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).
89 lines
4.1 KiB
Python
89 lines
4.1 KiB
Python
"""The adapter plane — per-request LoRA / ControlNet over one base (design_v3 §7.1, §9.19).
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Many adapters served over ONE resident base; a request picks which to apply (`DiffusionParams.adapters`).
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The `adapter_versions` cache-key field existed but the serving capability didn't. These assert:
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per-request selection changes output, multi-LoRA composes, ControlNet conditions on a control image,
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mixed-adapter requests interleave without smearing, hot-swap changes generation, and the cache key
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partitions by the active adapter stack (adapted ≠ base, and version A ≠ version B).
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"""
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from __future__ import annotations
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import numpy as np
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from v2.cache import CacheManager
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from v2.cache.keys import CacheKey
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from v2.card import load_card
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from v2.models.adapters import build_adapter_card, build_adapter_program
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from v2.parity import assert_interleave_parity
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from v2.request import DiffusionParams, TaskType, make_request
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from v2.request.modalpart import ImagePart, TextPart
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from v2.runtime import Engine
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def _engine():
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card = build_adapter_card()
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inst = load_card(card, cache_manager=CacheManager.from_card(card))
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eng = Engine()
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eng.register(card.model_id, inst, build_adapter_program())
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return eng, inst
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def _gen(eng, adapters=(), control=None, seed=3):
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inputs = (TextPart("a portrait"),) + ((ImagePart(pixels=control),) if control is not None else ())
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req = make_request(TaskType.T2V, "wan-adapters", "a portrait", inputs=inputs,
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diffusion=DiffusionParams(num_steps=6, seed=seed, adapters=adapters))
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return np.asarray(eng.run(req).artifacts["video"].frames)
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def test_per_request_adapter_changes_output():
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eng, _ = _engine()
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base = _gen(eng)
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anime = _gen(eng, ("lora_anime",))
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realistic = _gen(eng, ("lora_realistic",))
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assert not np.array_equal(base, anime)
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assert not np.array_equal(anime, realistic)
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assert np.array_equal(anime, _gen(eng, ("lora_anime",))) # deterministic
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def test_multi_lora_composes():
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eng, _ = _engine()
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a = _gen(eng, ("lora_anime",))
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b = _gen(eng, ("lora_realistic",))
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ab = _gen(eng, ("lora_anime", "lora_realistic"))
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assert not np.array_equal(ab, a) and not np.array_equal(ab, b)
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def test_controlnet_conditions_on_the_control_image():
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eng, _ = _engine()
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c_hi = _gen(eng, ("control_pose",), control=np.full((3, 1, 4, 6), 0.5, dtype="float32"))
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c_lo = _gen(eng, ("control_pose",), control=np.full((3, 1, 4, 6), -0.5, dtype="float32"))
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assert not np.array_equal(c_hi, c_lo) # different control ⇒ different video
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def test_mixed_adapter_requests_interleave_without_smearing():
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"""One base, different adapters per request — interleaving is bit-identical to serial (the active
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adapter set lives in the request/LoopState, never global)."""
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eng, _ = _engine()
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reqs = [make_request(TaskType.T2V, "wan-adapters", "a portrait",
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diffusion=DiffusionParams(num_steps=4, seed=s, adapters=ad))
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for s, ad in [(1, ()), (2, ("lora_anime",)), (3, ("lora_realistic",))]]
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assert not assert_interleave_parity(eng, reqs)
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def test_hot_swap_changes_generation():
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eng, inst = _engine()
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before = _gen(eng, ("lora_anime",))
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inst.component("lora_anime").update(seed=999) # swap the adapter's weights
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assert not np.array_equal(before, _gen(eng, ("lora_anime",)))
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def test_cache_key_partitions_by_adapter_stack():
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base = CacheKey(model_id="m", component_id="transformer", weights_version="w1",
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adapter_versions=(), precision="float32", input_hashes=())
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a = CacheKey(model_id="m", component_id="transformer", weights_version="w1",
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adapter_versions=CacheKey.adapters({"lora_anime": "v1"}), precision="float32", input_hashes=())
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b = CacheKey(model_id="m", component_id="transformer", weights_version="w1",
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adapter_versions=CacheKey.adapters({"lora_anime": "v2"}), precision="float32", input_hashes=())
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assert a != base # adapted ≠ base (no stale reuse)
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assert a != b # adapter version A ≠ version B (hot-swap)
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