v0.3.0 step 6: sample.py/rollout.py AR generation + class->PDG decode

- giant/sample.py: fix every sampler's call convention against
  Stage1Model/Stage2OneShot's actual forward signatures (was still
  calling model(x, t, cond_cont, cond_cat) positionally); add
  sample_secondaries_ar (free-running AR loop, unsnapped history feature)
  and sample_stage1/sample_stage2/resolve_n_sec dispatch helpers that read
  each stage's generator_kind/decoder off the model instance itself.
- giant/particles.py: decode_topn_class (argmax + other_policy) and
  decode_embedding_nearest (L1-snap + distance) turn a secondary's
  "onehot"/"embedding" type prediction into a concrete PDG.
- giant/rollout.py: decode_secondary_identity routes all three
  particle_type.target values to real mass/charge; per-stage generator
  dispatch (drops the single shared `mode` string, adds ddpm support);
  L1DistCollector accumulates the §11.3 embedding-distance diagnostic.
- giant/cli.py: drop the onehot/embedding-target rejection gate (narrowed
  to the still-unimplemented conditioning.particle/material.type=onehot
  axis); fix the dead model_cfg.get("mode") bug in predict/rollout.
- giant/analysis/: new type_embedding_l1_distance PlotSpec, wired through
  the rollout YAML sidecar (no live-model call needed, unlike
  router_gating -- the histogram is already pre-aggregated at rollout
  time).
- Un-xfail every test that was blocked on this step (test_rollout.py,
  test_flow.py, test_wgan.py, test_phase2.py, test_router.py,
  test_validate.py); add test_sample.py, test_type_embedding_distance.py.

Known follow-up: giant/validate.py still unpacks the training val-batch
as a stale 6-tuple and doesn't use the new per-stage dispatch, so
marginal validation during training degrades gracefully with a warning
rather than working -- not in this step's scope.

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

tests/test_cli_predict.py

@@ -6,14 +6,14 @@ import yaml
 
 from giant.cli import (
     _CEPH_PREDICTIONS,
-    _check_v030_onehot_support,
+    _check_conditioning_onehot_support,
     _resolve_prediction_output,
     _write_prediction_ref,
 )
 
 
 # ---------------------------------------------------------------------------
-# _check_v030_onehot_support
+# _check_conditioning_onehot_support
 # ---------------------------------------------------------------------------
 
 
@@ -29,42 +29,43 @@ def _nested_model_cfg(
     }
 
 
-def test_check_v030_onehot_support_allows_physical():
-    _check_v030_onehot_support(_nested_model_cfg(), "predict")  # no raise
+def test_check_conditioning_onehot_support_allows_physical():
+    _check_conditioning_onehot_support(_nested_model_cfg(), "predict")  # no raise
 
 
-def test_check_v030_onehot_support_rejects_onehot_particle_conditioning():
+def test_check_conditioning_onehot_support_rejects_onehot_particle_conditioning():
     cfg = _nested_model_cfg(particle_type="onehot")
     with pytest.raises(typer.Exit):
-        _check_v030_onehot_support(cfg, "predict")
+        _check_conditioning_onehot_support(cfg, "predict")
 
 
-def test_check_v030_onehot_support_rejects_onehot_material_conditioning():
+def test_check_conditioning_onehot_support_rejects_onehot_material_conditioning():
     cfg = _nested_model_cfg(material_type="onehot")
     with pytest.raises(typer.Exit):
-        _check_v030_onehot_support(cfg, "rollout")
+        _check_conditioning_onehot_support(cfg, "rollout")
 
 
-def test_check_v030_onehot_support_rejects_onehot_particle_type_target():
+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")
-    with pytest.raises(typer.Exit):
-        _check_v030_onehot_support(cfg, "predict")
+    _check_conditioning_onehot_support(cfg, "predict")  # no raise
 
 
-def test_check_v030_onehot_support_rejects_embedding_particle_type_target():
+def test_check_conditioning_onehot_support_allows_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")
+    _check_conditioning_onehot_support(cfg, "predict")  # no raise
 
 
-def test_check_v030_onehot_support_is_noop_for_v02_flat_model_config():
+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/embedding-target, so this must be a
-    silent no-op rather than crash on `.get("particle")` against a string."""
+    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_v030_onehot_support(cfg, "predict")  # no raise
+    _check_conditioning_onehot_support(cfg, "predict")  # no raise
 
 
 # ---------------------------------------------------------------------------