• Stars
    star
    279
  • Rank 147,967 (Top 3 %)
  • Language
    Python
  • License
    MIT License
  • Created over 3 years ago
  • Updated 8 months ago

Reviews

There are no reviews yet. Be the first to send feedback to the community and the maintainers!

Repository Details

An interpreter for RASP as described in the ICML 2021 paper "Thinking Like Transformers"

RASP

Setup

Mac or Linux

Run ./setup.sh . It will create a python3 virtual environment and install the dependencies for RASP. It will also try to install graphviz (the non-python part) and rlwrap on your machine. If these fail, you will still be able to use RASP, however: the interface will not be as nice without rlwrap, and drawing s-op computation flows will not be possible without graphviz. After having set up, you can run ./rasp.sh to start the RASP read-evaluate-print-loop.

Windows

Follow the instructions given in windows instructions.txt

The REPL

After having set up, if you are in mac/linux, you can run ./rasp.sh to start the RASP REPL. Otherwise, run python3 RASP_support/REPL.py Use Ctrl+C to quit a partially entered command, and Ctrl+D to exit the REPL.

Initial Environment

RASP starts with the base s-ops: tokens, indices, and length. It also has the base functions select, aggregate, and selector_width as described in the paper, a selector full_s created through select(1,1,==) that creates a "full" attention pattern, and several other library functions (check out RASP_support/rasplib.rasp to see them).

Additionally, the REPL begins with a base example, "hello", on which it shows the output for each created s-op or selector. This example can be changed, and toggled on and off, through commands to the REPL.

All RASP commands end with a semicolon. Commands to the REPL -- such as changing the base example -- do not.

Start by following along with the examples -- they are kept at the bottom of this readme.

Note on input types:

RASP expects inputs in four forms: strings, integers, floats, or booleans, handled respectively by tokens_str, tokens_int, tokens_float, and tokens_bool. Initially, RASP loads with tokens set to tokens_str, this can be changed by assignment, e.g.: tokens=tokens_int;. When changing the input type, you will also want to change the base example, e.g.: set example [0,1,2].

Note that assignments do not retroactively change the computation trees of existing s-ops!

Writing and Loading RASP files

To keep and load RASP code from files, save them with .rasp as the extension, and use the 'load' command without the extension. For example, you can load the examples file paper_examples.rasp in this repository to the REPL as follows:

>> load "paper_examples";

This will make (almost) all values in the file available in the loading environment (whether the REPL, or a different .rasp file): values whose names begin with an underscore remain private to the file they are written in. Loading files in the REPL will also print a list of all loaded values.

Syntax Highlighting

If you use the Sublime Text Editor or Vim, you can get RASP syntax highlighting by using the relevant provided syntax file and instructions in the syntax_highlighting folder of this repository. (Thank you to Emile Ferreira for making the Vim highlighter!)

If you use Emacs, you can get syntax highlighting, auto-indentation, and other cool features through Arthur Amalvy's Emacs major mode, which Arthur hosts (along with installation instructions) at https://gitlab.com/Aethor/rasp-mode .

Examples

Play around in the REPL!

Try simple elementwise manipulations of s-ops:

>>  threexindices =3 * indices;
     s-op: threexindices
 	 Example: threexindices("hello") = [0, 3, 6, 9, 12] (ints)
>> indices+indices;
     s-op: out
 	 Example: out("hello") = [0, 2, 4, 6, 8] (ints)

Change the base example, and create a selector that focuses each position on all positions before it:

>> set example "hey"
>> prevs=select(indices,indices,<);
     selector: prevs
 	 Example:
 			     h e y
 			 h |      
 			 e | 1    
 			 y | 1 1  

Check the output of an s-op on your new base example:

>> threexindices;
     s-op: threexindices
 	 Example: threexindices("hey") = [0, 3, 6] (ints)

Or on specific inputs:

>> threexindices(["hi","there"]);
	 =  [0, 3] (ints)
>> threexindices("hiya");
	 =  [0, 3, 6, 9] (ints)

Aggregate with the full selection pattern (loaded automatically with the REPL) to compute the proportion of a letter in your input:

>> full_s;
     selector: full_s
 	 Example:
 			     h e y
 			 h | 1 1 1
 			 e | 1 1 1
 			 y | 1 1 1
>> my_frac=aggregate(full_s,indicator(tokens=="e"));
     s-op: my_frac
 	 Example: my_frac("hey") = [0.333]*3 (floats)

Note: when an s-op's output is identical in all positions, RASP simply prints the output of one position, followed by " * X" (where X is the sequence length) to mark the repetition.

Check if a letter is in your input at all:

>> "e" in tokens;
     s-op: out
 	 Example: out("hey") = [T]*3 (bools)

Alternately, in an elementwise fashion, check if each of your input tokens belongs to some group:

>> vowels = ["a","e","i","o","u"];
     list: vowels = ['a', 'e', 'i', 'o', 'u']
>> tokens in vowels;
     s-op: out
 	 Example: out("hey") = [F, T, F] (bools)

Draw the computation flow for an s-op you have created, on an input of your choice: (this will create a pdf in a subfolder comp_flows of the current directory)

>> draw(my_frac,"abcdeeee");
	 =  [0.5]*8 (floats)

Or simply on the base example:

>> draw(my_frac);
	 =  [0.333]*3 (floats)

If they bother you, turn the examples off, and bring them back when you need them:

>> examples off
>> indices;
     s-op: indices
>> full_s;
     selector: full_s
>> examples on
>> indices;
     s-op: indices
 	 Example: indices("hey") = [0, 1, 2] (ints)

You can also do this selectively, turning only selector or s-op examples on and off, e.g.: selector examples off.

Create a selector that focuses each position on all other positions containing the same token. But first, set the base example to "hello" for a better idea of what's happening:

>> set example "hello"
>> same_token=select(tokens,tokens,==);
     selector: same_token
 	 Example:
 			     h e l l o
 			 h | 1        
 			 e |   1      
 			 l |     1 1  
 			 l |     1 1  
 			 o |         1

Then, use selector_width to compute, for each position, how many other positions the selector same_token focuses it on. This effectively computes an in-place histogram over the input:

>> histogram=selector_width(same_token);
     s-op: histogram
 	 Example: histogram("hello") = [1, 1, 2, 2, 1] (ints)

For more complicated examples, check out paper_examples.rasp!

Experiments on Transformers

The transformers in the paper were trained, and their attention heatmaps visualised, using the code in this repository: https://github.com/tech-srl/RASP-exps

Citation

This repo is an implementation of RASP as presented in the paper "Thinking Like Transformers" (https://arxiv.org/abs/2106.06981). You can cite it using:

@InProceedings{pmlr-v139-weiss21a,
  title = 	 {Thinking Like Transformers},
  author =       {Weiss, Gail and Goldberg, Yoav and Yahav, Eran},
  booktitle = 	 {Proceedings of the 38th International Conference on Machine Learning},
  pages = 	 {11080--11090},
  year = 	 {2021},
  editor = 	 {Meila, Marina and Zhang, Tong},
  volume = 	 {139},
  series = 	 {Proceedings of Machine Learning Research},
  month = 	 {18--24 Jul},
  publisher =    {PMLR},
  pdf = 	 {http://proceedings.mlr.press/v139/weiss21a/weiss21a.pdf},
  url = 	 {http://proceedings.mlr.press/v139/weiss21a.html}
  }

More Repositories

1

code2vec

TensorFlow code for the neural network presented in the paper: "code2vec: Learning Distributed Representations of Code"
Python
1,092
star
2

code2seq

Code for the model presented in the paper: "code2seq: Generating Sequences from Structured Representations of Code"
Python
547
star
3

how_attentive_are_gats

Code for the paper "How Attentive are Graph Attention Networks?" (ICLR'2022)
Python
299
star
4

Nero

Code and resources for the paper: "Neural Reverse Engineering of Stripped Binaries using Augmented Control Flow Graphs"
Python
185
star
5

bottleneck

Code for the paper: "On the Bottleneck of Graph Neural Networks and Its Practical Implications"
Python
91
star
6

slm-code-generation

TensorFlow code for the neural network presented in the paper: "Structural Language Models of Code" (ICML'2020)
Java
86
star
7

esh

statistical similarity of binaries (Esh)
C#
73
star
8

lstar_extraction

implementation of ICML 2018 paper, Extracting Automata from Recurrent Neural Networks Using Queries and Counterexamples
Jupyter Notebook
71
star
9

layer_norm_expressivity_role

Code for the paper "On the Expressivity Role of LayerNorm in Transformers' Attention" (Findings of ACL'2023)
Python
43
star
10

c3po

Code for the paper "A Structural Model for Contextual Code Changes"
Python
25
star
11

adversarial-examples

Code for the paper: "Adversarial Examples for Models of Code"
Python
17
star
12

weighted_lstar

implementation for "learning weighted deterministic automata from queries and counterexamples", neurips 2019
Python
17
star
13

RASP-exps

Code for running the transformers in the ICML 2021 paper "Thinking Like Transformers"
Python
16
star
14

prime

Java
14
star
15

safe

SAFE static analysis tools
Java
12
star
16

differential

differential
C
12
star
17

counting_dimensions

demonstration for our ACL 2018 paper, "On the Practical Computational Power of Finite Precision RNNs for Language Recognition"
Jupyter Notebook
10
star
18

id2vec

Python
9
star
19

RNN_to_PRS_CFG

Implementation of TACAS 2021 paper, "Extrapolating CFGs from RNNs"
Python
9
star
20

atam

Example programs for ATAM
C
3
star