OpenViSTAI · Agents · Blockchain · Crypto
Submit a project
Menu
AI Models

Ponder Transformer

Ponder Transformer is a Python/PyTorch implementation of a Transformer based on PonderNet that adapts its number of computation steps to input-sequence difficulty. It supports a documented training loss and, in evaluation mode, returns logits and the halting-layer index for each batch element.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Ponder Transformer — OpenViST project profile in AI Models.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use it when exploring a PonderNet-style Transformer and you want evaluation outputs to include per-sample exit-layer indices.

Who it is for

  • Researchers prototyping adaptive-computation sequence models.
  • Developers experimenting with causal language-model training.
  • Practitioners comparing model halting behavior using returned layer indices.

Documented uses

  • Train a Transformer on token sequences with labels and a mask.
  • Run causal evaluation and inspect model logits.
  • Compare the layer at which different batch elements halt.
Before you choose

Requirements & limits

The repository is public and lists an MIT license. The README documents installation with pip.

  • The documented implementation is Python and PyTorch based.
  • Evaluation stops when all samples in the batch have emitted a halting signal.
  • The examples demonstrate a training loss/backpropagation pass and evaluation outputs, but not a complete optimizer-driven training loop.

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

Set up the package and run the documented training example

Install the package and perform a training loss and backpropagation pass with the shown model inputs.

Before you start

  • Python environment with PyTorch available
  • A shell for the documented pip installation

Steps

  1. Install the package with the command below.

    Documented command
    pip install ponder-transformer

  2. Import torch and PonderTransformer.

  3. Create a model with num_tokens, dim and max_seq_len values.

  4. Prepare token inputs, labels and a boolean mask, then call the model with labels and mask.

  5. Call loss.backward() on the returned loss.

Expected outcome

The example produces a training loss and performs a backward pass.

Guide limitations

  • The README example uses randomly generated tensors rather than a real dataset.
  • It does not document optimizer setup or a complete training loop.
Documentation 1 ↗
Source-based practical guide

Inspect evaluation logits and halting-layer indices

Use the documented causal evaluation example to retrieve logits and the exit-layer index for each batch element.

Before you start

  • Python environment with PyTorch available
  • The ponder-transformer package installed

Steps

  1. Create a PonderTransformer with causal=True and the desired vocabulary, dimension and sequence-length settings.

  2. Prepare token inputs and a boolean mask.

  3. Set the model to evaluation mode with model.eval().

  4. Call the model with the inputs and mask to receive logits and layer indices.

Expected outcome

The example returns logits and a layer index for each batch element; evaluation terminates when all samples in the batch emit halting signals.

Guide limitations

  • The documented example uses randomly generated inputs.
  • The stopping condition is described for the batch as a whole, not as a separate runtime guarantee for each sample.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
18 October 2026
Original website title
lucidrains/ponder-transformer · Official homepage title · retained in its source language
How we review sourcesSuggest a correction