Files
hao-ai-lab-FastVideo/v2/training/__init__.py
T
SolitaryThinker 4e331eb7ff [refactor] v2: use absolute imports (v2.*) everywhere instead of relative
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).
2026-07-04 17:15:34 +00:00

40 lines
1.6 KiB
Python

"""Training plane — rollout records behavior on the same loops it serves (design_v3 §10).
Dependency rule (enforced): ``training`` imports ``card``/``loop``/``runtime``/``models``; the
engine never imports ``training``. The rollout forward IS the serve forward plus capture.
"""
from __future__ import annotations
from v2.training.behavior import BehaviorRecord, TrajectoryStatus
from v2.training.flywheel import derive_card, run_flywheel
from v2.training.methods import (
DiffusionNFTMethod,
DMD2Method,
FineTuneMethod,
JointMultiExpertRL,
SelfForcingMethod,
TrainingMethod,
UnifiedRLMethod,
WorkflowRLMethod,
build_diffusion_nft,
build_dmd2,
build_finetune,
build_joint_multi_rl,
build_self_forcing,
build_unified_rl,
build_workflow_rl,
)
from v2.training.rewards import MultiRewardScorer, ServedRewardScorer, build_multi_reward_scorer
from v2.training.rollout import RolloutContext, Trajectory, TrajectoryBuffer, rollout_loop
from v2.training.weight_sync import WeightRole, WeightSyncController, WeightSyncPlan
__all__ = [
"TrainingMethod", "FineTuneMethod", "DMD2Method", "DiffusionNFTMethod", "SelfForcingMethod",
"UnifiedRLMethod", "JointMultiExpertRL", "WorkflowRLMethod",
"build_finetune", "build_dmd2", "build_diffusion_nft", "build_self_forcing", "build_unified_rl",
"build_joint_multi_rl", "build_workflow_rl", "run_flywheel", "derive_card",
"BehaviorRecord", "TrajectoryStatus", "Trajectory", "TrajectoryBuffer", "RolloutContext",
"rollout_loop", "WeightSyncPlan", "WeightSyncController", "WeightRole",
"MultiRewardScorer", "ServedRewardScorer", "build_multi_reward_scorer",
]