openNMT-py CTranslate2性能对比流程

https://github.com/OpenNMT/OpenNMT-py

GitHub - OpenNMT/CTranslate2: Fast inference engine for Transformer models

安装软件包：

pip install OpenNMT-py
pip install ctranslate2

基于CTranslate2和openNMT-py对比性能（非基于官方镜像的方式）

参考OpenNMT-py github主页链接下载Pretrained models

基于WMT训练的English-German - Transformer

这个模型里面包含两个文件

averaged-10-epoch.pt和sentencepiece.model

前者是保存的模型，后者是sentencepiece tokenizer的模型

CTranslate2模型转换和量化参考：

https://github.com/OpenNMT/CTranslate2/blob/master/docs/quantization.md

下载测试数据（写在CTranslate2/tools/benchmark/benchmark_all.py）

import sacrebleu
 
# Benchmark configuration
test_set = "wmt14"
langpair = "en-de"
 
print("Downloading the test files...")
source_file = sacrebleu.get_source_file(test_set, langpair=langpair)
target_file = sacrebleu.get_reference_files(test_set, langpair=langpair)[0]
print("source_file:", source_file)
print("target_file:", target_file)

会下载翻译前后的原始文本，分别为en-de.en, en-de.de。

bench：参考CTranslate2/tools/benchmark/opennmt_ende_wmt14/目录的ctranslate2和opennmt_py两个目录，里面分别有tokenize.sh和translate.sh。

先对于opennmt_py目录，可以根据tokenize.sh创建custom_tokenize.py，内容如下：

import pyonmttok
 
sp_model_path = "transformer-ende-wmt-pyOnmt/sentencepiece.model"
 
src_file = "sacrebleu/wmt14/en-de.en"
tgt_file = src_file + ".tok"
 
pyonmttok.Tokenizer("none", sp_model_path=sp_model_path).tokenize_file(src_file, tgt_file)

执行后为输入文本生成对应的tokenized文本

然后执行翻译脚本为上一步tokenized输入文本生成翻译后的tokenized文本

修改translate.sh：

#!/bin/bash
 
# EXTRA_ARGS=""
# if [ $DEVICE = "GPU" ]; then
#     EXTRA_ARGS+=" -gpu 0"
# fi
# if [ ${INT8:-0} = "1" ]; then
#     EXTRA_ARGS+=" -int8"
# fi
 
model_path=transformer-ende-wmt-pyOnmt/averaged-10-epoch.pt
src_file=sacrebleu//wmt14/en-de.en.tok
out_file=${src_file}.onmt.out
 
# onmt_translate \
python -m onmt.bin.translate \
    -model ${model_path} \
    -src ${src_file} \
    -out  ${out_file} \
    -batch_size 32 \
    -beam_size 4 -gpu 0

最后进行detokenize，可以参考detokenize.sh编写custom_detokenize.py：

import sys;
import pyonmttok; 
 
sp_model_path = "transformer-ende-wmt-pyOnmt/sentencepiece.model"
 
src_file = "sacrebleu/wmt14/en-de.en.tok.onmt.out"
tgt_file = src_file + ".detok"
 
pyonmttok.Tokenizer("none", sp_model_path=sp_model_path).detokenize_file(src_file, tgt_file)

执行后会为翻译后的tokenized文本生成detokenize的文本文件，结果跟en-de.de应该相似。

ctranslate2执行跟上面流程除了translate步骤其他一样。

benchmark/ctranslate2/translate.sh里面使用了translate二进制可执行文件，但直接pip安装的ctranslate2并没有这个translate文件。

如果不使用镜像或自己构建镜像，直接使用Pip包推理，则应该自己读入文件调用python接口推理。为了公平的比较，openNMT-py的推理可以改成同样的方式并加warmup。

ctranslate2 python接口使用参考：

CTranslate2/python.md at master · OpenNMT/CTranslate2 · GitHub

这里采用自己编写ctans_custom_translate.py脚本读入文件推理:

import ctranslate2
import time
 
src_file = "sacrebleu/wmt14/en-de.en.tok"
tgt_file = src_file + ".ctrans.out"
 
model_path = "transformer-ende-wmt-pyOnmt/ende_ctranslate2/"
device = "cuda"  # "cpu", "cuda"
max_batch_size = 32
beam_size = 4
 
with open(src_file, "r") as f:
    lines = f.readlines()
    lines = [line.strip('\n').split(" ") for line in lines]
 
translator = ctranslate2.Translator(model_path, device=device)
 
# warmup
translator.translate_batch(lines[:max_batch_size * 4], max_batch_size=max_batch_size, beam_size=beam_size)
 
time1 = time.time()
trans_results = translator.translate_batch(lines, max_batch_size=max_batch_size, beam_size=beam_size)
time2 = time.time()
print("ctranslate2 translate time:", time2 - time1)
 
result_lines = [" ".join(result.hypotheses[0]) + "\n" for result in trans_results]
 
with open(tgt_file, "w") as f:
    f.writelines(result_lines)