Will Lin
51898f48e9
Frees v2/models/ to become the vendored-architecture namespace that mirrors fastvideo/models (part of making v2 self-contained / able to replace fastvideo). The v2 recipe layer (per-family card.py/loop.py/program.py + common.py + the build_*_engine re-exports) is the recipe, not the architectures, so it moves to v2/recipes/. The pure-numpy toy/parity implementations (ToyDiT etc.) move from v2/models/backend.py to v2/platform/backends/toy.py (alongside cpu.py/accel.py/torch_*). Mechanical: all imports are absolute, so v2.models.<x> -> v2.recipes.<x> and v2.models.backend -> v2.platform.backends.toy across v2/ + examples/ (89 files, 178 refs). No behavior change. CPU mini green (202 passed, 2 skipped); all v2 files compile.
71 lines
3.1 KiB
Python
71 lines
3.1 KiB
Python
"""Content-adaptive control flow — the loop owns it (design_v3 §2.2, §9.12).
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Because the model owns control flow (the driven loop), content-adaptive compute is natural: the
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`CacheDiTDenoiseLoop`'s `next()` reuses the cached velocity when consecutive predictions barely change
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(cache-dit / TeaCache — skip the DiT forward), and early-exits when the latent converges — a variable,
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content-dependent step count. These assert: skips happen and stay close to the full run; early-exit
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reduces steps; and the interleave parity gate still holds across requests with *different* step counts.
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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.card import load_card
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from v2.recipes import build_wan_t2v_program
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from v2.recipes.adaptive import build_adaptive_card
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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.runtime import Engine
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STEPS = 12
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def _engine(*, cache_threshold=0.0, exit_threshold=0.0):
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card = build_adaptive_card(cache_threshold=cache_threshold, exit_threshold=exit_threshold)
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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_wan_t2v_program())
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return eng
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def _run(eng, prompt="a calm sea", seed=3):
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return eng.run(make_request(TaskType.T2V, "wan-adaptive", prompt,
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diffusion=DiffusionParams(num_steps=STEPS, seed=seed)))
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def test_threshold_zero_is_plain_denoise():
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out = _run(_engine(cache_threshold=0.0))
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assert out.metrics["denoise_steps"] == float(STEPS)
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assert out.metrics["skipped_steps"] == 0.0 # subclass with threshold 0 ⇒ identical to base
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def test_cache_dit_skips_forwards_and_stays_close():
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full = _run(_engine(cache_threshold=0.0))
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cached = _run(_engine(cache_threshold=0.15))
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assert cached.metrics["skipped_steps"] > 0 # the loop chose to skip DiT forwards
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v_full = np.asarray(full.artifacts["video"].frames)
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v_cached = np.asarray(cached.artifacts["video"].frames)
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rel = float(np.linalg.norm(v_cached - v_full) / (np.linalg.norm(v_full) + 1e-8))
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assert rel < 0.05 # skipping is a close approximation, not exact
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def test_early_exit_reduces_steps_in_the_tail():
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eng = _engine(exit_threshold=0.05)
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out = _run(eng)
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assert out.metrics["early_exited"] == 1.0
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assert out.metrics["denoise_steps"] < float(STEPS) # stopped before the full schedule
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assert out.metrics["denoise_steps"] >= STEPS // 2 # ...but only in the low-noise tail, not at step 0
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def test_interleave_parity_holds_with_variable_step_counts():
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"""Different prompts skip/exit at different steps ⇒ ragged loops; serial == interleaved still."""
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eng = _engine(cache_threshold=0.15)
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reqs = [_make("alpha", 1), _make("beta gamma", 2), _make("alpha", 1)]
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assert not assert_interleave_parity(eng, reqs)
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def _make(prompt, seed):
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return make_request(TaskType.T2V, "wan-adaptive", prompt,
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diffusion=DiffusionParams(num_steps=STEPS, seed=seed))
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