Acelerando programas usando Cython - parte 1

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

Apesar de Python ser uma linguagem bastante conveniente para se trabalhar com computação científica (devido aos diversos pacotes disponíveis), ela não especialmente rápida para realizar cálculos. Pacotes como o Numpy e os diversos scikits são otimizados e contém diversas partes escritas em C, de modo que quando utilizamos suas funções na verdade estamos chamando um código eficiente escrito em C com uma pequena "casquinha" em Python. Porém, ao desenvolvermos nossos algoritmos escrever as partes "importantes" em outra linguagem acaba com a conveniência de Python. Cython é um framework meio termo que resolve justamente este problema e cujo objetivo é "compilar" programas em Python para C. A princípio qualquer programa em Python pode ser traduzido, sendo que a tradução em C será somente um conjunto de chamadas à API em C de Python fazendo as mesmas operações que o código original. Isto pode tornar alguns programas mais rápidos, mas o efeito é bem limitado. A grande vantagem de Cython é a possibilidade de adicionar tipos às funções/variáveis/etc. Com o conhecimento dos tipos, o Cython pode fazer traduções que utilizam menor quantidade de chamadas ao interpretador Python, o que torna o código muito mais rápido. Um for i in range(10) pode ser transformado diretamente em um for(i=0;i<10;i++){, que não chama o interpretador nenhuma vez e é muito rápido.

Instalação¶

O Cython está no PyPI e pode ser instalado pelo pip ou pelo conda de maneira bem simples:

\$ conda|pip install cython

Um primeiro exemplo¶

Como no último texto sobre processamento paralelo, usaremos como função de exemplo o gradiente morfológico de uma imagem. No início usaremos o iPython Notebook para executar nosso código Cython, mas nos próximos posts veremos como criar extensões e usá-las em scripts .py. Como sempre, começamos com uma versão em Python puro e serial.

In [1]:

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

def morpho_gradient(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_serial(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(img, out, (i, j))
    return out

def roda10(func, *args):
    times = []
    for i in range(10):
        start = time.clock()
        out = func(*args)
        times.append(time.clock() - start)
    return np.mean(times), out

def roda_e_plota(func, msg):
    img = sp.misc.lena()
    tempos, out = roda10(func, img)
    print(msg, tempos)
    plt.figure(figsize=(10, 10))
    plt.subplot(121)
    plt.gray()
    plt.imshow(img)
    plt.subplot(122)
    plt.gray()
    plt.imshow(out)


roda_e_plota(versao_serial, 'Python Puro')

Python Puro 2.714

A versão em Python puro é lenta e demora vários segundos para rodar. Nossa primeira versão em Cython é idêntica à em Python puro, mas será compilada usando Cython. Geralmente os programas ficam ligeiramente mais rápidos, mas os ganhos não costumam ultrapssar 50%.

Para criar funções usando Cython no iPython Notebook primeiramente precisamos carregar a extensão cython, o que pode ser feito usando a instrução na célula abaixo.

In [2]:

%load_ext cython

A partir de agora, qualquer célula que começar com a instrução %%cython será compilada usando Cython e suas funções serão disponíveis nas outras células do documento. Como cada célula Cython é compilada separadamente do notebook, é necessário importar todos os módulos que serão usados e é necessário definir todas as funções a serem usadas dentro da própria célula.

Junto com a célula abaixo, também mostramos uma listagem do código com linhas amareladas. Esta listagem é produzida pelo compilador Cython quando adicionamos a opção -a. Nela, quanto mais amarelada uma linha maior quantidade de chamadas ao interpretador Python ela faz. Logo, um bom código em Cython produz um código que utilize o menor número possível de linhas amareladas.

In [3]:

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

def morpho_gradient_cython(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_cython1(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_cython(img, out, (i, j))
    return out

Out[3]:

Generated by Cython 0.22.1

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+24:             morpho_gradient_cython(img, out, (i, j))

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In [4]:

roda_e_plota(versao_cython1, 'Cython I')

Cython I 1.989

Apesar de existirem ganhos de desempenho, a diferença não é muito significativa neste exemplo. Porém, não utilizamos nenhuma das extensões de acelaração de código e, portanto, nosso código tem quase todas as linhas amarelas. Neste post aprenderemos a utilizar 2 extensões que irão tornar o código muito mais rápido:

• tipagem de variáveis;
• memory views.

No exemplo anterior, todos os for estão amarelos, mesmo tendo traduções diretas para C. Adicionando tipos às variáveis de índice, podemos traduzir estas linhas diretamente e tornar nosso código mais rápido. Para declarar uma variável tipada usamos a instrução cdef (tipo) nome, onde tipo podem ser tanto os tipos básicos de C (incluindo ponteiros) quanto classes e tipos de objetos em Python (dict, list, tuple e qualquer classe que você possa construir).

In [5]:

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

def morpho_gradient_cython(img, 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

def versao_cython2(img):
    cdef int i, j, w, h
    h, w = img.shape
    out = np.zeros(img.shape, img.dtype)
    for i in range(1, h-1):
        for j in range(1, w-1):
            morpho_gradient_cython(img, out, (i, j))
    return out

Out[5]:

Generated by Cython 0.22.1

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: import numpy as np

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+02: import scipy as sp

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+03: import scipy.ndimage

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+04: import time

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+05: import matplotlib.pyplot as plt

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 06: 

+07: def morpho_gradient_cython(img, out, p):

/* Python wrapper */
static PyObject *__pyx_pw_46_cython_magic_5505d420540dd2e427bf660c7587472c_1morpho_gradient_cython(PyObject *__pyx_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
static PyMethodDef __pyx_mdef_46_cython_magic_5505d420540dd2e427bf660c7587472c_1morpho_gradient_cython = {"morpho_gradient_cython", (PyCFunction)__pyx_pw_46_cython_magic_5505d420540dd2e427bf660c7587472c_1morpho_gradient_cython, METH_VARARGS|METH_KEYWORDS, 0};
static PyObject *__pyx_pw_46_cython_magic_5505d420540dd2e427bf660c7587472c_1morpho_gradient_cython(PyObject *__pyx_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
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In [6]:

roda_e_plota(versao_cython2, 'Cython II')

Cython II 2.123

No nosso caso, adicionar tipos nos índices e nas outras variáveis não tem um efeito muito grande. Isto ocorre pois a operação mais lenta (acesso aos pixels das imagens) ainda é feita através do interpretador. Vamos então adicionar tipos às imagens utilizando memory views , que são uma maneira de acessar diretamente a memória de objetos que suportem o protocolo buffer em python. Arrays do Numpy suportam este protocolo e podemos, portanto, acessar sua memória diretamente.

In [7]:

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

def morpho_gradient_cython(long[:,:] img, long[:,:] out, p):
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    maxv = 0
    
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        for j in range(-1, 2):
            if img[pi+i, pj+j] < minv:
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def versao_cython3(long[:,:] img):
    cdef int i, j, w, h
    cdef long[:,:] out
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    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

Out[7]:

Generated by Cython 0.22.1

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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+04: import time

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+05: import matplotlib.pyplot as plt

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 06: import cython

 07: 

+08: def morpho_gradient_cython(long[:,:] img, long[:,:] out, p):

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 09:     cdef int i, j, pi, pj, minv, maxv

 10: 

+11:     pi, pj = p

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+12:     minv = 255

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+13:     maxv = 0

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 14: 

+15:     for i in range(-1, 2):

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+16:         for j in range(-1, 2):

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+17:             if img[pi+i, pj+j] < minv:

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+18:                 minv = img[pi+i, pj+j]

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+19:             if img[pi+i, pj+j] > maxv:

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+20:                 maxv = img[pi+i, pj+j]

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 22: 

+23: def versao_cython3(long[:,:] img):

/* Python wrapper */
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In [8]:

roda_e_plota(versao_cython3, 'Cython III')

Cython III 0.349

O último passo para a conversão de nosso código em Cython é adicionar as diretivas boundscheck e nonecheck à nossa função morpho_gradient_cython. Estes decorators desligam a checagem de limites de arrays e referência de objetos None. Agora, acessos à áreas de memórias irão dar Segmentation Fault, como qualquer programa em C, mas os acessos serão mais rápidos.

In [9]:

%%cython -a
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)
def 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

def versao_cython4(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

Out[9]:

Generated by Cython 0.22.1

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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+05: import matplotlib.pyplot as plt

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 06: import cython

 07: 

 08: @cython.boundscheck(False)

 09: @cython.nonecheck(False)

+10: def morpho_gradient_cython(long[:,:] img, long[:,:] out, p):

/* Python wrapper */
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In [10]:

roda_e_plota(versao_cython4,'Cython IV')

Cython IV 0.339

Isto conclui nossa introdução à utilização de Cython no iPython notebook. Como podemos ver, a utilização de Cython pode introduzir ganhos de mais de 50% em desempenho, porém ao custo de tornar o código menos flexível adicionando tipos. No próximo post veremos como utilizar Cython a partir de scripts independentes e como distribuir código Cython com o setuptools.