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Retro Pytorch

Retro Pytorch is a Python and PyTorch implementation of RETRO, a retrieval-based attention architecture for transformer models. It combines a model that attends to retrieved chunks with tooling to process text documents, build a Faiss index through autofaiss, and calculate nearest neighbors for those chunks.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Retro Pytorch — OpenViST project profile in RAG & Knowledge Systems.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use it when experimenting with retrieval inside a transformer model and you need a documented route from a text folder to indexed chunks and training batches.

Who it is for

  • Machine-learning developers prototyping retrieval-augmented transformer architectures.
  • Researchers studying retrieval-based attention and chunk-level nearest-neighbor search.
  • Practitioners preparing text documents as training data for a RETRO model.

Documented uses

  • Process a folder of text files into chunk, sequence and document-ID memory-mapped arrays for training.
  • Configure a transformer that receives retrieved chunks through retrieval-based attention.
  • Train using batches of sequences and retrieved neighbors, then generate with retrieval at chunk boundaries or from a prompt.
Before you choose

Requirements & limits

The repository is public and lists an Apache-2.0 license. The README documents installation with pip.

  • The implementation uses Faiss rather than the ScaNN library referenced by the paper, and rotary embeddings for relative positional encoding.
  • The TrainingWrapper example requires decisions about document paths, chunk size, k-nearest neighbors, array paths and memory limits.
  • The README shows both prompt-based generation and generation with retrieval at chunk boundaries.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Prepare text data and start RETRO training

Configure the documented TrainingWrapper workflow to turn text files into training data and obtain batches and an optimizer.

Before you start

  • Python environment with PyTorch and the installed retro-pytorch package.
  • A folder of text documents and chosen paths for chunk, sequence and document-ID memory-mapped arrays.
  • A RETRO model instance and configured chunk, neighbor, sequence and memory settings.

Steps

  1. Install the package with the command below and import RETRO and TrainingWrapper.

    Documented command
    pip install retro-pytorch

  2. Create a RETRO model and pass it to TrainingWrapper with the text-folder path, file glob, data-array paths and retrieval and memory settings.

  3. Obtain the dataloader with wrapper.get_dataloader(...) and the optimizer with wrapper.get_optimizer(...).

  4. Retrieve a batch, pass its sequence and retrieved-token tensors to RETRO with return_loss=True, then use the loss for training.

Expected outcome

The documented workflow provides batches containing sequences and retrieved neighbors, plus an optimizer, for model training.

Guide limitations

  • The README gives an example configuration; model dimensions, paths, chunk sizes, neighbor counts and memory limits must be set for the intended data and workload.
  • The guide describes the documented workflow, not a guarantee that a particular configuration will complete training.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
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Sources checked
15 October 2026
Original website title
lucidrains/RETRO-pytorch · Official homepage title · retained in its source language
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