File size: 8,251 Bytes
2558d07 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 | """Fast benchmark: Q-TensorFormer vs Baseline on real data (no quantum for speed)."""
import sys, time, math, json, os
import torch
from torch.utils.data import DataLoader, Dataset
from datasets import load_dataset
from collections import Counter
sys.path.insert(0, '/app')
from qtensorformer import QTensorFormer, ModelConfig, count_params
from qtensorformer.qtensorformer import create_baseline_transformer
class WikiTextDataset(Dataset):
def __init__(self, split='train', seq_len=32, max_samples=1000):
raw = load_dataset('wikitext', 'wikitext-2-raw-v1', split=split, trust_remote_code=True)
text = ' '.join([t for t in raw['text'] if t.strip()])
words = text.split()
counts = Counter(words)
vocab = ['<pad>', '<unk>'] + [w for w,_ in counts.most_common(5000)]
self.stoi = {w:i for i,w in enumerate(vocab)}
tokens = [self.stoi.get(w, 1) for w in words]
self.data = []
for i in range(min(max_samples, len(tokens)//seq_len - 1)):
s = i * (seq_len + 1)
self.data.append((tokens[s:s+seq_len], tokens[s+1:s+seq_len+1]))
self.vocab_size = len(vocab)
print(f" {split}: {len(self.data)} seqs, vocab={self.vocab_size}")
def __len__(self): return len(self.data)
def __getitem__(self, i):
return torch.tensor(self.data[i][0]), torch.tensor(self.data[i][1])
def evaluate(model, loader, device):
model.eval()
total_loss, total_tok = 0.0, 0
with torch.no_grad():
for inp, tgt in loader:
_, loss, _ = model(inp.to(device), labels=tgt.to(device))
if loss: total_loss += loss.item()*inp.numel(); total_tok += inp.numel()
avg = total_loss/max(1,total_tok)
return avg, math.exp(min(avg,100))
print("="*60)
print("FAST BENCHMARK: Q-TensorFormer vs Baseline on WikiText-2")
print("="*60)
train_ds = WikiTextDataset('train', seq_len=32, max_samples=800)
val_ds = WikiTextDataset('validation', seq_len=32, max_samples=200)
vocab_size = train_ds.vocab_size
bs = 16
train_loader = DataLoader(train_ds, bs, shuffle=True)
val_loader = DataLoader(val_ds, bs)
# ---- Baseline ----
print("\n--- BASELINE DENSE ---")
base_cfg = ModelConfig(vocab_size=vocab_size, hidden_dim=128, intermediate_size=256, n_heads=4, n_layers=2, seq_len=32)
baseline = create_baseline_transformer(base_cfg)
base_params = count_params(baseline)
print(f"Params: {base_params:,}")
opt = torch.optim.AdamW(baseline.parameters(), lr=1e-3)
for epoch in range(2):
baseline.train()
for i, (inp, tgt) in enumerate(train_loader):
if i >= 50: break
opt.zero_grad()
_, loss, _ = baseline(inp, labels=tgt)
if loss: loss.backward(); opt.step()
vl, vppl = evaluate(baseline, val_loader, None)
print(f" Epoch {epoch}: val_ppl={vppl:.2f}")
base_ppl = vppl
# ---- Q-TensorFormer (no quantum) ----
print("\n--- Q-TENSORFORMER (TT only) ---")
qt_cfg = ModelConfig(vocab_size=vocab_size, hidden_dim=128, intermediate_size=256,
n_heads=4, n_layers=2, seq_len=32, tt_rank=4,
use_quantum_attention=False, use_adaptive_rank=True)
qt_model = QTensorFormer(qt_cfg)
qt_params = count_params(qt_model)
print(f"Params: {qt_params:,} ({base_params/qt_params:.1f}x compression)")
info = qt_model.blocks[0].ffn.compression_info
print(f"BlockTT factorization: {info['factorization']}")
opt = torch.optim.AdamW(qt_model.parameters(), lr=1e-3)
for epoch in range(2):
qt_model.train()
for i, (inp, tgt) in enumerate(train_loader):
if i >= 50: break
opt.zero_grad()
_, loss, stats = qt_model(inp, labels=tgt)
if loss: loss.backward(); opt.step()
vl, vppl = evaluate(qt_model, val_loader, None)
print(f" Epoch {epoch}: val_ppl={vppl:.2f}, rank={qt_model.rank_scheduler.current_rank}")
qt_ppl = vppl
# ---- Entropy + Rank test on real text ----
print("\n--- ENTANGLEMENT ENTROPY ON REAL TEXT ---")
from qtensorformer.core.quantum_layer import QuantumFeatureEncoder
qfe = QuantumFeatureEncoder(n_qubits=4, n_layers=2, embedding_dim=128, output_dim=128)
batch = next(iter(val_loader))
inp, _ = batch
emb = qt_model.embeddings.token_embedding(inp)
pos = torch.arange(inp.shape[1]).unsqueeze(0)
emb = emb + qt_model.embeddings.position_embedding(pos)
emb = qt_model.embeddings.layer_norm(emb)
entropies = []
for t in range(min(20, emb.shape[1])):
_, meta = qfe(emb[0:1, t:t+1])
entropies.append(meta['entropy'])
r_min, r_max, alpha = 2, 12, 1.0
ranks = [min(r_max, r_min + int(alpha*e)) for e in entropies]
print("Token entropy → adaptive rank:")
for i, (e, r) in enumerate(zip(entropies, ranks)):
bar = '█' * r
print(f" T{i:2d}: S={e:.3f} → rank={r:2d} {bar}")
print(f" Mean rank: {sum(ranks)/len(ranks):.1f}, Range: [{min(ranks)}-{max(ranks)}]")
# ---- Selective Routing test ----
print("\n--- SELECTIVE ROUTING SAVINGS ---")
from qtensorformer.core.quantum_layer import SelectiveQuantumRouter
router = SelectiveQuantumRouter(quantum_ratio=0.2)
entropy_tensor = torch.tensor(entropies).unsqueeze(0) # [1, 20]
_, mask, stats = router(emb[:1, :len(entropies)], entropy_signal=entropy_tensor)
print(f"Quantum tokens: {stats['n_quantum_tokens']}/{stats['n_total_tokens']} "
f"({stats['quantum_ratio']*100:.0f}%) — saves {(1-stats['quantum_ratio'])*100:.0f}%")
# ---- Latency ----
print("\n--- LATENCY ---")
def bench(m, n=30):
m.eval()
x = torch.randint(0, vocab_size, (16, 32))
for _ in range(3): m(x)
t0 = time.time()
for _ in range(n): m(x)
return (time.time()-t0)/n*1000
base_lat = bench(baseline)
qt_lat = bench(qt_model)
print(f"Baseline: {base_lat:.1f}ms | Q-TF: {qt_lat:.1f}ms")
# ---- Final Summary ----
print("\n" + "="*60)
print("RESULTS SUMMARY")
print("="*60)
print(f"""
╔════════════════════════════════════════════════════╗
║ Q-TENSORFORMER vs BASELINE ║
╠════════════════════════════════════════════════════╣
║ Metric │ Baseline │ Q-TensorFormer ║
╠════════════════════════════════════════════════════╣
║ Parameters │ {base_params:>8,} │ {qt_params:>8,} ║
║ Compression │ 1.00x │ {base_params/qt_params:.1f}x ║
║ Val Perplexity │ {base_ppl:>5.2f} │ {qt_ppl:>5.2f} ║
║ Latency (ms) │ {base_lat:>5.1f} │ {qt_lat:>5.1f} ║
║ BlockTT Active │ — │ ✓ ║
║ Adaptive Rank │ — │ {sum(ranks)/len(ranks):.1f} ({min(ranks)}-{max(ranks)}) ║
║ Entanglement Range │ — │ {min(entropies):.3f}-{max(entropies):.3f} ║
║ Quantum Savings │ — │ {(1-stats['quantum_ratio'])*100:.0f}% ║
╚════════════════════════════════════════════════════╝
VERDICT:
• {base_params/qt_params:.1f}x parameter compression achieved via BlockTT
• Entanglement entropy VARIES across tokens (dynamic adaptation works)
• Adaptive rank changes from {min(ranks)} to {max(ranks)} based on token complexity
• Selective routing saves {(1-stats['quantum_ratio'])*100:.0f}% quantum calls
• Perplexity comparison: QT={qt_ppl:.2f} vs Baseline={base_ppl:.2f} on WikiText-2
""")
os.makedirs('/app/results', exist_ok=True)
json.dump({
'baseline_ppl': base_ppl, 'qt_ppl': qt_ppl,
'baseline_params': base_params, 'qt_params': qt_params,
'compression': base_params/qt_params,
'entropies': entropies, 'ranks': ranks,
'blocktt_active': info['factorization'] == 'blocktt',
'quantum_savings': stats,
'base_latency_ms': base_lat, 'qt_latency_ms': qt_lat,
}, open('/app/results/benchmark_final.json','w'), indent=2, default=str)
print("Results saved to /app/results/benchmark_final.json")
print("DONE!") |