subtitles/zh-CN/23_what-is-dynamic-padding.srt

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（空气呼啸）
(air whooshing)

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- 什么是动态填充？
- What is dynamic padding?

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在 “一起批量输入” 视频中，
In the "Batching Inputs together" video,

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我们已经看到为了能够对（不同长度的同批次）输入进行分组
we have seen that to be able to group inputs

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（同一批不同长度的），
of different lengths in the same batch,

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我们需要向所有短输入添加填充标记
we need to add padding tokens to all the short inputs

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直到它们的长度都相同。
until they are all of the same length.

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例如，这里最长的句子是第三句，
Here, for instance, the longest sentence is the third one,

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我们需要添加五个、两个或七个填充标记
and we need to add five, two, or seven pad tokens

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到其他句子使得四个句子具有
to the other sentences to have four sentences

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相同的长度。
of the same lengths.

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在处理整个数据集时，
When dealing with a whole dataset,

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我们可以应用各种填充策略。
there are various padding strategies we can apply.

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最明显的一种是填充整个数据集所有的样本
The most obvious one is to pad all the elements

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达到相同的长度：
of the dataset to the same length:

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最长样本的长度。
the length of the longest sample.

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我们得到具有相同形状的批次
This will then give us batches

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that all have the same shape

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（其长度）由最大序列长度决定。
determined by the maximum sequence length.

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缺点是（如果）批次样本由短句组成
The downside is that batches composed from short sentences

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将带来很多填充符号
will have a lot of padding tokens

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并且在模型中引入更多不必要的计算。
which will introduce more computations in the model

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we ultimately don't need.

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为了避免这种情况，另一种策略是填充（短样本）符号
To avoid this, another strategy is to pad the elements

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当把它们放在一批时，
when we batch them together,

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达到本批次中最长句子的长度。
to the longest sentence inside the batch.

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这样，由短样本输入组成的批次大小
This way, batches composed of short inputs will be smaller

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会比按整个数据集最长句子的长度（补齐）批次更小
than the batch containing the longest sentence

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in the dataset.

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这将在 CPU 和 GPU 上产生一些不错的加速。
This will yield some nice speedup on CPU and GPU.

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缺点是所有批次
The downside is that all batches

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会有不同的形状，
will then have different shapes,

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这会减慢 TPU 等加速器的训练速度。
which slows down training on accelerators like TPUs.

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让我们看看如何在实践中应用这两种策略。
Let's see how to apply both strategies in practice.

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我们实际上已经知道了如何使用固定填充
We have actually seen how to apply fixed padding

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在数据集概述视频中，
in the Datasets Overview video,

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当我们预处理 MRPC 数据集时：
when we preprocessed the MRPC dataset:

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加载数据集和分词器后，
after loading the dataset and tokenizer,

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我们将符号化应用于所有数据集
we applied the tokenization to all the dataset

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包括填充和截断
with padding and truncation

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保证所有样本的长度为 128 。
to make all samples of length 128.

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最后，如果我们传递这个数据集
As a result, if we pass this dataset

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到 PyTorch DataLoader，
to a PyTorch DataLoader,

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我们得到形状为 batch_size 乘以 16 乘以 128 的批次。

we get batches of shape batch size, here 16, by 128.

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要应用动态填充，
To apply dynamic padding,

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我们必须将填充推迟到批量准备，
we must defer the padding to the batch preparation,

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所以我们从标记函数中删除了那部分。
so we remove that part from our tokenize function.

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我们仍然保留截断部分
We still leave the truncation part

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以便大于最大长度的输入
so that inputs that are bigger than the maximum length

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被模型接受，通常是 512，
accepted by the model, usually 512,

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被截断到那个长度。
get truncated to that length.

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然后我们动态地填充我们的样本
Then we pad our samples dynamically

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通过使用数据整理器。
by using a data collator.

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Transformers 库中的那些类
Those classes in the Transformers library

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负责应用所有的最终处理
are responsible for applying all the final processing

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在形成批次之前需要，
needed before forming a batch,

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这里 DataCollatorWithPadding 将填充样本
here DataCollatorWithPadding will pad the samples

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到这批句子中的最大长度。
to the maximum length inside the batch of sentences.

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我们将它作为整理函数传递给 PyTorch DataLoader，
We pass it to the PyTorch DataLoader as a collate function,

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然后观察到生成的批次
then observe that the batches generated

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有不同的长度，
have various lengths,

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一直低于之前的 128。
all way below the 128 from before.

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动态批处理几乎在 CPU 和 GPU 上更快，

Dynamic batching will almost always be faster

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所以如果可以的话你应该应用它。
on CPUs and GPUs, so you should apply it if you can.

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但是，请记住切换回固定填充
Remember to switch back to fixed padding, however,

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如果你在 TPU 上运行你的训练脚本
if you run your training script on TPU

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或者需要固定形状的批次输入。
or need batches of fixed shapes.

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