M-Labs
/
nac3-spec
11
0
Fork 0
Code Issues 14 Pull Requests Releases Wiki Activity

updated after discussion with sb10q

This commit is contained in:
pca006132 2020-12-15 17:33:31 +08:00 committed by pca006132
parent 1c5795da3d
commit 8aa4fc18a7
1 changed files with 91 additions and 92 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

183
README.md
View File

@ -3,27 +3,19 @@
Specification and discussions about language design.
## Referencing Python Variables
> Not decided yet, whether require function annotation or only allow reference
> to variables.
The kernel is allowed to read Python variables.
Unbounded identifiers would be considered as Python variables, no object is
allowed. The value would be evaluated at compile time, subsequent modification
in the host would not be known by the kernel. Basically, only allow lookup, no
evaluation.
* Unbounded identifiers would be considered as Python variables, no object is
allowed, only primitive types and tuple/list of allowed types are allowed.
(not sure how to express the recursive concept neatly in English...)
* The value would be evaluated at compile time, subsequent modification
in the host would not be known by the kernel.
* Modification of global variable from within the kernel would be considered as
error.
* Calling non-RPC host function would be considered as an error. (RPC functions
must be annotated.)
(Bad) Alternative: Evaluate the unbounded identifier, allowing functions and
objects. It would easier to write the program as we don't have to manually
remove function calls in the kernel and store the result in a global variable.
However, this would potentially cause confusion, as the possibly side-effectful
function would be evaluated only once during compilation.
(Better) Alternative: only evaluate functions marked as pure? Not sure if we can
access custom function annotation. We have to rely on the user to uphold the
guarantee.
Example for a potentially confusing case:
Example code that would be disallowed:
```py
from artiq.experiment import *
counter = 0
@ -40,99 +32,106 @@ class Foo(EnvExperiment):
return result
```
Example for a totally valid case:
## 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 pointer is supported.
* Method pointer is a fat pointer (function pointer + object pointer), and
subject to lifetime check.
```py
from artiq.experiment import *
def get_param(x):
return x**2
class Foo(EnvExperiment):
@kernel
def run(self):
# unbounded function call disallowed
param = get_param(123)
# do something...
result = param
return result
```
This would not be allowed, and must be translated into this
```py
from artiq.experiment import *
def get_param(x):
return x**2
param_123 = get_param(123)
class Foo(EnvExperiment):
@kernel
def run(self):
param = param_123
# do something...
result = param
return result
```
## Type
### Decided
* Parametric polymorphism: use Python type variable.
* Normal functions: require full type signature.
* RPC: optional parameter type signature, require return type signature.
* No implicit coercion
### Undecided
#### Class Fields
Should we require the user to declare all class fields first?
## Generics
We use [type variable](https://docs.python.org/3/library/typing.html#typing.TypeVar) for denoting generics.
Example:
```py
class Foo:
a: int32
b: int32
def __init__(self, a: int32, b: int32):
self.a = a
self.b = b
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
```
#### Subtyping
Do we allow subtyping? Or is parametric polymorphism enough?
If subtyping is allowed, we might need virtual method table.
* Type variable can be limited to a fixed set of types. A shorthand for one-time
type variable limited to a fixed set of types is [union type & optional type](https://docs.python.org/3/library/typing.html#typing.Union).
e.g. `def run(self, a: Union[int, str])`
* Type variables support bounded generic, so we can access attributes of the
variable that are present in the boundary type, the type instantiated must be
the subtype of the boundary type. See [PEP484](https://www.python.org/dev/peps/pep-0484/#type-variables-with-an-upper-bound) for details.
* 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.
Example where parametric polymorphism is not enough:
## 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();
def foo(self) -> int:
return 1
class Foo(Base):
def foo();
class Derived(Base):
def foo(self) -> int:
return 2
def run_all(l: list[Base]):
return [x.foo() for x in l]
def bar(x: [Base]) -> int:
sum = 0
for v in x:
sum += v.foo()
return sum
run_all([Base(), Foo()])
# incorrect, the type signature of the list is `[virtual[Base]]`
bar([Base(), Derived()])
```
#### Union Type
As function overloading is not possible, should we allow union type, and const
evaluate all the type checks after monomorphization?
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.
This is mainly for performance consideration, as virtual method table that is
required for dynamic dispatch would penalize performance, and prohibits function
inlining etc.
Example:
```py
def foo(x: Union[int, bool]):
if type(x) == int:
return x
else:
return 1 if x else 0
def bar2(x: [virtual[Base]]) -> int:
sum = 0
for v in x:
sum += v.foo()
return sum
# correct
bar([Base(), Derived()])
```
## Function Pointers
- Lambda with no capturing are treated as normal functions.
- Lambda with capturing: a structure would be created to store *pointers* to
captured variables, and the lambda would be a method of the struct.
(Note: Storing pointers to meet the binding behavior of Python lambda)
- Method: implemented with fat pointer, i.e. function pointer + object pointer.
Subject to lifetime rules.
Structural subtyping support is not determined yet.