1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
|
"""UPH hyperparameter search for a given K.
Searches over (lr, steps, d) on a small N, then confirms top configs on full N.
Usage:
python scripts/uph_hyperparam_search.py --task review --setting user --K 8 --device cuda:0
python scripts/uph_hyperparam_search.py --task review --setting user --K 8 --N_screen 50 --N_confirm 200
"""
import sys
import os
import json
import time
import itertools
import numpy as np
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 data.style_features import compute_sfd, compute_feature_deltas
from models.qwen_wrapper import QwenWrapper
from models.cvh import UnconditionalHead
from adapt.cache_hidden import cache_support_hidden_states
from adapt.fit_theta import fit_theta
from eval.metrics import compute_rouge, compute_meteor
def run_uph_config(wrapper, examples, support_sets, references, support_texts,
d, lr, steps, device, N):
"""Run UPH with specific hyperparams, return mean R-L."""
H = wrapper.hidden_size
uncond = UnconditionalHead(H, d=d, alpha=0.1, 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
rl_scores = []
for i, (ex, support) in enumerate(zip(examples[:N], support_sets[:N])):
cached_h = cache_support_hidden_states(wrapper, support, ex['task'])
if not cached_h:
prompt = build_query_prompt(ex['query_input'], ex['task'])
# Base generation
chat_msgs = [{"role": "system", "content": "You are a helpful writing assistant."},
{"role": "user", "content": prompt}]
pt = wrapper.tokenizer.apply_chat_template(chat_msgs, tokenize=False, add_generation_prompt=True)
ids = wrapper.tokenizer.encode(pt, return_tensors="pt").to(device)
with torch.no_grad():
out = wrapper.model.generate(ids, max_new_tokens=512, min_new_tokens=128,
temperature=None, top_p=None, do_sample=False,
pad_token_id=wrapper.tokenizer.pad_token_id)
pred = wrapper.tokenizer.decode(out[0, ids.shape[1]:], skip_special_tokens=True)
else:
theta = fit_theta(
cached_h=cached_h,
lm_head_weight=wrapper.lm_head_weight,
lm_head_bias=lm_head_bias,
head_module=uncond,
d=d, lr=lr, steps=steps, beta=0.05, lam=1e-4,
max_grad_norm=5.0, device=device,
)
prompt = build_query_prompt(ex['query_input'], ex['task'])
pred = wrapper.generate_with_head_blended(
prompt, theta, uncond.forward_fn,
blend_gamma=0.5, max_new_tokens=512,
min_new_tokens=128, temperature=0.0,
)
del cached_h, theta
torch.cuda.empty_cache()
r = compute_rouge([pred], [references[i]])
rl_scores.append(r['rougeL'])
return np.mean(rl_scores)
def main():
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('--task', type=str, default='review')
parser.add_argument('--setting', type=str, default='user')
parser.add_argument('--K', type=int, default=8)
parser.add_argument('--N_screen', type=int, default=50)
parser.add_argument('--N_confirm', type=int, default=200)
parser.add_argument('--device', type=str, default='cuda:0')
parser.add_argument('--output_dir', type=str, default='outputs/hyperparam')
parser.add_argument('--top_k', type=int, default=3, help='Top configs to confirm')
args = parser.parse_args()
config_map = {
('review', 'user'): 'product_review_user',
('review', 'temporal'): 'product_review_temporal',
('topic', 'user'): 'topic_writing_user',
('topic', 'temporal'): 'topic_writing_temporal',
}
config_name = config_map[(args.task, args.setting)]
# Search grid
d_values = [32, 64, 128]
lr_values = [0.03, 0.05, 0.1]
steps_values = [30, 50, 100]
print(f"=== UPH Hyperparam Search: {args.task}_{args.setting}, K={args.K} ===")
print(f"Grid: d={d_values}, lr={lr_values}, steps={steps_values}")
print(f"Screen N={args.N_screen}, Confirm N={args.N_confirm}")
print("\nLoading data...")
examples = load_longlamp(config_name, split='val')[:args.N_confirm]
support_sets = [select_k_profile_items(ex['profile_items'], args.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(f"Loading model on {args.device}...")
wrapper = QwenWrapper('Qwen/Qwen2.5-1.5B-Instruct', device=args.device)
# Phase 1: Screen all configs on small N
print(f"\n--- Phase 1: Screening ({args.N_screen} examples) ---")
results = []
for d, lr, steps in itertools.product(d_values, lr_values, steps_values):
t0 = time.time()
mean_rl = run_uph_config(
wrapper, examples, support_sets, references, support_texts,
d=d, lr=lr, steps=steps, device=args.device, N=args.N_screen,
)
elapsed = time.time() - t0
results.append({'d': d, 'lr': lr, 'steps': steps, 'mean_rl': mean_rl, 'time': elapsed})
print(f" d={d:3d} lr={lr:.3f} steps={steps:3d}: R-L={mean_rl:.4f} ({elapsed:.0f}s)")
# Sort by R-L
results.sort(key=lambda x: x['mean_rl'], reverse=True)
print(f"\n--- Top {args.top_k} configs ---")
for i, r in enumerate(results[:args.top_k]):
print(f" #{i+1}: d={r['d']} lr={r['lr']} steps={r['steps']} R-L={r['mean_rl']:.4f}")
# Phase 2: Confirm top configs on full N
print(f"\n--- Phase 2: Confirming top {args.top_k} ({args.N_confirm} examples) ---")
confirmed = []
for r in results[:args.top_k]:
t0 = time.time()
mean_rl = run_uph_config(
wrapper, examples, support_sets, references, support_texts,
d=r['d'], lr=r['lr'], steps=r['steps'],
device=args.device, N=args.N_confirm,
)
elapsed = time.time() - t0
confirmed.append({**r, 'confirmed_rl': mean_rl, 'confirm_time': elapsed})
print(f" d={r['d']} lr={r['lr']} steps={r['steps']}: "
f"screen={r['mean_rl']:.4f} → confirm={mean_rl:.4f} ({elapsed:.0f}s)")
# Save
os.makedirs(args.output_dir, exist_ok=True)
output_path = os.path.join(args.output_dir,
f"{args.task}_{args.setting}_K{args.K}_search.json")
with open(output_path, 'w') as f:
json.dump({
'screening': results,
'confirmed': confirmed,
'task': args.task,
'setting': args.setting,
'K': args.K,
'N_screen': args.N_screen,
'N_confirm': args.N_confirm,
}, f, indent=2, default=str)
print(f"\nSaved to {output_path}")
if __name__ == '__main__':
main()
|
