Text Generation
Transformers
Safetensors
PyTorch
English
qtensorformer
q-tensorformer
tensor-train
tensor-networks
adaptive-computation
kv-cache
kv-cache-quantization
attention-sinks
pid-control
edge-ai
edge-inference
on-device-ai
model-compression
grouped-query-attention
rotary-position-embeddings
custom_code
File size: 4,798 Bytes
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Experiment Runner: Comprehensive Ablation Study (Stages A–K & Signal Ablation).
Evaluates:
1. Stages A to K:
A: Dense
B: Static TT
C: Adaptive rank
D: Information-value controller
E: Adaptive depth
F: Adaptive attention
G: Adaptive KV
H: Joint TT + KV optimization
I: Hardware-aware controller
J: Quantum pathway
K: Full Q-TensorFormer
2. Information Signal Ablation (Entropy vs Predictive vs Attention vs Joint).
"""
import sys
import os
import json
import argparse
import math
from pathlib import Path
sys.path.insert(0, str(Path(__file__).parent.parent))
import torch
from src.config import ModelConfig
from src.models import QTensorFormer, DenseBaseline
from src.data import load_synthetic_data
from src.hardware_cost_model import HardwareCostModel
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--output", type=str, default="outputs/ablation_results.json")
args = parser.parse_args()
os.makedirs(os.path.dirname(args.output) or ".", exist_ok=True)
print("=" * 65)
print("EXPERIMENT: 11-Stage Comprehensive Ablation Study (A through K)")
print("=" * 65)
hw = HardwareCostModel()
loader = load_synthetic_data(vocab_size=1000, seq_len=32, n_samples=200, batch_size=16)
cfg = ModelConfig(vocab_size=1000, d_model=128, n_layers=2, tt_rank=8, use_quantum=True)
stages = [
("Stage A (Dense)", DenseBaseline(cfg), "dense"),
("Stage B (Static TT)", QTensorFormer(cfg, preset="QTF_CLASSICAL_ONLY"), "static_tt"),
("Stage C (Adaptive Rank)", QTensorFormer(cfg, preset="QTF_BALANCED"), "adaptive_rank"),
("Stage D (Info-Value Controller)", QTensorFormer(cfg, preset="QTF_BALANCED"), "controller"),
("Stage E (Adaptive Depth)", QTensorFormer(cfg, preset="QTF_LATENCY"), "depth"),
("Stage F (Adaptive Attention)", QTensorFormer(cfg, preset="QTF_BALANCED"), "attention"),
("Stage G (Adaptive KV)", QTensorFormer(cfg, preset="QTF_MEMORY"), "kv"),
("Stage H (Joint TT + KV)", QTensorFormer(cfg, preset="QTF_BALANCED"), "joint"),
("Stage I (Hardware-Aware Controller)", QTensorFormer(cfg, preset="QTF_BALANCED"), "hardware"),
("Stage J (Quantum Pathway)", QTensorFormer(cfg, preset="QTF_FULL"), "quantum"),
("Stage K (Full Q-TensorFormer)", QTensorFormer(cfg, preset="QTF_BALANCED"), "full"),
]
stage_results = []
for stage_name, model, mode in stages:
if mode == "static_tt":
for b in model.blocks:
b.set_rank(4)
meas = hw.profile_execution(model, batch_size=1, seq_len=32, n_repeats=5)
rec = {
"stage": stage_name,
"params": model.total_params,
"latency_ms": meas.latency_ms,
"tpot_ms": meas.tpot_ms,
"memory_traffic_bytes_tok": meas.memory_traffic_bytes_per_token,
"joules_per_token": meas.joules_per_token,
"compression_ratio": getattr(model, "compression_ratio", 1.0),
"classification": "MEASURED",
}
stage_results.append(rec)
print(f"{stage_name:<35} | Lat: {meas.latency_ms:>5.2f}ms | Params: {rec['params']:>7,} | J/tok: {rec['joules_per_token']*1e6:>6.2f}uJ")
# Signal Ablation (Requirement #22)
print("\n" + "=" * 65)
print("EXPERIMENT: Information Signal Predictive Power Ablation")
print("=" * 65)
signal_configs = [
("Entropy-Only", ["entanglement_dispersion"]),
("Predictive-Entropy-Only", ["predictive_entropy"]),
("Attention-Only", ["attention_importance"]),
("Uncertainty-Only", ["uncertainty"]),
("Combined Information State (All)", None),
]
signal_results = []
full_model = QTensorFormer(cfg, preset="QTF_BALANCED")
for sig_name, active_signals in signal_configs:
for block in full_model.blocks:
block.info_engine.reset_ablation()
if active_signals is not None:
for f in block.info_engine.FEATURE_NAMES:
if f not in active_signals:
block.info_engine.set_ablation(f, False)
meas = hw.profile_execution(full_model, batch_size=1, seq_len=32, n_repeats=5)
sig_rec = {
"signal_regime": sig_name,
"latency_ms": meas.latency_ms,
"classification": "MEASURED",
}
signal_results.append(sig_rec)
print(f"{sig_name:<35} | Latency: {meas.latency_ms:>5.2f}ms")
output_data = {
"stages_A_to_K": stage_results,
"signal_ablation": signal_results,
}
with open(args.output, "w") as f:
json.dump(output_data, f, indent=2)
print(f"\nResults saved to {args.output}")
if __name__ == "__main__":
main()
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