[Core] Add realtime AR-Diffusion tick sessions for LingBot World 2.0 (#5491)
Signed-off-by: zhangbin.327 <zhangbin.327@bytedance.com> Signed-off-by: Jack47 <scsvip@gmail.com> Co-authored-by: zhangbin.327 <zhangbin.327@bytedance.com> Co-authored-by: Hongsheng Liu <liuhongsheng4@huawei.com>
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# Realtime AR-Diffusion sessions
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> **Status:** experimental. This document describes the internal execution
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> contract implemented under `vllm_omni.experimental.ar_diffusion`. It does not
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> define a public HTTP or WebSocket schema.
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## Scope
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Realtime AR-Diffusion serves world models that generate one autoregressive
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latent block per request while retaining model state across requests. The
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generic runtime owns identity, event ordering, session lifecycle, routing, and
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KV allocation. A model adapter owns model-specific controls and conditioning.
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LingBot World v2 is the first single-KV-branch integration. DreamZero uses the
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same runner capability with two logical CFG branches.
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## Identities
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Four identifiers have distinct lifetimes:
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| Identifier | Lifetime | Owner | Contract |
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| --- | --- | --- | --- |
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| `session_id` | One persistent world | Session manager | Selects runner KV and model-owned state. |
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| `event_id` | One control/prompt update | Transport adapter | Unique and monotonically increasing within a session, including across reset. |
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| `chunk_index` | One committed AR block position | Session | Contiguous from zero; reset starts again at zero. |
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| `request_id` | One chunk execution | Session | Correlates the tick with its returned metadata. |
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`AsyncOmni` keeps its normal UUID-suffixed engine routing ID. The tick
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`request_id` remains inside the immutable AR-Diffusion snapshot and the
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standard output metadata, while caller-visible `OmniRequestOutput.request_id`
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continues to use AsyncOmni's existing external-ID contract. Protocol identity
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therefore does not require a private entrypoint override.
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## Request lifecycle
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1. `ARDiffusionSession.accept_event()` validates and queues an immutable
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`ARDiffusionSessionEvent`. Prompt and opaque control tracks may be updated in
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the same event.
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2. `ARDiffusionSession.next_chunk()` serializes chunk execution and calls
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`_build_tick_locked()`. The resulting `ARDiffusionTickRequest` is a snapshot
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of the current prompt, reduced controls, ordered event IDs, and chunk index.
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3. `ARDiffusionOmniTickConsumer.execute_tick()` copies the stage sampling
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parameters, stores the typed tick in
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`sampling_params.extra_args["ar_diffusion_tick"]`, constructs the normal Omni
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prompt through its `prompt_provider`, and invokes `AsyncOmni.generate()`.
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4. `AsyncOmni` allocates its normal unique engine routing ID and submits one
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`OmniDiffusionRequest` through the orchestrator.
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5. `ARDiffusionModelRunner._request_session()` parses the typed tick.
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`_get_or_create_session()` obtains runner-owned paged KV, and
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`bind_ar_diffusion_state()` exposes it to the pipeline only for the duration
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of `execute_model()`.
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6. The model pipeline validates the tick and generates exactly one latent
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block. LingBot interprets camera controls in
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`LingBotCameraControlReducer` and
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`LingBotWorldCausalDMDPipeline.forward()`; the generic runtime does not parse
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camera or action schemas.
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7. The pipeline returns one `DiffusionOutput` with the generated payload and an
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`ARDiffusionChunkMetadata` mapping under `metadata["ar_diffusion"]`.
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`AsyncOmni` exposes it as
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`OmniRequestOutput.multimodal_output["metadata"]["ar_diffusion"]`.
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8. The consumer parses the standard envelope. The session commits its reducer
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state, prompt, controls, event IDs, and next chunk index only when returned
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metadata exactly equals the submitted tick snapshot.
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The metadata envelope is:
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```json
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{
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"metadata": {
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"ar_diffusion": {
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"session_id": "world-7",
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"request_id": "ar-world-7-3-...",
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"chunk_index": 3,
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"applied_event_ids": [10, 11]
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}
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}
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}
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```
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## Transaction and failure semantics
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Accepted events are not acknowledged as applied until the model output and
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metadata are validated. Reducer `prepare()` is speculative; reducer `commit()`
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and the session snapshot form one logical commit. A model, metadata, or reducer
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failure leaves the chunk index and queued events uncommitted and transitions the
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session to `FAILED`.
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After a tick failure, worker state is closed because a forward may have
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partially modified or already released KV. The same chunk is not retried in
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place. A caller must explicitly reset the session, starting again at chunk
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zero, or close it.
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Explicit close follows this state machine:
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```text
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ACTIVE or FAILED
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v
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CLOSING ---- worker cleanup succeeds ----> CLOSED
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+------- worker cleanup fails --------> CLEANUP_FAILED
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+-- close retry --> CLOSING
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```
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`CLEANUP_FAILED` is a local tombstone. The session manager retains it, rejects
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creation of the same `session_id`, and rejects reset. Only an idempotent close
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retry that succeeds removes the session. Transport disconnect uses the same
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path.
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## KV ownership and capacity
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`ARDiffusionKVCacheSpec` is the pipeline-owned immutable geometry contract. It
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declares:
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- TP-local self-attention geometry, AR block size, sink, and recent window;
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- logical KV branches and their worker-local slots;
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- fixed-length cross-attention caches;
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- scratch tokens required by an uncommitted forward;
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- persistent model-owned CUDA bytes per resident session; and
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- the requested resident-session capacity.
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The runner computes:
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```text
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required(capacity) =
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scratch
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+ managed_self_attention_pages(capacity)
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+ capacity * cross_attention_bytes_per_session
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+ capacity * model_owned_state_bytes_per_session
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```
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`gpu_memory_fraction` is a soft expansion budget:
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```text
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configured_budget = available_device_bytes * gpu_memory_fraction
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effective_budget = max(configured_budget, required(1))
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```
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If `required(1)` exceeds actual available device memory, initialization fails.
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Otherwise the runner always admits one viable session and uses the configured
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fraction to select additional resident sessions up to the model-declared cap.
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All model-owned reservations are deducted before the paged self-attention pool
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is allocated.
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LingBot reports its persistent full-horizon image-condition tensor. DreamZero
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reports the measured 603 MiB per-session Wan VAE causal-convolution state.
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## Concurrency and routing limits
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The current runner executes one request at a time:
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- `max_num_seqs` must be one;
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- request batching and diffusion step execution are rejected;
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- one AR block is generated per engine request; and
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- an AR-Diffusion stage must have exactly one replica.
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The single-replica restriction is intentional. Multi-replica support requires
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session-affine routing so every tick, reset, and close reaches the worker that
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owns the session. Random or round-robin routing is not correct.
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Multiple resident sessions may share one runner. Capacity selection accounts
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for all persistent pools, and the runner performs LRU eviction through the same
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model lifecycle hook used by explicit close.
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## Model boundary
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The generic runtime transports controls as opaque
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`ARDiffusionControlInput(track, schema, data)` values. Model adapters validate
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and reduce those values. This keeps target/velocity, key state, pose,
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intrinsics, and other model-specific conventions out of the session protocol.
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For LingBot World v2:
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- `lingbot_world/actions.py` parses key-state and trajectory controls;
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- `lingbot_world/camera.py` loads or constructs camera trajectories and Plücker embeddings;
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- `lingbot_world/transformer.py` implements checkpoint-compatible causal attention; and
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- `lingbot_world/pipeline.py` constructs conditioning, owns small non-KV session state, and
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produces one block plus the standard metadata envelope.
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## Non-goals
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This contract does not currently provide:
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- a public HTTP or WebSocket request schema (the structured-interaction
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frontend is tracked separately in
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[#5527](https://github.com/vllm-project/vllm-omni/pull/5527));
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- camera/action semantics shared by every world model;
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- session migration or replication across workers;
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- retry of an ambiguous partially executed chunk;
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- cross-stage KV transfer; or
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- stateful streaming VAE decode.
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Those features can be layered on top without adding LingBot-specific identity
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or lifecycle fields to the generic runtime.
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## Required regression coverage
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CPU contract tests cover:
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- separation of tick correlation IDs from internal engine routing IDs;
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- event ordering, metadata equality, and reducer fail-closed commit;
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- close/disconnect failure tombstones and cleanup retry;
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- capacity one under the default fraction for shipped LingBot geometry;
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- capacity expansion under a tuned fraction;
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- DreamZero model-owned state accounting;
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- runner session reuse, reset, close, LRU eviction, and forward cleanup; and
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- LingBot model registration, imports, camera/action reduction, and one-block
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output metadata.
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GPU validation must additionally exercise real model loading, action input,
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prompt switching, rolling-window replacement, reset, close, finite output
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latents, and exact metadata/request identity.
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