nac3-spec/toy-impl

Toy Implementation

Currently the rough implementation is done, works remain are the code for checking a real python script, getting the type variables, some implementation details etc.

These features are considered in the proposal, but would not be implemented here for simplicity reasons:

• Referencing Python Variables.
• Most of the types, only the following types are implemented:
  • int32
  • int64
  • float
  • bool
  • list[T]
  • tuple[T1,...]
  • virtual[T]
• Storing large constants as uint32, int64 or uint64.
• AugAssign, a += b etc.
• with, try except, etc.
• const indexing with tuple.
• method override check modulo variable renaming.
• more complicated type guard

Running Example

python main.py example/a.py

Files

All files named test_xxx are used for inspecting the result of algorithms, and can be ignored for now.

Here is the list of files and their purpose:

• helper.py: mainly for the error definition.
• inference.py: type-check for function invocation.
• inheritance.py: perform method and field inheritance.
• main.py: main script for checking an entire python script.
• parse_expr.py: type-check for expressions in python AST.
• parse_stmt.py: type-check for statements in python AST.
• primitives.py: definition of primitives, operations, and built-in functions.
• top_level.py: gather class, function and type variable definitions from python AST.
• type_def.py: python class for various types.

Variable Scope

There is no shadowing in Python, so we decided that variables with the same name in a function must have the same type. For example, the following is not allowed:

if foo():
    a = 1
else:
    a = None

Also, as variables has to be well typed, they must be initialized before using them. If a variable could be not initialized in some code path, then it is not readable. The following is also not allowed:

if foo():
    a = 1
a = a + 1

Generics

Generics are supported via type variables.

• Generic type variable:

  A = TypeVar('A')
  

• Bounded type variable (A can either be T1 or T2 or ...):

  A = TypeVar('A', T1, T2, ...)
  

Note:

1. In normal python, the bound of a type variable is actually about class inheritance. However, our type variable would be invariant and would not deal with subtyping.
2. Type variables cannot contain any type variable in their bound. For example, B = TypeVar('B', A, T3) is not allowed.
3. I did not really check the difference between the variable name and the name parameter of TypeVar, so idk what would happen if they are different. Please don't do that right now, would be fixed in later more serious implementations.

For generic type variables, you can't really do much with them, other than passing them around in parameters, dealing with their list, etc.

For bounded type variables, if an operation is supported by all the possible values of the variable, we can use that directly:

A = TypeVar('A', int32, int64)

def add(a: A, b: A) -> A:
    return a + b

If an operation is supported by some possible values, we can use type guard:

A = TypeVar('A', int32, list[int32])
def add2(a: int32, b: A) -> a:
    if type(b) == int32:
        # b is int32 here
        return a + b
    else:
        # b is list[int32] here
        for x in b:
            a = a + b
        return a

Note that we only support very simple kinds of type guards in this toy implementation. More specifically, the type guard has to meet the following conditions:

1. The if statement must be of the form type(*) == ** or type(*) != **. For example, if type(b) == int32 or type(b) == list[int32] is not allowed.
2. The type of * must be a type variable. For example, list[X] is not allowed.

Substitution

The crucial constraint and assumption in our system is that, every (sub-)expressions must have their types fully determined, and cannot depend on statements/expressions after them. Hence, in a function call, every arguments are well typed. We only have to determine the substitution of type variables present in the function type signature that makes the type agree.

There is a tiny difference between unification and our implementation. In our implementation, the substitution would only be applied to the type signature of the target function call but not the variables present in the function call. This way we don't have to make the type variables in the callee fresh before doing unification.

Consider the following example:

X = TypeVar('X')

def head(a: list[X]) -> X:
    return a[0]

head([1, 2, 3])

In this example, the expression [1, 2, 3] has type list[int32], so the algorithm tries to fit (list[int32]) into (list[X]), giving a substitution X -> int32.

Substitution can also substitute variables into another variable. Consider the following example:

X = TypeVar('X')
Y = TypeVar('Y', int32, int64)

def head(a: list[X]) -> X:
    return a[0]

def sum_of_heads(a: list[Y], b: list[Y]) -> Y:
    return head(a) + head(b)

In this example, a has type list[Y], so the algorithm would give a substitution X -> Y for the call head(a), and similarly for b. As Y can only range over int32 and int64, in the two instances of Y, the return statement would have type

• int32 + int32 : int32 : Y under Y -> int32, and
• int64 + int64 : int64 : Y under Y -> int64. So the function is well typed.

Note that variables are fresh in every invocation. Consider the following example:

I = TypeVar('I', int32, list[int32])

def add(a: int32, b: I) -> int32:
    if type(b) == int32:
        return a + b
    else:
        # b must be list[int32] in this branch.
        for x in b:
            a = add(a, x)
        return a

add(1, [1, 2, 3])

This one should type check. I -> list[int32] only affects 1 call, and the recursion inside could substitute I -> int32.

Example of a failure case:

A = TypeVar('A')
B = TypeVar('B')

def foo(a: A, b: A):
    pass

def bar(a: A, b: B):
    foo(a, b)

This would fail. From the first argument, we have A -> A, and from the second argument we need A -> B. In general, we may have A != B, so there is no substitution that meets the requirement and the type check failed.

Operator Overloading

Most operations are actually implemented via operator overloading. We currently support:

• Normal:
  • __init__
• Comparison:
  • __lt__
  • __le__
  • __gt__
  • __ge__
  • __eq__
  • __ne__
• Arithmetic:
  • __add__
  • __sub__
  • __mul__
  • __matmul__
  • __truediv__
  • __floordiv__
  • __mod__
  • __pow__
  • __lshift__
  • __rshift__
  • __and__
  • __or__
  • __xor__
  • __neg__