v0.3.0 post-implementation audit: resolve all 9 tracked discrepancies

Works through docs/v0.3.0-followups.md item by item, closing the gap
between the design doc and the shipped v0.3.0-stage2-autoregressive code:

1. validate.py: 7-tuple batch unpacking, sample_stage1/sample_stage2
   dispatch, stage-2 particle-type-class marginal.
2. Stage-prefixed --stage1-*/--stage2-* CLI flags for train/new-run.
3. Thread stage2_model.k_max through loader/transforms/dataset/pipeline/
   train instead of the hardcoded K_MAX constant.
4. Mixed conditioning.particle.type / conditioning.material.type support
   end-to-end (data pipeline + dwarf warm-cache).
5. conditioning.share_stages = true: one shared ConditionEncoder instance
   across both stages.
6. stage2_model.generator = "ddpm" formally deferred into design doc §11.2
   (was silently unimplemented).
7. giant predict/rollout: implement conditioning.*.type = "onehot" via the
   checkpoint's saved pdg_topn_map/mat_topn_map.
8. network.py's checkpoint-path model_config migration now fails loudly on
   non-zero legacy expert_hidden_dim/expert_n_blocks, matching config.py's
   TOML-load path (§4.2).
9. validate_config now rejects stage2_model.n_sec.mode = "truth" for a
   rollout-capable checkpoint (§9).

Also cleared all pre-existing `ty check` noise (44 -> 0 diagnostics),
mostly a test-helper dict-unpack pattern that made every unrelated
constructor keyword look like a type error.

Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>

tests/test_cli_predict.py

@@ -1,73 +1,14 @@
 import uuid
 
-import pytest
-import typer
 import yaml
 
 from giant.cli import (
     _CEPH_PREDICTIONS,
-    _check_conditioning_onehot_support,
     _resolve_prediction_output,
     _write_prediction_ref,
 )
 
 
-# ---------------------------------------------------------------------------
-# _check_conditioning_onehot_support
-# ---------------------------------------------------------------------------
-
-
-def _nested_model_cfg(
-    particle_type="physical", material_type="physical", target="physical"
-):
-    return {
-        "conditioning": {
-            "particle": {"type": particle_type, "emb_dim": 8},
-            "material": {"type": material_type, "emb_dim": 8},
-        },
-        "stage2_model": {"particle_type": {"target": target}},
-    }
-
-
-def test_check_conditioning_onehot_support_allows_physical():
-    _check_conditioning_onehot_support(_nested_model_cfg(), "predict")  # no raise
-
-
-def test_check_conditioning_onehot_support_rejects_onehot_particle_conditioning():
-    cfg = _nested_model_cfg(particle_type="onehot")
-    with pytest.raises(typer.Exit):
-        _check_conditioning_onehot_support(cfg, "predict")
-
-
-def test_check_conditioning_onehot_support_rejects_onehot_material_conditioning():
-    cfg = _nested_model_cfg(material_type="onehot")
-    with pytest.raises(typer.Exit):
-        _check_conditioning_onehot_support(cfg, "rollout")
-
-
-def test_check_conditioning_onehot_support_allows_onehot_particle_type_target():
-    """stage2_model.particle_type.target="onehot" is implemented (v0.3.0
-    step 6, giant.rollout.decode_secondary_identity) — it's a separate axis
-    from conditioning.particle.type, which this guard doesn't gate at all."""
-    cfg = _nested_model_cfg(target="onehot")
-    _check_conditioning_onehot_support(cfg, "predict")  # no raise
-
-
-def test_check_conditioning_onehot_support_allows_embedding_particle_type_target():
-    cfg = _nested_model_cfg(
-        particle_type="embedding", material_type="embedding", target="embedding"
-    )
-    _check_conditioning_onehot_support(cfg, "predict")  # no raise
-
-
-def test_check_conditioning_onehot_support_is_noop_for_v02_flat_model_config():
-    """A v0.2 checkpoint's flat model_config has conditioning as a plain
-    string, not a dict — never onehot, so this must be a silent no-op rather
-    than crash on `.get("particle")` against a string."""
-    cfg = {"conditioning": "embedding", "mode": "flow"}
-    _check_conditioning_onehot_support(cfg, "predict")  # no raise
-
-
 # ---------------------------------------------------------------------------
 # _resolve_prediction_output
 # ---------------------------------------------------------------------------