macro-kit 0.1.0

Macro recording and metaprogramming in Python

macro-kit

macro-kit is a package for efficient macro recording and metaprogramming in Python using abstract syntax tree (AST).

The design of AST in this package is strongly inspired by Julia metaprogramming. Similar methods are also implemented in builtin ast module but macro-kit is more focused on the macro generation and customization.

Installation

pip install git+https://github.com/hanjinliu/macro-kit

Examples

1. Define a macro-recordable function

from macrokit import Macro, Expr, Symbol
macro = Macro()

@macro.record
def str_add(a, b):
    return str(a) + str(b)

val0 = str_add(1, 2)
val1 = str_add(val0, "xyz")
macro

[Out]
var0x24fdc2d1530 = str_add(1, 2)
var0x24fdc211df0 = str_add(var0x24fdc2d1530, 'xyz')

# substitute identifiers of variables
# var0x24fdc2d1530 -> x
macro.format([(val0, "x")]) 

[Out]
x = str_add(1, 2)
var0x24fdc211df0 = str_add(x, 'xyz')

# substitute to _dict["key"], or _dict.__getitem__("key")
expr = Expr(head="getitem", args=[Symbol("_dict"), "key"])
macro.format([(val0, expr)])

[Out]
_dict['key'] = str_add(1, 2)
var0x24fdc211df0 = str_add(_dict['key'], 'xyz')

2. Record class

macro = Macro()

@macro.record
class C:
    def __init__(self, val: int):
        self.value = val
    
    @property
    def value(self):
        return self._value
    
    @value.setter
    def value(self, new_value: int):
        if not isinstance(new_value, int):
            raise TypeError("new_value must be an integer.")
        self._value = new_value
    
    def show(self):
        print(self._value)

c = C(1)
c.value = 5
c.value = -10
c.show()

[Out]
-10

macro.format([(c, "ins")])

[Out]
ins = C(1)
ins.value = -10     # setattr (and setitem) will not be recorded in duplicate
var0x7ffed09d2cd8 = ins.show()

macro.eval({"C": C})

[Out]
-10

3. Record module

import numpy as np
macro = Macro()
np = macro.record(np) # macro-recordable numpy

arr = np.random.random(30)
mean = np.mean(arr)

macro

[Out]
var0x2a0a2864090 = numpy.random.random(30)
var0x2a0a40daef0 = numpy.mean(var0x2a0a2864090)

from dask import array as da
dask_macro = macro.format([(np, "da")])
dask_macro

[Out]
var0x2a0a2864090 = da.random.random(30)
var0x2a0a40daef0 = da.mean(var0x2a0a2864090)

output = {}
dask_macro.eval({"da": da}, output)
output

[Out]
{:da: <module 'dask.array' from 'C:\\...\\__init__.py'>,
 :var0x2a0a2864090: dask.array<random_sample, shape=(30,), dtype=float64, chunksize=(30,), chunktype=numpy.ndarray>,
 :var0x2a0a40daef0: dask.array<mean_agg-aggregate, shape=(), dtype=float64, chunksize=(), chunktype=numpy.ndarray>}