Sweep all of v2/ through pre-commit (was previously only run on changed
files). Fixes surfaced across untouched modules:
- ruff: isinstance-tuple -> X | Y (UP038), try/except/pass ->
contextlib.suppress (SIM105), negated-return (SIM103), %-format ->
f-string (UP031).
- mypy: add annotations for no-untyped-call + var-annotated across recipes,
training methods, torch/toy backends, and serving.
- yapf reflow of the SF-Wan KV-cache call sites (semantics unchanged).
yapf/ruff/codespell/mypy all pass; v2 tests 237 passed / 5 pre-existing
(bucket_c torch-absent on CPU venv).
Sweep all 296 v2 modules: simplify verbose docstrings/comments and remove 376 dangling
"(design_vN §X)" citations to the now-deleted design docs (v2/README.md is the source of
truth). Comment/docstring-only — AST-verified code-identical; the CPU suite holds at 237
passed / 5 pre-existing. Also applies yapf + ruff --fix auto-fixes (import ordering,
forward-ref annotation de-quoting under `from __future__ import annotations`; behavior-
neutral, suite-confirmed) and adds the legitimate domain terms mot/clen/te to the codespell
ignore-list. Remaining ruff (24) + mypy (68 no-untyped-call) findings are pre-existing v2
debt, untouched here.
Frees v2/models/ to become the vendored-architecture namespace that mirrors fastvideo/models
(part of making v2 self-contained / able to replace fastvideo). The v2 recipe layer (per-family
card.py/loop.py/program.py + common.py + the build_*_engine re-exports) is the recipe, not the
architectures, so it moves to v2/recipes/. The pure-numpy toy/parity implementations (ToyDiT etc.)
move from v2/models/backend.py to v2/platform/backends/toy.py (alongside cpu.py/accel.py/torch_*).
Mechanical: all imports are absolute, so v2.models.<x> -> v2.recipes.<x> and v2.models.backend ->
v2.platform.backends.toy across v2/ + examples/ (89 files, 178 refs). No behavior change.
CPU mini green (202 passed, 2 skipped); all v2 files compile.
Mechanical conversion of every relative import under v2/ to an absolute v2.* path
(from .x / ..x / ...x -> from v2.<pkg>.x) so imports are unambiguous, grep-able, and
stable when code is copied/moved between entrypoints (VideoGenerator, CLI, server).
Surgical prefix-only rewrite: only the 'from <dots><module>' prefix changed — import
names, parentheses, multi-line formatting, comments, and ordering are byte-for-byte
preserved (no collapsing, no reorder, no unrelated reformatting).
- 471 imports across 125 files; v2/tests/ was already absolute (untouched).
- Validated: all 125 files compile, every 'from v2.* import' target resolves to a real
module/package, zero relative imports remain (full sweep), CPU mini suite green
(202 passed, 2 skipped).
Finish the kernel seam across the board so the platform's KernelTable is the
universal solver-dispatch path, not just wan21.
- Loops: ltx2, wan_causal, adapters, adaptive now resolve the flow-match solver
via model.platform.kernels.get(FLOW_MATCH_STEP) instead of importing the numpy
sampler directly (wan21 already did). On CPU this is bit-identical (the cpu
kernel IS the old function); a GPU/accel backend now overrides every loop.
- RL: the FlowGRPO log-prob recompute in unified_rl / joint_multi_rl /
workflow_rl dispatches FLOW_SDE_STEP through the platform, pinned to the SAME
kernel the rollout used (C2 kernel-pinning — otherwise the PPO ratio biases on
a real GPU where rollout and recompute kernels could differ).
- accel backend: add a kind-generic AccelComponent wrapper and override the vae
component too (text_encoder left unregistered to keep the device->cpu fallback
demonstrated), closing the "only dit is overridden" gap.
- tests: vae override assertion; a second-loop (wan-causal chunk rollout) parity
oracle proving accel == cpu bit-identical beyond wan21. README scope updated.
184 tests pass.
Targets the three design_v3 claims that were most load-bearing AND least
exercised (sessions/realtime, training-plane boundary, the WorkUnit-generality
falsifier). All fit with no new runtime primitive (127 tests pass).
1. Interactive world-model session (runtime/session.py, §9.8) — the Session
plane had ZERO coverage. WorldModelSession drives the causal chunk_rollout
loop as a long-lived session: persistent cross-request world state on the
Session.kv_handle, frame streaming, transactional step-boundary cancellation
(a cancelled act leaves the world resumable), no cross-session smearing.
Added only a continuation seam to the chunk loop (init seeds context from a
world_context slot; default empty = unchanged one-shot path). 5 tests.
2. End-to-end RL over a cross-model workflow (training/methods/workflow_rl.py,
§9.9) — trains BOTH flux-t2i and wan-i2v from ONE final-video reward. Rolls
out the whole workflow with SDE capture in both instances; the same final
advantage drives FlowGRPO PPO on each stage's transformer; two WeightSyncPlans
on two instances. The earlier model (T2I) is trained by a reward on the final
video — end-to-end credit across a model boundary — proven causal by a control
(constant reward => zero advantage => nothing moves). 4 tests.
3. Heterogeneous WorkUnit co-scheduling (models/tiled/, §9.10) — the §17
falsifier. VAETileLoop makes VAE decode a loop of VAE_TILE units; tiling is
exact (== one-shot, C0), and VAE_TILE + DIFFUSION_STEP pipelines interleave
bit-identically and co-run in one batch. Validates the mechanism; the
economic half (does it pay) stays a port-time measurement. 4 tests.
designv4 §9.8–§9.10 + falsifier/layout/closing updates (127 tests, 20 files).
Two more pipelines stress-testing the design, plus a BAGEL-placement note in
designv4. Both fit with no new runtime primitive (109 tests pass).
Pipeline 1 — cross-model T2I→I2V (program/workflow.py, models/image_video/):
- Realizes ProgramKind.WORKFLOW as a thin multi-instance orchestrator ABOVE the
engine (the hot path stays single-instance). A Program composes one model's
loops; a Workflow chains full engine.run calls across distinct cards, threading
artifacts. (LTX-2 already covers same-card multi-stage; cross-model — FLUX→Wan
— is the new capability the single-instance runner can't express.)
- flux-t2i (text→image) and wan-i2v (text+image→video) cards; the I2V program
folds the conditioning image into text_embeds so WanDenoiseLoop is unchanged.
- Each model keeps its own interleave-parity guarantee (crossing instances is a
Workflow boundary, not a loop step). 7 tests incl. video-depends-on-image.
Pipeline 2 — N-way joint RL (training/methods/joint_multi_rl.py, models/multi_expert/):
- JointMultiExpertRL generalizes UnifiedRLMethod (N=2) to N refiner LMs + a
generator: one reward → one group advantage → N token-PG updates + 1 FlowGRPO
PPO update, N+1 independent WeightSyncPlans. Proves the substrate was already
N-ready (card holds N components/loops; per-component weight-sync; dict grad
targets) — only the method body looped over two; now it loops over a list.
- credit="per_expert" learns all N cleanly; credit="shared" (faithful to UniRL)
works but is noisier — the honest multi-agent credit-assignment result, a
reward-shaping choice, not a substrate limit. 6 tests (N=1,3,4; prompt-only).
Fix — flow_sde_ml_velocity (loop/sampler.py): the toy FlowGRPO generator update
targeted the velocity the model already produced (a no-op once guidance_scale=1
was set for the C2 identity; the unified generator moved only on ~1e-7 noise).
The correct PG surrogate targets the max-likelihood velocity of the realized
sample — nonzero at ratio==1. Both UniRL and N-way generators now learn for real;
the C2 ratio==1 identity still holds (measured before the update).
BAGEL: MoT/shared-weight (one transformer on both loops), same row as Cosmos3;
real BAGEL's co-resident experts are expressible via the expert-routing policy
(partial sharing) — captured in designv4 §2.3.
Stress-tests the v2 Card/Loop/Program design with a UniRL/PromptRL-style
joint RL recipe: a prompt-refiner LM expert and a flow generator expert,
two separate experts driven by two loop types in one request, both updated
simultaneously from a single RL reward.
- loop/sampler.py: flow_sde_step_with_logprob — FlowGRPO SDE rollout sampler
(per-step Gaussian log-prob), distinct from the deterministic ODE serve step.
- request/params.py: gated sde_rollout/sde_noise_scale on DiffusionParams so
the serve path stays byte-identical (default ODE).
- models/wan21/loop.py: gated SDE-rollout capture in WanDenoiseLoop.advance.
- models/backend.py: ToyPromptRefiner — a real REINFORCE categorical policy
(the Qwen role), separate weights from the generator.
- models/unified/: the unified card+program — two disjoint experts (llm +
transformer) on ar_decode + diffusion_denoise; the topological opposite of
the Cosmos3 MoT card, same vocabulary.
- training/methods/unified_rl.py: joint GRPO — one reward -> group advantage
-> LM token policy gradient + DiT FlowGRPO PPO; two LRs; prompt-only/joint
flag; reuses the shared diffusion loop for rollout.
- training/weight_sync.py: WeightSyncPlan gains a component scope so the two
experts version + cache-invalidate independently (LM sync never flushes the
frozen text-encoder feature cache).
- tests/test_unified_rl.py: 9 tests incl. likelihood-based C2 identity, the
two-loop interleave parity gate, joint vs prompt-only. Full suite 91 passed.
designv4.md: unified design doc reflecting v2 as built+tested, with the joint
RL stress test as the validating case study (the design held — new card +
new method, no new runtime primitive).
Directory rename (git mv, history preserved) plus rewrite of all references: absolute imports in
tests, the zero-dep runner, docstrings, comments, and the README. No behavior change.
Run: python3 -m pytest v2/tests/ -q ; python3 v2/run_tests.py ; python3 -m v2.examples
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