Scallop is a language based on DataLog that supports differentiable logical and relational reasoning. Scallop program can be easily integrated in Python and even with a PyTorch learning module. You can also use it as another DataLog solver. Internally, Scallop is built on a generalized Provenance Semiring framework. It allows arbitrary semirings to be configured, supporting Scallop to perform discrete logical reasoning, probabilistic reasoning, and differentiable reasoning.

Here is a simple probabilistic DataLog program that is written in Scallop:

// Knowledge base facts
rel is_a("giraffe", "mammal")
rel is_a("tiger", "mammal")
rel is_a("mammal", "animal")

// Knowledge base rules
rel name(a, b) :- name(a, c), is_a(c, b)

// Recognized from an image, maybe probabilistic
rel name = {
  0.3::(1, "giraffe"),
  0.7::(1, "tiger"),
  0.9::(2, "giraffe"),
  0.1::(2, "tiger"),
}

// Count the animals
rel num_animals(n) :- n = count(o: name(o, "animal"))

Install rust with nightly channel set to default.

$ curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh
$ rustup default nightly

$ git clone https://github.com/scallop-lang/scallop.git
$ cd scallop

The following three binaries are available. Scroll down for more ways to use Scallop!

$ make install-scli # Scallop Interpreter
$ make install-sclc # Scallop Compiler
$ make install-sclrepl # Scallop REPL

Scallop interpreter (scli) interprets a scallop program (a file with extension .scl). You can install scli to your system using

$ make install-scli

Then since scli is in your system path, you can simply run

$ scli examples/animal.scl

Note that by default we don't accept probabilistic input. If your program is proabalistic and you want to obtain the resulting probabilities, do

$ scli examples/digit_sum_prob.scl -p minmaxprob

Note that the -p argument allows you to specify a provenance semiring. The minmaxprob is a simple provenance semiring that allows for probabilistic reasoning.

Scallop REPL (sclrepl) is an interactive command line interface for you to try various ideas with Scallop. You can install sclrepl to your system using

$ cargo install --path etc/sclrepl

Then you can run sclrepl. You can type scallop commands like the following

$ sclrepl
scl> rel edge = {(0, 1), (1, 2)}
scl> rel path(a, c) = edge(a, c) \/ path(a, b) /\ edge(b, c)
scl> query path
path: {(0, 1), (0, 2), (1, 2)}
scl>

scallopy is the python binding for Scallop. It provides an easy to use program construction/execution pipeline. With scallopy, you can write code like this:

import scallopy

# Create new context (with unit provenance)
ctx = scallopy.ScallopContext()

# Construct the program
ctx.add_relation("edge", (int, int))
ctx.add_facts("edge", [(0, 1), (1, 2)])
ctx.add_rule("path(a, c) = edge(a, c)")
ctx.add_rule("path(a, c) = edge(a, b), path(b, c)")

# Run the program
ctx.run()

# Inspect the result
print(list(ctx.relation("path"))) # [(0, 1), (0, 2), (1, 2)]

In addition, scallopy can be seamlessly integrated with PyTorch. Here's how one can write the mnist_sum_2 task with Scallop:

class MNISTSum2Net(nn.Module):
  def __init__(self, provenance="difftopkproofs", k):
    super(MNISTSum2Net, self).__init__()

    # MNIST Digit Recognition Network
    self.mnist_net = MNISTNet()

    # Scallop Context
    self.scl_ctx = scallopy.ScallopContext(provenance=provenance, k=k)
    self.scl_ctx.add_relation("digit_1", int, input_mapping=list(range(10)))
    self.scl_ctx.add_relation("digit_2", int, input_mapping=list(range(10)))
    self.scl_ctx.add_rule("sum_2(a + b) = digit_1(a), digit_2(b)")

    # The `sum_2` logical reasoning module
    self.sum_2 = self.scl_ctx.forward_function("sum_2", list(range(19)))

  def forward(self, x: Tuple[torch.Tensor, torch.Tensor]):
    (a_imgs, b_imgs) = x

    # First recognize the two digits
    a_distrs = self.mnist_net(a_imgs)
    b_distrs = self.mnist_net(b_imgs)

    # Then execute the reasoning module; the result is a size 19 tensor
    return self.sum_2(digit_1=a_distrs, digit_2=b_distrs)

To install, please do the following (also specified here):

Assume you are inside of the root scallop directory. First, we need to create a virtual environment for Scallop to operate in.

# Mac/Linux (venv, requirement: Python 3.8)
$ make py-venv # create a python virtual environment
$ source .env/bin/activate # if you are using fish, use .env/bin/activate.fish

# Linux (Conda)
$ conda create --name scallop-lab python=3.8 # change the name to whatever you want
$ conda activate scallop-lab

And let's install the core dependencies

$ pip install maturin

With this, we can build our scallopy library

$ make install-scallopy

If succeed, please run some examples just to confirm that scallopy is indeed installed successfully. When doing so (and all of the above), please make sure that you are inside of the virtual environment or conda environment.

$ python etc/scallopy/examples/edge_path.py

To install VSCode plugin from source, you can do the following, after making sure that npm is installed on your system

$ npm install -g vsce
$ make vscode-plugin

After this, a new .vsix plugin will appear in the etc/vscode-scl directory, named scallop-x.x.x.vsix. Next, please hold cmd + shift + p in VSCode and type "Install from VSIX". In the pop-up window, choose the .vsix plugin we just generated, and the plugin will be installed.

You can declare a single fact using the following syntax. In each line you define a single atom with every argument being constant.

rel digit(0, 1) // non-probabilitic
rel 0.3::digit(0, 1) // probabilistic

Alternatively, you can declare a set of facts using the following syntax.

rel digit = {
  0.4::(0, 1),
  0.3::(0, 2),
  0.1::(0, 3),
}

You can declare rule using traditional datalog syntax:

rel path(a, b) :- edge(a, b)
rel path(a, c) :- path(a, b), edge(b, c)

Alternatively, you can use a syntax similar to logic programming:

rel path(a, c) = edge(a, c) or (path(a, b) and edge(b, c))

It is possible to declare a probabilistic rule

rel 0.3::path(a, b) = edge(a, b)
rel 0.5::path(b, c) = edge(c, b)

Scallop supports stratified negation, with which you can write a rule like this:

scl> rel numbers(x) = x == 0 or (numbers(x - 1) and x <= 10)
scl> rel odd(1) = numbers(1)
scl> rel odd(x) = odd(x - 2), numbers(x)
scl> rel even(y) = numbers(y), ~odd(y)
scl> query even
even: {(0), (2), (4), (6), (8), (10)}

We support the following aggregations count, min, max, sum, and prod. For example, if you want to count the number of animals, you can write

scl> rel num_animals(n) :- n = count(o: name(o, "animal"))
scl> query num_animals
num_animals: {(2)}

Here n is the final count; o is the "key" variable that you want to count on; name(o, "animal") is the sub-formula that can pose constraint on o.

Naturally, the arguments that are not key and appears in both the sub-formula and outside of sub-formula will become a group-by variable. The following example counts the number of objects (n) of each color (c):

scl> rel object_color = {(0, "blue"), (1, "green"), (2, "blue")}
scl> rel color_count(c, n) :- n = count(o: object_color(o, c))
scl> query color_count
color_count: {("blue", 2), ("green", 1)}

The results says there are two "blue" objects and one "green" object, as expected.

For the aggregation such as min and max, it is possible to get the argmax and argmin at the same time. Building up from the previous object-color example, the following rule can extract the color that has the most number of objects:

scl> rel max_color(c) :- _ = max[c](n: color_count(c, n))
scl> query max_color
max_color: {("blue")}

Note that we have max[c] denoting that we want to get c as the argument for max. Also, we use a wildcard _ on the left hand side of the aggregation denoting that we don't care about the aggregation result. The final answer here is "blue" since there are 2 of them, which is greater than that of color "green".

Combining all of these, you can have a query containing group by and argument simultaneously. The following example builds on a table containing student, their class, and their grade:

rel class_student_grade = {
  (0, "tom", 50),
  (0, "jerry", 70),
  (0, "alice", 60),
  (1, "bob", 80),
  (1, "sherry", 90),
  (1, "frank", 30),
}

rel class_top_student(c, s) = _ = max[s](g: class_student_grade(c, s, g))

At the end, we will get {(0, "jerry"), (1, "sherry")}. Note that "jerry" is the one who got the highest score in class 0 and "sherry" is the one who got the highest score in class 1.

Scallop is a statically typed language which employs type inference, which is why you don't see the type definitions above. If you want, it is possible to define the type of the relations and even create new type aliases. For example,

type edge(usize, usize)

Defines that the relation edge will be a 2-relation and both of the arguments are of type usize, which follows rust's type idiomatic and represents an unsigned 64 bit numbers (in a 64-bit system).

Scallop supports the following primitive types:

• Signed Integers: i8, i16, i32, i64, i128, isize
• Unsigned Integers: u8, u16, u32, u64, u128, usize
• Floating Points: f32, f64
• Boolean: bool
• Character: char
• String:
  • &str (static string which could only be used in static Scallop compiler);
  • String

Some example type definition includes

type edge(usize, usize)
type obj_color(usize, String) // object is represented by a number, usize, and color is represented as string
type empty() // 0-arity relation
type binary_expr(usize, String, usize, usize) // expr id, operator, lhs expr id, rhs expr id

The following snippet shows how you can define subtype.

type Symbol <: usize
type ObjectId <: usize

It is possible to define a relation is an input relation that can be loaded from files.

@file("example/input_csv/edge.csv")
type edge(usize, usize)

Note that in this case it is essential to define the type of the relation. When loading .csv files, we accept extra loading options:

• deliminator: @file("FILE.csv", deliminator = "\t") with deliminator set to a tab ('\t')
• has header: @file("FILE.csv", has_header = true). It is default to false
• has probability: @file("FILE.csv", has_probability = true). When set to true, the first column of the CSV file will be treated as the probability of each tuple.