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, 122 insertions, 0 deletions

diff --git a/scripts/sweep_alpha.py b/scripts/sweep_alpha.py
new file mode 100644
index 0000000..bc35b0c
--- /dev/null
+++ b/scripts/sweep_alpha.py
@@ -0,0 +1,122 @@
+"""Quick sweep over alpha values to find the right perturbation scale."""
+
+import sys
+import os
+import json
+import time
+import torch
+
+sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+
+from data.longlamp import load_longlamp, select_k_profile_items
+from data.templates import build_query_prompt
+from models.qwen_wrapper import QwenWrapper
+from models.cvh import CVHHead
+from adapt.cache_hidden import cache_support_hidden_states
+from adapt.fit_theta import fit_theta
+from eval.metrics import evaluate_all
+
+
+def run_cvh_with_params(wrapper, examples, support_sets, alpha, beta, steps, d=64, lr=0.05):
+    """Run CVH with specific hyperparameters."""
+    device = 'cuda:1'
+    H = wrapper.hidden_size
+    head = CVHHead(H, d=d, alpha=alpha, basis_seed=42).to(device)
+
+    lm_head_bias = None
+    if hasattr(wrapper.model.lm_head, 'bias') and wrapper.model.lm_head.bias is not None:
+        lm_head_bias = wrapper.model.lm_head.bias.data
+
+    predictions = []
+    theta_norms = []
+
+    for i, (ex, support) in enumerate(zip(examples, support_sets)):
+        cached_h = cache_support_hidden_states(wrapper, support, ex['task'])
+        if not cached_h:
+            prompt = build_query_prompt(ex['query_input'], ex['task'])
+            pred = wrapper.generate_base(prompt, max_new_tokens=256)
+            predictions.append(pred)
+            continue
+
+        theta = fit_theta(
+            cached_h=cached_h,
+            lm_head_weight=wrapper.lm_head_weight,
+            lm_head_bias=lm_head_bias,
+            head_module=head,
+            d=d, lr=lr, steps=steps, beta=beta, lam=1e-4,
+            max_grad_norm=5.0, device=device, verbose=False,
+        )
+        theta_norms.append(theta.norm().item())
+
+        prompt = build_query_prompt(ex['query_input'], ex['task'])
+        pred = wrapper.generate_with_head(
+            prompt, theta, head.forward_fn,
+            max_new_tokens=256, temperature=0.0,
+        )
+        predictions.append(pred)
+
+        del cached_h, theta
+        torch.cuda.empty_cache()
+
+    avg_norm = sum(theta_norms) / max(len(theta_norms), 1)
+    return predictions, avg_norm
+
+
+def main():
+    print("Loading data...")
+    examples = load_longlamp('product_review_user', split='val')[:50]
+    K = 4
+    support_sets = [select_k_profile_items(ex['profile_items'], K, seed=0) for ex in examples]
+    references = [ex['target_output'] for ex in examples]
+    support_texts = [[s['support_output'] for s in ss] for ss in support_sets]
+
+    print("Loading model...")
+    wrapper = QwenWrapper('Qwen/Qwen2.5-1.5B-Instruct', device='cuda:1')
+
+    # Run base
+    print("\n=== Base ===")
+    base_preds = []
+    for ex in examples:
+        prompt = build_query_prompt(ex['query_input'], ex['task'])
+        pred = wrapper.generate_base(prompt, max_new_tokens=256, temperature=0.0)
+        base_preds.append(pred)
+    base_results = evaluate_all(base_preds, references, support_texts)
+    print(f"  ROUGE-L: {base_results['rougeL']:.4f}, METEOR: {base_results['meteor']:.4f}, SFD: {base_results['sfd']:.4f}")
+
+    # Sweep
+    configs = [
+        {'alpha': 0.1, 'beta': 0.05, 'steps': 30, 'lr': 0.05},
+        {'alpha': 0.3, 'beta': 0.05, 'steps': 30, 'lr': 0.05},
+        {'alpha': 0.5, 'beta': 0.05, 'steps': 30, 'lr': 0.05},
+        {'alpha': 0.3, 'beta': 0.01, 'steps': 50, 'lr': 0.05},
+        {'alpha': 0.5, 'beta': 0.01, 'steps': 50, 'lr': 0.05},
+        {'alpha': 0.3, 'beta': 0.01, 'steps': 50, 'lr': 0.1},
+    ]
+
+    all_results = {'Base': base_results}
+
+    for cfg in configs:
+        name = f"a{cfg['alpha']}_b{cfg['beta']}_s{cfg['steps']}_lr{cfg['lr']}"
+        print(f"\n=== CVH {name} ===")
+        t0 = time.time()
+        preds, avg_norm = run_cvh_with_params(
+            wrapper, examples, support_sets,
+            alpha=cfg['alpha'], beta=cfg['beta'],
+            steps=cfg['steps'], lr=cfg['lr'],
+        )
+        elapsed = time.time() - t0
+        results = evaluate_all(preds, references, support_texts)
+        all_results[name] = results
+        print(f"  ROUGE-L: {results['rougeL']:.4f}, METEOR: {results['meteor']:.4f}, "
+              f"SFD: {results['sfd']:.4f}, avg|theta|: {avg_norm:.3f}, time: {elapsed:.0f}s")
+
+    # Summary
+    print("\n" + "=" * 80)
+    print(f"{'Config':<40} {'ROUGE-L':<10} {'METEOR':<10} {'SFD':<10}")
+    print("-" * 80)
+    for name, r in all_results.items():
+        print(f"{name:<40} {r['rougeL']:<10.4f} {r['meteor']:<10.4f} {r['sfd']:<10.4f}")
+
+
+if __name__ == '__main__':
+    main()