我一直在玩弄tensorflow(CPU)和一些语言模型,到目前为止,一切都很好。
但是在看到我的旧CPU在所有的模型训练中慢慢地被淘汰之后,我决定是时候让我的RTX2080发挥一些作用了。我一直在遵循来自washinton university的指南:。我很快就让tensorflow gpu运行起来,在一些轻型预测之类的东西上运行它
但当我开始运行GPT2语言模型时,我遇到了一些小问题。我首先标记数据:
from tokenizers.models import BPE
from tokenizers import Tokenizer
from tokenizers.decoders import ByteLevel as ByteLevelDecoder
from tokenizers.normalizers import NFKC, Sequence
from tokenizers.pre_tokenizers import ByteLevel
from tokenizers.trainers import BpeTrainer
class BPE_token(object):
def __init__(self):
self.tokenizer = Tokenizer(BPE())
self.tokenizer.normalizer = Sequence([
NFKC()
])
self.tokenizer.pre_tokenizer = ByteLevel()
self.tokenizer.decoder = ByteLevelDecoder()
def bpe_train(self, paths):
trainer = BpeTrainer(vocab_size=50000, show_progress=True, inital_alphabet=ByteLevel.alphabet(), special_tokens=[
"<s>",
"<pad>",
"</s>",
"<unk>",
"<mask>"
])
self.tokenizer.train(trainer, paths)
def save_tokenizer(self, location, prefix=None):
if not os.path.exists(location):
os.makedirs(location)
self.tokenizer.model.save(location, prefix)
# ////////// TOKENIZE DATA ////////////
from pathlib import Pa th
import os# the folder 'text' contains all the files
paths = [str(x) for x in Path("./da_corpus/").glob("**/*.txt")]
tokenizer = BPE_token()# train the tokenizer model
tokenizer.bpe_train(paths)# saving the tokenized data in our specified folder
save_path = 'tokenized_data'
tokenizer.save_tokenizer(save_path)
上面的代码可以完美地工作并标记数据——就像tensorflow(CPU)一样。在将数据标记化后,我开始训练我的模型-但在它开始之前,我得到以下结果:
from transformers import GPT2Config, TFGPT2LMHeadModel, GPT2Tokenizer # loading tokenizer from the saved model path
ImportError: cannot import name 'TFGPT2LMHeadModel' from 'transformers' (unknown location)
Transformers软件包似乎已正确安装在站点软件包库中,我似乎能够使用其他变压器-但不能TFGPT2LMHeadModel 我读过谷歌上的所有内容,也尝试过tensorflow gpu、transformers、Tokenizer和许多其他软件包的不同版本,可惜没有任何帮助
套餐:
我刚刚使用了下面的命令,它按预期工作
通过安装tensorflow gpu=2.3.0&;cuda 10.1
遵循本指南: https://medium.com/analytics-vidhya/tensorflow-2-3-0-with-gpu-support-on-windows-10-f975a552ea7c
使用此命令安装gpu2.3.0:
python-mpip安装https://storage.googleapis.com/tensorflow/windows/gpu/tensorflow_gpu-2.3.0-cp37-cp37m-win_amd64.whl
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