Fix conditioning="physical" so it can actually generalize past training vocab

The whole point of conditioning="physical" is generalizing to a
species/material outside the training menu, but two independent code
paths still hard-required training-vocab membership:

- giant/data/transforms.py: build_cond_features unconditionally raised
  KeyError on an out-of-vocab pdg/material. _vectorized_map_lookup
  gains a strict=False mode (dummy index instead of raising), used only
  under conditioning="physical" where ConditionEncoder never reads
  cond_cat anyway; "embedding" mode is untouched and still raises,
  since cond_cat IS the conditioning signal there.
- giant/rollout.py: the known_pdg termination gate still killed a track
  on step 1 for any pdg outside pdg_map, regardless of conditioning
  mode. Now skipped entirely under conditioning="physical".
- giant/model/network.py: PdgRouter/ProcessRouter always build their
  own training-vocab nn.Embedding independent of conditioning, silently
  reintroducing the same limitation at the routing layer. build_models
  now raises loudly if conditioning="physical" is paired with either
  router type, rather than silently building a model that can't
  generalize the way it claims to.

This unblocks the held-out-species/material generalization experiment
against the multi-material dataset (see CLAUDE.md roadmap). Each fix
has a regression test, including an end-to-end rollout test seeded
with a resolvable-but-out-of-vocab PDG code.

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

giant/data/transforms.py

@@ -339,12 +339,22 @@ def sorted_membership(values: np.ndarray, sorted_arr: np.ndarray) -> np.ndarray:
     return sorted_arr[idx] == values
 
 
-def _vectorized_map_lookup(values: np.ndarray, mapping: dict) -> np.ndarray:
+def _vectorized_map_lookup(
+    values: np.ndarray, mapping: dict, strict: bool = True
+) -> np.ndarray:
     """Vectorized equivalent of `np.array([mapping[v] for v in values], dtype=np.int64)`.
 
     Replaces a per-element Python dict lookup with one `searchsorted` call.
     Raises `KeyError` if any value in `values` isn't a key of `mapping`,
-    matching the dict-comprehension it replaces (never silently misassigns).
+    matching the dict-comprehension it replaces (never silently misassigns)
+    — unless `strict=False`, in which case unmapped values get a dummy index
+    of 0 instead. Only pass `strict=False` where the caller has independently
+    verified the resulting index is never actually read (e.g.
+    `build_cond_features` under `conditioning="physical"`, where
+    `ConditionEncoder` ignores `cond_cat` entirely); it exists so a rollout
+    can be seeded with a species/material outside the training vocab without
+    a spurious `KeyError`, which is the entire point of physical-property
+    conditioning.
     """
     keys = np.asarray(list(mapping.keys()))
     vals = np.asarray(list(mapping.values()), dtype=np.int64)
@@ -355,6 +365,10 @@ def _vectorized_map_lookup(values: np.ndarray, mapping: dict) -> np.ndarray:
     pos = np.clip(pos, 0, len(keys_sorted) - 1)
     found = keys_sorted[pos] == values
     if not found.all():
+        if not strict:
+            out = np.zeros(values.shape, dtype=np.int64)
+            out[found] = vals_sorted[pos[found]]
+            return out
         missing = np.unique(values[~found])
         raise KeyError(f"value(s) not in mapping: {missing[:10].tolist()}")
     return vals_sorted[pos]
@@ -693,8 +707,15 @@ def build_cond_features(
         [cond_cont, _physical_cond_columns(data, conditioning)]
     ).astype(np.float32)
 
-    pdg_idx = _vectorized_map_lookup(data["pdg"], pdg_map)
-    mat_idx = _vectorized_map_lookup(data["material"], mat_map)
+    # In "physical" mode cond_cat is only a reporting/router convenience —
+    # ConditionEncoder never reads it (giant/model/network.py) — so a
+    # species/material outside the training vocab (the whole point of
+    # physical-property conditioning) gets a dummy index instead of raising.
+    # In "embedding" mode cond_cat IS the conditioning signal, so an unmapped
+    # value must still raise loudly rather than silently misassign.
+    strict = conditioning == "embedding"
+    pdg_idx = _vectorized_map_lookup(data["pdg"], pdg_map, strict=strict)
+    mat_idx = _vectorized_map_lookup(data["material"], mat_map, strict=strict)
     cond_cat = np.column_stack([pdg_idx, mat_idx])
 
     if cond_normalizer is not None:

giant/model/network.py

@@ -1228,7 +1228,40 @@ def _parse_composed_axes(router_cfg: dict) -> list[dict]:
     return [axes[i] for i in range(len(axes))]
 
 
-def _build_router_from_cfg(router_cfg: dict, pdg_vocab: int, mat_vocab: int) -> Router:
+# Router types that read cond_cat's pdg index through their own
+# nn.Embedding(pdg_vocab, ...), regardless of the trunk's `conditioning`
+# mode — see _check_router_conditioning_compat.
+_VOCAB_SCOPED_ROUTER_TYPES = ("pdg", "process")
+
+
+def _check_router_conditioning_compat(router_types: list[str], conditioning: str) -> None:
+    """Reject a router axis that reintroduces a training-vocab PDG lookup
+    under `conditioning="physical"`.
+
+    `PdgRouter`/`ProcessRouter` always build their own dataset-scoped
+    `nn.Embedding(pdg_vocab, ...)` (network.py's PdgRouter/ProcessRouter),
+    independent of `ConditionEncoder`'s `conditioning` mode. Pairing either
+    with `conditioning="physical"` would silently reintroduce a
+    training-menu-scoped lookup at the routing layer — defeating the entire
+    point of physical-property conditioning, which is to generalize to a
+    species/material outside that menu (see giant/rollout.py's
+    `build_cond_features(strict=...)` gate for the same concern on the
+    trunk side). Raised loudly at model-build time rather than left to
+    surface as a confusing rollout/generalization-benchmark result.
+    """
+    bad = sorted(set(router_types) & set(_VOCAB_SCOPED_ROUTER_TYPES))
+    if bad and conditioning == "physical":
+        raise ValueError(
+            f"router type(s) {bad} always use a training-vocab PDG embedding, "
+            "which is incompatible with conditioning='physical' (whose whole "
+            "point is generalizing beyond that vocab) — pick a different "
+            "router type (e.g. 'energy') or use conditioning='embedding'."
+        )
+
+
+def _build_router_from_cfg(
+    router_cfg: dict, pdg_vocab: int, mat_vocab: int, conditioning: str = "embedding"
+) -> Router:
     """Resolve one `model.router` config into a `Router`, single-axis or composed.
 
     `router_cfg["type"] == "composed"` reads `axis{i}_{field}` flat keys
@@ -1243,9 +1276,12 @@ def _build_router_from_cfg(router_cfg: dict, pdg_vocab: int, mat_vocab: int) ->
     """
     shared_vocab = dict(pdg_vocab=pdg_vocab, mat_vocab=mat_vocab)
     if router_cfg["type"] == "composed":
-        router = build_composed_router(_parse_composed_axes(router_cfg), **shared_vocab)
+        axes = _parse_composed_axes(router_cfg)
+        _check_router_conditioning_compat([a["type"] for a in axes], conditioning)
+        router = build_composed_router(axes, **shared_vocab)
         router.gumbel = bool(router_cfg.get("gumbel", False))
         return router
+    _check_router_conditioning_compat([router_cfg["type"]], conditioning)
     router_kwargs = {
         k: v for k, v in router_cfg.items() if k not in ("enabled", "type", "n_experts")
     }
@@ -1303,13 +1339,18 @@ def build_models(model_config: dict) -> tuple[nn.Module, nn.Module]:
             dropout=model_config.get("dropout", 0.1),
             conditioning=model_config.get("conditioning", "embedding"),
         )
+        conditioning = shared["conditioning"]
         stage1 = RoutedDenoisingMLP(
-            router=_build_router_from_cfg(router_cfg, pdg_vocab, mat_vocab),
+            router=_build_router_from_cfg(
+                router_cfg, pdg_vocab, mat_vocab, conditioning
+            ),
             k_max=model_config.get("k_max", K_MAX),
             **shared,
         )
         sec_decoder = RoutedSecondaryDecoder(
-            router=_build_router_from_cfg(router_cfg, pdg_vocab, mat_vocab),
+            router=_build_router_from_cfg(
+                router_cfg, pdg_vocab, mat_vocab, conditioning
+            ),
             **shared,
         )
         return stage1, sec_decoder

giant/rollout.py

@@ -407,7 +407,16 @@ def _step_chunk(
     tr["_material"] = material
     tr["_layer_id"] = layer_id
 
-    known_pdg = np.array([int(p) in pdg_map for p in tr["pdg"]], dtype=bool)
+    if conditioning == "physical":
+        # Under physical-property conditioning, mass/charge (already resolved
+        # on every track — see the cond_dict comment below) drive the model,
+        # not a training-vocab PDG embedding — build_cond_features passes
+        # strict=False for exactly this mode, so an out-of-vocab species no
+        # longer raises. Terminating on it here would defeat the entire
+        # point of physical conditioning: generalizing to a held-out species.
+        known_pdg = np.ones(n, dtype=bool)
+    else:
+        known_pdg = np.array([int(p) in pdg_map for p in tr["pdg"]], dtype=bool)
 
     # --- Pre-step termination gates (in priority order; each track picks one) ---
     stop = np.zeros(n, dtype=bool)

tests/test_rollout.py

@@ -119,6 +119,21 @@ def test_rollout_physical_conditioning_end_to_end(fake_material_props):
     assert set(np.unique(rec["pdg"]).tolist()) <= set(PDG_MAP.keys())
 
 
+def test_rollout_physical_conditioning_generalizes_to_out_of_vocab_pdg(
+    fake_material_props,
+):
+    """A real, giant.particles-resolvable species outside the training PDG
+    vocab (muon, 13) must run through physical-property conditioning rather
+    than terminate via TERM_UNKNOWN_PDG — that generalization is the entire
+    point of "physical" mode (see build_cond_features(strict=...))."""
+    seeds = _seeds(6)
+    seeds["pdg"] = np.full(6, 13, dtype=np.int64)
+    assert 13 not in PDG_MAP
+    rec = _run(seeds=seeds, conditioning="physical")
+    assert len(rec["event_id"]) > 0
+    assert TERM_UNKNOWN_PDG not in set(rec["termination_reason"].tolist())
+
+
 def test_seed_frontier_track_ids():
     seeds = _seeds(3)
     fr, counts = make_seed_frontier(**seeds)

tests/test_router.py

@@ -1,5 +1,6 @@
 """Tests for the mixture-of-experts routing prototype (giant/model/network.py)."""
 
+import pytest
 import torch
 
 from giant.constants import COND_DIM, K_MAX, SEC_DIM, X_DIM
@@ -484,6 +485,49 @@ def test_build_models_routed_with_pdg_router():
     assert stage1.router.pdg_emb.num_embeddings == 4
 
 
+def test_build_models_rejects_pdg_router_with_physical_conditioning():
+    """conditioning="physical" is meant to generalize beyond the training PDG
+    vocab; PdgRouter always uses a training-vocab nn.Embedding regardless of
+    conditioning, so the combination must raise rather than silently building
+    a model that can't actually generalize the way it claims to."""
+    model_config = dict(
+        pdg_vocab=4,
+        mat_vocab=2,
+        emb_dim=16,
+        dropout=0.1,
+        k_max=K_MAX,
+        expert_hidden_dim=16,
+        expert_n_blocks=2,
+        conditioning="physical",
+        router={"enabled": True, "type": "pdg", "n_experts": 3},
+    )
+    with pytest.raises(ValueError, match="physical"):
+        build_models(model_config)
+
+
+def test_build_models_rejects_composed_router_with_pdg_axis_and_physical_conditioning():
+    model_config = dict(
+        pdg_vocab=4,
+        mat_vocab=2,
+        emb_dim=16,
+        dropout=0.1,
+        k_max=K_MAX,
+        expert_hidden_dim=16,
+        expert_n_blocks=2,
+        conditioning="physical",
+        router={
+            "enabled": True,
+            "type": "composed",
+            "axis0_type": "energy",
+            "axis0_n_experts": 2,
+            "axis1_type": "pdg",
+            "axis1_n_experts": 3,
+        },
+    )
+    with pytest.raises(ValueError, match="physical"):
+        build_models(model_config)
+
+
 # ── ProcessRouter ────────────────────────────────────────────────────────────
 
 

tests/test_transforms.py

@@ -574,6 +574,51 @@ def test_vectorized_map_lookup_raises_keyerror_on_missing_value():
         _vectorized_map_lookup(values, mapping)
 
 
+def test_vectorized_map_lookup_strict_false_dummy_indexes_unmapped_values():
+    """strict=False must leave found values untouched and only dummy-index
+    (0) the unmapped ones — never raise, and never disturb a value that IS
+    in the mapping (e.g. one that happens to map to a nonzero index)."""
+    mapping = {1: 5, 2: 7}
+    values = np.array([1, 99, 2, 100])
+    result = _vectorized_map_lookup(values, mapping, strict=False)
+    np.testing.assert_array_equal(result, [5, 0, 7, 0])
+
+
+def test_build_cond_features_physical_mode_tolerates_out_of_vocab_pdg_and_material():
+    """conditioning="physical" must not KeyError on a pdg/material outside
+    the training-dataset vocab (mat_map/pdg_map) — that's the entire point
+    of the mode (see giant.rollout's known_pdg gate for the paired fix).
+    "embedding" mode must still raise, since cond_cat IS the conditioning
+    signal there. Note this is specifically about the dataset-scoped
+    vocab index, not giant.materials' physical-properties table — a
+    material must still be a real, known Geant4 material (e.g. "G4_Pb",
+    just not one *this* mat_map happened to include) for "physical" mode
+    to derive its Z_eff/A_eff/density/X0/λ_int; a genuinely unknown
+    material name correctly still raises via giant.materials, same as the
+    documented G4_LYSO precedent — that's a separate, intentional guard."""
+    pdg_map = {11: 0, 22: 1}
+    mat_map = {"G4_AIR": 0}
+    data = {
+        "pre_pos": np.zeros((1, 3), dtype=np.float32),
+        "pre_E": np.array([10.0], dtype=np.float32),
+        "pre_dir": np.array([[0.0, 0.0, 1.0]], dtype=np.float32),
+        "layer_id": np.array([0], dtype=np.int32),
+        "pdg": np.array([13], dtype=np.int64),  # not in pdg_map
+        "material": np.array(["G4_Pb"], dtype=object),  # not in mat_map
+        "mass": np.array([105.7], dtype=np.float32),
+        "charge": np.array([-1.0], dtype=np.float32),
+    }
+
+    cond_cont, cond_cat = build_cond_features(
+        data, pdg_map, mat_map, conditioning="physical"
+    )
+    assert cond_cont.shape[-1] == COND_DIM
+    np.testing.assert_array_equal(cond_cat, [[0, 0]])  # dummy indices, no raise
+
+    with pytest.raises(KeyError):
+        build_cond_features(data, pdg_map, mat_map, conditioning="embedding")
+
+
 # ── _WelfordAccumulator ──────────────────────────────────────────────────────