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mishmish66/
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This is forked from the main FAST tokenizer because that one catches errors and prints. I find it easier to use this one which throws the error and allows the user (me) to catch it :)

FAST: Efficient Action Tokenization for Vision-Language-Action Models

This is the official repo for the FAST action tokenizer.

The action tokenizer maps any sequence of robot actions into a sequence of dense, discrete action tokens for training autoregressive VLA models.

Here, we provide:

FAST+, our universal action tokenizer, trained on 1M real robot action sequences.
Code for quickly training new action tokenizers on your custom dataset.

Installation

FAST can be used as a convenient HuggingFace AutoProcessor. To use it, simply install the transformers package (and scipy for the underlying DCT algorithm).

pip install transformers scipy

Using the Universal Action Tokenizer

We recommend applying the tokenizer to 1-second action "chunks" that have been pre-normalized to a range of [-1...1] (we use quantile normalization for this step -- check our paper). Encoding and decoding support batched inference.

import numpy as np
from transformers import AutoProcessor

# Load the tokenizer from the Hugging Face hub
tokenizer = AutoProcessor.from_pretrained("physical-intelligence/fast", trust_remote_code=True)

# Tokenize & decode action chunks (we use dummy data here)
action_data = np.random.rand(256, 50, 14)    # one batch of action chunks
tokens = tokenizer(action_data)              # tokens = list[int]
decoded_actions = tokenizer.decode(tokens)

Note: During decoding, the tokenizer needs to map the decoded sequence of actions back into a [time_horizon, action_dim] matrix. There are multiple ways to provide the necessary dimensions to the tokenizer: (1) they automatically get saved on the first forward() call, (2) you can set them manually as arguments to the decode() call

Training a new Action Tokenizer on Your Own Data

In our experiments, we found the FAST+ universal tokenizer to work well across a wide range of robot setups, action dimensions, and control frequencies. If you, however, want to train a custom FAST tokenizer for your dataset at hand, it is very easy using the .fit() convenience function we provide. When called on a dataset of action chunks (of the same or different lengths), it returns a new tokenizer instance, which you can save and optionally push to the HuggingFace hub. Training should typically only take a few seconds to minutes.

# First, we download the tokenizer from the Hugging Face model hub
# Here, we will not use the pre-trained tokenizer weights, but only the source code
# to train a new tokenizer on our own data.
tokenizer = AutoProcessor.from_pretrained("physical-intelligence/fast", trust_remote_code=True)

# Load your action data for tokenizer training
# Chunks do not need to be of the same length, we will use dummy data
action_data = np.random.rand(4000, 50, 14)

# Train the new tokenizer, depending on your dataset size this can take a few minutes
tokenizer = tokenizer.fit(action_data)

# Save the new tokenizer, optionally push it to the Hugging Face model hub
tokenizer.save_pretrained("<your_local_path>")
tokenizer.push_to_hub("YourUsername/my_new_tokenizer")

mishmish66/throwing_fast

FAST: Efficient Action Tokenization for Vision-Language-Action Models

Installation

Using the Universal Action Tokenizer

Training a new Action Tokenizer on Your Own Data

mishmish66/
throwing_fast