Python code transformers that mimic pragma compiler directives

## Overview

PyPragma is a set of tools for performing in-place code modification, inspired by compiler directives in C. These modifications are intended to make no functional changes to the execution of code. In C, this is used to increase code performance or make certain tradeoffs (often between the size of the binary and its execution speed). In Python, these changes are most applicable when leveraging code generation libraries (such as Numba or Tangent) where the use of certain Python features is disallowed. By transforming the code in-place, disallowed features can be converted to allowed syntax at runtime without sacrificing the dynamic nature of python code.

For example, with Numba, it is not possible to compile a function which dynamically references and calls other functions (e.g., you may not select a function from a list and then execute it, you may only call functions by their explicit name):

fns = [sin, cos, tan]

@numba.jit
def call(i, x):
return fns[i](x)  # Not allowed, since it's unknown which function is getting called


If the dynamism is static by the time the function is defined, such as in this case, then these dynamic language features can be flattened to simpler features that such code generation libraries are more likely to support (e.g., the function can be extracted into a closure variable, then called directly by that name):

fns = [sin, cos, tan]

fns_0 = fns[0]
fns_1 = fns[1]
fns_2 = fns[2]

@numba.jit
def call(i, x):
if i == 0:
return fns_0(x)
if i == 1:
return fns_1(x)
if i == 2:
return fns_2(x)


Such a modification can only be done by the programmer if the dynamic features are known before runtime, that is, if fns is dynamically computed, then this modification cannot be performed by the programmer, even though this example demonstrates the the original function is not inherently dynamic, it just appears so. PyPragma enables this transformation at runtime, which for this example function would look like:

fns = [sin, cos, tan]

@numba.jit
@pragma.deindex(fns, 'fns')
@pragma.unroll(num_fns=len(fns))
def call(i, x):
for j in range(num_fns):
if i == j:
return fns[j](x)  # Still dynamic call, but decorators convert to static


This example is converted, in place and at runtime, to exactly the unrolled code above.

## Documentation

Complete documentation can be found over at RTFD.

## Installation

As usual, you have the choice of installing from PyPi:

pip install pragma


or directly from Github:

pip install git+https://github.com/scnerd/pypragma


## Usage

PyPragma has a small number of stackable decorators, each of which transforms a function in-place without changing its execution behavior. These can be imported as such:

import pragma


Each decorator can be applied to a function using either the standard decorator syntax, or as a function call:

@pragma.unroll
def pows(i):
for x in range(3):
yield i ** x

pows(5)

# Is identical to...

def pows(i):
for x in range(3):
yield i ** x

pragma.unroll(pows)(5)

# Both of which become...

def pows(i):
yield i ** 0
yield i ** 1
yield i ** 2

pows(5)


Each decorator can be used bare, as in the example above, or can be given initial parameters before decorating the given function. Any non-specified keyword arguments are added to the resulting function’s closure as variables. In addition, the decorated function’s closure is preserved, so external variables are also included. As a simple example, the above code could also be written as:

@pragma.unroll(num_pows=3)
def pows(i):
for x in range(num_pows):
yield i ** x

# Or...

num_pows = 3
@pragma.unroll
def pows(i):
for x in range(num_pows):
yield i ** x


Certain keywords are reserved, of course, as will be defined in the documentation for each decorator. Additionally, the resulting function is an actual, proper Python function, and hence must adhere to Python syntax rules. As a result, some modifications depend upon using certain variable names, which may collide with other variable names used by your function. Every effort has been made to make this unlikely by using mangled variable names, but the possibility for collision remains.

A side effect of the proper Python syntax is that functions can have their source code retrieved by any normal Pythonic reflection:

In [1]: @pragma.unroll(num_pows=3)
...: def pows(i):
...:    for x in range(num_pows):
...:       yield i ** x
...:

In [2]: pows??
Signature: pows(i)
Source:
def pows(i):
yield i ** 0
yield i ** 1
yield i ** 2
File:      /tmp/tmpmn5bza2j
Type:      function


As a utility, primarily for testing and debugging, the source code can be easily retrieved from each decorator instead of the transformed function by using the return_source=True argument.

## Quick Examples

### Collapse Literals

In [1]: @pragma.collapse_literals(x=5)
...: def f(y):
...:     z = x // 2
...:     return y * 10**z
...:

In [2]: f??
Signature: f(y)
Source:
def f(y):
z = 2
return y * 100


### De-index Arrays

In [1]: fns = [math.sin, math.cos, math.tan]

In [2]: @pragma.deindex(fns, 'fns')
...: def call(i, x):
...:     if i == 0:
...:         return fns[0](x)
...:     if i == 1:
...:         return fns[1](x)
...:     if i == 2:
...:         return fns[2](x)
...:

In [3]: call??
Signature: call(i, x)
Source:
def call(i, x):
if i == 0:
return fns_0(x)
if i == 1:
return fns_1(x)
if i == 2:
return fns_2(x)


Note that, while it’s not evident from the above printed source code, each variable fns_X is assigned to the value of fns[X] at the time when the decoration occurs:

In [4]: call(0, math.pi)
Out[4]: 1.2246467991473532e-16  # AKA, sin(pi) = 0

In [5]: call(1, math.pi)
Out[5]: -1.0  # AKA, cos(pi) = -1


### Unroll

In [1]: p_or_m = [1, -1]

In [2]: @pragma.unroll
...: def f(x):
...:     for j in range(3):
...:             yield sign * (x + j)
...:

In [3]: f??
Signature: f(x)
Source:
def f(x):
yield 1 * (x + 0)
yield -1 * (x + 0)
yield 1 * (x + 1)
yield -1 * (x + 1)
yield 1 * (x + 2)
yield -1 * (x + 2)


### Inline

In [1]: def sqr(x):
...:     return x ** 2
...:

In [2]: @pragma.inline(sqr)
...: def sqr_sum(a, b):
...:     return sqr(a) + sqr(b)
...:

In [3]: sqr_sum??
Signature: sqr_sum(a, b)
Source:
def sqr_sum(a, b):
_sqr_0 = dict(x=a)  # Prepare for 'sqr(a)'
for ____ in [None]:  # Wrap function in block
_sqr_0['return'] = _sqr_0['x'] ** 2  # Compute returned value
break  # 'return'
_sqr_return_0 = _sqr_0.get('return', None)  # Extract the returned value
del _sqr_0  # Delete the arguments dictionary, the function call is finished
_sqr_0 = dict(x=b)  # Do the same thing for 'sqr(b)'
for ____ in [None]:
_sqr_0['return'] = _sqr_0['x'] ** 2
break
_sqr_return_1 = _sqr_0.get('return', None)
del _sqr_0
return _sqr_return_0 + _sqr_return_1  # Substitute the returned values for the function calls


### Stacking Transformations

The above examples demonstrate how to perform pragma transformations to a function. It should be especially noted, however, that since each transformer returns a proper Python function, they can stack seamlessly:

In [1]: def make_dynamic_caller(*fns):
...:     @pragma.deindex(fns, 'fns')
...:     @pragma.unroll(num_fns=len(fns))
...:     def dynamic_call(i, x):
...:         for j in range(num_fns):
...:             if i == j:
...:                 return fns[j](x)
...:
...:     return dynamic_call

In [2]: f = make_dynamic_caller(math.sin, math.cos, math.tan)

In [3]: f??
Signature: f(i, x)
Source:
def dynamic_call(i, x):
if i == 0:
return fns_0(x)
if i == 1:
return fns_1(x)
if i == 2:
return fns_2(x)
File:      /tmp/tmpf9tjaffi
Type:      function

In [4]: g = pragma.collapse_literals(i=1)(f)

In [5]: g??
Signature: g(i, x)
Source:
def dynamic_call(i, x):
return fns_1(x)
File:      /tmp/tmpbze5i__2
Type:      function


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