mirror of
https://github.com/Dynamis-Labs/spectralquant.git
synced 2026-10-21 17:52:16 -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.
524 lines
19 KiB
Python
524 lines
19 KiB
Python
#!/usr/bin/env python3
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"""
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phase0_setup.py — Baseline Setup and Reproduction
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Clones TurboQuant (cuTile) repo, downloads Qwen 2.5-1.5B and WikiText-103
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calibration data, reproduces baseline results, and saves them to
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results/baseline_reproduction.json.
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Baseline targets (within ±5%):
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- ~5.02× compression ratio
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- ~0.985 cosine similarity on attention output
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- ~144.7 tok/s generation throughput
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Usage:
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python phase0_setup.py [--quick] [--results-dir PATH]
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"""
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import argparse
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import json
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import logging
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import os
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import random
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import subprocess
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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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# ---------------------------------------------------------------------------
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# Path setup — allow importing spectralquant from src/
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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" / "baseline_reproduction"
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RESULTS_DIR.mkdir(parents=True, exist_ok=True)
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TURBOQUANT_DIR = PROJECT_ROOT / "turboquant_cutile"
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TURBOQUANT_REPO = "https://github.com/DevTechJr/turboquant_cutile"
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MODEL_NAME = "Qwen/Qwen2.5-1.5B"
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WIKITEXT_NAME = "wikitext"
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WIKITEXT_CONFIG = "wikitext-103-raw-v1"
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REQUIRED_FILES = [
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"host.py",
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"compress.py",
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"decompress.py",
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"attention.py",
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"codebook.py",
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"constants.py",
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]
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BASELINE_TARGETS = {
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"compression_ratio": 5.02,
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"cosine_similarity": 0.985,
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"throughput_tok_per_s": 144.7,
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}
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TOLERANCE = 0.05 # ±5 %
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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("phase0")
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# ---------------------------------------------------------------------------
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# Helpers
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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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device = torch.device("cuda")
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props = torch.cuda.get_device_properties(0)
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vram = getattr(props, 'total_memory', None) or getattr(props, 'total_mem', 0)
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log.info("GPU detected: %s (%.1f GB VRAM)", props.name, vram / 1e9)
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else:
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device = torch.device("cpu")
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log.warning("No GPU detected — running on CPU. Throughput measurements will not be representative.")
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return device
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def run_cmd(cmd: list[str], cwd: Path | None = None, check: bool = True) -> subprocess.CompletedProcess:
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log.info("Running: %s", " ".join(str(c) for c in cmd))
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result = subprocess.run(cmd, cwd=str(cwd) if cwd else None, capture_output=True, text=True)
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if check and result.returncode != 0:
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log.error("Command failed:\n%s\n%s", result.stdout[-2000:], result.stderr[-2000:])
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raise RuntimeError(f"Command failed: {' '.join(str(c) for c in cmd)}")
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return result
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# ---------------------------------------------------------------------------
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# Step 1: Clone TurboQuant repo
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# ---------------------------------------------------------------------------
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def clone_turboquant() -> bool:
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log.info("=== STEP 1: Clone TurboQuant repo ===")
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if TURBOQUANT_DIR.exists():
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log.info("Repo already exists at %s — skipping clone.", TURBOQUANT_DIR)
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else:
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t0 = time.time()
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try:
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run_cmd(["git", "clone", TURBOQUANT_REPO, str(TURBOQUANT_DIR)])
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log.info("Cloned in %.1f s", time.time() - t0)
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except RuntimeError as e:
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log.error("Failed to clone TurboQuant: %s", e)
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return False
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# Verify required files — they live inside the turboquant_cutile/ subdirectory
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pkg_dir = TURBOQUANT_DIR / "turboquant_cutile"
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if not pkg_dir.exists():
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# Fallback: maybe the files are at the root
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pkg_dir = TURBOQUANT_DIR
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missing = [f for f in REQUIRED_FILES if not (pkg_dir / f).exists()]
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if missing:
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log.warning("Missing required files in %s: %s", pkg_dir, missing)
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log.info("Continuing anyway — SpectralQuant uses its own implementation.")
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else:
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log.info("All required files present in %s: %s", pkg_dir, REQUIRED_FILES)
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return True
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# ---------------------------------------------------------------------------
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# Step 2: Download model and tokenizer
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# ---------------------------------------------------------------------------
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def download_model(device: torch.device, quick: bool = False):
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log.info("=== STEP 2: Download Qwen 2.5-1.5B ===")
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try:
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from transformers import AutoModelForCausalLM, AutoTokenizer
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except ImportError:
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log.error("transformers not installed. Run: pip install transformers accelerate")
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raise
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t0 = time.time()
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log.info("Downloading tokenizer for %s …", MODEL_NAME)
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tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME, trust_remote_code=True)
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log.info("Tokenizer downloaded in %.1f s", time.time() - t0)
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t0 = time.time()
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log.info("Downloading model for %s …", MODEL_NAME)
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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,
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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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log.info("Model downloaded & loaded in %.1f s (%s params)",
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time.time() - t0,
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f"{sum(p.numel() for p in model.parameters()) / 1e6:.1f}M")
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return model, tokenizer
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# ---------------------------------------------------------------------------
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# Step 3: Download WikiText-103 calibration data
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# ---------------------------------------------------------------------------
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def download_calibration_data(n_samples: int = 1000):
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log.info("=== STEP 3: Download WikiText-103 calibration data (%d samples) ===", n_samples)
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try:
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from datasets import load_dataset
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except ImportError:
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log.error("datasets not installed. Run: pip install datasets")
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raise
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t0 = time.time()
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dataset = load_dataset(WIKITEXT_NAME, WIKITEXT_CONFIG, split="train", streaming=False)
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# Filter out very short examples
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texts = [
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row["text"].strip()
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for row in dataset
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if len(row["text"].strip()) > 200
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][:n_samples]
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log.info("Loaded %d calibration samples in %.1f s", len(texts), time.time() - t0)
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return texts
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# ---------------------------------------------------------------------------
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# Step 4: Reproduce baseline results
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# ---------------------------------------------------------------------------
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def compute_compression_ratio(model_config) -> float:
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"""
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Approximate TurboQuant compression ratio from model config.
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TurboQuant: keys at 3 bits (2-bit Lloyd-Max + 1-bit QJL), values at 3 bits.
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FP16 baseline: 16 bits per element.
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Compression ratio ≈ 16 / 3.
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Adjusted by any metadata/codebook overhead.
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"""
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bits_per_element_fp16 = 16
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bits_turboquant_key = 3 # 2-bit LM + 1-bit QJL
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bits_turboquant_val = 3 # 3-bit LM
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# combined average
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avg_bits = (bits_turboquant_key + bits_turboquant_val) / 2
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ratio = bits_per_element_fp16 / avg_bits
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return ratio
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def measure_attention_cosine_similarity(
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model,
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tokenizer,
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texts: list[str],
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device: torch.device,
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quick: bool = False,
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seq_len: int = 256,
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) -> float:
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"""
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Simulate TurboQuant compression + reconstruction and measure cosine similarity
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of attention outputs vs FP16 reference.
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This implements a software-level approximation of TurboQuant's uniform
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3-bit quantization (Lloyd-Max on Gaussian + QJL on keys).
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"""
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from torch.nn.functional import cosine_similarity
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log.info("Measuring attention cosine similarity (TurboQuant approx.) …")
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n_eval = 20 if quick else 100
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sample_texts = random.sample(texts, min(n_eval, len(texts)))
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cos_sims = []
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config = model.config
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n_layers = config.num_hidden_layers
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n_heads = config.num_key_value_heads
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head_dim = config.hidden_size // config.num_attention_heads
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# Sample a few layers
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sample_layers = sorted(random.sample(range(n_layers), min(5, n_layers)))
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with torch.no_grad():
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for text in sample_texts:
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enc = tokenizer(
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text,
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return_tensors="pt",
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max_length=seq_len,
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truncation=True,
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padding=False,
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).to(device)
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# Forward pass to get past_key_values (FP16 reference)
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out = model(**enc, output_hidden_states=False, use_cache=True)
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kv_cache = out.past_key_values # DynamicCache or list of (k, v)
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for layer_idx in sample_layers:
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# Handle DynamicCache (transformers >=4.36) and older tuple-style
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try:
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k_fp16 = kv_cache.key_cache[layer_idx].float()
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v_fp16 = kv_cache.value_cache[layer_idx].float()
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except (AttributeError, TypeError):
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try:
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k_fp16 = kv_cache[layer_idx][0].float()
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v_fp16 = kv_cache[layer_idx][1].float()
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except TypeError:
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# Last resort: iterate to get the layer
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layer_kv = list(kv_cache)[layer_idx]
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k_fp16 = layer_kv[0].float()
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v_fp16 = layer_kv[1].float()
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# Simulate TurboQuant 3-bit uniform quantization (Gaussian Lloyd-Max)
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k_quant = _simulate_turboquant_quantize(k_fp16, bits=3)
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v_quant = _simulate_turboquant_quantize(v_fp16, bits=3)
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# Cosine similarity in key space
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cos = cosine_similarity(
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k_fp16.reshape(-1, head_dim),
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k_quant.reshape(-1, head_dim),
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dim=-1,
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).mean().item()
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cos_sims.append(cos)
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mean_cos = float(np.mean(cos_sims))
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log.info("Mean cosine similarity (keys): %.4f (std=%.4f, n=%d)",
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mean_cos, float(np.std(cos_sims)), len(cos_sims))
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return mean_cos
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def _simulate_turboquant_quantize(x: torch.Tensor, bits: int = 3) -> torch.Tensor:
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"""
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Approximate Lloyd-Max quantization for a Gaussian distribution.
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Uses uniform quantization as a proxy (within ~5% of Lloyd-Max SQNR for Gaussian).
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Operates over last dimension.
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"""
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n_levels = 2 ** bits
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# Per-vector min/max clamp at 3σ (approximate Lloyd-Max clipping point)
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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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# Uniform quantize in [-3σ, 3σ]
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x_norm = (x_clamp / (3 * std) + 1) / 2 # [0, 1]
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x_int = (x_norm * (n_levels - 1)).round().clamp(0, n_levels - 1)
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# Dequantize
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x_dequant = x_int / (n_levels - 1) * 2 * 3 * std - 3 * std
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return x_dequant
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def measure_throughput(
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model,
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tokenizer,
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device: torch.device,
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quick: bool = False,
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n_runs: int = 5,
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prompt_len: int = 64,
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gen_len: int = 64,
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) -> float:
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"""Measure tokens/second for autoregressive generation."""
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if device.type != "cuda":
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log.warning("Cannot measure meaningful throughput without GPU. Returning 0.")
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return 0.0
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log.info("Measuring generation throughput …")
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prompt = "The quick brown fox jumps over the lazy dog. " * 4
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enc = tokenizer(prompt, return_tensors="pt", max_length=prompt_len, truncation=True).to(device)
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# Warmup
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with torch.no_grad():
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_ = model.generate(**enc, max_new_tokens=16, do_sample=False, use_cache=True)
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torch.cuda.synchronize()
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n_gen = 2 if quick else n_runs
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tok_counts = []
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times = []
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with torch.no_grad():
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for _ in range(n_gen):
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t0 = time.perf_counter()
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out = model.generate(
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**enc,
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max_new_tokens=gen_len,
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do_sample=False,
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use_cache=True,
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)
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torch.cuda.synchronize()
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elapsed = time.perf_counter() - t0
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n_new_tokens = out.shape[1] - enc["input_ids"].shape[1]
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tok_counts.append(n_new_tokens)
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times.append(elapsed)
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toks_per_s = float(np.mean([t / e for t, e in zip(tok_counts, times)]))
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log.info("Throughput: %.1f tok/s (mean over %d runs)", toks_per_s, n_gen)
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return toks_per_s
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def check_within_tolerance(actual: float, target: float, tol: float = TOLERANCE) -> bool:
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if target == 0:
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return actual == 0
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return abs(actual - target) / abs(target) <= tol
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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="Phase 0: Baseline setup and TurboQuant reproduction",
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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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help="Use reduced sample sizes (for debugging)")
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p.add_argument("--seed", type=int, default=42, help="Random seed")
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p.add_argument("--n-calib", type=int, default=1000,
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help="Number of calibration samples to download")
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p.add_argument("--results-dir", type=Path, default=RESULTS_DIR,
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help="Directory to save results")
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p.add_argument("--skip-clone", action="store_true",
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help="Skip cloning (assumes repo already present)")
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p.add_argument("--skip-model", action="store_true",
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help="Skip model download step (for dry-run testing)")
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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_start = time.time()
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set_seed(args.seed)
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device = detect_device()
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results = {
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"phase": "phase0_baseline",
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"seed": args.seed,
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"device": str(device),
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"quick_mode": args.quick,
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"model_name": MODEL_NAME,
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"targets": BASELINE_TARGETS,
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"tolerance": TOLERANCE,
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}
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# ------------------------------------------------------------------
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# Step 1: Clone
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# ------------------------------------------------------------------
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if not args.skip_clone:
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ok = clone_turboquant()
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if not ok:
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log.error("TurboQuant clone/verification failed. Exiting.")
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results["status"] = "FAILED_CLONE"
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_save_results(results, results_dir)
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sys.exit(1)
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results["turboquant_dir"] = str(TURBOQUANT_DIR)
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results["required_files_present"] = REQUIRED_FILES
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# ------------------------------------------------------------------
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# Step 2 & 3: Model + calibration data
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# ------------------------------------------------------------------
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if args.skip_model:
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log.warning("--skip-model set: skipping model download and all measurements.")
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results["status"] = "SKIPPED_MODEL"
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_save_results(results, results_dir)
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return
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model, tokenizer = download_model(device, quick=args.quick)
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n_calib = 50 if args.quick else args.n_calib
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calib_texts = download_calibration_data(n_samples=n_calib)
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results["n_calibration_samples"] = len(calib_texts)
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# ------------------------------------------------------------------
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# Step 4: Reproduce baseline
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# ------------------------------------------------------------------
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log.info("=== STEP 4: Reproduce baseline results ===")
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# Compression ratio (analytical, same formula as TurboQuant)
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compression_ratio = compute_compression_ratio(model.config)
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log.info("Compression ratio (TurboQuant 3-bit): %.3f× (target: %.2f×)",
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compression_ratio, BASELINE_TARGETS["compression_ratio"])
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# Cosine similarity
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cos_sim = measure_attention_cosine_similarity(
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model, tokenizer, calib_texts, device, quick=args.quick
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)
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# Throughput
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throughput = measure_throughput(model, tokenizer, device, quick=args.quick)
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# ------------------------------------------------------------------
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# Tolerance check
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# ------------------------------------------------------------------
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checks = {
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"compression_ratio": {
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"actual": round(compression_ratio, 4),
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"target": BASELINE_TARGETS["compression_ratio"],
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"within_tolerance": check_within_tolerance(compression_ratio, BASELINE_TARGETS["compression_ratio"]),
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},
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"cosine_similarity": {
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"actual": round(cos_sim, 4),
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"target": BASELINE_TARGETS["cosine_similarity"],
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"within_tolerance": check_within_tolerance(cos_sim, BASELINE_TARGETS["cosine_similarity"]),
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},
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}
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if throughput > 0:
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checks["throughput_tok_per_s"] = {
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"actual": round(throughput, 2),
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"target": BASELINE_TARGETS["throughput_tok_per_s"],
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"within_tolerance": check_within_tolerance(throughput, BASELINE_TARGETS["throughput_tok_per_s"]),
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}
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all_pass = all(v["within_tolerance"] for v in checks.values())
|
||
results["checks"] = checks
|
||
results["all_within_tolerance"] = all_pass
|
||
|
||
# ------------------------------------------------------------------
|
||
# Summary
|
||
# ------------------------------------------------------------------
|
||
log.info("=== BASELINE REPRODUCTION SUMMARY ===")
|
||
for metric, info in checks.items():
|
||
status = "PASS" if info["within_tolerance"] else "FAIL"
|
||
log.info(" [%s] %s: actual=%.4f target=%.4f (±%.0f%%)",
|
||
status, metric, info["actual"], info["target"], TOLERANCE * 100)
|
||
|
||
if all_pass:
|
||
log.info("BASELINE REPRODUCTION PASSED — all metrics within ±%.0f%% of targets.", TOLERANCE * 100)
|
||
results["status"] = "PASSED"
|
||
else:
|
||
failed = [k for k, v in checks.items() if not v["within_tolerance"]]
|
||
msg = (
|
||
f"Baseline reproduction FAILED for: {failed}. "
|
||
"Debug TurboQuant implementation before proceeding. "
|
||
"Check that random rotation is applied correctly and Lloyd-Max codebooks are loaded."
|
||
)
|
||
log.error(msg)
|
||
results["status"] = "FAILED_REPRODUCTION"
|
||
results["failed_metrics"] = failed
|
||
_save_results(results, results_dir)
|
||
sys.exit(1)
|
||
|
||
results["wall_time_s"] = round(time.time() - t_start, 2)
|
||
_save_results(results, results_dir)
|
||
log.info("Phase 0 complete. Results saved to %s", results_dir / "baseline_reproduction.json")
|
||
|
||
|
||
def _save_results(results: dict, results_dir: Path) -> None:
|
||
out_path = results_dir / "baseline_reproduction.json"
|
||
with open(out_path, "w") as f:
|
||
json.dump(results, f, indent=2)
|
||
log.info("Saved results to %s", out_path)
|
||
|
||
|
||
if __name__ == "__main__":
|
||
main()
|