python – Theano GPU计算比numpy慢

我正在学习使用theano.我想通过计算其中每个元素的二进制TF-IDF来填充term-document矩阵(numpy稀疏矩阵)：

import theano
import theano.tensor as T
import numpy as np
from time import perf_counter

def tfidf_gpu(appearance_in_documents,num_documents,document_words):
    start = perf_counter()
    APP = T.scalar('APP',dtype='int32')
    N = T.scalar('N',dtype='int32')
    SF = T.scalar('S',dtype='int32')
    F = (T.log(N)-T.log(APP)) / SF
    TFIDF = theano.function([N,APP,SF],F)
    ret = TFIDF(num_documents,appearance_in_documents,document_words)
    end = perf_counter()
    print("\nTFIDF_GPU ",end-start," secs.")
    return ret

def tfidf_cpu(appearance_in_documents,num_documents,document_words):
    start = perf_counter()
    tfidf = (np.log(num_documents)-np.log(appearance_in_documents))/document_words
    end = perf_counter()
    print("TFIDF_CPU ",end-start," secs.\n")
    return tfidf

但numpy版本比theano实现快得多：

Progress 1/43
TFIDF_GPU  0.05702276699594222  secs.
TFIDF_CPU  1.454801531508565e-05  secs.

Progress 2/43
TFIDF_GPU  0.023830442980397493  secs.
TFIDF_CPU  1.1073017958551645e-05  secs.

Progress 3/43
TFIDF_GPU  0.021920352999586612  secs.
TFIDF_CPU  1.0738993296399713e-05  secs.

Progress 4/43
TFIDF_GPU  0.02303648801171221  secs.
TFIDF_CPU  1.1675001587718725e-05  secs.

Progress 5/43
TFIDF_GPU  0.02359767400776036  secs.
TFIDF_CPU  1.4385004760697484e-05  secs.

....

我已经读过这可能是由于开销,小型操作可能会导致性能下降.

我的代码是坏的还是因为开销而应该避免使用GPU？

解决方法:

问题是你每次都在编译你的Theano功能.编译需要时间.尝试传递编译的函数,如下所示：

def tfidf_gpu(appearance_in_documents,num_documents,document_words,TFIDF):
    start = perf_counter()
    ret = TFIDF(num_documents,appearance_in_documents,document_words)
    end = perf_counter()
    print("\nTFIDF_GPU ",end-start," secs.")
    return ret

APP = T.scalar('APP',dtype='int32')
N = T.scalar('N',dtype='int32')
SF = T.scalar('S',dtype='int32')
F = (T.log(N)-T.log(APP)) / SF
TFIDF = theano.function([N,APP,SF],F)

tfidf_gpu(appearance_in_documents,num_documents,document_words,TFIDF)

您的TFIDF任务也是带宽密集型任务.通常,Theano和GPU最适合计算密集型任务.

当前任务将把数据带到GPU并返回相当大的开销,因为最终你需要读取每个元素O(1)次.但是如果你想做更多的计算,那么使用GPU是有意义的.

标签：python,numpy,tf-idf,theano
来源： https://codeday.me/bug/20190823/1695018.html