Add per-stage init_from/freeze (gitea #42) #67
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Reference: lars/giant#67
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stage{1,2}_model.active = false already trains one stage alone, but the
checkpoint it writes holds only that stage, so giant rollout refuses it --
the "retrain stage 2 alone against a fixed, known-good stage 1" experiment
the 2026-08-03 species failure calls for wasn't runnable end to end.
Adds stage{1,2}_model.init_from (a checkpoint .pt to load this stage's
weights from before training) and .freeze (never update them), symmetric
across both stages. Both stages stay active = true, so both get built and
both land in the output checkpoint -- the frozen stage is merely
initialized from disk instead of from scratch.
Decisions made during planning:
meaningful, no autograd special-casing), only optimizer.step() (and, for
the frozen stage, lr_sched.step()/EMA update) is skipped -- weights are
byte-identical for the whole run. This is StageTrainer._step_optimizer,
shared by the non-adversarial path and both halves (generator + critic)
of the WGAN path, so a frozen WGAN stage's critic freezes too.
is a --resume (a resumed frozen stage's weights come from the resume
checkpoint instead) -- freezing a randomly-initialized model is almost
certainly a mistake.
giant trainandgiant new-run(--stage{1,2}-init-from/--stage{1,2}-freeze), matching every other
per-stage model knob's existing treatment.
Co-Authored-By: Claude Opus 5 noreply@anthropic.com
stage{1,2}_model.active = false already trains one stage alone, but the checkpoint it writes holds only that stage, so giant rollout refuses it -- the "retrain stage 2 alone against a fixed, known-good stage 1" experiment the 2026-08-03 species failure calls for wasn't runnable end to end. Adds stage{1,2}_model.init_from (a checkpoint .pt to load this stage's weights from before training) and .freeze (never update them), symmetric across both stages. Both stages stay active = true, so both get built and both land in the output checkpoint -- the frozen stage is merely initialized from disk instead of from scratch. Decisions made during planning: - Soft freeze: forward/backward still run every batch (loss/grad_norm stay meaningful, no autograd special-casing), only optimizer.step() (and, for the frozen stage, lr_sched.step()/EMA update) is skipped -- weights are byte-identical for the whole run. This is StageTrainer._step_optimizer, shared by the non-adversarial path and both halves (generator + critic) of the WGAN path, so a frozen WGAN stage's critic freezes too. - validate_config requires init_from whenever freeze = true, unless the run is a --resume (a resumed frozen stage's weights come from the resume checkpoint instead) -- freezing a randomly-initialized model is almost certainly a mistake. - CLI flags on both `giant train` and `giant new-run` (--stage{1,2}-init-from/--stage{1,2}-freeze), matching every other per-stage model knob's existing treatment. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>