Acelerando programas usando Cython - parte 3

Série de posts sobre Cython:

1. Introdução ao desenvolvimento usando Cython
2. Distribuição de extensões em Cython
3. Análise de eficiência de operações
4. Paralelismo e NoGIL

Nos posts anteriores da séria vimos como acessar a memória diretamente usando Cython e também como distribuir módulos para utilização em qualquer script Python. Neste post irei focar na relação entre o código Cython gerado e o interpretador. Ou seja, iremos explorar quais instruções necessitam de acesso ao interpretador e quais funcionalidades podem ser traduzidas diretamente para C.

Primeiramente, vimos anteriormente que acessos a arrays do Numpy podem ser feitos diretamente, se interação com o interpretador. Vejamos abaixo uma função que faz operações com vários tipos nativos de Python e verifica a interação deles com o interpretador. Note que adicionamos tipos em todas as variáveis para mostrar que pode ser necessária interação com o interpretador mesmo se os tipos são conhecidos. Além disto, não conseguimos declarar uma variável do tipo ABC pois a classe é definida em tempo de execução. A alternativa é usar uma definição com o tipo object, que é a super classe de todos objetos em Python 3.

In [1]:

%load_ext cython

In [2]:

%%cython -a

class ABC:
    def pri(self):
        print('as')

def teste_operacoes():
    cdef list l
    cdef dict d
    cdef object o
    cdef str s
    
    s = 'abc'
    
    l = []
    l.append(2)
    
    d = {}
    d['str teste'] = 'as'
    
    o = ABC()
    o.pri()

c:\users\igor\miniconda3\lib\site-packages\IPython\utils\path.py:264: UserWarning: get_ipython_cache_dir has moved to the IPython.paths module
  warn("get_ipython_cache_dir has moved to the IPython.paths module")

Out[2]:

Generated by Cython 0.23

Yellow lines hint at Python interaction.
Click on a line that starts with a "+" to see the C code that Cython generated for it.

 01: 

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A utilização de objetos em Python é cara, principalmente se a classe for definida pelo usuário. Mesmo uma simples criação de string interage com o interpretador. Então, o que exatamente PODE ser traduzido eficientemente?

Acesso de memória

Este exemplo já foi explorado no primeiro texto da série.

Loops com tamanho "fixo"

Loops for que são executados em listas, dicionários ou arrays do numpy podem ser acelerados se transformados para uma instrução do tipo for i in range(...). Vemos abaixo alguns exemplos.

In [3]:

def list_test(l):
    for el in l:
        el

def dict_test(d):
    for k in d:
        k

def array_test(arr):
    for i in range(arr.shape[0]):
        for j in range(arr.shape[1]):
            arr[i, j] = arr[i, j] + 1

In [4]:

%%cython -a
def list_test_cy(l):
    n = len(l)
    for i in range(n):
        l[i]

def dict_test_cy(d):
    keys = list(d.keys())
    n = len(keys)
    for i in range(n):
        keys[i]
        
def array_test_cy(double[:, :] arr):
    for i in range(arr.shape[0]):
        for j in range(arr.shape[1]):
            arr[i, j] = arr[i, j] + 1

c:\users\igor\miniconda3\lib\site-packages\IPython\utils\path.py:264: UserWarning: get_ipython_cache_dir has moved to the IPython.paths module
  warn("get_ipython_cache_dir has moved to the IPython.paths module")

Out[4]:

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Yellow lines hint at Python interaction.
Click on a line that starts with a "+" to see the C code that Cython generated for it.

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In [5]:

import numpy as np

print('Iteração em listas')
l = list(range(1000000))
%timeit list_test(l)
%timeit list_test_cy(l)
print('Iteração em dicionários')
d = {str(i) + 'key': i**2 for i in range(10000)}
%timeit dict_test(d)
%timeit dict_test_cy(d)
print('Iteração em arrays do Numpy')
arr = np.ones((1000, 1000))
%timeit array_test(arr)
%timeit array_test_cy(arr)

Iteração em listas
10 loops, best of 3: 37.1 ms per loop
100 loops, best of 3: 4.68 ms per loop
Iteração em dicionários
1000 loops, best of 3: 568 µs per loop
1000 loops, best of 3: 430 µs per loop
Iteração em arrays do Numpy
1 loops, best of 3: 1.13 s per loop
100 loops, best of 3: 4.42 ms per loop

Esta otimização é muito efetiva pois evita interação com o interpretador a cada iteração do for e pode dar ganhos de desempenho significativos. Note que frequentemente não precisamos adicionar a diretiva cdef int i no início das nossas funções, pois o compilador Cython identifica que i é usado como índice do for e já ajusta seu tipo corretamente.

Definicao de classes em Cython

Podemos definir classes em Cython que da mesma maneira que as definimos em Python, mas com a diferença que classes em Cython são armazenadas em uma struct em vez de um objeto Python. Tanto métodos quanto atributos devem ser declarados previamente e não é possível adicionar nenhum dos dois em tempo de execução. Classes Cython também não suportam herança múltipla. Por outro lado, não é necessário determinar em tempo de execução se um atributo/método existe nem qual versão de um método é chamada em caso de herança.

Para tornar as diferenças mais claras, no exemplo abaixo adicionamos somente a declaração cdef class e tipamos o atributo p como object para isolar o efeito da chamada de método mais rápida.

In [6]:

class ABC:
    def __init__(self, p):
        self.p = p
    
    def add(self, q):
        self.p += q

In [7]:

%%cython -a
cdef class ABCy:
    cdef public object p
    def __init__(self, p):
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    def add(self, q):
        self.p += q

c:\users\igor\miniconda3\lib\site-packages\IPython\utils\path.py:264: UserWarning: get_ipython_cache_dir has moved to the IPython.paths module
  warn("get_ipython_cache_dir has moved to the IPython.paths module")

Out[7]:

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print('Criação de objeto')
%timeit a = ABC(0)
%timeit b = ABCy(0)

a = ABC(0)
b = ABCy(0)
print('Chamada de método')
%timeit for i in range(100): a.add(5)
%timeit for i in range(100): b.add(5)

Criação de objeto
The slowest run took 4.20 times longer than the fastest. This could mean that an intermediate result is being cached 
1000000 loops, best of 3: 977 ns per loop
1000000 loops, best of 3: 282 ns per loop
Chamada de método
10000 loops, best of 3: 64.9 µs per loop
10000 loops, best of 3: 32.7 µs per loop

Aritmética em geral

Python possui duas grandes diferenças em relação à aritmética em C. Primeiramente, todo número ponto flutuante é um double. Segundo, os tipos inteiros em Python tem precisão arbitrária e podem representar inteiros tão grande quanto quisermos. Podemos acelerar cálculos usando suas versões em C, que por outro lado estão sujeitas à overflows, underflows e todas as sutilezas que a implementação em Python esconde.

In [9]:

def add(a, b):
    for i in range(1000):
        c = a + b

def div_float(a, b):
    for i in range(1000):
        c = a / b

def div_int(a, b):
    for i in range(1000):
        c = a // b

def mul(a, b):
    for i in range(1000):
        c = a * b

In [10]:

%%cython -a
cimport cython

ctypedef fused int_or_double:
    cython.int
    cython.double

def cy_add(int_or_double a, int_or_double b):
    for i in range(10000):
        c = a + b
        
def cy_div_float(int_or_double a, int_or_double b):
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        c = a / b

def cy_div_int(int_or_double a, int_or_double b):
    for i in range(10000):
        c = a // b

def cy_mul(int_or_double a, int_or_double b):
    for i in range(10000):
        c = a * b

c:\users\igor\miniconda3\lib\site-packages\IPython\utils\path.py:264: UserWarning: get_ipython_cache_dir has moved to the IPython.paths module
  warn("get_ipython_cache_dir has moved to the IPython.paths module")

Out[10]:

Generated by Cython 0.23

Yellow lines hint at Python interaction.
Click on a line that starts with a "+" to see the C code that Cython generated for it.

 01: cimport cython

 02: 

 03: ctypedef fused int_or_double:

 04:     cython.int

 05:     cython.double

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In [11]:

print('Adição int')
%timeit add(1123, 34534)
%timeit cy_add(1123, 34534)

print('Adição doubles')
%timeit add(1123.5, 34534.5)
%timeit cy_add(1123.5, 34534.5)

print('\nDivisão float com int')
%timeit div_float(1123, 34534)
%timeit cy_div_float(1123, 34534)

print('Divisão float com doubles')
%timeit div_float(1123.5, 34534.5)
%timeit cy_div_float(1123.5, 34534.5)

print('\nDivisão int com int')
%timeit div_int(1123, 34534)
%timeit cy_div_int(1123, 34534)

print('Divisão int com doubles')
%timeit div_int(1123.5, 34534.5)
%timeit cy_div_int(1123.5, 34534.5)

print('\nMutltiplicação int')
%timeit mul(1123, 34534)
%timeit cy_mul(1123, 34534)

print('Mutltiplicação double')
%timeit mul(1123.5, 34534.5)
%timeit cy_mul(1123.5, 34534.5)

Adição int
10000 loops, best of 3: 203 µs per loop
The slowest run took 9.56 times longer than the fastest. This could mean that an intermediate result is being cached 
100000 loops, best of 3: 8.05 µs per loop
Adição doubles
10000 loops, best of 3: 119 µs per loop
The slowest run took 4.00 times longer than the fastest. This could mean that an intermediate result is being cached 
100000 loops, best of 3: 7.95 µs per loop

Divisão float com int
10000 loops, best of 3: 101 µs per loop
100000 loops, best of 3: 13.1 µs per loop
Divisão float com doubles
10000 loops, best of 3: 103 µs per loop
100000 loops, best of 3: 7.92 µs per loop

Divisão int com int
10000 loops, best of 3: 99.6 µs per loop
100000 loops, best of 3: 13.4 µs per loop
Divisão int com doubles
The slowest run took 6.63 times longer than the fastest. This could mean that an intermediate result is being cached 
1000 loops, best of 3: 244 µs per loop
100000 loops, best of 3: 7.98 µs per loop

Mutltiplicação int
1000 loops, best of 3: 169 µs per loop
The slowest run took 5.13 times longer than the fastest. This could mean that an intermediate result is being cached 
100000 loops, best of 3: 8 µs per loop
Mutltiplicação double
10000 loops, best of 3: 91.6 µs per loop
100000 loops, best of 3: 7.92 µs per loop

Chamada de funções definidas em Cython

Funções definidas em Cython podem existir em dois espaços de nomes diferentes. Funções definidas com def podem ser chamadas tanto em código Python quanto em código Cython, mas exigem interação com o interpretador para sua efetuar a chamada. Funções declaradas com cdef são compiladas diretamente para funções em C e não são exportadas para Python. Porém, elas estão acessíveis a partir de código escrito em Cython e sua chamada é bem rápida. Funções cpdef são um mixto dos dois tipo: podem ser chamadas em Python e em Cython, porém com um custo ligeiramente maior.

In [12]:

%%cython -a

def teste1():
    pass

cdef void teste1_c():
    pass

cpdef void teste1_cp():
    pass

cpdef call_10000_times1():
    for i in range(10000):
        teste1()

cpdef call_10000_times2():
    for i in range(10000):
        teste1_c()

cpdef call_10000_times3():
    for i in range(10000):
        teste1_cp()

c:\users\igor\miniconda3\lib\site-packages\IPython\utils\path.py:264: UserWarning: get_ipython_cache_dir has moved to the IPython.paths module
  warn("get_ipython_cache_dir has moved to the IPython.paths module")

Out[12]:

Generated by Cython 0.23

Yellow lines hint at Python interaction.
Click on a line that starts with a "+" to see the C code that Cython generated for it.

 01: 

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 09:     pass

 10: 

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In [13]:

%timeit call_10000_times1()
%timeit call_10000_times2()
%timeit call_10000_times3()

1000 loops, best of 3: 338 µs per loop
100000 loops, best of 3: 5.71 µs per loop
100000 loops, best of 3: 5.66 µs per loop

Como podemos ver, somente chamar uma função que não faz nada tem um custo muito menor para funções cdef e cpdef que para funções def. Isto é especialmente importante em situações em que uma função é chamada diversas vezes dentro de um loop. Vemos agora um outro exemplo em que a utilização de funções cpdef é ainda mais vantajosa.

In [14]:

%%cython -a

cdef class Rect:
    cdef int w, h
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def test_rect2():
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c:\users\igor\miniconda3\lib\site-packages\IPython\utils\path.py:264: UserWarning: get_ipython_cache_dir has moved to the IPython.paths module
  warn("get_ipython_cache_dir has moved to the IPython.paths module")

Out[14]:

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In [15]:

%timeit test_rect1()
%timeit test_rect2()

10000 loops, best of 3: 163 µs per loop
10000 loops, best of 3: 79.4 µs per loop

Como podemos ver, se declaramos o tipo do objecto como Rect podemos chamar funções cdef sem interação com o interpretador e, portanto, com um custo por chamada bem menor.

---

Como vimos, pequenas modificações no código podem resultar em grandes ganhos de desempenho, chegando em diversas vezes em 50% de ganho. Conhecer quais operações acessam o interpretador é essencial para tirar o melhor que o Cython tem a oferecer. No próximo texto da série veremos como utilizar a diretiva nogil para indicar que um código não utiliza o interpretador e como podemos paralelizar fors em funções nogil. Até lá.