Acelerando programas usando Cython - parte 4

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

Na parte 4 da série sobre Cython falaremos sobre funções que liberam completamente o interpretador e podem ser paralelizadas usando OpenMP. No texto anterior vimos que chamadas de funções cdef e cpdef são muito rápidas pois elas podem ser feitas "por fora" do interpretador. Também vimos quais operaçõe podem ser feitas sem tocar no interpretador. Podemos levar esta idéia mais além e criar funções (ou contextos) nogil, que liberam explicitamente o GIL e permitem a execução simultânea de várias threads em Python. Já vimos nos textos sobre multiprocessing (partes 1 e 2) que a utilização de vários processadores pode trazer ganhos de desempenho significativos. Neste texto exploramos algumas situações em que podemos fazer processamento paralelo em Cython.

In [7]:

%load_ext cython

The cython extension is already loaded. To reload it, use:
  %reload_ext cython

In [8]:

%%cython
# exemplo de funcoes nogil e de contextos nogil
cdef void funcao_nogil(int a, float b, char[:] c) nogil:
    cdef double d
    pass
    # toda função nogil precisa ter todos os tipos declarados e só usar operações "brancas"
    with gil:
        pass
        # esta parte requer o GIL, ou seja, é executada SEMPRE de maneira serial.
    
def funcao_gil(a, b, c):
    cdef double d
    with nogil:
        pass
        # aqui só são permitidas operações "brancas" com variáveis tipadas explicitamente.

Funções (e contextos) nogil possuem diversas limitações (parecidas com as do modo nopython que vimos no post sobre Numba). Não é possível interagir com nenhum tipo de objeto em Python, somente com variáveis que possuem tipo declarado e que o tipo está disponível diretamente em C. Isto inclui os tipos C habituais (int, float, double, char, etc) e suas versões memory view (int[:], float[:, :], etc). É importante notar que todos os tipos permitidos acessam a memória diretamente e não passam pela contagem de referências do interpretador.

A maneira mais fácil de checar se um trecho de código pode ser executado como nogil é simplesmente executando o cython -a como temos feito nos últimos posts. Toda linha completamente branca pode ser executada como nogil. Qualquer traço de amarelo indica interação com o interpretador e, portanto, é necessário obter o GIL.

Apresentamos abaixo duas versões do gradiente morfológico que usamos nos últimos posts. Primeiramente, adicionamos um tipo de retorno à função morpho_gradient_cython e a marcamos como nogil, o que significa que ela não interage nenhuma vez com o interpretador e explicitamente o libera ao ser executada. A versão versao_cython_omp usa a construção cython.parallel.prange(link), que executa cada iteração do for em uma thread diferente. Esta construção só funciona em modo nogil.

Para compilar extensões usando OpenMP no ipython notebook é necessário adicionar as flags abaixo na %%cython magic. Se você está usando um setup.py, é necessário adicioanar as opções

In [9]:

%%cython --compile-args=-fopenmp --link-args=-fopenmp --force
import numpy as np
import cython

from cython.parallel cimport prange
cimport openmp


@cython.boundscheck(False)
@cython.nonecheck(False)
cdef void morpho_gradient_cython(long[:,:] img, long[:,:] out, int pi, int pj) nogil:
    cdef int i, j, minv, maxv
    minv = 255
    maxv = 0
    
    for i in range(-3, 4):
        for j in range(-3, 4):
            if img[pi+i, pj+j] < minv:
                minv = img[pi+i, pj+j]
            if img[pi+i, pj+j] > maxv:
                maxv = img[pi+i, pj+j]
    out[pi, pj] = maxv - minv

@cython.boundscheck(False)
@cython.nonecheck(False)
@cython.cdivision(True)
cpdef void versao_cython_omp(long[:,:] img, long[:,:] out) nogil:
    cdef int i, j, k, w, h
    h = img.shape[0]; w = img.shape[1]
    n = (h-2)*(w-2)
    for k in prange(n, nogil=True):
        i = k / w
        j = k % w
        morpho_gradient_cython(img, out, i, j)

@cython.boundscheck(False)
@cython.nonecheck(False)
@cython.cdivision(True)
cpdef void versao_cython(long[:,:] img, long[:,:] out) nogil:
    cdef int i, j, k, w, h
    h = img.shape[0]; w = img.shape[1]
    n = (h-2)*(w-2)
    for k in range(n):
        i = k / w
        j = k % w
        morpho_gradient_cython(img, out, i, j)

Para comparar os resultados, precisamos de imagens maiores para sentir a diferença entre as implementações. Processamento paralelo sempre involve uma quantidade significativa de overhead e o código em Cython já é muito rápido para imagens 512x512, como é o caso da Lena. Logo, "copiamos" a Lena em uma imagem bem maior.

In [10]:

import scipy
import scipy.misc
import numpy as np

img_temp = scipy.misc.lena()
img2 = np.c_[img_temp, img_temp]
img3 = np.r_[img2, img2]
img4 = np.c_[img3, img3]
img = np.r_[img4, img4]
img = np.r_[img, img]
img = np.c_[img, img]
print(img.shape)

(4096, 4096)

In [11]:

out = img.copy()
print('Versão Cython')
%timeit versao_cython(img, out)
print('Versão Cython OMP')
%timeit versao_cython_omp(img, out)

Versão Cython
1 loops, best of 3: 4.9 s per loop
Versão Cython OMP
1 loops, best of 3: 2.3 s per loop

Como podemos ver, existe uma diferença significativa entre as duas implementações. Este é principal caso de uso de paralelismo em Cython: execução de um loop em paralelo em que as iterações são independentes e poderiam ser executadas em qualquer ordem. Os ganhos de desempenho podem ser supreendentes, principalmente em processadores com um grande número de cores (8, 16, 24, ...).

Um dos grandes problemas da utilização de OpenMP em Cython é que é proibida qualquer interação com objetos Python. Porém, dependendo do tipo de tarefa podemos mesmo assim explorar código paralelo usando o módulo multiprocessing, que já exploramos em textos anteriores. Toda função cpdef ou def acelerada usando Cython pode ser chamada normalmente usando o multiprocessing. No exemplo abaixo, aplicamos o gradiente morfológico em um grande número de imagens e comparamos a versão OpenMP com uma versão Cython "regular" (sem OpenMP) mas utilizando multiprocessing.

Este problema possui as mesmas propriedades do anterior: é um conjunto de tarefas independentes que podem ser executadas em qualquer ordem. Porém, desta vez analisamos a situação em que não conseguimos paralelizar o código de cada tarefa individualmente, então executamos várias tarefas em paralelo. Um ponto importante é que não existem benefícios em criar um número maior de processos/threads que o número de processadores disponíveis. Isto pode, inclusive, tornar a execução mais lenta por causa da competição entre os processos. Logo, executar uma função com OpenMP usando multiprocessing provavelmente não é uma boa ideia.

In [12]:

import os
import numpy as np
import scipy as sp
import scipy.ndimage

from multiprocessing.pool import Pool

def le_e_processa_omp(t):
    imgpath = t
    img = sp.ndimage.imread(imgpath, mode='L').astype('long')
    out = img.copy()
    versao_cython_omp(img, out)

def serial_omp(path, n=-1):    
    images = [x for x in os.listdir(path) if x[-3:] == 'png'][:n]
    images = ['%s/%s'%(path, img) for img in images]
    list(map(le_e_processa_omp, images))

def le_e_processa(t):
    imgpath = t
    img = sp.ndimage.imread(imgpath, mode='L').astype('long')
    out = img.copy()
    versao_cython(img, out)

def parallel_thread(path, n=-1):
    images = [x for x in os.listdir(path) if x[-3:] == 'png'][:n]
    images = ['%s/%s'%(path, img) for img in images]
    pool = Pool()
    pool.map(le_e_processa, images)
    pool.close()
    del pool

    
path = '/media/igor/Data1/datasets/staffs/test'
%timeit -r 1 -n 1 serial_omp(path, 50)
%timeit -r 1 -n 1 parallel_thread(path, 50)

1 loops, best of 1: 50.8 s per loop
1 loops, best of 1: 45.3 s per loop

Como podemos ver, o desempenho dos dois testes é bem parecido, mesmo usando uma versão muito mais lenta do gradiente morfológico na versão multiprocessing. Logo, seja usando OpenMP, seja usando multiprocessing, processamento paralelo com Cython pode trazer ganhos significativos de performance.

Acelerando Python usando Numba

Implementações em Python puro são frequentemente muito lentas e qualquer tentativa de acelerar estes programas impacta diretamente a produtividade de um cientista que uma Python. Falamos anteriormente de Processamento Paralelo com multiprocessing e Cython como maneiras de acelerar experimentos. Ambas soluções, entretanto, exigem modificações não triviais (e potencialmente extensivas) no código, aumentando sua complexidade.

Neste texto irei apresentar uma alternativa chamada Numba, um compilador Just in Time(JIT) que compila um subconjunto da linguagem Python em código de máquina eficiente. Numba é muito mais limitado que Cython e não é capaz de traduzir todas (a maioria das?) construções Python de maneira eficiente. As funcionalidades suportadas, porém, podem ser aceleradas significativamente sem grande esforço.

Primeiramente, um compilador JIT traduz, durante a execução, código escrito em uma linguagem de alto nível como Python em instruções nativas da CPU em que está sendo executado. Otimizações são aplicadas somente nas partes mais lentas do código. A vantagem disto é que não é necessário compilar nada antes da execução do programa, porém as primeira execuções de uma função "jitted" são mais lentas.

Uma das grandes vantagens de se usar Numba é que o código compilado é Python puro. Não é necessário adicionar nenhum tipo de anotação ou código extra. Tudo é feito automaticamente pelo Numba e o código gerado é bastante rápido em alguns casos específicos. A maneira mais fácil de instalar este pacote é utilizando o Anaconda Python. Um simples conda install numba instala todo o necessário sem nenhuma complicação, mesmo em Windows.

Vamos usar como exemplo nosso já cansado gradiente morfológico. Também manteremos a versão Cython que criamos em um post anterior.

Versão Python¶

In [1]:

import numpy as np
import scipy as sp
import scipy.ndimage
import time
import matplotlib.pyplot as plt

def morpho_gradient_py(img, out, p):
    pi, pj = p
    minv = 255
    maxv = 0
    
    for i in range(-1, 2):
        for j in range(-1, 2):
            if img[pi+i, pj+j] < minv:
                minv = img[pi+i, pj+j]
            if img[pi+i, pj+j] > maxv:
                maxv = img[pi+i, pj+j]
    out[pi, pj] = maxv - minv

def versao_python(img):
    out = np.zeros(img.shape, img.dtype)
    for i in range(1, img.shape[0]-1):
        for j in range(1, img.shape[1]-1):
            morpho_gradient_py(img, out, (i, j))
    return out

Versão Cython¶

In [2]:

%load_ext cython

In [3]:

%%cython
import numpy as np
import scipy as sp
import scipy.ndimage
import time
import matplotlib.pyplot as plt
import cython

@cython.boundscheck(False)
@cython.nonecheck(False)
cdef morpho_gradient_cython(long[:,:] img, long[:,:] out, p):
    cdef int i, j, pi, pj, minv, maxv
    
    pi, pj = p
    minv = 255
    maxv = 0
    
    for i in range(-1, 2):
        for j in range(-1, 2):
            if img[pi+i, pj+j] < minv:
                minv = img[pi+i, pj+j]
            if img[pi+i, pj+j] > maxv:
                maxv = img[pi+i, pj+j]
    out[pi, pj] = maxv - minv

@cython.boundscheck(False)
@cython.nonecheck(False)
cpdef versao_cython(long[:,:] img):
    cdef int i, j, w, h
    cdef long[:,:] out
    h = img.shape[0]; w = img.shape[1]
    out = np.zeros((h, w), np.int)
    for i in range(1, h-1):
        for j in range(1, w-1):
            morpho_gradient_cython(img, out, (i, j))
    return out

Versão Numba¶

In [4]:

import numba

@numba.jit
def morpho_gradient_numba(img, out, p):
    pi, pj = p
    minv = 255
    maxv = 0
    
    for i in range(-1, 2):
        for j in range(-1, 2):
            if img[pi+i, pj+j] < minv:
                minv = img[pi+i, pj+j]
            if img[pi+i, pj+j] > maxv:
                maxv = img[pi+i, pj+j]
    out[pi, pj] = maxv - minv

@numba.jit
def versao_numba(img):
    out = np.zeros(img.shape, img.dtype)
    for i in range(1, img.shape[0]-1):
        for j in range(1, img.shape[1]-1):
            morpho_gradient_numba(img, out, (i, j))
    return out

A versão Numba é basicamente a versão Python com um decorador adicionado às funções. Diferentemente de Cython, não adicionamos nenhum tipo manualmente nem precisamos compilar nosso código explicitamente. Vamos agora aos resultados numéricos.

In [5]:

import scipy

img = scipy.misc.lena()

print('Versão Python')
%timeit versao_python(img)
print('Versão Cython')
%timeit versao_cython(img)
print('Versão Numba')
%timeit versao_numba(img)

Versão Python
1 loops, best of 3: 3.23 s per loop
Versão Cython
10 loops, best of 3: 39.7 ms per loop
Versão Numba
The slowest run took 25.12 times longer than the fastest. This could mean that an intermediate result is being cached 
1 loops, best of 3: 26.6 ms per loop

Ambas as versões aceleradas obtiveram performance muito superior ao programa em Python puro. Surpreendentemente, o código em Numba obteve um desempenho superior ao código em Cython. Isto ocorre, principalmente, pois usamos somente funcionalidades suportadas no modo nativo do Numba, chamado de nopython. Isto inclui, basicamente, manipulação de arrays com Numpy (funções suportadas), chamadas de função com decorador @jit e controle de fluxo padrão de Python (if, for i in range(..), while). Existe suporte limitado para listas e é melhor evitar funções recursivas. Uma lista completa das funcionalidades suportadas pode ser vista na documentação do projeto. Funções nopython podem ser compiladas para código nativo bastante rápido e o próprio processo de compilação não só é invisível para o programador com também é bem rápido, diferentemente de Cython.

Porém, o modo nopython só é ativado se a função só utilizar as funcionalidades permitidas. Se uma única operação não for suportada o Numba ativa o modo object e trata todas as variáveis como objetos Python. Ou seja, apesar de algumas otimizações ainda serem possíveis, o programa será muito mais lento. Não é possível, como em Cython, misturar código que interage com o interpretador e código compilado. Vejamos abaixo um exemplo do mesmo código acima compilado no modo object.

In [6]:

@numba.jit(forceobj=True)
def morpho_gradient_numba_ruim(img, out, p):
    pi, pj = p
    minv = 255
    maxv = 0
    
    for i in range(-1, 2):
        for j in range(-1, 2):
            if img[pi+i, pj+j] < minv:
                minv = img[pi+i, pj+j]
            if img[pi+i, pj+j] > maxv:
                maxv = img[pi+i, pj+j]
    out[pi, pj] = maxv - minv

@numba.jit(forceobj=True)
def versao_numba_ruim(img):
    out = np.zeros(img.shape, img.dtype)
    for i in range(1, img.shape[0]-1):
        for j in range(1, img.shape[1]-1):
            morpho_gradient_numba_ruim(img, out, (i, j))
    return out


print('Versão Numba Object')
%timeit versao_numba_ruim(img)

Versão Numba Object
1 loops, best of 3: 18.3 s per loop

Como podemos ver, o resultado pode ser desastroso. Apesar da aparente limitação, Numba é muito útil para o que se propõe a fazer: acelerar código em Python que contém diversos loops e acessa arrays do Numpy. Uma boa maneira de checar se um código poderá ser acelerado é usar o decorador @jit(nopython=True). Esta opção força a compilação em modo nopython e levanta uma exceção se o código não puder ser compilado deste modo.

Mesmo que a função inteira não possa ser compilada, se houver algum loop que só possui operações nopython o Numba pode executar uma otimização chamada loop lifting. Vejamos o exemplo abaixo.

In [7]:

@numba.jit
def morpho_gradient_numba_lift(img, out, p):
    pi, pj = p
    minv = 255
    maxv = 0
    for i in range(-1, 2):
        for j in range(-1, 2):
            if img[pi+i, pj+j] < minv:
                minv = img[pi+i, pj+j]
            if img[pi+i, pj+j] > maxv:
                maxv = img[pi+i, pj+j]
    out[pi, pj] = maxv - minv

@numba.jit
def versao_numba_lift(img):
    out = np.zeros(img.shape, np.uint8)
    d = {}

    for i in range(1, img.shape[0]-1):
        for j in range(1, img.shape[1]-1):
            morpho_gradient_numba_lift(img, out, (i, j))

    d[5] = 0

    return out


print('Versão Numba Object')
%timeit versao_numba_lift(img)

Versão Numba Object
The slowest run took 19.17 times longer than the fastest. This could mean that an intermediate result is being cached 
1 loops, best of 3: 25.3 ms per loop

A função versao_numba_lift não pode ser compilada em modo nopython, mas os dois for podem. Logo, ele extrai esta parte da função, aplica loop lifting e termina com um código tão rápido quanto o anterior. Porém, isto não funcionaria bem se fizéssemos o mesmo na função morpho_gradient_numba_lift. Apesar do loop da outra função ser realmente acelerado, uma funcionalidade importante do Numba é que a chamada de funções em modo nopython é muito rápida para outras funções nopython, mas lenta para funções Python (ref). Como em Cython, é possível ganhar muita velocidade se todas as nossas funções forem compiladas no modo acelerado.

---

Como pudemos ver, Numba pode ser uma excelente alternativa para acelerar código escrito em Python. Em um próximo post apresentarei as funções (e limitações) de Numba para rodar código em GPUs NVidia usando CUDA.

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.

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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
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def dict_test_cy(d):
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        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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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:
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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")

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In [8]:

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):
    for i in range(10000):
        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

 06: 

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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
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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

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def test_rect1():
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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
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Out[14]:

Generated by Cython 0.23

Yellow lines hint at Python interaction.
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 01: 

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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.