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, 141 insertions, 0 deletions

diff --git a/scripts/test_svd_cvh.py b/scripts/test_svd_cvh.py
new file mode 100644
index 0000000..1f93bd1
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+++ b/scripts/test_svd_cvh.py
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+"""Test SVD-based CVH vs random basis CVH vs Unconditional."""
+
+import sys
+import os
+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, UnconditionalHead
+from models.svd_cvh import SVDCVHHead, SVDUncondHead
+from adapt.cache_hidden import cache_support_hidden_states
+from adapt.fit_theta import fit_theta
+from eval.metrics import evaluate_all
+
+
+def run_head(wrapper, examples, support_sets, head_module, d=64,
+             beta=0.05, steps=30, lr=0.05, max_new_tokens=512,
+             min_new_tokens=64):
+    device = 'cuda:1'
+    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=max_new_tokens)
+            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_module,
+            d=d, lr=lr, steps=steps, beta=beta, lam=1e-4,
+            max_grad_norm=5.0, device=device, verbose=(i == 0),
+        )
+        theta_norms.append(theta.norm().item())
+
+        prompt = build_query_prompt(ex['query_input'], ex['task'])
+        pred = wrapper.generate_with_head(
+            prompt, theta, head_module.forward_fn,
+            max_new_tokens=max_new_tokens, temperature=0.0,
+            min_new_tokens=min_new_tokens,
+        )
+        predictions.append(pred)
+
+        del cached_h, theta
+        torch.cuda.empty_cache()
+
+        if (i + 1) % 20 == 0:
+            print(f"    {i+1}/{len(examples)}")
+
+    avg_norm = sum(theta_norms) / max(len(theta_norms), 1)
+    # Check avg output length
+    avg_len = sum(len(p.split()) for p in predictions) / max(len(predictions), 1)
+    return predictions, avg_norm, avg_len
+
+
+def main():
+    N = 50
+    print(f"Loading data ({N} examples)...")
+    examples = load_longlamp('product_review_user', split='val')[:N]
+    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]
+
+    avg_ref_len = sum(len(r.split()) for r in references) / len(references)
+    print(f"Avg reference length: {avg_ref_len:.0f} words")
+
+    print("Loading model...")
+    wrapper = QwenWrapper('Qwen/Qwen2.5-1.5B-Instruct', device='cuda:1')
+    H = wrapper.hidden_size
+    device = 'cuda:1'
+
+    # 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=512, temperature=0.0)
+        base_preds.append(pred)
+    base_r = evaluate_all(base_preds, references, support_texts)
+    base_len = sum(len(p.split()) for p in base_preds) / len(base_preds)
+    print(f"  ROUGE-L: {base_r['rougeL']:.4f}, METEOR: {base_r['meteor']:.4f}, "
+          f"SFD: {base_r['sfd']:.4f}, avg_len: {base_len:.0f}")
+
+    results = {}
+    results['Base'] = {**base_r, 'avg_len': base_len}
+
+    # SVD-based heads
+    print("\nComputing SVD of lm_head...")
+    svd_cvh = SVDCVHHead(wrapper.lm_head_weight, d=64, alpha=0.1).to(device)
+    svd_uncond = SVDUncondHead(wrapper.lm_head_weight, d=64, alpha=0.1).to(device)
+
+    configs = [
+        ('Random CVH d=64', CVHHead(H, d=64, alpha=0.1, basis_seed=42).to(device), 64),
+        ('Random Uncond d=64', UnconditionalHead(H, d=64, alpha=0.1, basis_seed=42).to(device), 64),
+        ('SVD CVH d=64', svd_cvh, 64),
+        ('SVD Uncond d=64', svd_uncond, 64),
+        # Try different alpha with SVD
+        ('SVD CVH d=64 a=0.05', SVDCVHHead(wrapper.lm_head_weight, d=64, alpha=0.05).to(device), 64),
+        ('SVD CVH d=64 a=0.2', SVDCVHHead(wrapper.lm_head_weight, d=64, alpha=0.2).to(device), 64),
+    ]
+
+    for name, head, d in configs:
+        print(f"\n=== {name} ===")
+        t0 = time.time()
+        preds, avg_norm, avg_len = run_head(
+            wrapper, examples, support_sets, head, d=d,
+            beta=0.05, steps=30, lr=0.05, max_new_tokens=512,
+            min_new_tokens=64,
+        )
+        elapsed = time.time() - t0
+        r = evaluate_all(preds, references, support_texts)
+        results[name] = {**r, 'avg_len': avg_len}
+        print(f"  ROUGE-L: {r['rougeL']:.4f}, METEOR: {r['meteor']:.4f}, "
+              f"SFD: {r['sfd']:.4f}, avg|theta|: {avg_norm:.3f}, "
+              f"avg_len: {avg_len:.0f}, time: {elapsed:.0f}s")
+
+    # Summary
+    print("\n" + "=" * 100)
+    print(f"{'Config':<25} {'R-1':<8} {'R-L':<8} {'METEOR':<8} {'SFD':<8} {'Len':<6}")
+    print("-" * 100)
+    for name, r in results.items():
+        print(f"{name:<25} {r['rouge1']:<8.4f} {r['rougeL']:<8.4f} "
+              f"{r['meteor']:<8.4f} {r['sfd']:<8.4f} {r.get('avg_len', 0):<6.0f}")
+
+
+if __name__ == '__main__':
+    main()