Pylate Rs

PyLate efficient inference engine

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Install / Use

npx skills add lightonai/pylate-rs

Installs into whichever agent you are using.

About this skill

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README

<div align="center"> <h1>pylate-rs</h1> </div> <p align="center"><img width=500 src="https://github.com/lightonai/pylate-rs/blob/01ee9895d83d6bc0a52ba826f6b634d33be479ce/docs/logo.jpg"/></p> <div align="center"> <a href="https://lightonai.github.io/pylate-rs/"><img src="https://img.shields.io/badge/blog-%23000000.svg?style=for-the-badge&logoColor=white" alt="blog"></a> <a href="https://docs.rs/pylate-rs/latest/pylate_rs/all.html"><img src="https://img.shields.io/badge/crate-%23000000.svg?style=for-the-badge&logoColor=white" alt="crate"></a> </div> <div align="center"> <b>Efficient Inference for PyLate</b> </div>

⭐️ Overview

pylate-rs is a high-performance inference engine for PyLate models, meticulously crafted in Rust for optimal speed and efficiency.

While model training is handled by PyLate, which supports a variety of late interaction models, pylate-rs is engineered to execute these models at speeds.

Accelerated Performance: Experience significantly faster model loading and rapid cold starts, making it ideal for serverless environments and low-latency applications.
Lightweight Design: Built on the Candle ML framework, pylate-rs maintains a minimal footprint suitable for resource-constrained systems like serverless functions and edge computing.
Broad Hardware Support: Optimized for diverse hardware, with dedicated builds for standard CPUs, Intel (MKL), Apple Silicon (Accelerate & Metal), and NVIDIA GPUs (CUDA).
Cross-Platform Integration: Seamlessly integrate pylate-rs into your projects with bindings for Python, Rust, and JavaScript/WebAssembly.

For a complete, high-performance multi-vector search pipeline, pair pylate-rs with its companion library, FastPlaid, at inference time.

Explore our WebAssembly live demo.

💻 Installation

Install the version of pylate-rs that matches your hardware for optimal performance.

Python

Python GPU support

To install pylate-rs with GPU support, please built it from source using the following command:

pip install git+https://github.com/lightonai/pylate-rs.git

or by cloning the repository and installing it locally:

git clone https://github.com/lightonai/pylate-rs.git
cd pylate-rs
pip install .

Any help in pre-building and distributing CUDA wheels would be greatly appreciated.

Rust

Add pylate-rs to your Cargo.toml by enabling the feature flag that corresponds to your backend.

⚡️ Quick Start

Python

Get started in just a few lines of Python.

from pylate_rs import models

# Initialize the model for your target device ("cpu", "cuda", or "mps")
model = models.ColBERT(
    model_name_or_path="lightonai/GTE-ModernColBERT-v1",
    device="cpu"
)

# Encode queries and documents
queries_embeddings = model.encode(
    sentences=["What is the capital of France?", "How big is the sun?"],
    is_query=True
)

documents_embeddings = model.encode(
    sentences=["Paris is the capital of France.", "The sun is a star."],
    is_query=False
)

# Calculate similarity scores
similarities = model.similarity(queries_embeddings, documents_embeddings)

print(f"Similarity scores:\n{similarities}")

# Use hierarchical pooling to reduce document embedding size and speed up downstream tasks
pooled_documents_embeddings = model.encode(
    sentences=["Paris is the capital of France.", "The sun is a star."],
    is_query=False,
    pool_factor=2, # Halves the number of token embeddings
)

similarities_pooled = model.similarity(queries_embeddings, pooled_documents_embeddings)

print(f"Similarity scores with pooling:\n{similarities_pooled}")

Rust

use anyhow::Result;
use candle_core::Device;
use pylate_rs::{hierarchical_pooling, ColBERT};

fn main() -> Result<()> {
    // Set the device (e.g., Cpu, Cuda, Metal)
    let device = Device::Cpu;

    // Initialize the model
    let mut model: ColBERT = ColBERT::from("lightonai/GTE-ModernColBERT-v1")
        .with_device(device)
        .try_into()?;

    // Encode queries and documents
    let queries = vec!["What is the capital of France?".to_string()];
    let documents = vec!["Paris is the capital of France.".to_string()];

    let query_embeddings = model.encode(&queries, true)?;
    let document_embeddings = model.encode(&documents, false)?;

    // Calculate similarity
    let similarities = model.similarity(&query_embeddings, &document_embeddings)?;
    println!("Similarity score: {}", similarities.data[0][0]);

    // Use hierarchical pooling
    let pooled_document_embeddings = hierarchical_pooling(&document_embeddings, 2)?;
    let pooled_similarities = model.similarity(&query_embeddings, &pooled_document_embeddings)?;
    println!("Similarity score after hierarchical pooling: {}", pooled_similarities.data[0][0]);

    Ok(())
}

📊 Benchmarks

Device    backend        Queries per seconds        Documents per seconds        Model loading time
cpu       PyLate         350.10                     32.16                        2.06
cpu       pylate-rs      386.21 (+10%)              42.15 (+31%)                 0.07 (-97%)

cuda      PyLate         2236.48                    882.66                       3.62
cuda      pylate-rs      4046.88 (+81%)             976.23 (+11%)                1.95 (-46%)

mps       PyLate         580.81                     103.10                       1.95
mps       pylate-rs      291.71 (-50%)              23.26 (-77%)                 0.08 (-96%)

Benchmarks were run with Python. pylate-rs provide significant performance improvement, especially in scenarios requiring fast startup times. While on a Mac it takes up to 5 seconds to load a model with the Transformers backend and encode a single query, pylate-rs achieves this in just 0.11 seconds, making it ideal for low-latency applications. Don't expect pylate-rs to be much faster than PyLate to encode a lot of content at the same time as PyTorch is heavily optimized.

📦 Using Custom Models

pylate-rs is compatible with any model saved in the PyLate format, whether from the Hugging Face Hub or a local directory. PyLate itself is compatible with a wide range of models, including those from Sentence Transformers, Hugging Face Transformers, and custom models. So before using pylate-rs, ensure your model is saved in the PyLate format. You can easily convert and upload your own models using PyLate.

Pushing a model to the Hugging Face Hub in PyLate format is straightforward. Here’s how you can do it:

pip install pylate

Then, you can use the following Python code snippet to push your model:

from pylate import models

# Load your model
model = models.ColBERT(model_name_or_path="your-base-model-on-hf")

# Push in PyLate format
model.push_to_hub(
    repo_id="YourUsername/YourModelName",
    private=False,
    token="YOUR_HUGGINGFACE_TOKEN",
)

If you want to save a model in PyLate format locally, you can do so with the following code snippet:

from pylate import models

# Load your model
model = models.ColBERT(model_name_or_path="your-base-model-on-hf")

# Save in PyLate format
model.save_pretrained("path/to/save/GTE-ModernColBERT-v1-pylate")

An existing set of models compatible with pylate-rs is available on the Hugging Face Hub under the LightOn namespace.

Retrieval pipeline

pip install pylate-rs fast-plaid

Here is a sample code for running ColBERT with pylate-rs and fast-plaid.

import torch
from fast_plaid import search
from pylate_rs import models

model = models.ColBERT(
    model_name_or_path="lightonai/GTE-ModernColBERT-v1",
    device="cpu", # mps or cuda
)

documents = [
    "1st Arrondissement: Louvre, Tuileries Garden, Palais Royal, historic, tourist.",
    "2nd Arrondissement: Bourse, financial, covered passages, Sentier, business.",
    "3rd Arrondissement: Marais, Musée Picasso, galleries, trendy, historic.",
    "4th Arrondissement: Notre-Dame, Marais, Hôtel de Ville, LGBTQ+.",
    "5th Arrondissement: Latin Quarter, Sorbonne, Panthéon, student, intellectual.",
    "6th Arrondissement: Saint-Germain-des-Prés, Luxembourg Gardens, chic, artistic, cafés.",
    "7th Arrondissement: Eiffel Tower, Musée d'Orsay, Les Invalides, affluent, prestigious.",
    "8th Arrondissement: Champs-Élysées, Arc de Triomphe, luxury, shopping, Élysée.",
    "9th Arrondissement: Palais Garnier, department stores, shopping, theaters.",
    "10th Arrondissement: Gare du Nord, Gare de l'Est, Canal Saint-Martin.",
    "11th Arrondissement: Bastille, nightlife, Oberkampf, revolutionary, hip.",
    "12th Arrondissement: Bois