pyturboquant 0.0.2

High-performance implementation of Google's TurboQuant: online vector quantization with near-optimal distortion rates

pyturboquant

A GPU-accelerated Python implementation of Google's TurboQuant: Online Vector Quantization with Near-Optimal Distortion Rates (arXiv:2504.19874).

pyturboquant provides data-oblivious vector quantization that achieves near-Shannon-optimal distortion with zero indexing time -- no codebook training, no k-means, no data passes. Vectors are quantized independently using random rotations and precomputed Lloyd-Max codebooks, making it ideal for online settings and massive-scale nearest neighbor search.

Features

• MSE-Optimal Quantizer (Algorithm 1) -- per-coordinate scalar quantization after random rotation, approaching the Shannon lower bound at 1-8 bits
• Inner Product Quantizer (Algorithm 2) -- two-stage MSE + 1-bit QJL residual for unbiased inner product estimation
• FAISS-inspired NN Search -- TurboQuantIndex with .add() / .search() API, asymmetric distance computation, save/load persistence
• LangChain VectorStore -- drop-in TurboQuantVectorStore for low-RAM RAG pipelines
• Pure PyTorch -- no custom C++ extensions; runs on CPU and CUDA out of the box
• Deterministic -- seed-based rotation matrices and QJL projections for full reproducibility

Installation

# Core library (torch only)
pip install pyturboquant

# With LangChain RAG support
pip install pyturboquant[langchain]

# Development (tests, linting, codebook generation)
pip install pyturboquant[dev]

# Everything
pip install pyturboquant[all]

Requires Python >= 3.12 and PyTorch >= 2.4.

Quick Start

Pure PyTorch Building Blocks

import torch
from pyturboquant.core import (
    mse_quantize, mse_dequantize,
    ip_quantize, estimate_inner_product,
    random_rotate, random_rotate_inverse,
)

x = torch.randn(1000, 256)

# MSE-optimal quantization (Algorithm 1)
qt = mse_quantize(x, bits=3, seed=42)
x_hat = mse_dequantize(qt)
mse = ((x - x_hat) ** 2).sum(dim=-1).mean()
print(f"Normalized MSE: {mse / (x ** 2).sum(dim=-1).mean():.4f}")  # ~0.034

# Inner-product-preserving quantization (Algorithm 2)
y = torch.randn(1000, 256)
qt_ip = ip_quantize(x, bits=4, seed=42)
ip_est = estimate_inner_product(qt_ip, y)
ip_true = (x * y).sum(dim=-1)
print(f"IP error: {(ip_est - ip_true).abs().mean():.4f}")

Nearest Neighbor Search

import torch
from pyturboquant.search import TurboQuantIndex

# Build index -- near-zero indexing time (no training step)
index = TurboQuantIndex(dim=128, bits=4, metric="ip")
database = torch.randn(100_000, 128)
index.add(database)

print(f"Vectors: {index.ntotal}")
print(f"Memory:  {index.memory_usage_mb:.1f} MB")
print(f"Index time: {index.last_add_time_ms:.0f} ms")

# Search
queries = torch.randn(10, 128)
distances, indices = index.search(queries, k=10)

# Save / load
index.save("my_index.pt")
loaded = TurboQuantIndex.load("my_index.pt")

LangChain RAG Pipeline

from langchain_huggingface import HuggingFaceEmbeddings
from pyturboquant.search.langchain import TurboQuantVectorStore

embeddings = HuggingFaceEmbeddings(
    model_name="sentence-transformers/all-MiniLM-L6-v2"
)

store = TurboQuantVectorStore.from_texts(
    texts=["Document 1...", "Document 2...", "Document 3..."],
    embedding=embeddings,
    bits=4,
)

docs = store.similarity_search("my query", k=3)
for doc in docs:
    print(doc.page_content)

# Use as a standard LangChain retriever
retriever = store.as_retriever(search_kwargs={"k": 5})

Class API (Stateful Quantizers)

from pyturboquant.core import MSEQuantizer, InnerProductQuantizer

# Reusable quantizer -- rotation matrix and codebook created once
mse_q = MSEQuantizer(dim=256, bits=3, seed=42)
ip_q = InnerProductQuantizer(dim=256, bits=4, seed=42)

for batch in dataloader:
    qt = mse_q.quantize(batch)
    reconstructed = mse_q.dequantize(qt)

Architecture

pyturboquant
├── core/              Pure PyTorch math (depends only on torch)
│   ├── rotation       Random orthogonal matrices via QR decomposition
│   ├── codebook       Lloyd-Max codebooks for Gaussian coordinates
│   ├── mse_quantizer  Algorithm 1: MSE-optimal vector quantization
│   ├── qjl            1-bit Quantized Johnson-Lindenstrauss transform
│   ├── prod_quantizer Algorithm 2: MSE + QJL for unbiased inner products
│   ├── packed         Bit-packing utilities for compact storage
│   └── types          QuantizedMSE, QuantizedIP, Codebook dataclasses
├── search/            Nearest neighbor search engine
│   ├── index          TurboQuantIndex with FAISS-like API
│   ├── distance       Asymmetric IP and L2 computation
│   └── langchain      LangChain VectorStore wrapper (optional)
└── utils/
    ├── beta_distribution  Sphere coordinate PDF (Lemma 1)
    └── metrics            MSE distortion, IP error, Shannon bounds

The core package has no dependencies beyond PyTorch. The search package builds on core. LangChain integration is guarded behind the [langchain] extra.

How It Works

TurboQuant exploits a key insight: after applying a random orthogonal rotation to a unit vector in high dimensions, each coordinate becomes approximately independent and Gaussian with variance 1/d. This allows optimal per-coordinate scalar quantization using precomputed Lloyd-Max codebooks.

MSE Quantizer (Algorithm 1):

1. Extract and store the vector norm
2. Normalize to the unit sphere
3. Apply a seeded random rotation
4. Quantize each coordinate independently via searchsorted against Lloyd-Max boundaries
5. Pack indices into compact bit representation

Inner Product Quantizer (Algorithm 2):

1. Apply the MSE quantizer at (bits - 1) bits
2. Compute the quantization residual
3. Apply a 1-bit QJL transform (random projection + sign) to the residual
4. At query time, estimate <x, y> ≈ <x_hat_mse, y> + QJL_estimate(<residual, y>)

The result is an unbiased inner product estimator that enables high-recall approximate nearest neighbor search with zero indexing time.

Empirical Distortion

Measured on 5,000 random unit vectors at d=256:

Bits	Empirical MSE	Shannon Lower Bound	Ratio
1	0.362	0.250	1.45x
2	0.117	0.0625	1.87x
3	0.034	0.0156	2.19x
4	0.009	0.00391	2.41x

These match the paper's Theorem 1 values within 1%.

Development

git clone https://github.com/jorgbahlmann/pyturboquant.git
cd pyturboquant
python -m venv .venv && source .venv/bin/activate
pip install -e ".[dev]"

# Run tests (133 tests)
pytest

# Lint
ruff check src/ tests/

# Precompute codebooks (requires scipy)
python scripts/precompute_codebooks.py

# Run benchmarks
python benchmarks/bench_distortion.py
python benchmarks/bench_nn_search.py

Roadmap

• v0.1.0 (current) -- Core quantizers, NN search index, LangChain VectorStore
• v0.2.0 -- Triton fused kernels for GPU hot paths
• v0.3.0 -- Hugging Face Transformers KV-cache integration (TurboQuantCache)
• v0.4.0 -- IVF partitioning for sublinear search

Citation

@article{chen2025turboquant,
  title={TurboQuant: Online Vector Quantization with Near-Optimal Distortion Rate},
  author={Chen, Vincent and others},
  journal={arXiv preprint arXiv:2504.19874},
  year={2025}
}

License

MIT