feat: implement ChordTransformer (pre-norm decoder-only transformer)

Adds src/model.py with a weight-tied autoregressive transformer and
tests/test_model.py with shape, weight-tying, and causal-masking checks.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

tests/test_model.py

@@ -0,0 +1,87 @@
+"""Tests for src/model.py — ChordTransformer."""
+
+import torch
+import pytest
+
+from src.model import ChordTransformer
+
+
+VOCAB_SIZE = 85
+SMALL = dict(vocab_size=VOCAB_SIZE, d_model=32, n_layers=2, n_heads=4, d_ff=64)
+
+
+# ---------------------------------------------------------------------------
+# Smoke tests
+# ---------------------------------------------------------------------------
+
+
+def test_output_shape_no_mask():
+    model = ChordTransformer(**SMALL)
+    model.eval()
+    ids = torch.randint(0, VOCAB_SIZE, (2, 16))
+    with torch.no_grad():
+        logits = model(ids)
+    assert logits.shape == (2, 16, VOCAB_SIZE)
+
+
+def test_output_shape_with_padding_mask():
+    model = ChordTransformer(**SMALL)
+    model.eval()
+    ids = torch.randint(0, VOCAB_SIZE, (3, 20))
+    mask = torch.ones(3, 20, dtype=torch.long)
+    mask[0, 15:] = 0  # last 5 positions are padding for batch item 0
+    with torch.no_grad():
+        logits = model(ids, attention_mask=mask)
+    assert logits.shape == (3, 20, VOCAB_SIZE)
+
+
+def test_single_token_sequence():
+    model = ChordTransformer(**SMALL)
+    model.eval()
+    ids = torch.randint(0, VOCAB_SIZE, (1, 1))
+    with torch.no_grad():
+        logits = model(ids)
+    assert logits.shape == (1, 1, VOCAB_SIZE)
+
+
+def test_max_seq_len_raises():
+    model = ChordTransformer(**SMALL, max_seq_len=8)
+    ids = torch.randint(0, VOCAB_SIZE, (1, 9))
+    with pytest.raises(ValueError, match="exceeds max_seq_len"):
+        model(ids)
+
+
+def test_weight_tying():
+    model = ChordTransformer(**SMALL)
+    assert model.lm_head.weight is model.token_emb.weight
+
+
+# ---------------------------------------------------------------------------
+# Causal masking
+# ---------------------------------------------------------------------------
+
+
+def test_causal_masking_future_tokens_do_not_affect_past_logits():
+    """Changing tokens at positions [k:] must not alter logits at positions [:k]."""
+    torch.manual_seed(0)
+    model = ChordTransformer(**SMALL)
+    model.eval()
+
+    seq_len = 12
+    base_ids = torch.randint(0, VOCAB_SIZE, (1, seq_len))
+
+    with torch.no_grad():
+        logits_base = model(base_ids)
+
+    # Mutate the second half of the sequence
+    modified_ids = base_ids.clone()
+    modified_ids[:, seq_len // 2 :] = torch.randint(0, VOCAB_SIZE, (1, seq_len // 2))
+
+    with torch.no_grad():
+        logits_mod = model(modified_ids)
+
+    # Logits for the first half must be bit-exact (no stochastic ops in eval)
+    assert torch.equal(
+        logits_base[:, : seq_len // 2, :],
+        logits_mod[:, : seq_len // 2, :],
+    ), "Causal masking violated: future tokens affected past logits"