Text Generation
Transformers
Safetensors
PyTorch
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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
Premchandyadav369
Transform Q-TensorFormer into an Information-Value Adaptive Resource Allocation Architecture
eaeea8f | """ | |
| 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() | |