Practical overview
When it fits
Use it when a project needs an encoder-only Fastformer implementation in PyTorch and its documented token-and-mask interface fits the intended sequence workflow.
Who it is for
- PyTorch developers adding an encoder component to an NLP project.
- Practitioners prototyping token-sequence modeling with Fastformer.
Documented uses
- Install the Fastformer implementation as a Python package.
- Configure an encoder using vocabulary and sequence-model parameters.
- Pass token IDs and a mask to obtain sequence logits.
Requirements & limits
The repository specifies an MIT license and documents installation through pip.
- The project is explicitly encoder-only.
- The README demonstrates relative positional encoding by default and an absolute positional embedding option.
- The documented output is logits with sequence and vocabulary dimensions; a complete downstream task pipeline is not shown.
Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.
Install and initialize the Fastformer encoder
Set up the documented package and construct an encoder with token and sequence parameters.
Before you start
- Python environment with PyTorch available.
- Install the package with pip install fast-transformer-pytorch.
Steps
Import torch and FastTransformer from fast_transformer_pytorch.
Construct FastTransformer with num_tokens, dim, depth and max_seq_len values appropriate to the intended sequence model.
Use relative positional encoding by default, or set absolute_pos_emb=True if the default is not working.
Expected outcome
A FastTransformer encoder instance configured with vocabulary, dimension, depth and maximum sequence length.
Guide limitations
- The implementation is encoder-only.
Pass token IDs and a mask to the encoder
Use the documented model call to obtain sequence logits.
Before you start
- An initialized FastTransformer model.
- Token IDs and a Boolean mask, with dimensions matching the batch and sequence length.
Steps
Create or prepare token IDs within the configured vocabulary range.
Prepare a Boolean mask for the sequence.
Call the model with the token IDs and mask argument.
Expected outcome
The documented example returns logits with batch, sequence-length and vocabulary dimensions.
Guide limitations
- The README does not specify downstream classification, training, or interpretation steps.
Sources & review
Source record details
- Source availability
- Active
- Sources checked
- 18 October 2026
- Original website title
- lucidrains/fast-transformer-pytorch · Official homepage title · retained in its source language