Will Lin
1fe50c0092
The v2 top level had grown to ~27 dirs + 13 loose files — half of them
single-concept abstraction shells (memory/ 86 LOC, transport/ 222, parity/ 178,
extend/ 226) and half vendored fastvideo code sitting as peers to the actual v2
design. Regroup to mirror README section 3 "Planes & dependency order":
core/ enums+types, card, loop, program, parity, request, parallel
(the model-native contracts; no kernels)
runtime/ + folded-in substrate: cache, memory, transport, extend
(the import graph shows only runtime consumes them; compile/cudagraph
already lived here)
serving/ + deploy/ (products / fleet)
_vendor/ all copied fastvideo: models, layers, attention, configs, distributed,
platforms, api, hooks, logging_utils, third_party + fastvideo_args/
utils/logger/envs/forward_context — internal layout unchanged, still
mirrors upstream for diffing
28 dirs + 13 root files -> 8 dirs + 6 root files. Pure mechanical move: 947
absolute-import paths rewritten (v2.X -> v2.{core,runtime,serving,_vendor}.X),
boundary-anchored so platform/ (native dispatch) and platforms/ (vendored CUDA
detect) no longer collide and neither does hooks/ vs extend/. Deleted the empty
v2/loader/. README section 3 + 16 updated; stale test count corrected
(34 files/216 tests -> 22 files/143 tests).
Validated: `import v2` stays torch-free; `v2/run_tests.py` and `pytest v2/tests/`
-> 143 passed, 2 skipped on the numpy toy backend. (On a GPU box force the toy
backend with CUDA_VISIBLE_DEVICES="" or detect() picks cuda.) No external importer
changed — examples use the re-exported `from v2 import VideoGenerator`.
v2_examples/omni — MoT & cascade models
Runnable examples of the omni models: one request that spans multiple loop types. Two topologies (designv4 §2.3):
- MoT / shared-weight (Cosmos3, BAGEL) — ONE resident instance whose
transformerruns both anar_decodeloop and adiffusion_denoiseloop on the same weights. Every AR token and denoise step is a runtime-visible WorkUnit (the differentiation vs vllm-omni's opaque DIFFUSION stage). - Cascade / separate experts (Qwen-Omni) — three distinct experts on three loop types
(
thinker → talker → vocoder), cross-stage conditioning, streaming codec→waveform.
Run from anywhere:
python3 v2_examples/omni/01_cosmos3.py
| Script | Model | Loops in one request |
|---|---|---|
01_cosmos3.py |
Cosmos3 (shared MoT) | ar_decode (reason) → diffusion_denoise (joint video) |
02_bagel.py |
BAGEL (shared MoT) | ar_decode (generate_text) → diffusion_denoise (generate_image) |
03_qwen_omni.py |
Qwen-Omni (3 separate experts) | ar_decode → ar_decode → audio_decode (text + speech) |
04_interleave_across_loop_types.py |
— | the interleave parity gate holds across AR + diffusion steps |
build_omni_engine() registers all three; see designv4 §9.5 (Qwen-Omni cascade).