# NAC3 Specification

Specification and discussions about language design.

A toy implementation is in [`toy-impl`](./toy-impl), requires python 3.9.


## Referencing Python Variables
The kernel is allowed to read host Python variables, but has to specify with
`global` before referencing them. This is to simplify and speed-up
implementation, and also warn the user about the variable being global. (prevent
calling the interpreter many times during compilation if there are many
references to host variables)

Only primitive types and tuple/list of primitive types are allowed.

The value would be substituted at compile time, subsequent modification in the
host would not be known by the kernel.

Modification in kernel code to the global variables is not allowed.

## Class and Functions
* Class fields must be annotated:
    ```py
    class Foo:
        a: int
        b: int
        def __init__(self, a: int, b: int):
            self.a = a
            self.b = b
    ```
* Functions require full type signature, including type annotation to every
  parameter and return type.
  ```py
  def add(a: int, b: int) -> int:
    return a + b
  ```
* No implicit coercion, require implicit cast. Integers are int32 by default,
  floating point numbers are double by default.
* RPCs: optional parameter type signature, require return type signature.
* Function default parameters are not allowed, as changes to the default value
  would not be kept across kernel calls, which is a potential source of
  confusion. (maybe we can allow primitive default types?)
* Cannot construct objects within kernel code.

Questions:
* Can we construct objects within kernel code?
* Should we support function pointers? What about subtyping with function
  pointers, and generic types?

## Built-in Types
* Primitive types include:
    * `bool`
    * `byte`
    * `int32`
    * `int64`
    * `uint32`
    * `uint64`
    * `float`
    * `str` (note: fixed length, provide builtin methods?)
    * `bytes` (a list of `byte`, but with more convenient syntax)
* Collections include:
    * `list`: homogeneous (elements must be of the same type) fixed-size (no
      append) list.
    * `tuple`: inhomogeneous fixed-size list, only pattern
      matching (e.g. `a, b, c = (1, True, 1.2)`) and constant indexing
      is supported:
      ```
      t = (1, True)
      # OK
      a, b = t
      # OK
      a = t[0]
      # Not OK
      i = 0
      a = t[i]
      ```
    * `range` (over numerical types) (not sure if this is really useful)

### Numerical Types
* All binary operations expect the values to have the same type, no implicit
  coercion would be performed, explicit casting is required.
* Casting can be done by `T(v)` where `T` is the target type, and `v` is the
  original value. Examples: `int64(123)`
* Constant integers are treated as `int32` by default. If the value cannot
  be stored in `int32`, `uint64` would be used if the integer is non-negative,
  and `int64` would be used it the integer is negative.
* Only `uint32`, `int32` (and range of them) can be used as index.

## Generics
We use [type variable](https://docs.python.org/3/library/typing.html#typing.TypeVar) for denoting generics.

Example:
```py
from typing import TypeVar
T = TypeVar('T')

class Foo(EnvExperiment):
    @kernel
    # type of a is the same as type of b
    def run(self, a: T, b: T) -> bool:
        return a == b
```

* Type variables can only be used in functions/methods, but not in classes.
    (this can be relaxed, only allow those with type variables fully defined
    from the constructor)
* Type variable can be limited to a fixed set of types.
* Type variables are invariant, same as the default in Python. We disallow
  covariant or contravariant. The compiler should mark as error if it encounters
  a type variable used in kernel that is declared covariant or contravariant.
* Code region protected by a type check, such as `if type(x) == int:`, would
  treat `x` as `int`, similar to how [typescript type guard](https://www.typescripttutorial.net/typescript-tutorial/typescript-type-guards/) works.
  ```py
  def add1(x: Union[int, bool]) -> int:
    if type(x) == int:
        # x is int
        return x + 1
    else:
        # x must be bool
        return 2 if x else 1
  ```
* Generics are instantiated at compile time, all the type checks like
  `type(x) == int` would be evaluated as constants. Type checks are not allowed
  in area outside generics.
* Type variable cannot occur alone in the result type, i.e. must be bound to the
  input parameters.

Questions:
* Should we support things like optional type? (like the one in rust)
* Would it be better to assert on the type variable directly instead of
  `type(x)` for type guards?

## Dynamic Dispatch
Type annotations are invariant, so subtype (derived types) cannot be used
when the base type is expected. Example:
```py
class Base:
    def foo(self) -> int:
        return 1

class Derived(Base):
    def foo(self) -> int:
        return 2

def bar(x: list[Base]) -> int:
    sum = 0
    for v in x:
        sum += v.foo()
    return sum

# incorrect, this list cannot be typed (inhomogeneous)
bar([Base(), Derived()])
```

Dynamic dispatch is supported, but requires explicit annotation, similar to
[trait object](https://doc.rust-lang.org/book/ch17-02-trait-objects.html) in rust.
`virtual[T]` is the type for `T` and its subtypes(derived types).

This is mainly for performance consideration, as virtual method table that is
required for dynamic dispatch would penalize performance, and prohibits function
inlining etc.

Type variables cannot be used inside `virtual[...]`, and type variables would not
range over `virtual[...]`.

> Not sure what is the best syntax for `virtual[...]`

Example:
```py
def bar2(x: list[virtual[Base]]) -> int:
    sum = 0
    for v in x:
        sum += v.foo()
    return sum
```