SolitaryThinker
431f4daddb
Three more capabilities on distinct untested surfaces (167 tests pass, no new runtime primitive). A — Adapter plane (§9.19): one base + swappable LoRA/ControlNet adapters, selected per request (DiffusionParams.adapters); AdapterDenoiseLoop applies each active adapter's velocity delta. Per-request selection changes output, multi-LoRA composes, ControlNet conditions on a control image, mixed-adapter requests interleave without smearing, hot-swap changes generation, cache key partitions by adapter stack (the adapter_versions field, previously declared-only). ToyLoRA/ToyControlNet; models/adapters/. 6 tests. B — Non-linear workflows (§9.17): ParallelWorkflow (fan-out: one input → N models → merged) and BestOfNWorkflow (generate N → score with the served reward card → return best; inference-time scaling). The shapes a linear chain can't express. 4 tests. D — RL→distill flywheel (§9.18): run_flywheel RL-improves the base (NFT), then distills FROM the RL'd model (DMD2 teacher = RL'd policy) into a faster card, recording the base→rl→distilled provenance chain in RecipeSpec.parents. The distilled student is measurably closer to the RL'd teacher than the base; the distilled card serves few-step. training/flywheel.py. 4 tests. designv4 §9.17–§9.19 + layout/closing/counts (167 tests, 29 files).
40 lines
1.6 KiB
Python
40 lines
1.6 KiB
Python
"""Training plane — rollout records behavior on the same loops it serves (design_v3 §10).
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Dependency rule (enforced): ``training`` imports ``card``/``loop``/``runtime``/``models``; the
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engine never imports ``training``. The rollout forward IS the serve forward plus capture.
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"""
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from __future__ import annotations
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from .behavior import BehaviorRecord, TrajectoryStatus
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from .flywheel import derive_card, run_flywheel
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from .methods import (
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DiffusionNFTMethod,
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DMD2Method,
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FineTuneMethod,
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JointMultiExpertRL,
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SelfForcingMethod,
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TrainingMethod,
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UnifiedRLMethod,
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WorkflowRLMethod,
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build_diffusion_nft,
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build_dmd2,
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build_finetune,
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build_joint_multi_rl,
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build_self_forcing,
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build_unified_rl,
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build_workflow_rl,
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)
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from .rewards import MultiRewardScorer, ServedRewardScorer, build_multi_reward_scorer
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from .rollout import RolloutContext, Trajectory, TrajectoryBuffer, rollout_loop
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from .weight_sync import WeightRole, WeightSyncController, WeightSyncPlan
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__all__ = [
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"TrainingMethod", "FineTuneMethod", "DMD2Method", "DiffusionNFTMethod", "SelfForcingMethod",
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"UnifiedRLMethod", "JointMultiExpertRL", "WorkflowRLMethod",
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"build_finetune", "build_dmd2", "build_diffusion_nft", "build_self_forcing", "build_unified_rl",
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"build_joint_multi_rl", "build_workflow_rl", "run_flywheel", "derive_card",
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"BehaviorRecord", "TrajectoryStatus", "Trajectory", "TrajectoryBuffer", "RolloutContext",
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"rollout_loop", "WeightSyncPlan", "WeightSyncController", "WeightRole",
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"MultiRewardScorer", "ServedRewardScorer", "build_multi_reward_scorer",
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]
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