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.

Usando Anaconda Python para Computação Científica

Durante o processo de pesquisa, frequentemente utilizo máquinas remotas de grande porte para executar meus códigos. Eventualmente também uso Windows e é necessário que todo meu código não só funcione como seja fácil de instalar. Uma das soluções padrão seria utilizar virtualenv para instalar pacotes somente para o meu usuário, mas além de isto não ajudar muito no caso Windows, tive diversos problemas com virtualenvs quebrando quando utilizados em máquinas diferentes. Recentemente resolvi testar uma distribuição chamada Anaconda Python que é voltada para computação científica. O Anaconda Python possui diversas vantagens em relação aos pacotes Python "padrão":

1. A instalação padrão do Anaconda contém a maioria das bibliotecas científicas comumente usadas;
2. Não é necessário possuir privilégios de administrador para instalar pacotes nem mexer com virtualenvs;
3. O comando conda, incluso na distribuição, é de fácil utilização e permite instalar pacotes pré-compilados (muito útil em Windows) e diversas versões de Python ao mesmo tempo. Em muitas situações substitui ambos pip e virtualenv.
4. Em Windows, ele já vem configurado para compilar extensões em C (ou Cython) usando o mingw (somente 32 bits, por enquanto).

É claro que também podem existir desvantagens, sendo que na minha opinião a principal é que pacotes comerciais instalados no sistema podem não funcionar imediatamente.

Instalação¶

A instalação é bem simples e não acho que valha muito a pena perder com ela aqui. Em Windows, é um instalador Next -> Next -> Next como todos os outros. Em Linux basta baixar o instalador e executá-lo no terminal. Tudo muito simples. Nas minhas instalações, mesmo em Windows, eu sempre escolho como destino uma pasta dentro do meu home e deixo o instalador sobrescrever o PATH para que o python padrão nos meus terminais seja o Anaconda.

Instalação de pacotes¶

Primeiramente, o comando pip funciona normalmente, assim como todos os outros módulos relacionados à instalação de pacotes (easy_install, setuptools, etc). O Anaconda Python vem com uma ferramenta adicional chamada conda, que permite a instalação de pacotes pré-compilados. A sintaxe do comando é a mais simples possível:

\$ conda install nome_do_pacote

Nem todos os pacotes do PyPI estão disponíveis, mas a quantidade que está é bem grande mesmo em Windows. Existem duas grandes vantagens em utilizar o conda:

1. Pacotes enormes, como Numpy, Scipy ou Scikit-learn, ou cheios de dependências e complicados de instalar, como o Numba, podem ser instalados rapidamente, sem esperar um bom tempo pela sua compilação ou sem precisar caçar dependências manualmente;
2. É muito mais fácil instalar pacotes que usam extensões em Windows, já que compilar pacotes nesta plataforma é sempre complicado;

Lembrando que como a instalação é normalmente feita na área do usuário, nem pip nem conda necessitam de privilégios especiais para instalar novos pacotes.

Ambientes virtuais com conda¶

O conda também suporta a instalação de diversas versões de Python utilizando ambientes virtuais. Um ambiente virtual é uma instalação Python feita inteiramente em um diretório. Podemos ter diversos ambientes instalados e cada um terá sua versão do Python e seus pacotes armazenados separadamente. Ao ativarmos um ambiente virtual, os comandos python, pip, etc e o caminho de busca dos pacotes são substituídos pelos caminhos do ambiente virtual. A partir da ativação, qualquer comando Python usará as bibliotecas e o interpretador do ambiente virtual. A utilização de ambientes virtuais é comum quando desenvolvemos vários projetos paralelamente, de modo que cada projeto tem seu ambiente separado com somente os pacotes necessários instalados. É prático e evita inconvenientes como versões de pacotes incompatíveis ou diferentes para cada projeto.

Para criar um ambiente virtual utilizamos

\$ conda create -n nome python=X.Y lista de pacotes desejados

E para ativá-lo

• \$ source activate nome (Linux, MacOSX)
• \$ activate nome (Windows)

Também podemos listar os ambientes instalados:

\$ conda info --envs

O Anaconda também disponibiliza um resumão de todos os comandos do conda que é muito útil.

Minha experiência pessoal¶

Trocar os meus virtualenvs pelo Anaconda Python me ajudou a migrar definitivamente para Python 3, além de acabar com o inferno de virtualenvs quebrando sozinhos quando usados em máquinas usando NFS. Também é muito conveniente instalar os pacotes rapidamente sem precisar esperar o tempo de compilação e ter a grande maioria dos pacotes que uso disponíveis na instalação padrão. E a grande vantagem de tudo é que é tudo Python, não existe nenhuma extensão ou diferença em usar o Anaconda ou a distribuição padrão. Particularmente, eu recomendo a todos que precisam de um ambiente completo rapidamente.

Bônus - IDE Spyder¶

A instalação padrão do Anaconda contém a IDE Spyder que é muito útil para desenvolvimento de aplicações científicas. De certa maneira, Spyder tem várias características em comum com o Matlab, mas usa Python e é Open Source. Acho que a imagem abaixo mostra claramente o poder deste ambiente. Vale a pena dar uma conferida ;)