Practical overview
When it fits
Use Linformer when developing PyTorch models for inputs with a known, fixed sequence length and you want a complete language-model or transformer component, or a reusable self-attention layer. The contextual-keys interface is relevant when the context sequence differs in length from the input.
Who it is for
- PyTorch developers prototyping language models for fixed-length inputs.
- Researchers and engineers comparing key/value projection and sharing configurations.
- Developers integrating a Linformer self-attention layer into an encoder or using a separate contextual-key sequence.
Documented uses
- Build a Linformer language-model prototype with a specified vocabulary size, sequence length, depth and number of heads.
- Use the provided transformer module to map fixed-length feature sequences to output feature sequences.
- Add a single Linformer self-attention layer to a model.
- Configure key/value projection size, head sharing and projection sharing.
- Apply the attention layer to an input sequence with a distinct context sequence.
Requirements & limits
The repository is public and lists an MIT license. Its README gives `pip install linformer` as the installation command.
- The implementation assumes a fixed sequence length, so the configured length matters.
- The README states that the implementation does not work for autoregressive use.
- The
kparameter controls the sequence-dimension projection size for keys and values. - The contextual-keys example validates sequence length against the context rather than the source input.
Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.
Set up a Linformer self-attention layer
Install the package and configure a single self-attention layer for a fixed-length PyTorch input.
Before you start
- Python environment with PyTorch available.
- Input feature dimension and sequence length for the layer.
Steps
Install the package with the command below.
pip install linformerImport
LinformerSelfAttentionfromlinformer.Construct the layer with the input dimension, fixed sequence length, head count and projection size
k.Pass an input tensor shaped as batch, sequence length and feature dimension to the layer.
Expected outcome
A configured self-attention layer that returns an output with the same sequence and feature dimensions as the example input.
Guide limitations
- The implementation assumes a fixed sequence length.
- The README says it does not work for the autoregressive case.
Configure attention with a separate context sequence
Set up the documented self-attention interface with contextual keys whose sequence length differs from the input.
Before you start
- Python environment with PyTorch available.
- Input and context tensors with the same feature dimension.
- A configured context sequence length.
Steps
Install the package with the command below.
pip install linformerImport
LinformerSelfAttentionand configure itsseq_lento the context length.Pass the input tensor and context tensor to the layer.
Expected outcome
The example produces an output aligned to the input sequence length; the configured sequence length is validated against the contextual keys.
Guide limitations
- The implementation assumes a fixed sequence length.
- The README says it does not work for the autoregressive case.
Sources & review
Source record details
- Source availability
- Active
- Sources checked
- 15 October 2026
- Original website title
- lucidrains/linformer · Official homepage title · retained in its source language