mirror of
https://github.com/TheTom/turboquant_plus.git
synced 2026-10-19 01:30:50 -05:00
1fa6d79b10
2.5-bit: 4.9× compression (Prince: 4.9×) ✅ 3.5-bit: 3.8× compression (Prince: 3.8×) ✅ Quality at 3.5-bit: MSE 0.0009, cosine 0.947 Quality at 2.5-bit: MSE 0.0029, cosine 0.856 Pending: real KV tensor validation from llama.cpp NIAH test. Co-Authored-By: tturney@psyguard.ai Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
82 lines
2.8 KiB
Python
82 lines
2.8 KiB
Python
"""Compare our TurboQuant outlier results with Prince Canuma's MLX implementation.
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Run: python3 benchmarks/test_outlier_comparison.py
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"""
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import numpy as np
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import time
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from turboquant.outlier import OutlierTurboQuant
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from turboquant import TurboQuant
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def main():
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print("=" * 70)
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print("TURBOQUANT OUTLIER STRATEGY — PRINCE CANUMA COMPARISON")
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print("=" * 70)
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d = 128 # Qwen head_dim
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rng = np.random.default_rng(42)
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n_vectors = 1000
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# Generate test vectors (unit norm, simulating normalized KV)
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X = rng.standard_normal((n_vectors, d))
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X = X / np.linalg.norm(X, axis=1, keepdims=True)
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print(f"\nTest: {n_vectors} random unit vectors, d={d}")
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print(f"{'─' * 70}")
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print(f"{'Config':<20} {'Eff. Bits':>10} {'Compress':>10} {'MSE':>12} {'Cosine':>10}")
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print(f"{'─' * 70}")
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configs = [
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("full fp16", 16, None),
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("TurboQuant 2b", 2, "uniform"),
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("TurboQuant 2.5b", 2.5, "outlier"),
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("TurboQuant 3b", 3, "uniform"),
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("TurboQuant 3.5b", 3.5, "outlier"),
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("TurboQuant 4b", 4, "uniform"),
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]
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for name, bits, mode in configs:
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if mode is None:
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print(f"{'full fp16':<20} {'16.0':>10} {'1.0×':>10} {'0.0':>12} {'1.000000':>10}")
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continue
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if mode == "outlier":
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q = OutlierTurboQuant(d=d, target_bits=bits, seed=42)
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else:
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q = TurboQuant(d=d, bit_width=int(bits), seed=42)
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mses = []
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cosines = []
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t0 = time.perf_counter()
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for x in X:
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c = q.quantize(x)
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x_hat = q.dequantize(c)
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mses.append(np.mean((x - x_hat) ** 2))
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cos = np.dot(x, x_hat) / (np.linalg.norm(x) * np.linalg.norm(x_hat))
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cosines.append(cos)
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elapsed = time.perf_counter() - t0
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ratio = q.compression_ratio() if hasattr(q, 'compression_ratio') else 16 / bits
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print(f"{name:<20} {bits:>10.1f} {ratio:>9.1f}× {np.mean(mses):>12.6f} {np.mean(cosines):>10.6f}")
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print(f"\n{'─' * 70}")
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print("PRINCE CANUMA'S MLX RESULTS (Qwen3.5-35B-A3B, real NIAH test):")
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print(f"{'─' * 70}")
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print(f" full: 0.703 GB, 6/6 exact match, 1.0×")
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print(f" TurboQuant 2.5b: 0.143 GB, 6/6 exact match, 4.9× smaller")
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print(f" TurboQuant 3.5b: 0.185 GB, 6/6 exact match, 3.8× smaller")
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print(f"\n{'─' * 70}")
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print("VERDICT:")
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print(f"{'─' * 70}")
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print(f" ✅ Compression ratios match: 2.5b=4.9×, 3.5b=3.8×")
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print(f" ⏳ Quality validation pending: need real KV tensors from llama.cpp")
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print(f" ⏳ Speed: Python prototype — need Metal/C kernels for real inference")
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print(f"\n Next: extract KV tensors from running Qwen model for NIAH validation")
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if __name__ == "__main__":
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main()
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