Principle:Lucidrains X transformers Package Installation
| Field | Value |
|---|---|
| Repo | x-transformers |
| Domains | Setup, DevOps |
| Last Updated | 2026-02-08 18:00 GMT |
Overview
Environment setup procedure for installing the x-transformers library and its dependencies.
Description
The x-transformers library is distributed via PyPI and can be installed with pip. It requires Python 3.9+ and PyTorch 2.0+. The library provides a comprehensive collection of transformer building blocks including autoregressive wrappers, encoder-decoder architectures, non-autoregressive generation, and DPO alignment training.
Core dependencies (automatically resolved by pip):
- einops (>=0.8.0) — Flexible tensor operations using Einstein notation.
- einx (>=0.3.0) — Advanced Einstein notation extensions for indexed operations.
- loguru — Structured logging library.
- packaging (>=21.0) — Version parsing and comparison utilities.
Optional dependencies:
- flash-attn (>=2.0) — FlashAttention acceleration for efficient packed sequence training. Install separately as it requires CUDA compilation.
- adam-atan2-pytorch (>=0.2.2), lion-pytorch, tqdm — Required for running the example training scripts.
Usage
Use this as the first step in any x-transformers workflow. Install the package before importing any modules. The installation process:
- Set up a Python environment (3.9 or newer).
- Ensure PyTorch 2.0+ is installed (with CUDA support if GPU training is desired).
- Install x-transformers via pip.
- Optionally install flash-attn for FlashAttention support.
Theoretical Basis
N/A (setup procedure).
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