Initial commit: UPH project codebase and experiment results

Includes model code, evaluation scripts, configs, analysis outputs,
and experiment results for the User Prior Head personalization method.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

1 files changed, 107 insertions, 0 deletions

diff --git a/adapt/fit_theta.py b/adapt/fit_theta.py
new file mode 100644
index 0000000..f5b047b
--- /dev/null
+++ b/adapt/fit_theta.py
@@ -0,0 +1,107 @@
+"""Fit theta_u on cached hidden states.
+
+Loss = CE(lm_head(h + alpha * B(theta ⊙ A@h)), y) + beta * KL(p_theta || p_0) + lambda * ||theta||^2
+"""
+
+import torch
+import torch.nn.functional as F
+
+# Maximum chunk size for logit computation to avoid OOM
+CHUNK_SIZE = 128
+
+
+def _chunked_ce_kl(h_prime, h_base, lm_w, lm_bias, y, beta):
+    """Compute CE + KL in chunks to avoid OOM from huge vocab logits."""
+    seq_len = h_prime.shape[0]
+    total_ce = 0.0
+    total_kl = 0.0
+
+    for start in range(0, seq_len, CHUNK_SIZE):
+        end = min(start + CHUNK_SIZE, seq_len)
+        hp_chunk = h_prime[start:end]
+        hb_chunk = h_base[start:end]
+        y_chunk = y[start:end]
+
+        logits = F.linear(hp_chunk, lm_w, lm_bias)
+        base_logits = F.linear(hb_chunk, lm_w, lm_bias)
+
+        total_ce = total_ce + F.cross_entropy(logits, y_chunk, reduction='sum')
+
+        if beta > 0:
+            log_p = F.log_softmax(logits, dim=-1)
+            p0 = F.softmax(base_logits.detach(), dim=-1)
+            total_kl = total_kl + F.kl_div(log_p, p0, reduction='sum')
+
+        # Free intermediates
+        del logits, base_logits
+        if beta > 0:
+            del log_p, p0
+
+    return total_ce, total_kl
+
+
+def fit_theta(
+    cached_h: list,       # list of (h_states: (T_i, H), label_ids: (T_i,))
+    lm_head_weight: torch.Tensor,  # (vocab_size, H)
+    lm_head_bias: torch.Tensor | None,
+    head_module,           # CVHHead or UnconditionalHead
+    d: int = 64,
+    lr: float = 0.05,
+    steps: int = 30,
+    beta: float = 0.05,
+    lam: float = 1e-4,
+    max_grad_norm: float = 5.0,
+    device: str = "cuda:1",
+    verbose: bool = False,
+) -> torch.Tensor:
+    """Fit a user vector theta_u on cached hidden states.
+
+    Memory-efficient: computes logits in chunks, no pre-computation of base logits.
+    """
+    theta = torch.zeros(d, device=device, requires_grad=True, dtype=torch.float32)
+    optimizer = torch.optim.Adam([theta], lr=lr)
+
+    lm_w = lm_head_weight.float()
+    lm_b = lm_head_bias.float() if lm_head_bias is not None else None
+
+    for step in range(steps):
+        total_loss = 0.0
+        total_tokens = 0
+
+        for h_cpu, y_cpu in cached_h:
+            h = h_cpu.to(device).float()
+            y = y_cpu.to(device)
+
+            # Apply head to get personalized hidden states
+            h_prime = head_module(h, theta)
+
+            # Compute CE + KL in chunks
+            ce, kl = _chunked_ce_kl(h_prime, h.detach(), lm_w, lm_b, y, beta)
+
+            total_loss = total_loss + ce + beta * kl
+            total_tokens += y.shape[0]
+
+            # Free GPU memory
+            del h, y, h_prime
+
+        # Average over tokens + L2 reg
+        loss = total_loss / max(total_tokens, 1) + lam * theta.square().sum()
+
+        optimizer.zero_grad()
+        loss.backward()
+        torch.nn.utils.clip_grad_norm_([theta], max_norm=max_grad_norm)
+        optimizer.step()
+
+        # Clip theta L2 norm
+        with torch.no_grad():
+            norm = theta.norm()
+            if norm > max_grad_norm:
+                theta.mul_(max_grad_norm / norm)
+
+        if verbose and (step % 10 == 0 or step == steps - 1):
+            print(f"  Step {step:3d}: loss={loss.item():.4f}, |theta|={theta.norm().item():.4f}")
+
+        # Free graph
+        del total_loss, loss
+
+    return theta.detach()