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Triton Transformer

Triton Transformer is a Python library implementing a configurable Transformer model with Triton-based GPU operations and PyTorch tensor support. It demonstrates model execution and labels-based cross-entropy training, while serving as a work-in-progress learning project for lower-level neural-network code.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Triton Transformer — OpenViST project profile in AI Developer Tools & SDKs.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use it when you want a compact, inspectable starting point for Transformer training experiments or for learning about Triton GPU operations within a model implementation.

Who it is for

  • Developers learning how Transformer operations can be implemented with Triton.
  • Researchers or engineers prototyping small Transformer training experiments with PyTorch.
  • GPU-kernel developers exploring or adapting selected Transformer computation paths.

Documented uses

  • Configure a causal or non-causal Transformer model for an experiment.
  • Run a model on token tensors and obtain logits.
  • Pass labels to the model to obtain cross-entropy loss for backpropagation.
  • Study or adapt Triton implementations of layer normalization, softmax, cross-entropy and forward batch matrix multiplication with fused activation.
Before you choose

Requirements & limits

The repository is public and specifies the MIT license. Installation instructions use pip.

  • The README labels the project work in progress and describes it as a learning experience.
  • Usage examples use PyTorch tensors moved to CUDA.
  • The README shows causal model configuration and a labels-and-mask training example.
  • Fused attention, additional Triton projection matmuls, some backward work, inference-specific kernels, and benchmarking or optimization are listed as TODOs.
  • The repository does not document a complete environment or compatibility matrix.

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

Install the package and configure a Transformer

Install Triton Transformer and prepare a model configuration as shown in the README.

Before you start

  • Python package installation with pip
  • PyTorch
  • CUDA tensors for the documented model examples

Steps

  1. Install the package with the command below.

    Documented command
    pip install triton-transformer

  2. Import torch and Transformer from triton_transformer.

  3. Create a Transformer with the desired vocabulary size, maximum sequence length, dimensions, depth, heads, and dropout settings; set causal and use_triton as needed.

  4. Move the model to CUDA as in the example.

Expected outcome

A configured Transformer model following the README's CUDA-based usage pattern.

Guide limitations

  • The repository describes itself as work in progress.
  • The README does not specify a complete environment or compatibility matrix.
  • Fused attention and several other kernel paths remain TODOs.
Documentation 1 ↗
Source-based practical guide

Prepare a labels-based training loss

Pass input tokens and labels to the model to obtain cross-entropy loss for backpropagation.

Before you start

  • An installed Triton Transformer package
  • PyTorch tensors for inputs and labels
  • A model and tensors placed on CUDA, following the README example

Steps

  1. Create a Transformer with vocabulary and sequence-length settings appropriate to the example.

  2. Prepare input token IDs and labels, and optionally a boolean mask.

  3. Call the model with labels=labels and the mask if used.

  4. Use the returned loss with .backward(), as shown in the README.

Expected outcome

A labels-based cross-entropy loss value that can be used for backpropagation.

Guide limitations

  • The README's training example is illustrative and does not specify a full training loop or dataset.
  • The repository is marked as work in progress; several backward and attention-related kernels are listed as TODOs.
Documentation 1 ↗
Evidence and transparency

Sources & review

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