giant predict/rollout rebuilt models straight from ckpt["model_config"] with
no way to change sampling-only keys (e.g. stage2_model.n_sec.stop_sampling)
without retraining. Adds config_overrides to load_for_inference, validated
against giant.config.INFERENCE_OVERRIDES so a typo or shape-bearing key
raises CheckpointCompatibilityError up front instead of an opaque
load_state_dict mismatch. Wired as a repeatable --set dotted.path=value on
both CLI commands, recorded in the rollout YAML sidecar, and surfaced in
`giant model summary`'s output.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
conditioning.particle.emb_dim and stage2_model.particle_type.target="onehot"'s
class count were silently the same number everywhere (pipeline.py's PDG
top-N map build, Stage2OneShot/Stage2Autoregressive's type head, StageSpec's
training loss width, the checkpoint's shared pdg_topn_map), fixing the
secondary-species vocabulary at whatever width the unrelated
physical-conditioning MLP happened to use — the exact vocabulary the v0.3.0
pivot exists to fix.
Adds stage2_model.particle_type.n_classes (default 0 = inherit
conditioning.particle.emb_dim, preserving today's behavior and every
existing checkpoint) and a single resolve_type_n_classes helper used
everywhere the coupling used to be implicit. Splits the checkpoint's shared
pdg_topn_map into a conditioning-only pdg_topn_map and a new
sec_type_topn_map, built independently through the existing
(axis, n_classes)-keyed setup cache (no extra scan when they still resolve
to the same N) and threaded through giant predict/giant rollout's decode
path. A checkpoint with no sec_type_topn_map key (pre-#29) falls back to
reusing pdg_topn_map, reproducing the old shared behavior exactly.
Decided with the user during planning: commit directly on this branch;
represent the split as an additive sec_type_topn_map checkpoint key rather
than conditionally reusing pdg_topn_map; build the two top-N maps
independently rather than the issue's proposed build-at-max-and-slice, since
the setup cache already avoids redundant scans across runs.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
giant predict and giant rollout each carried a ~65-line, independently
drifting copy of "load checkpoint -> validate -> resolve conditioning axes
-> restore normalizers/vocab maps -> build models -> load weights", plus a
third partial copy of _conditioning_axes in analysis/router_gating.py. A
silent divergence there doesn't crash, it makes the two commands run
different physics from the same checkpoint with no test coverage anywhere
along that path.
giant/checkpoint_io.py now holds the single implementation:
load_for_inference() + an InferenceContext dataclass, raising
CheckpointCompatibilityError (verbatim message text preserved) instead of
calling typer directly, so it can be unit-tested and imported from
non-Typer code. router_gating.py's load_router imports conditioning_axes
from it lazily, keeping its "no torch at module scope" contract intact.
Adds 17 direct unit tests for load_for_inference/conditioning_axes/stage_cfg
plus CLI smoke tests confirming the error surfaces as typer.Exit(1) through
predict and rollout — previously zero coverage on this path.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
lars