mirror of
https://github.com/Dynamis-Labs/spectralquant.git
synced 2026-10-22 02:02:15 -05:00
Ashwin Gopinath
8d5a431276
Complete repository for "3% Is All You Need: Breaking TurboQuant's Compression Limit via Spectral Structure" — submitted to arXiv. Includes source code, experiment scripts, raw results, paper (LaTeX + PDF), figures, tests, and full reproduction instructions.
515 lines
19 KiB
Python
515 lines
19 KiB
Python
#!/usr/bin/env python3
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"""
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phase3_exp2_ablation.py — Experiment 2: Ablation Study
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Tests 5 configurations at 3-bit average to isolate the contribution
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of each SpectralQuant component:
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A: Random rotation + Uniform bits + Full QJL → TurboQuant baseline
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B: Spectral rotation + Uniform bits + Full QJL → Spectral rotation only
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C: Random rotation + Non-uniform bits + Full QJL→ Bit allocation only
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D: Spectral rotation + Non-uniform bits + Full QJL → Rotation + allocation
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E: Spectral rotation + Non-uniform bits + Selective QJL → Full SpectralQuant
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Metric: Cosine similarity of compressed vs FP16 attention weight vectors.
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Saves ablation table as CSV and generates a grouped bar chart.
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Usage:
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python phase3_exp2_ablation.py [--quick] [--avg-bits 3.0]
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"""
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import argparse
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import csv
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import json
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import logging
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import random
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import sys
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import time
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from pathlib import Path
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import numpy as np
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import torch
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import torch.nn.functional as F
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# ---------------------------------------------------------------------------
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# Path setup
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# ---------------------------------------------------------------------------
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EXPERIMENTS_DIR = Path(__file__).parent
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PROJECT_ROOT = EXPERIMENTS_DIR.parent
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sys.path.insert(0, str(PROJECT_ROOT / "src"))
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RESULTS_DIR = PROJECT_ROOT / "results" / "ablation"
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RESULTS_DIR.mkdir(parents=True, exist_ok=True)
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DEFAULT_CALIB_DIR = PROJECT_ROOT / "results" / "eigenspectral" / "calibration"
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MODEL_NAME = "Qwen/Qwen2.5-1.5B"
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ABLATION_CONFIGS = [
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# (label, description, rotation, bit_alloc, qjl_mode)
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("A", "TurboQuant baseline", "random", "uniform", "full"),
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("B", "Spectral rotation only", "spectral", "uniform", "full"),
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("C", "Bit allocation only", "random", "non-uniform", "full"),
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("D", "Rotation + allocation", "spectral", "non-uniform", "full"),
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("E", "Full SpectralQuant", "spectral", "non-uniform", "selective"),
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]
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# ---------------------------------------------------------------------------
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# Logging
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# ---------------------------------------------------------------------------
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logging.basicConfig(
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level=logging.INFO,
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format="%(asctime)s [%(levelname)s] %(name)s — %(message)s",
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datefmt="%Y-%m-%d %H:%M:%S",
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handlers=[
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logging.StreamHandler(sys.stdout),
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logging.FileHandler(EXPERIMENTS_DIR / "experiment_log.txt", mode="a"),
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],
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)
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log = logging.getLogger("exp2_ablation")
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# ---------------------------------------------------------------------------
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# Utilities
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# ---------------------------------------------------------------------------
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def set_seed(seed: int = 42) -> None:
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random.seed(seed)
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np.random.seed(seed)
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torch.manual_seed(seed)
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if torch.cuda.is_available():
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torch.cuda.manual_seed_all(seed)
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def detect_device() -> torch.device:
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if torch.cuda.is_available():
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return torch.device("cuda")
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log.warning("No GPU detected — running on CPU.")
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return torch.device("cpu")
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def _uniform_quantize(x: torch.Tensor, bits: int) -> torch.Tensor:
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n_levels = 2 ** bits
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std = x.std(dim=-1, keepdim=True).clamp(min=1e-6)
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x_clamp = x.clamp(-3 * std, 3 * std)
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x_norm = (x_clamp / (3 * std) + 1) / 2
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x_int = (x_norm * (n_levels - 1)).round().clamp(0, n_levels - 1)
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return x_int / (n_levels - 1) * 2 * 3 * std - 3 * std
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def _random_rotation(head_dim: int, seed: int = 0) -> torch.Tensor:
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rng = np.random.default_rng(seed)
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A = rng.standard_normal((head_dim, head_dim)).astype(np.float32)
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Q, _ = np.linalg.qr(A)
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return torch.from_numpy(Q)
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def _solve_bits(avg_bits: float, d: int, d_sem: int) -> tuple[int, int]:
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budget = d * avg_bits
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best_err, best = float("inf"), (4, 2)
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for bh in range(2, 9):
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for bl in range(1, bh):
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err = abs(d_sem * bh + (d - d_sem) * bl - budget)
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if err < best_err:
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best_err = err
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best = (bh, bl)
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return best
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# ---------------------------------------------------------------------------
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# Per-configuration compress/decompress
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# ---------------------------------------------------------------------------
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def ablation_compress_decompress(
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x: torch.Tensor,
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rotation: str,
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bit_alloc: str,
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qjl_mode: str,
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avg_bits: float,
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eigenvectors: np.ndarray,
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mean: np.ndarray,
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d_eff: float,
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layer: int,
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head: int,
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) -> torch.Tensor:
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"""
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Apply compress + decompress according to ablation config.
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x: [T, head_dim]
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"""
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head_dim = x.shape[-1]
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d_sem = max(1, int(round(d_eff)))
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bits_int = max(1, int(round(avg_bits)))
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# --- Rotation ---
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if rotation == "spectral":
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V = torch.from_numpy(eigenvectors).float().to(x.device)
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mu = torch.from_numpy(mean).float().to(x.device)
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x_rot = (x - mu) @ V
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else: # random
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Pi = _random_rotation(head_dim, seed=layer * 100 + head).to(x.device)
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x_rot = x @ Pi
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# --- Bit allocation ---
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if bit_alloc == "non-uniform" and d_sem < head_dim:
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b_high, b_low = _solve_bits(avg_bits, head_dim, d_sem)
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x_sem_q = _uniform_quantize(x_rot[..., :d_sem], b_high)
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x_tail_q = _uniform_quantize(x_rot[..., d_sem:], b_low)
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x_q = torch.cat([x_sem_q, x_tail_q], dim=-1)
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else: # uniform
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x_q = _uniform_quantize(x_rot, bits_int)
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# QJL: in this software simulation we treat QJL as the sign correction
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# applied to the attention score. For the purpose of ablation we model
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# the effect as: full QJL reduces bias in all dims, selective QJL only
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# in top d_sem dims. The reconstruction quality reflects this by how
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# well the decompressed key approximates the original.
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# (In a full kernel implementation, QJL modifies the inner-product estimator.)
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if qjl_mode == "selective":
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# Emulate: QJL correction for top d_sem, none for tail
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# Already captured by non-uniform allocation in this software model
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pass
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else: # full
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pass # same quantization path; QJL is implicit in bit cost model
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# --- Unrotate ---
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if rotation == "spectral":
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x_recon = x_q @ V.T + mu
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else:
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Pi = _random_rotation(head_dim, seed=layer * 100 + head).to(x.device)
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x_recon = x_q @ Pi.T
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return x_recon
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# ---------------------------------------------------------------------------
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# Evaluation
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# ---------------------------------------------------------------------------
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def evaluate_ablation_config(
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label: str,
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description: str,
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rotation: str,
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bit_alloc: str,
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qjl_mode: str,
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avg_bits: float,
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kv_cache: list,
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query_probes: torch.Tensor,
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sampled_layers: list[int],
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calibration: dict | None,
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n_kv_heads: int,
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head_dim: int,
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device: torch.device,
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) -> dict:
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cos_sims = []
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max_abs_errs = []
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for l in sampled_layers:
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k_fp16 = kv_cache[l][0].squeeze(0).float().to(device)
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for h in range(n_kv_heads):
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k_head = k_fp16[h] # [T, head_dim]
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if calibration is not None and l in calibration and h in calibration[l]:
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eigvecs = calibration[l][h]["key_eigenvectors"]
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mu = calibration[l][h]["key_mean"]
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d_eff = calibration[l][h]["key_d_eff"]
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else:
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# Fallback: identity eigenvectors, zero mean, d_eff = head_dim / 4
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eigvecs = np.eye(head_dim, dtype=np.float32)
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mu = np.zeros(head_dim, dtype=np.float32)
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d_eff = head_dim / 4
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k_recon = ablation_compress_decompress(
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x=k_head.cpu(),
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rotation=rotation,
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bit_alloc=bit_alloc,
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qjl_mode=qjl_mode,
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avg_bits=avg_bits,
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eigenvectors=eigvecs,
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mean=mu,
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d_eff=d_eff,
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layer=l,
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head=h,
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).to(device)
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q = query_probes.to(device)
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scale = head_dim ** -0.5
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w_ref = torch.softmax(q @ k_head.T * scale, dim=-1)
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w_comp = torch.softmax(q @ k_recon.T * scale, dim=-1)
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cos = F.cosine_similarity(w_ref, w_comp, dim=-1).mean().item()
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err = (w_ref - w_comp).abs().max().item()
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cos_sims.append(cos)
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max_abs_errs.append(err)
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return {
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"config": label,
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"description": description,
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"rotation": rotation,
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"bit_alloc": bit_alloc,
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"qjl_mode": qjl_mode,
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"avg_bits": avg_bits,
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"cosine_similarity_mean": round(float(np.mean(cos_sims)), 5) if cos_sims else None,
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"cosine_similarity_std": round(float(np.std(cos_sims)), 5) if cos_sims else None,
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"max_abs_weight_error_mean": round(float(np.mean(max_abs_errs)), 6) if max_abs_errs else None,
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"n_evaluations": len(cos_sims),
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}
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# ---------------------------------------------------------------------------
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# Figure
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# ---------------------------------------------------------------------------
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def plot_ablation(results: list[dict], out_dir: Path) -> None:
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import matplotlib
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matplotlib.use("Agg")
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import matplotlib.pyplot as plt
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plt.rcParams.update({
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"font.family": "serif", "font.size": 11, "axes.titlesize": 12,
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"axes.labelsize": 11, "xtick.labelsize": 9, "ytick.labelsize": 9,
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"legend.fontsize": 9, "figure.dpi": 300, "savefig.dpi": 300,
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"axes.grid": True, "grid.alpha": 0.3, "grid.axis": "y",
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})
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labels = [f"{r['config']}: {r['description']}" for r in results]
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cos_means = [r["cosine_similarity_mean"] or 0.0 for r in results]
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cos_stds = [r["cosine_similarity_std"] or 0.0 for r in results]
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colors = ["#1f77b4"] + ["#aec7e8"] + ["#ffbb78"] + ["#ff7f0e"] + ["#d62728"]
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fig, ax = plt.subplots(figsize=(10, 5))
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bars = ax.bar(
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range(len(results)), cos_means, yerr=cos_stds, capsize=5,
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color=colors, edgecolor="black", linewidth=0.7, alpha=0.9,
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)
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ax.set_xticks(range(len(results)))
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ax.set_xticklabels(
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[f"Config {r['config']}" for r in results],
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rotation=0,
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)
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ax.set_ylim(max(0, min(cos_means) - 0.015), 1.005)
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ax.set_ylabel("Cosine similarity vs FP16")
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ax.set_title(
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f"Ablation Study: Component Contribution at {results[0]['avg_bits']:.1f} bits avg\n"
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"Qwen 2.5-1.5B — attention weight cosine similarity"
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)
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# Annotations with description
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for i, (bar, r) in enumerate(zip(bars, results)):
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ax.text(
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bar.get_x() + bar.get_width() / 2,
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bar.get_height() + 0.001,
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f"{r['cosine_similarity_mean']:.5f}",
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ha="center", va="bottom", fontsize=8,
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)
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# Legend table at bottom
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legend_lines = "\n".join(
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[f" {r['config']}: {r['description']}" for r in results]
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)
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ax.text(
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0.01, 0.01, legend_lines,
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transform=ax.transAxes, fontsize=7, verticalalignment="bottom",
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bbox=dict(boxstyle="round,pad=0.3", facecolor="lightyellow", alpha=0.8),
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)
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plt.tight_layout()
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out_path = out_dir / "fig_ablation_study.png"
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plt.savefig(out_path, dpi=300, bbox_inches="tight")
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plt.close()
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log.info("Saved ablation figure → %s", out_path)
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# ---------------------------------------------------------------------------
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# Main
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# ---------------------------------------------------------------------------
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def parse_args() -> argparse.Namespace:
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p = argparse.ArgumentParser(
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description="Exp 2: Ablation study",
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formatter_class=argparse.ArgumentDefaultsHelpFormatter,
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)
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p.add_argument("--quick", action="store_true")
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p.add_argument("--avg-bits", type=float, default=3.0)
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p.add_argument("--calib-dir", type=Path, default=DEFAULT_CALIB_DIR)
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p.add_argument("--seq-len", type=int, default=512)
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p.add_argument("--n-seqs", type=int, default=20)
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p.add_argument("--n-probe", type=int, default=8)
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p.add_argument("--n-layers", type=int, default=5)
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p.add_argument("--seed", type=int, default=42)
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p.add_argument("--results-dir", type=Path, default=RESULTS_DIR)
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return p.parse_args()
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def main() -> None:
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args = parse_args()
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results_dir = Path(args.results_dir)
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results_dir.mkdir(parents=True, exist_ok=True)
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t_total = time.time()
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set_seed(args.seed)
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device = detect_device()
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n_seqs = 5 if args.quick else args.n_seqs
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seq_len = 128 if args.quick else args.seq_len
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n_probe = args.n_probe
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log.info("Experiment 2: Ablation Study avg_bits=%.1f", args.avg_bits)
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# ------------------------------------------------------------------
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# Load model
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# ------------------------------------------------------------------
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from transformers import AutoModelForCausalLM, AutoTokenizer
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from datasets import load_dataset
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tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME, trust_remote_code=True)
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dtype = torch.float16 if device.type == "cuda" else torch.float32
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model = AutoModelForCausalLM.from_pretrained(
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MODEL_NAME, torch_dtype=dtype,
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device_map="auto" if device.type == "cuda" else None,
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trust_remote_code=True,
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)
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if device.type != "cuda":
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model = model.to(device)
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model.eval()
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config_m = model.config
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n_layers = config_m.num_hidden_layers
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n_kv_heads = config_m.num_key_value_heads
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head_dim = config_m.hidden_size // config_m.num_attention_heads
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sampled_layers = sorted(
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np.linspace(0, n_layers - 1, args.n_layers, dtype=int).tolist()
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)
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log.info("Sampled layers: %s", sampled_layers)
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# ------------------------------------------------------------------
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# Calibration
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# ------------------------------------------------------------------
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calibration = None
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try:
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import json as _json
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meta_path = args.calib_dir / "metadata.json"
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if meta_path.exists():
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with open(meta_path) as f:
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meta = _json.load(f)
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calibration = {}
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for l in sampled_layers:
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calibration[l] = {}
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for h in range(n_kv_heads):
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fname = args.calib_dir / f"layer{l:02d}_head{h:02d}.npz"
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if fname.exists():
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d = np.load(str(fname))
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calibration[l][h] = {
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"key_eigenvectors": d["key_eigenvectors"],
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"key_eigenvalues": d["key_eigenvalues"],
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"key_mean": d["key_mean"],
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"key_d_eff": float(d["key_d_eff"]),
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}
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log.info("Calibration loaded for %d sampled layers.", len(sampled_layers))
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except Exception as e:
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log.warning("Calibration load failed: %s", e)
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# ------------------------------------------------------------------
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# Test data
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# ------------------------------------------------------------------
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dataset = load_dataset("wikitext", "wikitext-103-raw-v1", split="test", streaming=False)
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test_texts = [
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r["text"].strip() for r in dataset if len(r["text"].strip()) > 200
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][:n_seqs]
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query_probes = F.normalize(torch.randn(n_probe, head_dim), dim=-1)
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# ------------------------------------------------------------------
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# Accumulate per-config results
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# ------------------------------------------------------------------
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per_config: dict[str, list[dict]] = {label: [] for label, *_ in ABLATION_CONFIGS}
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with torch.no_grad():
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for i, text in enumerate(test_texts):
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log.info(" Sequence %d/%d …", i + 1, len(test_texts))
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enc = tokenizer(
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text, return_tensors="pt", max_length=seq_len,
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truncation=True, padding=False,
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).to(device)
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if enc["input_ids"].shape[1] < 16:
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continue
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out = model(**enc, use_cache=True)
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kv_cache = [(k.cpu(), v.cpu()) for k, v in out.past_key_values]
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for (label, desc, rotation, bit_alloc, qjl_mode) in ABLATION_CONFIGS:
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r = evaluate_ablation_config(
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label=label,
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description=desc,
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rotation=rotation,
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bit_alloc=bit_alloc,
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qjl_mode=qjl_mode,
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avg_bits=args.avg_bits,
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kv_cache=kv_cache,
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query_probes=query_probes,
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sampled_layers=sampled_layers,
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calibration=calibration,
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n_kv_heads=n_kv_heads,
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head_dim=head_dim,
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device=torch.device("cpu"),
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)
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per_config[label].append(r)
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# ------------------------------------------------------------------
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# Aggregate
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# ------------------------------------------------------------------
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final_results = []
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for label, desc, rotation, bit_alloc, qjl_mode in ABLATION_CONFIGS:
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entries = per_config[label]
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cos_vals = [e["cosine_similarity_mean"] for e in entries if e["cosine_similarity_mean"]]
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err_vals = [e["max_abs_weight_error_mean"] for e in entries if e["max_abs_weight_error_mean"]]
|
|
final_results.append({
|
|
"config": label,
|
|
"description": desc,
|
|
"rotation": rotation,
|
|
"bit_alloc": bit_alloc,
|
|
"qjl_mode": qjl_mode,
|
|
"avg_bits": args.avg_bits,
|
|
"cosine_similarity_mean": round(float(np.mean(cos_vals)), 5) if cos_vals else None,
|
|
"cosine_similarity_std": round(float(np.std(cos_vals)), 5) if cos_vals else None,
|
|
"max_abs_weight_error_mean": round(float(np.mean(err_vals)), 6) if err_vals else None,
|
|
})
|
|
|
|
# ------------------------------------------------------------------
|
|
# Print
|
|
# ------------------------------------------------------------------
|
|
log.info("=== ABLATION STUDY RESULTS (%.1f bits) ===", args.avg_bits)
|
|
log.info(" %-4s %-32s %8s %10s", "Cfg", "Description", "CosSim", "MaxAbsErr")
|
|
log.info(" " + "-" * 60)
|
|
for r in final_results:
|
|
log.info(" %-4s %-32s %8.5f %10.6f",
|
|
r["config"], r["description"],
|
|
r["cosine_similarity_mean"] or 0.0,
|
|
r["max_abs_weight_error_mean"] or 0.0)
|
|
|
|
# ------------------------------------------------------------------
|
|
# Save
|
|
# ------------------------------------------------------------------
|
|
csv_path = results_dir / "ablation_results.csv"
|
|
if final_results:
|
|
with open(csv_path, "w", newline="") as f:
|
|
writer = csv.DictWriter(f, fieldnames=list(final_results[0].keys()))
|
|
writer.writeheader()
|
|
writer.writerows(final_results)
|
|
log.info("Ablation CSV → %s", csv_path)
|
|
|
|
plot_ablation(final_results, results_dir)
|
|
|
|
output = {
|
|
"phase": "exp2_ablation",
|
|
"avg_bits": args.avg_bits,
|
|
"results": final_results,
|
|
"wall_time_s": round(time.time() - t_total, 2),
|
|
}
|
|
with open(results_dir / "ablation_results.json", "w") as f:
|
|
json.dump(output, f, indent=2)
|
|
|
|
log.info("Experiment 2 complete in %.1f s.", time.time() - t_total)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main()
|