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Tracr: TRAnsformer Compiler for RASP.

Tracr is a compiler for converting RASP programs (Weiss et al. 2021) into transformer weights. Please see our tech report for a detailed description of the compiler.

Directory structure:

• rasp contains an implementation of RASP embedded in Python.
• compiler contains the compiler itself.
• transformer contains the implementation of the transformer.
• craft contains the intermediate representation used by the compiler: essentially a small linear algebra-based library with named dimensions.

This is not an officially supported Google product.

Installation

Just clone and pip install:

git clone https://github.com/deepmind/tracr
cd tracr
pip3 install .

Usage example: RASP reverse program

Consider the RASP reverse program:

opp_index = length - indices - 1;
flip = select(indices, opp_index, ==);
reverse = aggregate(flip, tokens);

To compile this with Tracr, we would first implement the program using Tracr's RASP library:

from tracr.rasp import rasp

length = make_length()  # `length` is not a primitive in our implementation.
opp_index = length - rasp.indices - 1
flip = rasp.Select(rasp.indices, opp_index, rasp.Comparison.EQ)
reverse = rasp.Aggregate(flip, rasp.tokens)

Where:

def make_length():
  all_true_selector = rasp.Select(rasp.tokens, rasp.tokens, rasp.Comparison.TRUE)
  return rasp.SelectorWidth(all_true_selector)

We can then compile the RASP program to a transformer with:

from tracr.compiler import compiling

bos = "BOS"
model = compiling.compile_rasp_to_model(
    reverse,
    vocab={1, 2, 3},
    max_seq_len=5,
    compiler_bos=bos,
)

This yields a transformer as a Haiku model. This model isn't intended to provide everything you might need, but rather serves as a kind of "documentation-in-code" for the semantics of the generated parameters. The expectation is that the user can then write or contribute an adapter that converts parameters from this reference model to another transformer implementation.

Using this model we can perform a forward pass:

>>> out = model.apply([bos, 1, 2, 3])
>>> out.decoded
["BOS", 3, 2, 1]

Success! We have a transformer that reverses its input tokens.

Note: compiled models always expect a BOS token in order to support selectors which don't attend to any of the input tokens. This is necessary to preserve intuitive RASP semantics; the alternative would have been to treat all-False selector rows as equivalent to all-True (which is what softmax in an attention layer would naturally do). For more details, see our paper.

You can also inspect some of the intermediate activations of the model, using out.residuals, out.layer_outputs, and out.attn_logits.

For more examples of RASP programs we can compile, check out compiler/lib.py.

For an interactive example of compiling a model and visualizing its computation, check out the notebook at examples/Visualize_Tracr_Models.ipynb.

Developer README

If you'd like to extend Tracr to fit your purposes, here's some information on how Tracr works under the hood.

How Tracr works conceptually

To compile a program, Tracr does the following.

1. Trace RASP program into a graph representation. This involves creating a graph node for each RASP expression and inferring dependencies between these graph nodes.

2. Infer bases. Tracr is designed to have each node output to a separate subspace of the residual stream. To do this, we first infer the set of all possible token values that each node can take, then using that information, decide on a subspace for each node, and augment each node in the graph with the basis vectors for that node's subspace.

3. Convert nodes to Craft components. Craft is the name of our internal intermediate representation that does linear algebra on named subspaces. In this stage, each expression node is converted to a Craft component that actually performs the linear algebra operations necessary to implement the expression. This includes converting sequence operators to MLP weights, and selectors to weights of attention heads. (We compute the appropriate weights directly using the theory of universal approximation for MLPs - no gradient descent required!)

4. Convert Craft graph to Craft model. In this stage, we convert from a graph representation to a layout that looks more like an actual transformer. At this stage, we essentially have a working model, but with the linear algebra done using Craft rather than JAX + Haiku.

5. Convert Craft model to Haiku model. Finally, we convert our intermediate representation of the model to a full Haiku model.

Two details worth expanding on here are subspaces and corresponding bases. Each node writes to a separate subspace of the residual stream, where each subspace is simply a unique chunk of the residual stream vector. For example, the first node might write to the first 5 components of the residual stream; the second node the next 5; and so on. In terms of what the embeddings actually associated with each node, Tracr employs two different kinds of bases:

• Categorical representation - in which each unique token value is represented as a unique one-hot vector in that node's subspace. This is the representation used by default.
• Numerical representation - in which each unique token value is mapped to a unique scalar value. This is necessary for some uses of the aggregate operation - essentially, ones which involve taking a mean - and some other operations are represented more efficiently with this representation.

A final detail is BOS tokens. The compiler relies on beginning-of-sequence tokens to in order to implement a number of operations. This is why token sequences fed into the final model must start with a BOS token.

How Tracr works in practice

The flow of compilation execution begins in compiler/compiling.py, in the compile_rasp_to_model function. This function is fairly short and maps directly to the stages outlined above, so don't be afraid to read the source!

Running tests

We use absltest, which is unittest-compatible, and is therefore in turn pytest-compatible.

First, install test dependencies:

pip3 install absl-py pytest

Then, in the checkout directory, simply run pytest. This should take about 60 seconds.

Superposition

One topic that we've investigated using Tracr is superposition (see e.g. Elhage et al 2023): in this work, we learn a compressed embedding of the residual stream in such a way as to keep the computation faithful to the uncompressed program.

This is an example showing the dot products between embedding vectors for the frac_prevs example program from compiler/lib.py:

The code for learning these embeddings is not included in this repository, but you can read more about it in Section 5 of the tech report.

Citing Tracr

Please use the bibtex for our tech report:

@article{lindner2023tracr,
  title = {Tracr: Compiled Transformers as a Laboratory for Interpretability},
  author = {Lindner, David and Kramár, János and Rahtz, Matthew and McGrath, Thomas and Mikulik, Vladimir},
  journal={arXiv preprint arXiv:2301.05062},
  year={2023}
}