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Bump ruff line-length to 120 and reformat
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Rejoins lines that only wrapped because they exceeded the old 88-char
limit; ruff check and the full test suite (725 passed) are unaffected.
This commit is contained in:
Lars Bogner
2026-08-12 13:33:09 +02:00
parent 9ce7b32324
commit 55332db67a
66 changed files with 757 additions and 2413 deletions
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scripts/check_migration_v02_v03.py
+5 -16
View File
@@ -84,19 +84,12 @@ def main() -> int:
mode = model_config.get("mode", "flow")
routed = bool((model_config.get("router") or {}).get("enabled"))
print(f"checkpoint: {args.checkpoint}")
print(
f" mode={mode!r} conditioning={model_config.get('conditioning')!r} "
f"routed={routed} ema={args.ema}"
)
print(f" mode={mode!r} conditioning={model_config.get('conditioning')!r} routed={routed} ema={args.ema}")
stage1_key = "model_ema" if args.ema and "model_ema" in ckpt else "model"
stage2_key = (
"sec_decoder_ema" if args.ema and "sec_decoder_ema" in ckpt else "sec_decoder"
)
stage2_key = "sec_decoder_ema" if args.ema and "sec_decoder_ema" in ckpt else "sec_decoder"
if args.ema and stage1_key == "model":
print(
" warning: --ema requested but no model_ema in checkpoint, using raw weights"
)
print(" warning: --ema requested but no model_ema in checkpoint, using raw weights")
# --- old side: the frozen v0.2 snapshot, loaded with the checkpoint's own weights ---
old_stage1, old_stage2 = legacy.build_models(model_config)
@@ -119,9 +112,7 @@ def main() -> int:
print("PASS (construction only, routed checkpoint)")
return 0
remapped1, remapped2 = net.migrate_legacy_state_dict(
ckpt[stage1_key], ckpt[stage2_key]
)
remapped1, remapped2 = net.migrate_legacy_state_dict(ckpt[stage1_key], ckpt[stage2_key])
missing1, unexpected1 = new_stage1.load_state_dict(remapped1, strict=True)
missing2, unexpected2 = new_stage2.load_state_dict(remapped2, strict=True)
if missing1 or unexpected1 or missing2 or unexpected2:
@@ -132,9 +123,7 @@ def main() -> int:
new_stage1.eval()
new_stage2.eval()
cond_cont, cond_cat, x1, x2, t, z1, z2 = _random_batch(
model_config, args.batch, args.seed
)
cond_cont, cond_cat, x1, x2, t, z1, z2 = _random_batch(model_config, args.batch, args.seed)
ok = True
with torch.no_grad():