v0.3.0 step 4: type map + particle_type.target = "onehot"/"embedding"

Builds the shared top-N-plus-other PDG/material maps (pooling both primary
and secondary occurrences for PDG, directly targeting the meeting's
species-collapse failure mode) and wires up conditioning.{particle,material}
= "onehot" plus stage2_model.particle_type.target in ("onehot", "embedding")
end-to-end: setup-cache persistence, Stage2OneShot's type_head (flow/ddpm)
vs. folded+ST-Gumbel-relaxed adversarial slice (wgan), and the corresponding
CE/MSE training losses. particle_type.target = "physical" stays byte-for-byte
unchanged, keeping the v0.2 migration shim's bit-identical guarantee intact.
giant predict/rollout fail loudly on a onehot/embedding checkpoint until
full decode support lands in step 6.

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

tests/test_cli_predict.py

@@ -1,14 +1,72 @@
 import uuid
 
+import pytest
+import typer
 import yaml
 
 from giant.cli import (
     _CEPH_PREDICTIONS,
+    _check_v030_onehot_support,
     _resolve_prediction_output,
     _write_prediction_ref,
 )
 
 
+# ---------------------------------------------------------------------------
+# _check_v030_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_v030_onehot_support_allows_physical():
+    _check_v030_onehot_support(_nested_model_cfg(), "predict")  # no raise
+
+
+def test_check_v030_onehot_support_rejects_onehot_particle_conditioning():
+    cfg = _nested_model_cfg(particle_type="onehot")
+    with pytest.raises(typer.Exit):
+        _check_v030_onehot_support(cfg, "predict")
+
+
+def test_check_v030_onehot_support_rejects_onehot_material_conditioning():
+    cfg = _nested_model_cfg(material_type="onehot")
+    with pytest.raises(typer.Exit):
+        _check_v030_onehot_support(cfg, "rollout")
+
+
+def test_check_v030_onehot_support_rejects_onehot_particle_type_target():
+    cfg = _nested_model_cfg(target="onehot")
+    with pytest.raises(typer.Exit):
+        _check_v030_onehot_support(cfg, "predict")
+
+
+def test_check_v030_onehot_support_rejects_embedding_particle_type_target():
+    cfg = _nested_model_cfg(
+        particle_type="embedding", material_type="embedding", target="embedding"
+    )
+    with pytest.raises(typer.Exit):
+        _check_v030_onehot_support(cfg, "predict")
+
+
+def test_check_v030_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/embedding-target, so this must be a
+    silent no-op rather than crash on `.get("particle")` against a string."""
+    cfg = {"conditioning": "embedding", "mode": "flow"}
+    _check_v030_onehot_support(cfg, "predict")  # no raise
+
+
 # ---------------------------------------------------------------------------
 # _resolve_prediction_output
 # ---------------------------------------------------------------------------