Question

我正在 Huggingface 中测试 Bert 基础和 Bert 蒸馏模型，有 4 种速度场景，batch_size = 1：

1) bert-base-uncased: 154ms per request
2) bert-base-uncased with quantifization: 94ms per request
3) distilbert-base-uncased: 86ms per request
4) distilbert-base-uncased with quantifization: 69ms per request

我使用 IMDB 文本作为实验数据并设置 max_length=512，所以它很长。 Ubuntu 18.04 上的 CPU 信息如下：

cat /proc/cpuinfo  | grep 'name'| uniq
model name  : Intel(R) Xeon(R) Platinum 8163 CPU @ 2.50GHz

机器有 3 个 GPU 可供使用：

Tesla V100-SXM2

对于实时应用程序来说似乎很慢。对于 bert 基础模型，这些速度是否正常？

测试代码如下：

import pandas as pd
import torch.quantization

from transformers import AutoTokenizer, AutoModel, DistilBertTokenizer, DistilBertModel

def get_embedding(model, tokenizer, text):
    inputs = tokenizer(text, return_tensors="pt", max_length=512, truncation=True)
    outputs = model(**inputs)
    output_tensors = outputs[0][0]
    output_numpy = output_tensors.detach().numpy()
    embedding = output_numpy.tolist()[0]

def process_text(model, tokenizer, text_lines):
    for index, line in enumerate(text_lines):
        embedding = get_embedding(model, tokenizer, line)
        if index % 100 == 0:
            print('Current index: {}'.format(index))

import time
from datetime import timedelta
if __name__ == "__main__":

    df = pd.read_csv('../data/train.csv', sep='\t')
    df = df.head(1000)
    text_lines = df['review']
    text_line_count = len(text_lines)
    print('Text size: {}'.format(text_line_count))

    start = time.time()

    tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
    model = AutoModel.from_pretrained("bert-base-uncased")
    process_text(model, tokenizer, text_lines)

    end = time.time()
    print('Total time spent with bert base: {}'.format(str(timedelta(seconds=end - start))))

    model = torch.quantization.quantize_dynamic(model, {torch.nn.Linear}, dtype=torch.qint8)
    process_text(model, tokenizer, text_lines)

    end2 = time.time()
    print('Total time spent with bert base quantization: {}'.format(str(timedelta(seconds=end2 - end))))

    tokenizer = DistilBertTokenizer.from_pretrained("distilbert-base-uncased")
    model = DistilBertModel.from_pretrained("distilbert-base-uncased")
    process_text(model, tokenizer, text_lines)

    end3 = time.time()
    print('Total time spent with distilbert: {}'.format(str(timedelta(seconds=end3 - end2))))

    model = DistilBertModel.from_pretrained("distilbert-base-uncased")
    model = torch.quantization.quantize_dynamic(model, {torch.nn.Linear}, dtype=torch.qint8)
    process_text(model, tokenizer, text_lines)

    end4 = time.time()
    print('Total time spent with distilbert quantization: {}'.format(str(timedelta(seconds=end4 - end3))))

编辑：根据建议，我改为以下内容：

inputs = tokenizer(text_batch, padding=True, return_tensors="pt")
outputs = model(**inputs)

其中 text_batch 是作为输入的文本列表。

Answer 1

不，你可以加快速度。

首先，为什么要使用批量大小 1 进行测试？

tokenizer 和 model 都接受批量输入。基本上，您可以传递每行包含一个样本的二维数组/列表。请参阅分词器的文档：https://huggingface.co/transformers/main_classes/tokenizer.html#transformers.PreTrainedTokenizer.__call__ 这同样适用于模型。

此外，即使您使用的批量大小大于 1，您的 for 循环也是顺序的。您可以创建一个测试数据，然后使用 Trainer 类和 trainer.predict()

另请参阅我在 HF 论坛上的讨论：https://discuss.huggingface.co/t/urgent-trainer-predict-and-model-generate-creates-totally-different-predictions/3426

这些是 PyTorch 中 Bert 预训练模型推理的正常速度吗

1 个答案: