inputs.data import OmniDiffusionSamplingParams
-
-pytestmark = [pytest.mark.core_model, pytest.mark.cpu, pytest.mark.diffusion]
-
-
-def test_dense_legacy_preprocess_does_not_initialize_layout_tokenizer(monkeypatch) -> None:
-    hf_config = SimpleNamespace(vae_downsample_factor=(8, 8), patch_size=2)
-    image_processor = SimpleNamespace(vision_encoder_processor=SimpleNamespace(patch_size=1))
-    monkeypatch.setattr(pipeline_module, "get_config", lambda *_args, **_kwargs: hf_config)
-    monkeypatch.setattr(pipeline_module, "HunyuanImage3ImageProcessor", lambda _config: image_processor)
-    monkeypatch.setattr(
-        pipeline_module,
-        "TokenizerWrapper",
-        lambda _model: pytest.fail("dense_legacy must not initialize the Diffusion KV tokenizer"),
-    )
-
-    get_hunyuan_image_3_pre_process_func(
-        SimpleNamespace(model="model", diffusion_kv_mode=DiffusionKVCacheMode.DENSE_LEGACY)
-    )
-
-
-class _FakeTokenizerWrapper:
-    def __init__(self, *, prefix_lens: list[int]) -> None:
-        self.prefix_lens = prefix_lens
-        self.calls: list[dict] = []
-        self.pad_token_id = 0
-        self.eos_token_id = 2
-        self.boi_token_id = 3
-        self.end_recaption_token_id = 4
-        self.end_answer_token_id = 5
-        self.special_token_map = {f"<img_ratio_{index}>": 100 + index for index in range(33)}
-
-    def apply_chat_template(self, **kwargs):
-        self.calls.append(kwargs)
-        rows = len(self.prefix_lens)
-        has_cond_image = kwargs.get("batch_cond_image_info") is not None
-        seq_lens = [prefix_len + 20 for prefix_len in self.prefix_lens]
-        padded_seq_len = max(seq_lens)
-        positions = torch.arange(padded_seq_len)
-        joint_image_slices = [[slice(1, 3), slice(3, 5)] if has_cond_image else [] for _ in range(rows)]
-        gen_image_slices = [[slice(length + 1, length + 17)] for length in self.prefix_lens]
-        all_image_slices = [joint + generated for joint, generated in zip(joint_image_slices, gen_image_slices)]
-        cond_vae_image_mask = (
-            torch.stack([(positions >= 1) & (positions < 3) for _ in range(rows)]) if has_cond_image else None
-        )
-        cond_vit_image_mask = (
-            torch.stack([(positions >= 3) & (positions < 5) for _ in range(rows)]) if has_cond_image else None
-        )
-        sections = []
-        for _ in range(rows):
-            row_sections = []
-            if has_cond_image:
-                row_sections.append(
-                    {
-                        "type": "joint_image",
-                        "token_height": [1, 1],
-                        "token_width": [2, 2],
-                    }
-                )
-            row_sections.append({"type": "gen_image", "token_height": 4, "token_width": 4})
-            sections.append(row_sections)
-        return {
-            "output": TokenizerEncodeOutput(
-                tokens=torch.arange(rows * padded_seq_len, dtype=torch.long).reshape(rows, padded_seq_len),
-                gen_timestep_scatter_index=torch.tensor(self.prefix_lens, dtype=torch.long).reshape(rows, 1),
-                cond_timestep_scatter_index=(torch.zeros(rows, 1, dtype=torch.long) if has_cond_image else None),
-                all_image_slices=all_image_slices,
-                gen_image_mask=torch.stack(
-                    [(positions >= length + 1) & (positions < length + 17) for length in self.prefix_lens]
-                ),
-                cond_vae_image_mask=cond_vae_image_mask,
-                cond_vit_image_mask=cond_vit_image_mask,
-                joint_image_slices=joint_image_slices,
-                gen_image_slices=gen_image_slices,
-                real_pos=torch.tensor(seq_lens, dtype=torch.long).reshape(rows, 1),
-            ),
-            "sections": sections,
-        }
-
-
-class _FakeImageProcessor:
-    def __init__(self, image_token_length: int = 16) -> None:
-        self.image_token_length = image_token_length
-        self.image_sizes: list[tuple[int, int]] = []
-        self.vision_encoder_processor = SimpleNamespace(patch_size=1)
-
-    def build_image_info(self, image_size):
-        self.image_sizes.append(image_size)
-        return ImageInfo(
-            image_type="gen_image",
-            image_width=image_size[1],
-            image_height=image_size[0],
-            token_width=4,
-            token_height=4,
-            image_token_length=self.image_token_length,
-            base_size=1024,
-            ratio_index=0,
-        )
-
-
-def _components(prefix_lens: list[int]):
-    return _FakeTokenizerWrapper(prefix_lens=prefix_lens), _FakeImageProcessor()
-
-
-def _prepare(
-    request: OmniDiffusionRequest,
-    tokenizer: _FakeTokenizerWrapper,
-    image_processor: _FakeImageProcessor,
-) -> HunyuanPreparedLayout:
-    prepared_layout = prepare_hunyuan_layout(
-        request,
-        tokenizer_wrapper=tokenizer,
-        image_processor=image_processor,
-        generation_config=SimpleNamespace(sequence_template="instruct", drop_think=False),
-        image_base_size=1024,
-    )
-    request.prepared_layout = prepared_layout
-    return prepared_layout
-
-
-def _request(
-    *,
-    guidance_scale: float,
-    prompt="draw a cat",
-    request_id: str = "req",
-) -> OmniDiffusionRequest:
-    return OmniDiffusionRequest(
-        prompt=prompt,
-        sampling_params=OmniDiffusionSamplingParams(
-            height=768,
-            width=1024,
-            guidance_scale=guidance_scale,
-            num_inference_steps=4,
-        ),
-        request_id=request_id,
-    )
-
-
-def test_builds_kv_request_lengths_without_model_execution() -> None:
-    tokenizer, image_processor = _components([12])
-    request = _request(guidance_scale=1.0)
-    prepared_layout = _prepare(request, tokenizer, image_processor)
-
-    kv_requests = build_hunyuan_diffusion_kv_requests(request, prepared_layout)
-
-    assert isinstance(request.prepared_layout, HunyuanPreparedLayout)
-    assert all(isinstance(item, DiffusionKVRequest) for item in kv_requests)
-    assert [(item.prefix_len, item.target_len, item.seq_len) for item in kv_requests] == [(12, 17, 32)]
-    assert image_processor.image_sizes == [(768, 1024)]
-    assert tokenizer.calls[0]["sequence_template"] == "instruct"
-    assert tokenizer.calls[0]["cfg_factor"] == 1
-    assert kv_requests[0].block_hashes == []
-    assert kv_requests[0].kv_contexts == ()
-    assert kv_requests[0].skip_reading_prefix_cache is True
-
-
-def test_builds_one_kv_request_per_cfg_row() -> None:
-    tokenizer, image_processor = _components([12, 14])
-    request = _request(guidance_scale=5.0)
-    prepared_layout = _prepare(request, tokenizer, image_processor)
-
-    kv_requests = build_hunyuan_diffusion_kv_requests(request, prepared_layout)
-
-    assert [item.sequence_id for item in kv_requests] == [0, 1]
-    assert [item.prefix_len for item in kv_requests] == [12, 14]
-    assert [item.seq_len for item in kv_requests] == [32, 34]
-    assert tokenizer.calls[0]["cfg_factor"] == 2
-
-
-def _reference_image() -> JointImageInfo:
-    return JointImageInfo(
-        vae_image_info=ImageInfo(
-            image_type="vae",
-            token_width=8,
-            token_height=8,
-            image_token_length=64,
-        ),
-        vision_image_info=ImageInfo(
-            image_type="siglip2",
-            token_width=4,
-            token_height=4,
-            image_token_length=16,
-        ),
-        vision_encoder_kwargs={
-            "spatial_shapes": torch.tensor([4, 4]),
-            "pixel_attention_mask": torch.ones(4, dtype=torch.bool),
-        },
-    )
-
-
-def test_passes_preprocessed_reference_image_geometry_to_tokenizer() -> None:
-    tokenizer, image_processor = _components([20])
-    joint_image = _reference_image()
-    prompt = {
-        "prompt": "edit this image",
-        "additional_information": {"batch_cond_image_info": [joint_image]},
-    }
-    request = _request(guidance_scale=1.0, prompt=prompt)
-    prepared_layout = _prepare(request, tokenizer, image_processor)
-
-    kv_requests = build_hunyuan_diffusion_kv_requests(request, prepared_layout)
-
-    assert tokenizer.calls[0]["batch_cond_image_info"] == [[joint_image]]
-    assert kv_requests[0].prefix_len == 20
-    assert kv_requests[0].target_len == 17
-    assert kv_requests[0].seq_len == 40
-    assert kv_requests[0].kv_contexts == ()
-
-
-def _assert_nested_equal(actual, expected) -> None:
-    if isinstance(expected, torch.Tensor):
-        torch.testing.assert_close(actual, expected)
-    elif isinstance(expected, dict):
-        assert actual.keys() == expected.keys()
-        for key in expected:
-            _assert_nested_equal(actual[key], expected[key])
-    elif isinstance(expected, list | tuple):
-        assert isinstance(actual, type(expected))
-        assert len(actual) == len(expected)
-        for actual_item, expected_item in zip(actual, expected):
-            _assert_nested_equal(actual_item, expected_item)
-    else:
-        assert actual == expected
-
-
-@pytest.mark.parametrize(
-    ("prefix_lens", "guidance_scale", "with_reference_image"),
-    [
-        pytest.param([12], 1.0, False, id="cfg-1"),
-        pytest.param([12, 14], 5.0, False, id="cfg-2"),
-        pytest.param([20], 1.0, True, id="cfg-1-reference-image"),
-        pytest.param([20, 22], 5.0, True, id="cfg-2-reference-image"),
-    ],
-)
-def test_prepared_model_inputs_match_local_tokenization(
-    monkeypatch,
-    prefix_lens: list[int],
-    guidance_scale: float,
-    with_reference_image: bool,
-) -> None:
-    tokenizer, image_processor = _components(prefix_lens)
-    prompt: str | dict = "draw a cat"
-    if with_reference_image:
-        prompt = {
-            "prompt": "edit this image",
-            "additional_information": {"batch_cond_image_info": [_reference_image()]},
-        }
-    request = _request(guidance_scale=guidance_scale, prompt=prompt)
-    prepared_layout = _prepare(request, tokenizer, image_processor)
-
-    prompts, cot_texts, system_prompt, batch_cond_image_info, bot_task = extract_hunyuan_prompt_inputs(
-        [request.prompt],
-        request.sampling_params.extra_args or {},
-        request_id=request.request_id,
-        allow_cond_image=True,
-    )
-    cot_text = (
-        [normalize_hunyuan_cot_text(text) for text in cot_texts]
-        if any(text is not None for text in cot_texts)
-        else None
-    )
-
-    pipeline = object.__new__(HunyuanImage3Pipeline)
-    pipeline._tkwrapper = tokenizer
-    pipeline.image_processor = image_processor
-    pipeline.generation_config = SimpleNamespace(sequence_template="instruct", drop_think=False)
-    pipeline.config = SimpleNamespace(
-        image_base_size=1024,
-        attention_head_dim=2,
-        rope_theta=10000.0,
-    )
-    monkeypatch.setattr(HunyuanImage3Pipeline, "device", property(lambda _self: torch.device("cpu")))
-
-    def fake_encode_cond_image(batch_cond_image_info_list, cfg_factor=1, generator=None):
-        del generator
-        rows = len(batch_cond_image_info_list) * cfg_factor
-        return (
-            torch.arange(rows * 4, dtype=torch.float32).reshape(rows, 1, 2, 2),
-            torch.arange(rows, dtype=torch.float32),
-            torch.arange(rows * 3, dtype=torch.float32).reshape(rows, 3),
-        )
-
-    pipeline._encode_cond_image = fake_encode_cond_image
-
-    def fake_build_batch_2d_rope(*, image_infos, seq_len, **_kwargs):
-        geometry_sum = sum(
-            int(image_slice.start or 0) + int(image_slice.stop or 0) + height + width
-            for row in image_infos
-            for image_slice, (height, width) in row
-        )
-        values = torch.tensor([len(image_infos), seq_len, geometry_sum], dtype=torch.float32)
-        return values, values + 1
-
-    monkeypatch.setattr(pipeline_module, "build_batch_2d_rope", fake_build_batch_2d_rope)
-    common_kwargs = dict(
-        prompt=prompts,
-        cot_text=cot_text,
-        system_prompt=system_prompt,
-        mode="gen_image",
-        guidance_scale=guidance_scale,
-        image_size=(request.sampling_params.height, request.sampling_params.width),
-        generator=[torch.Generator().manual_seed(0)],
-        batch_cond_image_info=batch_cond_image_info,
-        bot_task=bot_task,
-    )
-
-    local_inputs = pipeline.prepare_model_inputs(**common_kwargs)
-    prepared_inputs = pipeline.prepare_model_inputs(**common_kwargs, prepared_layout=prepared_layout)
-
-    comparable_fields = (
-        "input_ids",
-        "position_ids",
-        "custom_pos_emb",
-        "image_mask",
-        "gen_timestep_scatter_index",
-        "cond_vae_images",
-        "cond_timestep",
-        "cond_vae_image_mask",
-        "cond_vit_images",
-        "cond_vit_image_mask",
-        "vit_kwargs",
-        "cond_timestep_scatter_index",
-    )
-    for field in comparable_fields:
-        _assert_nested_equal(prepared_inputs[field], local_inputs[field])
-
-    tokenizer_fields = (
-        "tokens",
-        "gen_image_mask",
-        "cond_vae_image_mask",
-        "cond_vit_image_mask",
-        "gen_timestep_scatter_index",
-        "cond_timestep_scatter_index",
-        "real_pos",
-        "all_image_slices",
-        "joint_image_slices",
-        "gen_image_slices",
-    )
-    for field in tokenizer_fields:
-        _assert_nested_equal(
-            getattr(prepared_inputs["tokenizer_output"], field),
-            getattr(local_inputs["tokenizer_output"], field),
-        )
-
-    local_mask = pipeline._prepare_attention_mask_for_generation(
-        local_inputs["input_ids"],
-        pipeline.generation_config,
-        local_inputs,
-    )
-    prepared_mask = pipeline._prepare_attention_mask_for_generation(
-        prepared_inputs["input_ids"],
-        pipeline.generation_config,
-        prepared_inputs,
-    )
-    torch.testing.assert_close(prepared_mask, local_mask)
-    assert prepared_inputs["full_attn_spans"] == local_inputs["full_attn_spans"]
-
-    num_image_tokens = hunyuan_num_image_tokens(prepared_layout.generated_image_info)
-    local_inputs.update(attention_mask=local_mask, num_image_tokens=num_image_tokens)
-    prepared_inputs.update(attention_mask=prepared_mask, num_image_tokens=num_image_tokens)
-    local_step_inputs = pipeline._update_model_kwargs_for_generation(ModelOutput(), local_inputs)
-    prepared_step_inputs = pipeline._update_model_kwargs_for_generation(ModelOutput(), prepared_inputs)
-
-    for field in ("position_ids", "attention_mask", "gen_timestep_scatter_index", "full_attn_spans"):
-        _assert_nested_equal(prepared_step_inputs[field], local_step_inputs[field])
-
-
-def test_rejects_tokenizer_cfg_row_mismatch() -> None:
-    tokenizer, image_processor = _components([12])
-    request = _request(guidance_scale=5.0)
-    prepared_layout = _prepare(request, tokenizer, image_processor)
-
-    with pytest.raises(ValueError, match="sequence count does not match"):
-        build_hunyuan_diffusion_kv_requests(request, prepared_layout)
-
-
-def test_sender_endpoint_does_not_change_local_kv_request() -> None:
-    tokenizer, image_processor = _components([12])
-    request = _request(guidance_scale=1.0)
-    request.kv_sender_info = {"host": "127.0.0.1", "zmq_port": 5000}
-    prepared_layout = _prepare(request, tokenizer, image_processor)
-
-    kv_requests = build_hunyuan_diffusion_kv_requests(request, prepared_layout)
-
-    assert kv_requests[0].seq_len == 32
-    assert kv_requests[0].num_computed_tokens == 0
-
-
-def test_paged_preprocess_attaches_layout_and_scheduler_kv_requests(monkeypatch) -> None:
-    tokenizer, image_processor = _components([12])
-    hf_config = SimpleNamespace(
-        vae_downsample_factor=(8, 8),
-        patch_size=2,
-        image_base_size=1024,
-    )
-    monkeypatch.setattr(pipeline_module, "get_config", lambda *_args, **_kwargs: hf_config)
-    monkeypatch.setattr(pipeline_module, "HunyuanImage3ImageProcessor", lambda _config: image_processor)
-    monkeypatch.setattr(pipeline_module, "TokenizerWrapper", lambda _model: tokenizer)
-    monkeypatch.setattr(
-        pipeline_module.GenerationConfig,
-        "from_pretrained",
-        lambda _model: SimpleNamespace(sequence_template="instruct", drop_think=False),
-    )
-    preprocess = get_hunyuan_image_3_pre_process_func(
-        SimpleNamespace(model="model", diffusion_kv_mode=DiffusionKVCacheMode.PAGED_SCHEDULER)
-    )
-    request = _request(guidance_scale=1.0)
-
-    prepared_request = preprocess(request)
-
-    assert prepared_request is request
-    assert isinstance(request.prepared_layout, HunyuanPreparedLayout)
-    assert request.diffusion_kv_requests is not None
-    assert [(item.prefix_len, item.target_len, item.seq_len) for item in request.diffusion_kv_requests] == [
-        (12, 17, 32)
-    ]
-    assert len(tokenizer.calls) == 1

tests/diffusion/models/hunyuan_image3/test_hunyuan_image3_step_execution.py

@@ -7,10 +7,7 @@ import pytest
 import torch
 
 import vllm_omni.diffusion.models.hunyuan_image3.pipeline_hunyuan_image3 as hy3_module
-import vllm_omni.diffusion.models.hunyuan_image3.request_layout as hy3_layout_module
 from vllm_omni.diffusion.data import AttentionConfig, AttentionSpec
-from vllm_omni.diffusion.models.hunyuan_image3.hunyuan_image3_tokenizer import TokenizerEncodeOutput
-from vllm_omni.diffusion.models.hunyuan_image3.hunyuan_image3_transformer import ImageInfo
 from vllm_omni.diffusion.models.hunyuan_image3.pipeline_hunyuan_image3 import (
     _STEP_AR_KV,
     _STEP_CFG_FACTOR,
@@ -21,7 +18,6 @@ from vllm_omni.diffusion.models.hunyuan_image3.pipeline_hunyuan_image3 import (
     _STEP_PROMPT_KV,
     HunyuanImage3Pipeline,
 )
-from vllm_omni.diffusion.models.hunyuan_image3.request_layout import HunyuanPreparedLayout
 from vllm_omni.diffusion.worker.input_batch import InputBatch
 from vllm_omni.diffusion.worker.request_batch import DiffusionRequestBatch
 from vllm_omni.diffusion.worker.utils import StepRequestState
@@ -80,72 +76,6 @@ def _sampling_params(**extra_args):
     )
 
 
-def _prepared_layout() -> HunyuanPreparedLayout:
-    return HunyuanPreparedLayout(
-        tokenizer_output=TokenizerEncodeOutput(
-            tokens=torch.arange(21).reshape(1, 21),
-            gen_image_mask=torch.ones(1, 21, dtype=torch.bool),
-            gen_timestep_scatter_index=torch.tensor([[4]]),
-            all_image_slices=[[slice(5, 21)]],
-            joint_image_slices=[[]],
-            gen_image_slices=[[slice(5, 21)]],
-            real_pos=torch.tensor([[21]]),
-        ),
-        rope_image_info=[[(slice(5, 21), (4, 4))]],
-        generated_image_info=ImageInfo(
-            image_type="gen_image",
-            image_width=512,
-            image_height=512,
-            token_width=4,
-            token_height=4,
-            image_token_length=16,
-        ),
-    )
-
-
-def test_prepare_model_inputs_reuses_prepared_layout(monkeypatch):
-    pipeline = _pipeline()
-    monkeypatch.setattr(HunyuanImage3Pipeline, "device", property(lambda self: torch.device("cpu")))
-    rope_kwargs = {}
-
-    def fake_build_batch_2d_rope(**kwargs):
-        rope_kwargs.update(kwargs)
-        return torch.zeros(1), torch.zeros(1)
-
-    monkeypatch.setattr(hy3_module, "build_batch_2d_rope", fake_build_batch_2d_rope)
-
-    def fail_apply_chat_template(**_kwargs):
-        pytest.fail("prepared Hunyuan execution must not apply the chat template again")
-
-    pipeline._tkwrapper = SimpleNamespace(
-        apply_chat_template=fail_apply_chat_template,
-        eos_token_id=2,
-        boi_token_id=3,
-        end_recaption_token_id=4,
-        end_answer_token_id=5,
-    )
-    pipeline.config = SimpleNamespace(
-        attention_head_dim=2,
-        rope_theta=10000.0,
-    )
-    prepared = _prepared_layout()
-
-    model_inputs = pipeline.prepare_model_inputs(
-        prompt="prompt",
-        mode="gen_image",
-        guidance_scale=1.0,
-        image_size=(512, 512),
-        device=torch.device("cpu"),
-        generator=[torch.Generator().manual_seed(0)],
-        prepared_layout=prepared,
-    )
-
-    assert model_inputs["tokenizer_output"] is prepared.tokenizer_output
-    assert model_inputs["batch_gen_image_info"] == [prepared.generated_image_info]
-    assert rope_kwargs["image_infos"] is prepared.rope_image_info
-    torch.testing.assert_close(model_inputs["input_ids"], prepared.tokenizer_output.tokens)
-
-
 def test_hunyuan_step_group_key_ignores_step_index_for_later_steps():
     pipeline = _pipeline()
     states = [_state("req-0", 1), _state("req-1", 3)]
@@ -201,13 +131,12 @@ def test_prepare_encode_preserves_normal_hunyuan_bot_task_semantics(
         captured.update(kwargs)
         raise RuntimeError("stop after prepare_model_inputs")
 
-    monkeypatch.setattr(hy3_layout_module, "get_system_prompt", fake_get_system_prompt)
+    monkeypatch.setattr(hy3_module, "get_system_prompt", fake_get_system_prompt)
     pipeline.prepare_model_inputs = fake_prepare_model_inputs
     state = StepRequestState(
         request_id="req-bot-task",
         sampling=sampling,
         prompt=prompt_item,
-        prepared_layout=_prepared_layout(),
     )
 
     with pytest.raises(RuntimeError, match="stop after prepare_model_inputs"):
@@ -215,7 +144,6 @@ def test_prepare_encode_preserves_normal_hunyuan_bot_task_semantics(
 
     assert captured["bot_task"] == expected_model_bot_task
     assert captured["system_prompt_bot_task"] == expected_system_bot_task
-    assert captured["prepared_layout"] is state.prepared_layout
 
 
 def test_forward_uses_same_hunyuan_bot_task_semantics(monkeypatch):
@@ -231,22 +159,23 @@ def test_forward_uses_same_hunyuan_bot_task_semantics(monkeypatch):
         captured.update(kwargs)
         raise RuntimeError("stop after prepare_model_inputs")
 
-    monkeypatch.setattr(hy3_layout_module, "get_system_prompt", fake_get_system_prompt)
+    monkeypatch.setattr(hy3_module, "get_system_prompt", fake_get_system_prompt)
     pipeline.prepare_model_inputs = fake_prepare_model_inputs
-    request = SimpleNamespace(
-        request_id="req-forward-bot-task",
-        sampling_params=_sampling_params(bot_task="think_recaption", use_system_prompt="dynamic"),
-        prompt={"prompt": "prompt", "bot_task": "vanilla"},
-        prepared_layout=_prepared_layout(),
+    req = DiffusionRequestBatch(
+        requests=[
+            SimpleNamespace(
+                request_id="req-forward-bot-task",
+                sampling_params=_sampling_params(bot_task="think_recaption", use_system_prompt="dynamic"),
+                prompt={"prompt": "prompt", "bot_task": "vanilla"},
+            )
+        ]
     )
-    req = DiffusionRequestBatch(requests=[request])
 
     with pytest.raises(RuntimeError, match="stop after prepare_model_inputs"):
         pipeline.forward(req)
 
     assert captured["bot_task"] == "think"
     assert captured["system_prompt_bot_task"] == "think"
-    assert captured["prepared_layout"] is request.prepared_layout
 
 
 def test_grouped_denoise_rejects_non_sdpa_attention_backend():

tests/diffusion/models/lingbot_video/test_image_condition.py

@@ -1,133 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-
-from types import SimpleNamespace
-
-import numpy as np
-import pytest
-import torch
-from PIL import Image
-from torch import nn
-
-from vllm_omni.diffusion.models.lingbot_video.image_condition import (
-    apply_clean_prefix,
-    geometry_align_image,
-    prepare_ti2v_image_condition,
-)
-
-pytestmark = [pytest.mark.core_model, pytest.mark.cpu, pytest.mark.diffusion]
-
-
-def _gradient_image(width: int, height: int) -> Image.Image:
-    x = np.linspace(0, 255, width, dtype=np.uint8)[None, :]
-    y = np.linspace(0, 255, height, dtype=np.uint8)[:, None]
-    rgb = np.stack(
-        [
-            np.broadcast_to(x, (height, width)),
-            np.broadcast_to(y, (height, width)),
-            np.full((height, width), 127, dtype=np.uint8),
-        ],
-        axis=-1,
-    )
-    return Image.fromarray(rgb, mode="RGB")
-
-
-@pytest.mark.parametrize("source_size", [(160, 64), (64, 160), (96, 96)])
-def test_geometry_align_image_supports_source_aspect_ratios(source_size):
-    pixel = geometry_align_image(_gradient_image(*source_size), 64, 96)
-
-    assert pixel.shape == (1, 3, 1, 64, 96)
-    assert pixel.dtype == torch.float32
-    assert 0 <= pixel.min() <= pixel.max() <= 1
-
-
-@pytest.mark.parametrize(
-    ("source_size", "target_size"),
-    [((160, 64), (64, 96)), ((64, 160), (96, 64))],
-)
-def test_geometry_align_image_center_crops_without_stretching(source_size, target_size):
-    width, height = source_size
-    target_height, target_width = target_size
-    rgb = np.full((height, width, 3), (255, 0, 0), dtype=np.uint8)
-    if width > target_width:
-        left = (width - target_width) // 2
-        rgb[:, left : left + target_width] = (0, 255, 0)
-    else:
-        top = (height - target_height) // 2
-        rgb[top : top + target_height] = (0, 255, 0)
-
-    pixel = geometry_align_image(
-        Image.fromarray(rgb, mode="RGB"),
-        target_height,
-        target_width,
-    )
-
-    assert pixel.shape == (1, 3, 1, target_height, target_width)
-    expected_green = torch.tensor((0.0, 1.0, 0.0)).view(1, 3, 1, 1, 1)
-    assert torch.equal(pixel, expected_green.expand_as(pixel))
-
-
-class _LatentDistribution:
-    def __init__(self, latent: torch.Tensor):
-        self.latent = latent
-
-    def sample(self, generator=None):
-        del generator
-        return self.latent
-
-
-class _CapturingVAE(nn.Module):
-    def __init__(self):
-        super().__init__()
-        self.anchor = nn.Parameter(torch.zeros(()))
-        self.config = SimpleNamespace(
-            latents_mean=[0.0, 0.0, 0.0],
-            latents_std=[1.0, 1.0, 1.0],
-        )
-        self.encoded_pixel = None
-
-    def encode(self, pixel):
-        self.encoded_pixel = pixel.detach().clone()
-        return SimpleNamespace(latent_dist=_LatentDistribution(pixel))
-
-
-def test_ti2v_vlm_and_vae_paths_share_geometry_aligned_image():
-    vae = _CapturingVAE()
-    condition = prepare_ti2v_image_condition(
-        _gradient_image(160, 64),
-        height=64,
-        width=96,
-        vae=vae,
-        vision_patch_size=16,
-        device=torch.device("cpu"),
-    )
-
-    vae_pixel = (vae.encoded_pixel + 1.0) / 2.0
-    vae_image = vae_pixel[0, :, 0].permute(1, 2, 0).mul(255).byte().numpy()
-    assert condition.vlm_image.size == (96, 64)
-    np.testing.assert_allclose(
-        np.asarray(condition.vlm_image),
-        vae_image,
-        rtol=0,
-        atol=1,
-    )
-
-
-def test_apply_clean_prefix_only_overwrites_condition_frames():
-    latents = torch.arange(20, dtype=torch.float32).reshape(1, 1, 5, 2, 2)
-    original_tail = latents[:, :, 2:].clone()
-    clean = torch.full((1, 1, 2, 2, 2), -3.0)
-
-    returned = apply_clean_prefix(latents, clean)
-
-    assert returned is latents
-    assert torch.equal(latents[:, :, :2], clean)
-    assert torch.equal(latents[:, :, 2:], original_tail)
-
-
-def test_apply_clean_prefix_rejects_incompatible_shapes():
-    with pytest.raises(ValueError, match="shape must match"):
-        apply_clean_prefix(
-            torch.zeros(1, 2, 3, 4, 4),
-            torch.zeros(1, 3, 1, 4, 4),
-        )

tests/diffusion/models/lingbot_video/test_pipeline_lingbot_video.py

@@ -6,10 +6,9 @@ from types import SimpleNamespace
 import numpy as np
 import pytest
 import torch
-from PIL import Image
 from torch import nn
 
-from vllm_omni.diffusion.request import DUMMY_DIFFUSION_REQUEST_ID, OmniDiffusionRequest
+from vllm_omni.diffusion.request import OmniDiffusionRequest
 from vllm_omni.diffusion.worker.request_batch import DiffusionRequestBatch
 from vllm_omni.inputs.data import OmniDiffusionSamplingParams
 
@@ -23,8 +22,6 @@ def _make_pipeline():
     nn.Module.__init__(pipeline)
     pipeline.device = torch.device("cpu")
     pipeline.default_negative_prompt = "default negative"
-    pipeline.default_image_negative_prompt = "default image negative"
-    pipeline.img_prompt_template = "<image>"
     pipeline.od_config = SimpleNamespace(flow_shift=None)
     return pipeline
 
@@ -40,24 +37,17 @@ def test_lingbot_video_pipeline_import_and_registry():
         LingBotVideoPipeline,
         LingBotVideoTransformer3DModel,
         get_lingbot_video_post_process_func,
-        get_lingbot_video_pre_process_func,
-    )
-    from vllm_omni.diffusion.registry import (
-        _DIFFUSION_MODELS,
-        _DIFFUSION_POST_PROCESS_FUNCS,
-        _DIFFUSION_PRE_PROCESS_FUNCS,
     )
+    from vllm_omni.diffusion.registry import _DIFFUSION_MODELS, _DIFFUSION_POST_PROCESS_FUNCS
 
     assert LingBotVideoPipeline is not None
     assert LingBotVideoTransformer3DModel is not None
-    assert get_lingbot_video_pre_process_func is not None
     assert get_lingbot_video_post_process_func is not None
     assert _DIFFUSION_MODELS["LingBotVideoPipeline"] == (
         "lingbot_video",
         "pipeline_lingbot_video",
         "LingBotVideoPipeline",
     )
-    assert _DIFFUSION_PRE_PROCESS_FUNCS["LingBotVideoPipeline"] == "get_lingbot_video_pre_process_func"
     assert _DIFFUSION_POST_PROCESS_FUNCS["LingBotVideoPipeline"] == "get_lingbot_video_post_process_func"
 
 
@@ -68,7 +58,6 @@ def test_component_discovery_declarations():
     assert LingBotVideoPipeline._encoder_modules == ["text_encoder"]
     assert LingBotVideoPipeline._vae_modules == ["vae"]
     assert LingBotVideoPipeline.supports_step_execution is False
-    assert LingBotVideoPipeline.max_outputs_per_prompt == 1
 
 
 def test_extra_body_params_include_video_flow_shift_alias():
@@ -80,47 +69,17 @@ def test_extra_body_params_include_video_flow_shift_alias():
     assert "batch_cfg" in params
 
 
-def test_preprocess_preserves_geometry_and_converts_rgb(tmp_path):
-    from vllm_omni.diffusion.models.lingbot_video import (
-        get_lingbot_video_pre_process_func,
-    )
+def test_extract_prompt_string_and_mapping():
+    from vllm_omni.diffusion.models.lingbot_video.pipeline_lingbot_video import _extract_prompt
 
-    image_path = tmp_path / "reference.png"
-    Image.new("L", (48, 32), color=127).save(image_path)
-    request = OmniDiffusionRequest(
-        prompt={
-            "prompt": "a robot walks",
-            "modalities": ["video"],
-            "multi_modal_data": {"image": str(image_path)},
-        },
-        sampling_params=OmniDiffusionSamplingParams(),
-        request_id="lingbot-preprocess-test",
-    )
+    string_req = SimpleNamespace(prompt="a robot waves")
+    assert _extract_prompt(string_req) == ("a robot waves", None)
 
-    result = get_lingbot_video_pre_process_func(SimpleNamespace())(request)
+    mapping_req = SimpleNamespace(prompt={"prompt": "a robot walks", "negative_prompt": "low quality"})
+    assert _extract_prompt(mapping_req) == ("a robot walks", "low quality")
 
-    image = result.prompt["multi_modal_data"]["image"]
-    assert image.mode == "RGB"
-    assert image.size == (48, 32)
-
-
-def test_preprocess_marks_internal_image_warmup_as_video():
-    from vllm_omni.diffusion.models.lingbot_video import (
-        get_lingbot_video_pre_process_func,
-    )
-
-    request = OmniDiffusionRequest(
-        prompt={
-            "prompt": "dummy run",
-            "multi_modal_data": {"image": Image.new("RGB", (32, 32))},
-        },
-        sampling_params=OmniDiffusionSamplingParams(),
-        request_id=DUMMY_DIFFUSION_REQUEST_ID,
-    )
-
-    result = get_lingbot_video_pre_process_func(SimpleNamespace())(request)
-
-    assert result.prompt["modalities"] == ["video"]
+    with pytest.raises(TypeError, match="Unsupported LingBot prompt type"):
+        _extract_prompt(SimpleNamespace(prompt=["not", "supported"]))
 
 
 def test_check_inputs_accepts_t2v_shapes_and_rejects_invalid_values():
@@ -145,59 +104,6 @@ def test_postprocess_keeps_torch_by_default_and_converts_np_when_requested():
     array = post(frames, SimpleNamespace(output_type="np"))
     assert isinstance(array, np.ndarray)
     assert array.shape == (2, 4, 4, 3)
-    video = post({"video": frames}, SimpleNamespace(output_type="pt"))
-    assert set(video) == {"video"}
-    assert video["video"] is frames
-    image = post(
-        {"image": torch.ones(4, 4, 3)},
-        SimpleNamespace(output_type="pt"),
-    )
-    assert set(image) == {"image"}
-    assert isinstance(image["image"], np.ndarray)
-    assert image["image"].shape == (4, 4, 3)
-    latent = torch.ones(1, 4, 1, 2, 2)
-    latent_output = post({"image": latent}, SimpleNamespace(output_type="latent"))
-    assert set(latent_output) == {"image"}
-    assert latent_output["image"] is latent
-
-
-def test_postprocess_preserves_image_contract_for_serving():
-    from vllm_omni.diffusion.data import DiffusionOutput
-    from vllm_omni.diffusion.models.lingbot_video import get_lingbot_video_post_process_func
-    from vllm_omni.diffusion.output_formatter import (
-        format_diffusion_outputs,
-        normalize_diffusion_postprocess_output,
-    )
-    from vllm_omni.entrypoints.openai.api_server import _extract_images_from_result
-
-    sampling = OmniDiffusionSamplingParams(
-        num_inference_steps=1,
-        output_type="pt",
-        resolution=64,
-    )
-    request = OmniDiffusionRequest(
-        prompt={"prompt": "a robot", "modalities": ["image"]},
-        sampling_params=sampling,
-        request_id="lingbot-image-contract-test",
-    )
-    raw_output = {"image": torch.ones(8, 12, 3)}
-    processed = get_lingbot_video_post_process_func(SimpleNamespace())(raw_output, sampling)
-    normalized = normalize_diffusion_postprocess_output(processed)
-
-    assert normalized.primary_key == "image"
-    [result] = format_diffusion_outputs(
-        request=request,
-        od_config=SimpleNamespace(model_class_name="LingBotVideoPipeline"),
-        diffusion_output=DiffusionOutput(output=raw_output),
-        output_data=raw_output,
-        postprocess_output=normalized,
-    )
-
-    assert len(result.images) == 1
-    assert isinstance(result.images[0], np.ndarray)
-    endpoint_images = _extract_images_from_result(result)
-    assert len(endpoint_images) == 1
-    assert endpoint_images[0].size == (12, 8)
 
 
 def test_forward_resolves_t2v_sampling_and_flow_shift_alias():
@@ -210,11 +116,7 @@ def test_forward_resolves_t2v_sampling_and_flow_shift_alias():
 
     pipeline._generate = fake_generate
     req = _make_request_batch(
-        {
-            "prompt": "a robot arm",
-            "negative_prompt": "bad hands",
-            "modalities": ["video"],
-        },
+        {"prompt": "a robot arm", "negative_prompt": "bad hands"},
         height=192,
         width=320,
         num_frames=9,
@@ -227,12 +129,10 @@ def test_forward_resolves_t2v_sampling_and_flow_shift_alias():
 
     out = pipeline.forward(req)
 
-    assert torch.equal(out.output["video"], torch.zeros(9, 192, 320, 3))
+    assert torch.equal(out.output, torch.zeros(9, 192, 320, 3))
     assert len(calls) == 1
     call = calls[0]
     assert call["prompt"] == "a robot arm"
-    assert call["mode"].value == "t2v"
-    assert call["input_image"] is None
     assert call["negative_prompt"] == "bad hands"
     assert call["height"] == 192
     assert call["width"] == 320
@@ -294,222 +194,6 @@ def test_forward_prefers_shift_over_flow_shift_and_defaults_negative_prompt():
     assert calls[0]["negative_prompt"] == "default negative"
 
 
-@pytest.mark.parametrize(
-    ("prompt", "num_frames", "expected_mode", "output_key", "default_negative"),
-    [
-        (
-            {"prompt": "a still robot", "modalities": ["image"]},
-            1,
-            "t2i",
-            "image",
-            "default image negative",
-        ),
-        (
-            {
-                "prompt": "a moving robot",
-                "modalities": ["video"],
-                "multi_modal_data": {"image": Image.new("RGB", (48, 32), color="blue")},
-            },
-            9,
-            "ti2v",
-            "video",
-            "default negative",
-        ),
-    ],
-)
-def test_forward_dispatches_generation_mode_and_output(
-    prompt,
-    num_frames,
-    expected_mode,
-    output_key,
-    default_negative,
-):
-    pipeline = _make_pipeline()
-    calls = []
-
-    def fake_generate(**kwargs):
-        calls.append(kwargs)
-        return torch.zeros(192, 320, 3) if output_key == "image" else torch.zeros(num_frames, 192, 320, 3)
-
-    pipeline._generate = fake_generate
-    output = pipeline.forward(
-        _make_request_batch(
-            prompt,
-            height=192,
-            width=320,
-            num_frames=num_frames,
-            num_inference_steps=2,
-            guidance_scale=3.0,
-            seed=42,
-            output_type="pt",
-        )
-    )
-
-    assert set(output.output) == {output_key}
-    assert calls[0]["mode"].value == expected_mode
-    assert calls[0]["negative_prompt"] == default_negative
-    if expected_mode == "ti2v":
-        assert isinstance(calls[0]["input_image"], Image.Image)
-
-
-def test_batched_cfg_pads_image_conditioned_embeddings_and_masks():
-    from vllm_omni.diffusion.models.lingbot_video.pipeline_lingbot_video import (
-        _batch_cfg_prompt_inputs,
-    )
-
-    positive = torch.ones(1, 7, 4)
-    positive_mask = torch.ones(1, 7, dtype=torch.long)
-    negative = torch.full((1, 9, 4), 2.0)
-    negative_mask = torch.ones(1, 9, dtype=torch.long)
-
-    embeds, mask = _batch_cfg_prompt_inputs(
-        positive,
-        positive_mask,
-        negative,
-        negative_mask,
-    )
-
-    assert embeds.shape == (2, 9, 4)
-    assert mask.shape == (2, 9)
-    assert torch.equal(mask[0], torch.tensor([1, 1, 1, 1, 1, 1, 1, 0, 0]))
-    assert torch.equal(mask[1], torch.ones(9, dtype=torch.long))
-
-
-class _RecordingTransformer(nn.Module):
-    def __init__(self):
-        super().__init__()
-        self.anchor = nn.Parameter(torch.zeros(()))
-        self.config = SimpleNamespace(in_channels=1)
-        self.latent_inputs = []
-
-    def forward(
-        self,
-        latents,
-        timestep,
-        prompt_embeds,
-        *,
-        encoder_attention_mask,
-        return_dict,
-    ):
-        del timestep, prompt_embeds, encoder_attention_mask, return_dict
-        self.latent_inputs.append(latents.detach().clone())
-        return (torch.zeros_like(latents),)
-
-
-class _CorruptingScheduler:
-    sigma_max = 1.0
-    sigma_min = 0.0
-
-    def set_timesteps(self, num_inference_steps, *, device, shift, **kwargs):
-        del num_inference_steps, shift, kwargs
-        self.timesteps = torch.tensor([1000.0, 500.0], device=device)
-
-    def step(
-        self,
-        noise_pred,
-        timestep,
-        latents,
-        *,
-        return_dict,
-        generator,
-    ):
-        del noise_pred, timestep, return_dict, generator
-        return (latents + 7.0,)
-
-
-@pytest.mark.parametrize(
-    ("guidance_scale", "batch_cfg", "expected_prompts", "expected_transformer_calls"),
-    [
-        pytest.param(2.0, False, ["positive", "negative"], 4, id="sequential-cfg"),
-        pytest.param(2.0, True, ["positive", "negative"], 2, id="batched-cfg"),
-        pytest.param(1.0, False, ["positive"], 2, id="cfg-disabled"),
-    ],
-)
-def test_ti2v_reinjects_clean_prefix_across_cfg_modes(
-    guidance_scale,
-    batch_cfg,
-    expected_prompts,
-    expected_transformer_calls,
-):
-    from vllm_omni.diffusion.models.lingbot_video import (
-        LingBotGenerationMode,
-        LingBotImageCondition,
-    )
-
-    pipeline = _make_pipeline()
-    pipeline.transformer = _RecordingTransformer()
-    pipeline.scheduler = _CorruptingScheduler()
-    pipeline.progress_bar = lambda timesteps: timesteps
-    pipeline.prepare_latents = lambda *args, **kwargs: torch.zeros(1, 1, 3, 1, 1)
-    vlm_image = Image.new("RGB", (32, 32), color="red")
-    condition = LingBotImageCondition(
-        vlm_image=vlm_image,
-        clean_latent=torch.full((1, 1, 1, 1, 1), 5.0),
-    )
-    pipeline.prepare_ti2v_image_condition = lambda *args, **kwargs: condition
-    encode_calls = []
-
-    def fake_encode(prompt, *, images, device):
-        del device
-        encode_calls.append((prompt, images))
-        return torch.ones(1, 2, 4), torch.ones(1, 2, dtype=torch.long)
-
-    pipeline.encode_prompt = fake_encode
-
-    latents = pipeline._generate(
-        prompt="positive",
-        mode=LingBotGenerationMode.TI2V,
-        input_image=vlm_image,
-        negative_prompt="negative",
-        height=16,
-        width=16,
-        num_frames=9,
-        num_inference_steps=2,
-        guidance_scale=guidance_scale,
-        shift=3.0,
-        output_type="latent",
-        batch_cfg=batch_cfg,
-    )
-
-    assert [call[0] for call in encode_calls] == expected_prompts
-    assert all(call[1] == [vlm_image] for call in encode_calls)
-    assert len(pipeline.transformer.latent_inputs) == expected_transformer_calls
-    for item in pipeline.transformer.latent_inputs:
-        expected_prefix = condition.clean_latent.expand(item.shape[0], -1, -1, -1, -1)
-        assert torch.equal(item[:, :, :1], expected_prefix)
-    assert torch.equal(latents[:, :, :1], condition.clean_latent)
-    assert torch.equal(latents[:, :, 1:], torch.full((1, 1, 2, 1, 1), 14.0))
-
-
-def test_t2i_decodes_and_returns_the_unique_frame():
-    from vllm_omni.diffusion.models.lingbot_video import LingBotGenerationMode
-
-    pipeline = _make_pipeline()
-    pipeline.transformer = _RecordingTransformer()
-    pipeline.scheduler = _CorruptingScheduler()
-    pipeline.progress_bar = lambda timesteps: timesteps
-    pipeline.prepare_latents = lambda *args, **kwargs: torch.zeros(1, 1, 1, 2, 2)
-    pipeline.encode_prompt = lambda *args, **kwargs: (
-        torch.ones(1, 2, 4),
-        torch.ones(1, 2, dtype=torch.long),
-    )
-    pipeline._decode_latents = lambda latents: torch.ones(1, 16, 16, 3)
-
-    image = pipeline._generate(
-        prompt="a still robot",
-        mode=LingBotGenerationMode.T2I,
-        height=16,
-        width=16,
-        num_frames=1,
-        num_inference_steps=2,
-        guidance_scale=1.0,
-        shift=3.0,
-        output_type="pt",
-    )
-
-    assert image.shape == (16, 16, 3)
-
-
 def test_forward_rejects_multi_request_batches():
     pipeline = _make_pipeline()
     first = OmniDiffusionRequest(
@@ -527,45 +211,6 @@ def test_forward_rejects_multi_request_batches():
         pipeline.forward(DiffusionRequestBatch([first, second]))
 
 
-def test_forward_marks_invalid_request_contract_as_client_error():
-    from vllm_omni.errors import OmniClientError
-
-    pipeline = _make_pipeline()
-    request = _make_request_batch(
-        {
-            "prompt": "unsupported image edit",
-            "modalities": ["image"],
-            "multi_modal_data": {"image": Image.new("RGB", (32, 32), color="blue")},
-        },
-        height=192,
-        width=320,
-        num_frames=1,
-    )
-
-    with pytest.raises(OmniClientError, match="does not accept") as exc_info:
-        pipeline.forward(request)
-
-    assert exc_info.value.status_code == 400
-
-
-def test_forward_marks_unsupported_output_count_as_client_error():
-    from vllm_omni.errors import OmniClientError
-
-    pipeline = _make_pipeline()
-    request = _make_request_batch(
-        {"prompt": "motion", "modalities": ["video"]},
-        height=192,
-        width=320,
-        num_frames=9,
-        num_outputs_per_prompt=2,
-    )
-
-    with pytest.raises(OmniClientError, match="one output per prompt") as exc_info:
-        pipeline.forward(request)
-
-    assert exc_info.value.status_code == 400
-
-
 def test_load_weights_rejects_external_weight_stream():
     pipeline = _make_pipeline()
 

tests/diffusion/models/lingbot_video/test_request_utils.py

@@ -1,350 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-
-from types import SimpleNamespace
-
-import pytest
-from PIL import Image
-
-from vllm_omni.diffusion.models.lingbot_video.request_utils import (
-    LingBotGenerationMode,
-    caption_from_lingbot_prompt,
-    normalize_lingbot_num_frames,
-    normalize_lingbot_request,
-    resolve_lingbot_mode,
-    resolve_lingbot_num_frames,
-    resolve_lingbot_output_dimensions,
-    resolve_lingbot_size,
-)
-from vllm_omni.inputs.data import OmniDiffusionSamplingParams
-
-pytestmark = [pytest.mark.core_model, pytest.mark.cpu, pytest.mark.diffusion]
-
-
-def _request(prompt, **sampling_overrides):
-    return SimpleNamespace(
-        prompt=prompt,
-        sampling_params=OmniDiffusionSamplingParams(**sampling_overrides),
-    )
-
-
-def _normalize(prompt, **sampling_overrides):
-    return normalize_lingbot_request(
-        _request(prompt, **sampling_overrides),
-        default_negative_prompt="video default",
-        default_image_negative_prompt="image default",
-    )
-
-
-@pytest.mark.parametrize(
-    ("prompt", "expected"),
-    [
-        ({"prompt": "still", "modalities": ["image"]}, LingBotGenerationMode.T2I),
-        ({"prompt": "motion", "modalities": ["video"]}, LingBotGenerationMode.T2V),
-        (
-            {
-                "prompt": "motion",
-                "modalities": ["video"],
-                "multi_modal_data": {"image": Image.new("RGB", (8, 8))},
-            },
-            LingBotGenerationMode.TI2V,
-        ),
-    ],
-)
-def test_resolve_lingbot_mode(prompt, expected):
-    assert resolve_lingbot_mode(prompt) is expected
-
-
-@pytest.mark.parametrize(
-    ("prompt", "message"),
-    [
-        (
-            {"prompt": "x", "modalities": ["image"], "multi_modal_data": {"image": Image.new("RGB", (8, 8))}},
-            "does not accept",
-        ),
-        (
-            {
-                "prompt": "x",
-                "modalities": ["video"],
-                "multi_modal_data": {"image": [Image.new("RGB", (8, 8)), Image.new("RGB", (8, 8))]},
-            },
-            "exactly one",
-        ),
-        ({"prompt": "x", "modalities": ["video"], "multi_modal_data": {"video": [1]}}, "reference modalities"),
-        ({"prompt": "x", "multi_modal_data": {"image": Image.new("RGB", (8, 8))}}, "explicitly set"),
-        ({"prompt": "x", "modalities": ["audio"]}, "output modality"),
-    ],
-)
-def test_resolve_lingbot_mode_rejects_invalid_media_combinations(prompt, message):
-    with pytest.raises(ValueError, match=message):
-        resolve_lingbot_mode(prompt)
-
-
-def test_caption_from_lingbot_prompt_preserves_text_and_compact_json():
-    assert caption_from_lingbot_prompt("plain text") == "plain text"
-    assert caption_from_lingbot_prompt({"caption": "caption only", "duration": 4}) == "caption only"
-    assert caption_from_lingbot_prompt({"caption": {"中文": True, "nested": {"value": 2}}}) == (
-        '{"中文":true,"nested":{"value":2}}'
-    )
-    assert (
-        caption_from_lingbot_prompt(
-            {"scene": "海边", "camera": {"move": "pan"}, "duration": 5, "fps": 24, "size": "320x192"}
-        )
-        == '{"scene":"海边","camera":{"move":"pan"}}'
-    )
-
-
-@pytest.mark.parametrize("prompt", [{"duration": 4, "fps": 24}, {"caption": ""}, ""])
-def test_caption_from_lingbot_prompt_rejects_empty_caption(prompt):
-    with pytest.raises(ValueError, match="empty|must not be empty"):
-        caption_from_lingbot_prompt(prompt)
-
-
-@pytest.mark.parametrize(
-    ("num_frames", "expected"),
-    [(1, 1), (2, 5), (4, 5), (5, 5), (96, 97), (98, 101), (121, 121)],
-)
-def test_normalize_lingbot_num_frames_rounds_up_to_4n_plus_1(num_frames, expected):
-    assert normalize_lingbot_num_frames(num_frames) == expected
-
-
-@pytest.mark.parametrize("num_frames", [0, -1, 1.5, "invalid"])
-def test_normalize_lingbot_num_frames_rejects_invalid_values(num_frames):
-    with pytest.raises(ValueError, match="num_frames"):
-        normalize_lingbot_num_frames(num_frames)
-
-
-@pytest.mark.parametrize(
-    ("duration", "fps", "expected"),
-    [(4, 24, 97), (5, 24, 121), (8, 24, 193), (4, 12, 49), (4, 30, 121)],
-)
-def test_resolve_lingbot_num_frames_rounds_up(duration, fps, expected):
-    assert resolve_lingbot_num_frames(duration, fps) == expected
-
-
-@pytest.mark.parametrize(("duration", "fps"), [(0, 24), (-1, 24), (0.01, 24), (1, 0), (1, -1)])
-def test_resolve_lingbot_num_frames_rejects_non_positive_or_empty_results(duration, fps):
-    with pytest.raises(ValueError, match="duration|fps|at least one"):
-        resolve_lingbot_num_frames(duration, fps)
-
-
-@pytest.mark.parametrize(
-    ("resolution", "ratio", "expected"),
-    [
-        ("192p", "1:1", (192, 192)),
-        ("192p", "9:16", (192, 320)),
-        ("192p", "16:9", (320, 192)),
-        ("192p", "3:4", (192, 256)),
-        ("192p", "4:3", (256, 192)),
-        ("480p", "1:1", (480, 480)),
-        ("480p", "9:16", (480, 832)),
-        ("480p", "16:9", (832, 480)),
-        ("480p", "3:4", (480, 640)),
-        ("480p", "4:3", (640, 480)),
-        ("720p", "1:1", (736, 736)),
-        ("720p", "9:16", (736, 1280)),
-        ("720p", "16:9", (1280, 736)),
-        ("720p", "3:4", (736, 960)),
-        ("720p", "4:3", (960, 736)),
-        ("1080p", "1:1", (1088, 1088)),
-        ("1080p", "9:16", (1088, 1920)),
-        ("1080p", "16:9", (1920, 1088)),
-        ("1080p", "3:4", (1088, 1440)),
-        ("1080p", "4:3", (1440, 1088)),
-        ("2k", "1:1", (1440, 1440)),
-        ("2k", "9:16", (1440, 2560)),
-        ("2k", "16:9", (2560, 1440)),
-        ("2k", "3:4", (1440, 1920)),
-        ("2k", "4:3", (1920, 1440)),
-        ("4k", "1:1", (2176, 2176)),
-        ("4k", "9:16", (2176, 3840)),
-        ("4k", "16:9", (3840, 2176)),
-        ("4k", "3:4", (2176, 2880)),
-        ("4k", "4:3", (2880, 2176)),
-    ],
-)
-def test_resolve_lingbot_size_supports_all_official_presets(resolution, ratio, expected):
-    assert resolve_lingbot_size(resolution=resolution, ratio=ratio) == expected
-
-
-def test_resolve_lingbot_size_validates_sources_and_alignment():
-    assert resolve_lingbot_size(size="320x192") == (192, 320)
-    assert resolve_lingbot_size(width=320, height=192) == (192, 320)
-    with pytest.raises(ValueError, match="provided together"):
-        resolve_lingbot_size(width=320)
-    with pytest.raises(ValueError, match="provided together"):
-        resolve_lingbot_size(resolution="192p")
-    with pytest.raises(ValueError, match="mutually exclusive"):
-        resolve_lingbot_size(width=320, height=192, resolution="192p", ratio="9:16")
-    with pytest.raises(ValueError, match="multiples of 16"):
-        resolve_lingbot_size(width=321, height=192)
-
-
-def test_resolve_lingbot_output_dimensions_matches_request_priority():
-    assert resolve_lingbot_output_dimensions(
-        sampling_width=480,
-        sampling_height=480,
-        prompt_fields={"resolution": "720p", "ratio": "16:9"},
-    ) == (736, 1280)
-
-    assert resolve_lingbot_output_dimensions(
-        sampling_width=480,
-        sampling_height=480,
-        prompt_fields={"width": 320, "height": 192},
-    ) == (320, 192)
-
-    assert resolve_lingbot_output_dimensions(
-        sampling_width=320,
-        sampling_height=192,
-    ) == (320, 192)
-
-    assert resolve_lingbot_output_dimensions() == (480, 480)
-
-
-def test_normalize_lingbot_request_uses_mode_defaults_and_preserves_empty_negative():
-    t2i = _normalize(
-        {"prompt": "still", "modalities": ["image"], "negative_prompt": ""},
-        width=320,
-        height=192,
-        num_frames=1,
-    )
-    assert t2i.mode is LingBotGenerationMode.T2I
-    assert t2i.num_frames == 1
-    assert t2i.negative_prompt == ""
-
-    t2v = _normalize(
-        {"prompt": "motion", "modalities": ["video"]},
-        width=320,
-        height=192,
-        num_frames=9,
-    )
-    assert t2v.mode is LingBotGenerationMode.T2V
-    assert t2v.negative_prompt == "video default"
-
-
-def test_normalize_lingbot_request_rejects_explicit_zero_flow_shift():
-    with pytest.raises(ValueError, match="shift.*positive"):
-        _normalize(
-            {"prompt": "still", "modalities": ["image"]},
-            width=320,
-            height=192,
-            num_frames=1,
-            extra_args={"flow_shift": 0},
-        )
-
-
-def test_normalize_lingbot_request_rejects_explicit_zero_width():
-    with pytest.raises(ValueError, match="width"):
-        _normalize(
-            {"prompt": "still", "modalities": ["image"]},
-            width=0,
-            height=192,
-            num_frames=1,
-        )
-
-
-def test_normalize_lingbot_request_rounds_all_video_frame_counts():
-    config = _normalize(
-        {"prompt": "motion", "modalities": ["video"]},
-        width=320,
-        height=192,
-        num_frames=96,
-        fps=24,
-        frame_rate=24,
-    )
-    assert config.num_frames == 97
-
-    config = _normalize(
-        {"prompt": "motion", "modalities": ["video"]},
-        width=320,
-        height=192,
-        num_frames=121,
-        fps=24,
-        frame_rate=24,
-    )
-    assert config.num_frames == 121
-
-
-def test_normalize_lingbot_request_prefers_explicit_frames_over_duration():
-    online = _normalize(
-        {"prompt": "motion", "modalities": ["video"]},
-        width=320,
-        height=192,
-        num_frames=9,
-        fps=24,
-        extra_args={"duration": 5},
-    )
-    assert online.num_frames == 9
-
-    offline = _normalize(
-        {
-            "prompt": {"caption": "motion", "duration": 5, "num_frames": 9},
-            "modalities": ["video"],
-        },
-        width=320,
-        height=192,
-        fps=24,
-    )
-    assert offline.num_frames == 9
-
-
-def test_normalize_lingbot_request_uses_duration_then_model_default():
-    duration = _normalize(
-        {"prompt": {"caption": "motion", "duration": 5}, "modalities": ["video"]},
-        width=320,
-        height=192,
-        fps=24,
-    )
-    assert duration.num_frames == 121
-
-    default = _normalize(
-        {"prompt": "motion", "modalities": ["video"]},
-        width=320,
-        height=192,
-    )
-    assert default.num_frames == 81
-
-
-def test_normalize_lingbot_request_rejects_contract_conflicts():
-    with pytest.raises(ValueError, match="text-to-image.*duration"):
-        _normalize(
-            {"prompt": {"caption": "still", "duration": 4}, "modalities": ["image"]},
-            width=320,
-            height=192,
-            num_frames=1,
-        )
-    with pytest.raises(ValueError, match="text-to-image requires"):
-        _normalize(
-            {"prompt": "still", "modalities": ["image"]},
-            width=320,
-            height=192,
-            num_frames=5,
-        )
-    with pytest.raises(ValueError, match="seconds.*duration"):
-        _normalize(
-            {"prompt": "motion", "modalities": ["video"]},
-            width=320,
-            height=192,
-            num_frames=96,
-            extra_args={"seconds": 4, "duration": 4},
-        )
-
-
-def test_normalize_lingbot_request_model_size_overrides_sampling_dimensions():
-    # A prompt-provided size overrides the sampling width/height.
-    config = _normalize(
-        {"prompt": {"caption": "motion", "size": "320x192"}, "modalities": ["video"]},
-        width=480,
-        height=480,
-        num_frames=81,
-    )
-    assert (config.height, config.width) == (192, 320)
-
-    # Without a prompt size, the sampling dimensions are used.
-    config = _normalize(
-        {"prompt": "motion", "modalities": ["video"]},
-        width=480,
-        height=480,
-        num_frames=81,
-    )
-    assert (config.height, config.width) == (480, 480)

tests/diffusion/models/minimax_h3/test_minimax_h3_contract.py

@@ -9,7 +9,6 @@ from unittest.mock import Mock
 import numpy as np
 import pytest
 import torch
-import torch.nn as nn
 
 pytestmark = [pytest.mark.core_model, pytest.mark.cpu, pytest.mark.diffusion]
 
@@ -492,10 +491,6 @@ def test_cudnn_packed_attention_uses_python_length_without_padding_mask():
     class FakeBackend:
         supports_prefix_kv_slicing = True
 
-        @classmethod
-        def supports_packed_mask_free(cls) -> bool:
-            return False
-
     class FakeAttention(torch.nn.Module):
         attn_backend = FakeBackend
 
@@ -530,48 +525,6 @@ def test_cudnn_packed_attention_uses_python_length_without_padding_mask():
     assert attention.attention.metadata.extra["valid_kv_length"] == 5
 
 
-def test_packed_attention_skips_mask_for_packed_mask_free_backend():
-    from vllm_omni.diffusion.models.minimax_h3.minimax_h3_transformer import (
-        MiniMaxH3Attention,
-    )
-
-    class FakeBackend:
-        supports_prefix_kv_slicing = False
-
-        @classmethod
-        def supports_packed_mask_free(cls) -> bool:
-            return True
-
-    class FakeAttention(torch.nn.Module):
-        attn_backend = FakeBackend
-
-        def __init__(self):
-            super().__init__()
-            self.metadata = None
-
-        def forward(self, query, key, value, metadata):
-            self.metadata = metadata
-            return query
-
-    attention = object.__new__(MiniMaxH3Attention)
-    torch.nn.Module.__init__(attention)
-    attention.attention = FakeAttention()
-    q = torch.randn(8, 2, 4)
-
-    attention._run_packed_attention(
-        q,
-        q,
-        q,
-        cu_seqlens=torch.tensor([0, 5, 8], dtype=torch.int32),
-        max_seqlen=5,
-        packed_total=8,
-    )
-
-    assert attention.attention.metadata.attn_mask is None
-    assert attention.attention.metadata.extra["valid_kv_length"] == 5
-    assert attention.attention.metadata.extra["npu_attn_varlen"] is True
-
-
 def test_packed_attention_keeps_padding_mask_for_other_backends():
     from vllm_omni.diffusion.models.minimax_h3.minimax_h3_transformer import (
         MiniMaxH3Attention,
@@ -580,10 +533,6 @@ def test_packed_attention_keeps_padding_mask_for_other_backends():
     class FakeBackend:
         supports_prefix_kv_slicing = False
 
-        @classmethod
-        def supports_packed_mask_free(cls) -> bool:
-            return False
-
     class FakeAttention(torch.nn.Module):
         attn_backend = FakeBackend
 
@@ -1516,149 +1465,3 @@ def test_g4_reference_video_metadata_validation(field, value, message, tmp_path)
     metadata[field] = value
     with pytest.raises(ValueError, match=message):
         _validate_reference_video_metadata(metadata, index=0, source=str(tmp_path / "reference.mp4"))
-
-
-_ENCODER_HIDDEN = 4
-_ENCODER_HEAD_DIM = 2
-_ENCODER_NUM_HEADS = 2
-_ENCODER_NUM_KV_HEADS = 1
-_ENCODER_INTERMEDIATE = 4
-
-# One distinct value per checkpoint tensor, none of them a parameter initializer
-# (fused weights start as `torch.empty`, norms as ones). A uniform fill would
-# prove only that every row was written, not that each source landed in its own
-# row range, which is the invariant the fused loaders have to get right.
-_SOURCE_FILL = {
-    "self_attn.q_proj.weight": 3.0,
-    "self_attn.k_proj.weight": 4.0,
-    "self_attn.v_proj.weight": 5.0,
-    "mlp.gate_proj.weight": 6.0,
-    "mlp.up_proj.weight": 7.0,
-    "input_layernorm.weight": 8.0,
-}
-
-_QKV_TARGET = "text_model.layers.0.self_attn.qkv_proj.weight"
-_GATE_UP_TARGET = "text_model.layers.0.mlp.gate_up_proj.weight"
-_NORM_TARGET = "text_model.layers.0.input_layernorm.weight"
-
-
-def _one_layer_text_encoder():
-    """Encoder stub holding both fused weights plus one plain parameter."""
-    from vllm_omni.diffusion.models.minimax_h3.encoder import (
-        MiniMaxH3Qwen3VLEncoder,
-        MiniMaxH3Qwen3VLMergedColumnParallelLinear,
-        MiniMaxH3Qwen3VLQKVParallelLinear,
-        MiniMaxH3Qwen3VLRMSNorm,
-    )
-    from vllm_omni.diffusion.models.minimax_h3.pipeline_minimax_h3 import _SingleRankEncoderGroup
-
-    group = _SingleRankEncoderGroup(0)
-    layer = nn.Module()
-    layer.self_attn = nn.Module()
-    layer.self_attn.qkv_proj = MiniMaxH3Qwen3VLQKVParallelLinear(
-        group,
-        hidden_size=_ENCODER_HIDDEN,
-        num_heads=_ENCODER_NUM_HEADS,
-        num_kv_heads=_ENCODER_NUM_KV_HEADS,
-        head_dim=_ENCODER_HEAD_DIM,
-        dtype=torch.float32,
-    )
-    layer.mlp = nn.Module()
-    layer.mlp.gate_up_proj = MiniMaxH3Qwen3VLMergedColumnParallelLinear(
-        group,
-        input_size=_ENCODER_HIDDEN,
-        intermediate_size=_ENCODER_INTERMEDIATE,
-        dtype=torch.float32,
-    )
-    layer.input_layernorm = MiniMaxH3Qwen3VLRMSNorm(_ENCODER_HIDDEN)
-
-    encoder = object.__new__(MiniMaxH3Qwen3VLEncoder)
-    nn.Module.__init__(encoder)
-    encoder.text_model = nn.Module()
-    encoder.text_model.layers = nn.ModuleList([layer])
-    return encoder
-
-
-def _write_text_encoder_checkpoint(path, *, omit=()):
-    """Write a one-layer Qwen3-VL-named checkpoint, one fill value per source."""
-    import json
-
-    import safetensors.torch
-
-    prefix = "model.language_model.layers.0"
-    q_rows = _ENCODER_NUM_HEADS * _ENCODER_HEAD_DIM
-    kv_rows = _ENCODER_NUM_KV_HEADS * _ENCODER_HEAD_DIM
-    shapes = {
-        "self_attn.q_proj.weight": (q_rows, _ENCODER_HIDDEN),
-        "self_attn.k_proj.weight": (kv_rows, _ENCODER_HIDDEN),
-        "self_attn.v_proj.weight": (kv_rows, _ENCODER_HIDDEN),
-        "mlp.gate_proj.weight": (_ENCODER_INTERMEDIATE, _ENCODER_HIDDEN),
-        "mlp.up_proj.weight": (_ENCODER_INTERMEDIATE, _ENCODER_HIDDEN),
-        "input_layernorm.weight": (_ENCODER_HIDDEN,),
-    }
-    tensors = {f"{prefix}.{source}": torch.full(shape, _SOURCE_FILL[source]) for source, shape in shapes.items()}
-    for source in omit:
-        del tensors[f"{prefix}.{source}"]
-    safetensors.torch.save_file(tensors, str(path / "model.safetensors"))
-    index = {"weight_map": dict.fromkeys(tensors, "model.safetensors")}
-    (path / "model.safetensors.index.json").write_text(json.dumps(index))
-
-
-@pytest.mark.parametrize(
-    ("omitted_sources", "expected_detail", "unreported_target"),
-    [
-        (("self_attn.q_proj.weight",), f"{_QKV_TARGET}: ['q']", _GATE_UP_TARGET),
-        (("self_attn.k_proj.weight",), f"{_QKV_TARGET}: ['k']", _GATE_UP_TARGET),
-        (("self_attn.v_proj.weight",), f"{_QKV_TARGET}: ['v']", _GATE_UP_TARGET),
-        (("mlp.gate_proj.weight",), f"{_GATE_UP_TARGET}: ['gate']", _QKV_TARGET),
-        (("mlp.up_proj.weight",), f"{_GATE_UP_TARGET}: ['up']", _QKV_TARGET),
-        (
-            ("self_attn.q_proj.weight", "self_attn.k_proj.weight", "self_attn.v_proj.weight"),
-            f"{_QKV_TARGET}: ['q', 'k', 'v']",
-            _GATE_UP_TARGET,
-        ),
-    ],
-)
-def test_encoder_load_reports_missing_fused_source_shards(
-    tmp_path, omitted_sources, expected_detail, unreported_target
-):
-    encoder = _one_layer_text_encoder()
-    _write_text_encoder_checkpoint(tmp_path, omit=omitted_sources)
-
-    with pytest.raises(RuntimeError) as excinfo:
-        encoder._load_weights(str(tmp_path))
-
-    message = str(excinfo.value)
-    assert expected_detail in message
-    assert unreported_target not in message
-
-
-def test_encoder_load_rejects_unloaded_plain_param(tmp_path):
-    encoder = _one_layer_text_encoder()
-    _write_text_encoder_checkpoint(tmp_path, omit=("input_layernorm.weight",))
-
-    with pytest.raises(RuntimeError, match=r"weights not loaded.*input_layernorm"):
-        encoder._load_weights(str(tmp_path))
-
-
-def test_encoder_load_places_every_source_in_its_own_rows(tmp_path):
-    encoder = _one_layer_text_encoder()
-    _write_text_encoder_checkpoint(tmp_path)
-
-    encoder._load_weights(str(tmp_path))
-
-    params = dict(encoder.named_parameters())
-    q_rows = _ENCODER_NUM_HEADS * _ENCODER_HEAD_DIM
-    kv_rows = _ENCODER_NUM_KV_HEADS * _ENCODER_HEAD_DIM
-    qkv = params[_QKV_TARGET]
-    gate_up = params[_GATE_UP_TARGET]
-    slices = {
-        "self_attn.q_proj.weight": qkv[:q_rows],
-        "self_attn.k_proj.weight": qkv[q_rows : q_rows + kv_rows],
-        "self_attn.v_proj.weight": qkv[q_rows + kv_rows :],
-        "mlp.gate_proj.weight": gate_up[:_ENCODER_INTERMEDIATE],
-        "mlp.up_proj.weight": gate_up[_ENCODER_INTERMEDIATE:],
-        "input_layernorm.weight": params[_NORM_TARGET],
-    }
-    for source, rows in slices.items():
-        assert torch.equal(rows, torch.full_like(rows, _SOURCE_FILL[source])), source

tests/diffusion/models/minimax_h3/test_minimax_h3_e2e.py

@@ -19,7 +19,7 @@ MODEL_ENV = "VLLM_TEST_MINIMAX_H3_FL2VA_MODEL"
 def _assert_joint_output(outputs):
     assert len(outputs) == 1
     frames = np.asarray(outputs[0].images[0])
-    assert frames.shape == (107, 256, 448, 3)
+    assert frames.shape == (39, 256, 448, 3)
     multimodal = outputs[0].multimodal_output
     assert multimodal is not None
     assert np.asarray(multimodal["audio"]).shape[1] == 2
@@ -122,58 +122,6 @@ def test_minimax_h3_t2va_fp8_single_gpu_smoke():
                 output_type="np",
                 extra_args={
                     "task": "t2va",
-                    "duration": 4.0,
-                    "aspect_ratio": "16:9",
-                    "flow_shift": 12.0,
-                    "audio_flow_shift": 3.0,
-                },
-            ),
-            use_tqdm=False,
-        )
-    finally:
-        engine.close()
-
-    _assert_joint_output(outputs)
-
-
-@pytest.mark.skipif(
-    not os.environ.get(MODEL_ENV),
-    reason=f"set {MODEL_ENV} to an authorized FL2VA checkpoint path",
-)
-def test_minimax_h3_t2va_teacache_single_gpu_smoke():
-    import torch
-
-    from vllm_omni.diffusion.data import DiffusionParallelConfig
-    from vllm_omni.entrypoints.omni import Omni
-    from vllm_omni.inputs.data import OmniDiffusionSamplingParams
-
-    if torch.accelerator.device_count() < 1:
-        pytest.skip("MiniMax H3 TeaCache smoke requires a CUDA device")
-
-    engine = Omni(
-        model=os.environ[MODEL_ENV],
-        cache_backend="tea_cache",
-        cache_config={"rel_l1_thresh": 0.17},
-        parallel_config=DiffusionParallelConfig(ulysses_degree=1),
-        trust_remote_code=True,
-        enable_cpu_offload=True,
-        enforce_eager=True,
-    )
-    try:
-        outputs = engine.generate(
-            "A quiet cinematic night scene with matching ambient sound.",
-            OmniDiffusionSamplingParams(
-                height=256,
-                width=448,
-                num_frames=29,
-                fps=24,
-                num_inference_steps=2,
-                seed=42,
-                output_type="np",
-                extra_args={
-                    "task": "t2va",
-                    "duration": 4.0,
-                    "aspect_ratio": "16:9",
                     "flow_shift": 12.0,
                     "audio_flow_shift": 3.0,
                 },

tests/diffusion/models/minimax_h3/test_minimax_h3_parallel.py

@@ -69,7 +69,6 @@ def test_h3_fused_rope_matches_reference_and_preserves_unrotated_dims():
 
     attention = object.__new__(MiniMaxH3Attention)
     nn.Module.__init__(attention)
-    attention.rot_dim = 96
     attention.rope = RotaryEmbedding(is_neox_style=True, half_head_dim=False)
     attention.rope._forward_method = attention.rope.forward_native
 

tests/diffusion/test_diffusion_config_propagation.py

@@ -15,7 +15,6 @@ from vllm_omni.diffusion.data import (
     DiffusionParallelConfig,
     OmniDiffusionConfig,
 )
-from vllm_omni.diffusion.diffusion_kv.config import DiffusionKVCacheMode
 from vllm_omni.diffusion.model_metadata import (
     HUNYUAN_IMAGE3_MAX_INPUT_IMAGES,
     QWEN_IMAGE_EDIT_PLUS_MAX_INPUT_IMAGES,
@@ -125,11 +124,6 @@ class TestCreateDefaultDiffusion:
         assert ea["enforce_eager"] is True
         assert ea["lora_path"] == "/tmp/lora"
 
-    def test_diffusion_kv_mode_roundtrip(self):
-        od = _roundtrip_diffusion_config(model="x", diffusion_kv_mode="paged_scheduler")
-
-        assert od.diffusion_kv_mode is DiffusionKVCacheMode.PAGED_SCHEDULER
-
 
 def test_qwen_image_edit_plus_sets_generic_multimodal_limit():
     od_config = OmniDiffusionConfig(model="Qwen/Qwen-Image-Edit-2511", model_class_name="QwenImageEditPlusPipeline")

tests/diffusion/test_diffusion_engine.py

@@ -25,7 +25,6 @@ from vllm_omni.diffusion.request import OmniDiffusionRequest
 from vllm_omni.diffusion.sched.interface import (
     CachedRequestData,
     NewRequestData,
-    _AdmissionWaitDecision,
 )
 from vllm_omni.diffusion.sched.interface import (
     DiffusionSchedulerOutput as RealDiffusionSchedulerOutput,
@@ -76,34 +75,6 @@ class MockScheduler:
     def num_running_requests(self) -> int:
         return 0
 
-    def get_admission_wait_decision(
-        self,
-        *,
-        now: float,
-        dp_concurrent: bool = False,
-    ) -> _AdmissionWaitDecision:
-        del dp_concurrent
-        return _AdmissionWaitDecision(
-            should_wait=True,
-            deadline=now + 0.5,
-            stable_window_s=0.05,
-            max_batch=self.max_num_running_reqs,
-        )
-
-    def should_end_admission_wait(
-        self,
-        decision: _AdmissionWaitDecision,
-        *,
-        now: float,
-        stable_since: float,
-    ) -> bool:
-        waiting = self.num_waiting_requests()
-        return (
-            waiting >= decision.max_batch
-            or (waiting > 0 and now - stable_since >= decision.stable_window_s)
-            or (decision.deadline is not None and now >= decision.deadline)
-        )
-
     def schedule(self) -> DiffusionSchedulerOutput:
         if not self._waiting_queue:
             return DiffusionSchedulerOutput(step_id=self._step_id)
@@ -137,17 +108,6 @@ class MockScheduler:
         pass
 
 
-def _make_admission_engine(pre_process_func) -> DiffusionEngine:
-    engine = object.__new__(DiffusionEngine)
-    engine.scheduler = MockScheduler()
-    engine.pre_process_func = pre_process_func
-    engine._out_streams = {}
-    engine._rpc_lock = threading.RLock()
-    engine._cv = threading.Condition(engine._rpc_lock)
-    engine._closed = False
-    return engine
-
-
 class _BatchCapablePipeline:
     supports_request_batch = True
 
@@ -565,11 +525,6 @@ class TestRequestBatchAdmission:
         with pytest.raises(ValueError, match="request_batch_max_wait_ms"):
             OmniDiffusionConfig(model="test", request_batch_max_wait_ms=-1.0)
 
-    @pytest.mark.parametrize("bad_wait", [float("nan"), float("inf"), float("-inf")])
-    def test_config_rejects_nonfinite_request_batch_max_wait_ms(self, bad_wait: float) -> None:
-        with pytest.raises(ValueError, match="request_batch_max_wait_ms"):
-            OmniDiffusionConfig(model="test", request_batch_max_wait_ms=bad_wait)
-
     def test_config_normalizes_request_batch_max_wait_ms_to_float(self) -> None:
         config = OmniDiffusionConfig(model="test", request_batch_max_wait_ms=5)
 
@@ -637,7 +592,7 @@ class TestRequestBatchAdmission:
 
         start = time.monotonic()
         with engine._cv:
-            engine._wait_for_admission_if_needed_locked()
+            engine._wait_for_request_batch_admission_locked()
         waited_s = time.monotonic() - start
 
         # Stable-window exit (~50ms), not the full 1000ms deadline.
@@ -728,34 +683,6 @@ async def _consume_final_output(generator):
     return final_output
 
 
-@pytest.mark.cpu
-@pytest.mark.parametrize("entrypoint", ["add_request", "async_add_req_and_stream_response"])
-def test_engine_admission_preprocesses_request_once(entrypoint: str) -> None:
-    raw_request = OmniDiffusionRequest(
-        prompt="raw",
-        sampling_params=OmniDiffusionSamplingParams(num_inference_steps=1),
-        request_id="admission",
-    )
-    prepared_request = OmniDiffusionRequest(
-        prompt="prepared",
-        sampling_params=OmniDiffusionSamplingParams(num_inference_steps=1),
-        request_id="admission",
-        prepared_layout=object(),
-    )
-    preprocess_calls = []
-
-    def preprocess(request):
-        preprocess_calls.append(request)
-        return prepared_request
-
-    engine = _make_admission_engine(preprocess)
-
-    getattr(engine, entrypoint)(raw_request)
-
-    assert preprocess_calls == [raw_request]
-    assert engine.scheduler._waiting_queue == [prepared_request]
-
-
 @pytest.mark.cpu
 @pytest.mark.asyncio
 async def test_async_add_req_and_stream_response():