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).
67 lines
2.7 KiB
Python
67 lines
2.7 KiB
Python
"""Request parameters: AR sampling vs diffusion params + output/capture spec.
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design_v3 §12: "AR ``sampling`` vs ``diffusion`` params, an ``OutputSpec``
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(requested modalities + streaming + capture flags), and per-node overrides."
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design.md §6.1: ``sampling``/``diffusion`` are *defaults*; ``node_params`` override
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per graph node (validated against the PipelineSpec's node-parameter schema).
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"""
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from __future__ import annotations
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from dataclasses import dataclass, field
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from enum import Enum
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@dataclass(frozen=True)
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class SamplingParams:
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"""AR decode knobs (used by ar_decode loops: reasoner/thinker/talker — phase 2)."""
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max_tokens: int = 512
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temperature: float = 1.0
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top_p: float = 1.0
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top_k: int = 0
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stop: tuple[str, ...] = ()
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seed: int | None = None
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@dataclass(frozen=True)
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class DiffusionParams:
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"""Denoise knobs. ``guidance`` may be a scalar or per-modality dict (joint A/V)."""
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num_steps: int = 50
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guidance_scale: float = 5.0
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# Per-modality guidance for joint denoise (LTX-2 A/V, Cosmos3 t2vs). Empty => use scalar.
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guidance_per_modality: dict[str, float] = field(default_factory=dict)
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shift: float = 5.0 # flow-match sigma shift
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sigmas: tuple[float, ...] | None = None # explicit schedule (distilled few-step)
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negative_prompt: str = ""
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height: int = 480
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width: int = 832
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num_frames: int = 81
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fps: int = 16
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seed: int | None = None
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# FlowGRPO-style RL rollout: use the SDE sampler (with per-step log-prob capture) instead of the
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# deterministic ODE serve sampler (PromptRL/UniRL §6). Default False ⇒ the serve path is unchanged.
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sde_rollout: bool = False
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sde_noise_scale: float = 0.7
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# Active adapters for this request (LoRA / ControlNet ids on the base) — the adapter plane (§9.19).
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# Empty ⇒ the base model, unmodified. Part of the cache key (adapter_versions) so adapted ≠ base.
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adapters: tuple[str, ...] = ()
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class CaptureMode(str, Enum):
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"""What the loop captures while running (design_v3 §9.4, §10)."""
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NONE = "none" # serve forward: no-grad, no capture
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BEHAVIOR = "behavior" # rollout forward: emit BehaviorRecord slices for RL/distill
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@dataclass(frozen=True)
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class OutputSpec:
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modalities: frozenset[str] = frozenset({"video"})
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# streaming flags per modality: True | chunk size (ms / frames). {} => no streaming.
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stream: dict[str, object] = field(default_factory=dict)
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return_latents: bool = False
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return_trajectory: bool = False # full denoise trajectory (parity / debug)
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capture: CaptureMode = CaptureMode.NONE
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@property
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def streaming(self) -> bool:
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return bool(self.stream)
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