pytype

A static type analyzer for Python code

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Abstract values

Objects, Types and Values

The regular python interpreter tracks the values of objects. That is, given the code

x = "hello world"

it will create an object (a block of memory) whose contents are the string “hello world”, and any part of the code that holds a reference to the object (e.g. the variable x here) can retrieve that value.

If an object is mutable, calling one of the mutation methods will change the contents of the object while retaining the object’s identity, for example

x = [1, 2, 3]
y = x  # y and x now point to the same list object
x[0] = 4
print(y)  # => [4, 2, 3]

Pytype likewise creates and maintains objects, but it tracks the types of those objects rather than their values. In the examples above,

x = "hello world"

will create an object whose contents are essentially “this is a string”, and

x = [1, 2, 3]

will create an object whose contents are “this is a list of integers”. Following this strategy, mutating the object will not necessarily produce any changes in its pytype representation - e.g. x[0] = 4 as above will continue to store the object as “this is a list of integers”, but x[0] = "foo" will change its contents to “this is a list of strings and integers”.

Abstract Values

In broad terms, python objects can be divided into classes and instances. An instance contains a reference to a class, and the class is referred to as the “type” of the instance. Again in broad terms, every python object is a dictionary of key/value pairs, where the entries are the object properties, methods, and metadata like type annotations. Pytype models this object system with a hierarchy of python classes whose instances act as abstract representations of python objects.

This is easier to explain with a concrete example, so consider the following code:

class A(object):
  def __init__(self, x):
    self.x = x

foo = A(10)

Pytype would execute the following pseudocode to model it:

# Create a "class" object for A
obj1 = abstract.InterpreterClass(
  name = "A",
  bases = [builtinclass_object],
  members = {}
)

# Create a "method" object for __init__, setting its containing class to A
obj2 = abstract.Method(
  name = "__init__",
  containing_class = obj1,
  signature = (args=['x'], return=None)
  bytecode = <bytecode>
)

# Fill in the member dictionary for class A
# Note that we have no information about the type of A.x so we set it to the Any
# type, which matches everything when type checked.
obj1.members['__init__'] = obj2
obj1.members['x'] = builtinclass_Any

# Create an "instance" object for foo
obj3 = abstract.Instance(
  class = obj1,
  initializers = {'x': 10},
  members = {}
)

# Fill in the members for foo, based on the class and the initializer
obj3.members['__init__'] = obj2
obj3.members['x'] = builtinclass_int

# Fill in the variable name assignments
globals = {'A': obj1, 'foo': obj3}

The abstract.* classes are defined in abstract.py. They all inherit from the base class AtomicAbstractValue, which is the pytype representation of a python object, and store various metadata that is relevant to type inference and checking (e.g. the InterpreterClass object stores a list of base classes and a dictionary of members, and the Instance object stores a reference to the InterpreterClass object it was instantiated from).

TIP: The abstract_utils module contains many useful functions for working with abstract values. Additionally, all abstract values have a vm attribute that references the current virtual machine, through which various handlers for abstract values can be accessed.

Type Information

In python, the type of an object is determined (at runtime) by the class it is created from, as can be seen from this ipython session:

In [1]: class A: pass
In [2]: x = A()
In [3]: y = [x]

In [4]: type(A)
Out[4]: type

In [5]: type(x)
Out[5]: __main__.A

In [6]: type(y)
Out[6]: list

Pytype determines the same information at “compile” time, by analysing the bytecode without actually running it. The “type” of an object within pytype is determined by a combination of several factors:

The final two points are important - pytype has a richer (and stricter) type system than python itself does, but this type system usually represents the intent of the code better.

For instance, given the following code:

x: List[int] = []
x.append("hello")

python will consider the type of the object x points to to be list throughout, whereas pytype will first create it as List[int], and then raise a type error because we are trying to mutate it to List[Union[int, string]] which contradicts the type annotation.

Python will not raise a type error for the same code, because (a) type annotations are treated as comments and not directives, and (b) because the type of all lists is simply list, and is not parametrised by the type of its contents, so there was no type violation.

Matching

Most of the errors that pytype reports are detected via a mismatch between an expected and an observed type. pytype/matcher.py contains the logic for matching abstract values against each other. For example, when analyzing:

def f(x: int): ...
f(0)

pytype will call

matcher.match_var_against_type(
    Variable(Binding(PythonConstant(0))), PyTDClass(int))

in order to determine whether f(0) is a valid function call. Here, match_var_against_type will return True, since the value PythonConstant(0) is compatible with the type PyTDClass(int).

A second important function of the matcher is to compute type parameter substitutions. Consider this code snippet:

T = TypeVar('T')
def f(x: T, y: T): ...
f(0, 1)

When matching (0, 1) against (T, T), the matcher determines that the call is valid because we can find a substitution, {T: int}, that matches the types of the arguments for x and y. The matcher also returns this substitution dictionary so that the type T is mapped to can be propagated.