fix: clone grammar bias per step in generate_period

_grammar_bias returned a shared module-level singleton that the loop
mutated in place (EOS block + repetition penalty). The penalty thus
accumulated across positions within a call and persisted across calls,
collapsing output to HOLD/NC until process restart. Clone the bias each
step so edits stay local. Add regression tests guarding the invariant.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>

tests/test_generate.py

@@ -4,6 +4,7 @@ import pytest
 import torch
 import torch.nn as nn
 
+import src.generate as gen
 from src.generate import _encode_prefix, _EOS, _HOLD, _NC, _ROOT_START, _BASS_START, _BASS_END, generate_period
 from src.tokenizer import TOKEN_TO_ID, VOCAB
 
@@ -152,3 +153,52 @@ def test_generate_no_bars_arg_still_works():
         max_tokens=64, seed=0,
     )
     assert len(period.bars) >= 1
+
+
+# ---------------------------------------------------------------------------
+# Regression: generation must not mutate shared module-level bias tensors.
+#
+# _grammar_bias() returns shared singletons; the loop applied the EOS block and
+# the repetition penalty *in place*, permanently corrupting them. Within one call
+# the penalty accumulated across positions (collapsing output to HOLD/NC); across
+# calls the corruption persisted in the process until restart. Both symptoms are
+# the same root cause: the per-step bias must be a fresh copy.
+# ---------------------------------------------------------------------------
+
+# Module-level bias singletons that the generation loop must never mutate.
+_SHARED_BIASES = [
+    "_BIAS_FREE", "_BIAS_FREE_EOS", "_BIAS_QUAL",
+    "_BIAS_EXT", "_BIAS_EXT_NONE", "_BIAS_BASS",
+]
+
+
+def test_generation_does_not_mutate_shared_bias_tensors():
+    before = {name: getattr(gen, name).clone() for name in _SHARED_BIASES}
+
+    model = _UniformModel()
+    generate_period(
+        model=model, mode="major", time="4/4", subdivision=4,
+        style="H1K0", function="verse", key="C_major",
+        n_bars=8, repetition_penalty=1.0, seed=0,
+    )
+
+    for name in _SHARED_BIASES:
+        assert torch.equal(getattr(gen, name), before[name]), (
+            f"{name} was mutated in place by generate_period"
+        )
+
+
+def test_repeated_generation_is_reproducible_with_penalty():
+    # Same seed + same params must give identical output no matter how many times
+    # generation ran before. State bleed between calls made this fail.
+    model = _UniformModel()
+    kwargs = dict(
+        model=model, mode="major", time="4/4", subdivision=4,
+        style="H1K0", function="verse", key="C_major",
+        n_bars=8, repetition_penalty=1.0, seed=123,
+    )
+    first = generate_period(**kwargs)
+    # A differently-seeded call in between perturbs any shared state.
+    generate_period(**{**kwargs, "seed": 7})
+    second = generate_period(**kwargs)
+    assert first.bars == second.bars