implement Experiment 1: data pipeline, tied-RNN w/ ACT halting, transformer baseline, train/eval/plot, 16 tests

1 files changed, 77 insertions, 0 deletions

diff --git a/tests/test_models.py b/tests/test_models.py
new file mode 100644
index 0000000..7994e86
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+++ b/tests/test_models.py
@@ -0,0 +1,77 @@
+import torch
+import torch.nn.functional as F
+
+from src.config import Config
+from src.data import build_examples, make_batch
+from src.model_api import build_model
+
+
+def _batch(cfg, inputs=(2, 7, 42, 99)):
+    ex = build_examples(list(inputs), cfg)
+    return make_batch(ex, cfg)
+
+
+def test_rnn_shapes_and_halting_range():
+    cfg = Config(model="rnn", max_steps=20, min_steps=2)
+    m = build_model(cfg)
+    b = _batch(cfg)
+    out = m(b["x"], b["y_in"])
+    assert out["logits"].shape == (4, b["y"].shape[1], cfg.vocab)
+    s = out["halt_steps"]
+    assert s.shape == (4,)
+    assert bool((s >= cfg.min_steps).all())
+    assert bool((s <= cfg.max_steps).all())
+
+
+def test_rnn_fixed_k_ablation():
+    cfg = Config(model="rnn", halting=False, max_steps=20)
+    m = build_model(cfg)
+    b = _batch(cfg)
+    out = m(b["x"], b["y_in"])
+    assert torch.allclose(out["halt_steps"], torch.full((4,), 20.0))
+
+
+def test_transformer_shapes():
+    cfg = Config(model="transformer")
+    m = build_model(cfg)
+    b = _batch(cfg)
+    out = m(b["x"], b["y_in"])
+    assert out["logits"].shape == (4, b["y"].shape[1], cfg.vocab)
+    assert out["halt_steps"] is None
+
+
+def test_transformer_integers_mode():
+    cfg = Config(model="transformer", vocab_mode="integers")
+    m = build_model(cfg)
+    b = _batch(cfg)
+    out = m(b["x"], b["y_in"])
+    assert out["logits"].shape == (4, b["y"].shape[1], cfg.vocab)
+
+
+def test_param_counts_logged_not_gated():
+    r = build_model(Config(model="rnn")).param_count()
+    t = build_model(Config(model="transformer")).param_count()
+    assert r > 1000 and t > 1000
+
+
+def test_forward_backward_no_nan_both_models():
+    for model_name in ("rnn", "transformer"):
+        cfg = Config(model=model_name)
+        m = build_model(cfg)
+        b = _batch(cfg)
+        out = m(b["x"], b["y_in"])
+        y_safe = b["y"].clamp(max=cfg.vocab - 1)
+        loss = F.cross_entropy(out["logits"].reshape(-1, cfg.vocab), y_safe.reshape(-1), reduction="none")
+        mask = b["y_mask"].float().reshape(-1)
+        loss = (loss * mask).sum() / mask.sum()
+        loss.backward()
+        assert torch.isfinite(loss).item(), f"{model_name} loss NaN"
+
+
+def test_greedy_decode_shape():
+    from src.model_api import greedy_decode
+    cfg = Config(model="rnn")
+    m = build_model(cfg)
+    x = torch.tensor([[4, 2], [9, 9]], dtype=torch.long)
+    gen = greedy_decode(m, x, cfg)
+    assert gen.shape == (2, cfg.max_out_len)