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

1 files changed, 38 insertions, 0 deletions

diff --git a/src/model_api.py b/src/model_api.py
new file mode 100644
index 0000000..66c2ace
--- /dev/null
+++ b/src/model_api.py
@@ -0,0 +1,38 @@
+"""Model interface contract + build dispatch + shared greedy decode."""
+import torch
+import torch.nn as nn
+
+from src.config import Config
+
+
+class PrimeModel(nn.Module):
+    """Contract: forward(x, y_in) -> {"logits": (B,T_out,vocab), "halt_steps": (B,) or None}."""
+
+    def forward(self, x, y_in):
+        raise NotImplementedError
+
+    def param_count(self) -> int:
+        return sum(p.numel() for p in self.parameters())
+
+
+def build_model(cfg: Config) -> PrimeModel:
+    if cfg.model == "rnn":
+        from src.models.rnn import TiedRNN
+        return TiedRNN(cfg)
+    if cfg.model == "transformer":
+        from src.models.transformer import TransformerBaseline
+        return TransformerBaseline(cfg)
+    raise ValueError(f"unknown model: {cfg.model}")
+
+
+@torch.no_grad()
+def greedy_decode(model: PrimeModel, x: torch.Tensor, cfg: Config, max_len: int | None = None) -> torch.Tensor:
+    """Autoregressive greedy decode of output digits. Returns (B, max_len) tokens (BOS stripped)."""
+    max_len = max_len or cfg.max_out_len
+    B = x.shape[0]
+    y_in = torch.full((B, 1), cfg.eos_id, dtype=torch.long, device=x.device)
+    for _ in range(max_len):
+        out = model(x, y_in)
+        nxt = out["logits"][:, -1].argmax(-1)
+        y_in = torch.cat([y_in, nxt[:, None]], dim=1)
+    return y_in[:, 1:]