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"""Sweep d values and test multi-basis CVH."""
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 adapt.cache_hidden import cache_support_hidden_states
from adapt.fit_theta import fit_theta
from eval.metrics import evaluate_all
class MultiBasisCVH(torch.nn.Module):
"""Two-basis CVH: h'_t = h_t + a1*B1(theta⊙A1*h) + a2*B2(theta⊙A2*h)"""
def __init__(self, hidden_size, d=64, alpha=0.1, basis_seed=42):
super().__init__()
self.hidden_size = hidden_size
self.d = d
self.alpha = alpha
gen1 = torch.Generator()
gen1.manual_seed(basis_seed)
gen2 = torch.Generator()
gen2.manual_seed(basis_seed + 500)
scale_a = 1.0 / (hidden_size ** 0.5)
scale_b = 1.0 / (d ** 0.5)
self.register_buffer('A1', torch.randn(d, hidden_size, generator=gen1) * scale_a)
self.register_buffer('B1', torch.randn(hidden_size, d, generator=gen1) * scale_b)
self.register_buffer('A2', torch.randn(d, hidden_size, generator=gen2) * scale_a)
self.register_buffer('B2', torch.randn(hidden_size, d, generator=gen2) * scale_b)
def forward(self, h, theta):
proj1 = (self.A1.float() @ h.T).T
gated1 = theta.unsqueeze(0) * proj1
res1 = (self.B1.float() @ gated1.T).T
proj2 = (self.A2.float() @ h.T).T
gated2 = theta.unsqueeze(0) * proj2
res2 = (self.B2.float() @ gated2.T).T
return h + self.alpha * (res1 + res2)
def forward_fn(self, h, theta):
return self.forward(h, theta)
def run_head(wrapper, examples, support_sets, head_module, d=64, alpha=0.1,
beta=0.05, steps=30, lr=0.05, max_new_tokens=512):
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=False,
)
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,
)
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)
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')
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)
print(f" ROUGE-L: {base_r['rougeL']:.4f}, METEOR: {base_r['meteor']:.4f}, SFD: {base_r['sfd']:.4f}")
results = {'Base': base_r}
configs = [
('CVH d=64', CVHHead(H, d=64, alpha=0.1, basis_seed=42).to(device), 64),
('CVH d=128', CVHHead(H, d=128, alpha=0.1, basis_seed=42).to(device), 128),
('CVH d=256', CVHHead(H, d=256, alpha=0.1, basis_seed=42).to(device), 256),
('Uncond d=64', UnconditionalHead(H, d=64, alpha=0.1, basis_seed=42).to(device), 64),
('Uncond d=128', UnconditionalHead(H, d=128, alpha=0.1, basis_seed=42).to(device), 128),
('MultiBasis d=64', MultiBasisCVH(H, d=64, alpha=0.1, basis_seed=42).to(device), 64),
# Higher beta to preserve content
('CVH d=64 b=0.1', CVHHead(H, d=64, alpha=0.1, basis_seed=42).to(device), 64),
('CVH d=64 b=0.2', CVHHead(H, d=64, alpha=0.1, basis_seed=42).to(device), 64),
]
betas = {
'CVH d=64 b=0.1': 0.1,
'CVH d=64 b=0.2': 0.2,
}
for name, head, d in configs:
beta = betas.get(name, 0.05)
print(f"\n=== {name} (beta={beta}) ===")
t0 = time.time()
preds, avg_norm = run_head(
wrapper, examples, support_sets, head, d=d,
alpha=0.1, beta=beta, steps=30, lr=0.05, max_new_tokens=512,
)
elapsed = time.time() - t0
r = evaluate_all(preds, references, support_texts)
results[name] = r
print(f" ROUGE-L: {r['rougeL']:.4f}, METEOR: {r['meteor']:.4f}, "
f"SFD: {r['sfd']:.4f}, avg|theta|: {avg_norm:.3f}, time: {elapsed:.0f}s")
# Summary
print("\n" + "=" * 90)
print(f"{'Config':<25} {'ROUGE-1':<10} {'ROUGE-L':<10} {'METEOR':<10} {'SFD':<10}")
print("-" * 90)
for name, r in results.items():
print(f"{name:<25} {r['rouge1']:<10.4f} {r['rougeL']:<10.4f} {r['meteor']:<10.4f} {r['sfd']:<10.4f}")
if __name__ == '__main__':
main()
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