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Update: This post has now become a book! Check out LLM-book.com which contains (Chapter 3) an updated and expanded version of this post speaking about the latest Transformer models and how they've evolved in the seven years since the original Transformer (like Multi-Query Attention and RoPE Positional embeddings).

In the previous post, we looked at Attention – a ubiquitous method in modern deep learning models. Attention is a concept that helped improve the performance of neural machine translation applications. In this post, we will look at The Transformer – a model that uses attention to boost the speed with which these models can be trained. The Transformer outperforms the Google Neural Machine Translation model in specific tasks. The biggest benefit, however, comes from how The Transformer lends itself to parallelization. It is in fact Google Cloud’s recommendation to use The Transformer as a reference model to use their Cloud TPU offering. So let’s try to break the model apart and look at how it functions.在之前的文章中，我们讨论了注意力机制——现代深度学习模型中一种普遍使用的方法。注意力是一个帮助提升神经机器翻译应用性能的概念。在本文中，我们将探讨Transformer——一个利用注意力机制来加速模型训练的模型。Transformer在特定任务上超越了谷歌神经机器翻译模型。然而，最大的优势在于Transformer易于并行化。事实上，谷歌云推荐使用Transformer作为参考模型来使用其Cloud TPU服务。那么，让我们尝试拆解这个模型，看看它是如何工作的。

The Transformer was proposed in the paper Attention is All You Need. A TensorFlow implementation of it is available as a part of the Tensor2Tensor package. Harvard’s NLP group created a guide annotating the paper with PyTorch implementation. In this post, we will attempt to oversimplify things a bit and introduce the concepts one by one to hopefully make it easier to understand to people without in-depth knowledge of the subject matter.Transformer在论文《Attention is All You Need》中被提出。其TensorFlow实现作为Tensor2Tensor包的一部分提供。哈佛大学NLP小组创建了一份指南，使用PyTorch实现注释了该论文。在本文中，我们将尝试简化一些内容，并逐一介绍这些概念，希望能让没有深入专业知识的人更容易理解。

2025 Update: We’ve built a free short course that brings the contents of this post up-to-date with animations:2025年更新：我们制作了一门免费的短期课程，通过动画将本文内容更新到最新状态：

A High-Level Look高层概览

Let’s begin by looking at the model as a single black box. In a machine translation application, it would take a sentence in one language, and output its translation in another.让我们首先将模型视为一个黑盒。在机器翻译应用中，它接收一种语言的句子，并输出其另一种语言的翻译。

Popping open that Optimus Prime goodness, we see an encoding component, a decoding component, and connections between them.打开这个擎天柱般的好东西，我们看到一个编码组件、一个解码组件以及它们之间的连接。

The encoding component is a stack of encoders (the paper stacks six of them on top of each other – there’s nothing magical about the number six, one can definitely experiment with other arrangements). The decoding component is a stack of decoders of the same number.编码组件是一堆编码器（论文中堆叠了六个——数字六并没有什么神奇之处，完全可以尝试其他排列）。解码组件是相同数量的解码器堆叠而成。

The encoders are all identical in structure (yet they do not share weights). Each one is broken down into two sub-layers:所有编码器在结构上是相同的（但它们不共享权重）。每个编码器分为两个子层：

The encoder’s inputs first flow through a self-attention layer – a layer that helps the encoder look at other words in the input sentence as it encodes a specific word. We’ll look closer at self-attention later in the post.编码器的输入首先经过一个自注意力层——该层帮助编码器在编码特定单词时查看输入句子中的其他单词。我们将在后文详细讨论自注意力。

The outputs of the self-attention layer are fed to a feed-forward neural network. The exact same feed-forward network is independently applied to each position.自注意力层的输出被送入一个前馈神经网络。完全相同的前馈网络独立地应用于每个位置。

The decoder has both those layers, but between them is an attention layer that helps the decoder focus on relevant parts of the input sentence (similar what attention does in seq2seq models).解码器也有这两个层，但在它们之间有一个注意力层，帮助解码器关注输入句子的相关部分（类似于seq2seq模型中注意力的作用）。

Bringing The Tensors Into The Picture引入张量

Now that we’ve seen the major components of the model, let’s start to look at the various vectors/tensors and how they flow between these components to turn the input of a trained model into an output.现在我们已经了解了模型的主要组件，让我们开始看看各种向量/张量以及它们如何在组件之间流动，将训练好的模型的输入转化为输出。

As is the case in NLP applications in general, we begin by turning each input word into a vector using an embedding algorithm.与NLP应用的一般情况一样，我们首先使用嵌入算法将每个输入单词转换为向量。

The embedding only happens in the bottom-most encoder. The abstraction that is common to all the encoders is that they receive a list of vectors each of the size 512 – In the bottom encoder that would be the word embeddings, but in other encoders, it would be the output of the encoder that’s directly below. The size of this list is hyperparameter we can set – basically it would be the length of the longest sentence in our training dataset.嵌入只发生在最底层的编码器中。所有编码器共有的抽象是它们接收一个大小为512的向量列表——在底层编码器中，这些是词嵌入，但在其他编码器中，则是直接下方编码器的输出。这个列表的大小是一个我们可以设置的超参数——基本上就是训练数据集中最长句子的长度。

After embedding the words in our input sequence, each of them flows through each of the two layers of the encoder.在嵌入输入序列中的单词后，每个单词都流经编码器的两个层。

Here we begin to see one key property of the Transformer, which is that the word in each position flows through its own path in the encoder. There are dependencies between these paths in the self-attention layer. The feed-forward layer does not have those dependencies, however, and thus the various paths can be executed in parallel while flowing through the feed-forward layer.这里我们开始看到Transformer的一个关键特性：每个位置的单词在编码器中沿着自己的路径流动。在自注意力层中，这些路径之间存在依赖关系。然而，前馈层没有这些依赖关系，因此各种路径可以在流经前馈层时并行执行。

Next, we’ll switch up the example to a shorter sentence and we’ll look at what happens in each sub-layer of the encoder.接下来，我们将示例切换为一个较短的句子，并查看编码器每个子层中发生了什么。

Now We’re Encoding!现在开始编码！

As we’ve mentioned already, an encoder receives a list of vectors as input. It processes this list by passing these vectors into a ‘self-attention’ layer, then into a feed-forward neural network, then sends out the output upwards to the next encoder.正如我们已经提到的，编码器接收一个向量列表作为输入。它通过将这些向量传递到“自注意力”层，然后传递到前馈神经网络来处理这个列表，然后将输出向上发送到下一个编码器。

Self-Attention at a High Level高层自注意力

Don’t be fooled by me throwing around the word “self-attention” like it’s a concept everyone should be familiar with. I had personally never came across the concept until reading the Attention is All You Need paper. Let us distill how it works.不要被我随意抛出“自注意力”这个词所迷惑，好像这是一个每个人都应该熟悉的概念。我个人在阅读《Attention is All You Need》论文之前从未遇到过这个概念。让我们来剖析它的工作原理。

Say the following sentence is an input sentence we want to translate:假设以下句子是我们想要翻译的输入句子：

”The animal didn't cross the street because it was too tired”“那只动物没有过马路，因为它太累了”

What does “it” in this sentence refer to? Is it referring to the street or to the animal? It’s a simple question to a human, but not as simple to an algorithm.这个句子中的“它”指的是什么？是指马路还是动物？对人类来说这是一个简单的问题，但对算法来说并不简单。

When the model is processing the word “it”, self-attention allows it to associate “it” with “animal”.当模型处理单词“它”时，自注意力允许它将“它”与“动物”关联起来。

As the model processes each word (each position in the input sequence), self attention allows it to look at other positions in the input sequence for clues that can help lead to a better encoding for this word.当模型处理每个单词（输入序列中的每个位置）时，自注意力允许它查看输入序列中的其他位置，以获取有助于更好编码该单词的线索。

If you’re familiar with RNNs, think of how maintaining a hidden state allows an RNN to incorporate its representation of previous words/vectors it has processed with the current one it’s processing. Self-attention is the method the Transformer uses to bake the “understanding” of other relevant words into the one we’re currently processing.如果你熟悉RNN，可以想想维护隐藏状态如何允许RNN将之前处理过的单词/向量的表示与当前正在处理的表示结合起来。自注意力是Transformer用来将其他相关单词的“理解”融入当前正在处理的单词的方法。

Be sure to check out the Tensor2Tensor notebook where you can load a Transformer model, and examine it using this interactive visualization.请务必查看Tensor2Tensor笔记本，您可以在其中加载Transformer模型，并通过交互式可视化进行检查。

Self-Attention in Detail自注意力详解

Let’s first look at how to calculate self-attention using vectors, then proceed to look at how it’s actually implemented – using matrices.让我们首先看看如何使用向量计算自注意力，然后继续看它实际上是如何实现的——使用矩阵。

The first step in calculating self-attention is to create three vectors from each of the encoder’s input vectors (in this case, the embedding of each word). So for each word, we create a Query vector, a Key vector, and a Value vector. These vectors are created by multiplying the embedding by three matrices that we trained during the training process.计算自注意力的第一步是从每个编码器的输入向量（在本例中，是每个单词的嵌入）创建三个向量。因此，对于每个单词，我们创建一个查询向量、一个键向量和一个值向量。这些向量是通过将嵌入乘以我们在训练过程中训练的三个矩阵来创建的。

Notice that these new vectors are smaller in dimension than the embedding vector. Their dimensionality is 64, while the embedding and encoder input/output vectors have dimensionality of 512. They don’t HAVE to be smaller, this is an architecture choice to make the computation of multiheaded attention (mostly) constant.请注意，这些新向量的维度比嵌入向量小。它们的维度是64，而嵌入和编码器输入/输出向量的维度是512。它们不一定非要更小，这是一种架构选择，目的是使多头注意力的计算（基本上）保持恒定。

What are the “query”, “key”, and “value” vectors?

They’re abstractions that are useful for calculating and thinking about attention. Once you proceed with reading how attention is calculated below, you’ll know pretty much all you need to know about the role each of these vectors plays.什么是“查询”、“键”和“值”向量？ 它们是用于计算和思考注意力的抽象概念。一旦你继续阅读下面关于注意力如何计算的内容，你几乎就会知道每个向量所扮演的角色。

The second step in calculating self-attention is to calculate a score. Say we’re calculating the self-attention for the first word in this example, “Thinking”. We need to score each word of the input sentence against this word. The score determines how much focus to place on other parts of the input sentence as we encode a word at a certain position.计算自注意力的第二步是计算一个分数。假设我们正在计算本例中第一个单词“Thinking”的自注意力。我们需要对输入句子中的每个单词相对于该单词进行评分。分数决定了当我们编码某个位置的单词时，对输入句子其他部分的关注程度。

The score is calculated by taking the dot product of the query vector with the key vector of the respective word we’re scoring. So if we’re processing the self-attention for the word in position #1, the first score would be the dot product of q1 and k1. The second score would be the dot product of q1 and k2.分数是通过将查询向量与所评分单词的键向量进行点积来计算的。因此，如果我们正在处理位置#1的单词的自注意力，第一个分数将是q1和k1的点积。第二个分数将是q1和k2的点积。

The third and fourth steps are to divide the scores by 8 (the square root of the dimension of the key vectors used in the paper – 64. This leads to having more stable gradients. There could be other possible values here, but this is the default), then pass the result through a softmax operation. Softmax normalizes the scores so they’re all positive and add up to 1.第三步和第四步是将分数除以8（论文中使用的键向量维度的平方根——64。这有助于获得更稳定的梯度。这里可能有其他可能的值，但这是默认值），然后将结果通过softmax操作。Softmax将分数归一化，使它们全部为正且总和为1。

This softmax score determines how much each word will be expressed at this position. Clearly the word at this position will have the highest softmax score, but sometimes it’s useful to attend to another word that is relevant to the current word.这个softmax分数决定了每个单词在该位置被表达的程度。显然，该位置的单词将具有最高的softmax分数，但有时关注与当前单词相关的另一个单词是有用的。

The fifth step is to multiply each value vector by the softmax score (in preparation to sum them up). The intuition here is to keep intact the values of the word(s) we want to focus on, and drown-out irrelevant words (by multiplying them by tiny numbers like 0.001, for example).第五步是将每个值向量乘以softmax分数（为求和做准备）。这里的直觉是保持我们想要关注的单词的值不变，并淹没不相关的单词（例如，通过将它们乘以像0.001这样的小数）。

The sixth step is to sum up the weighted value vectors. This produces the output of the self-attention layer at this position (for the first word).第六步是求和加权后的值向量。这产生了该位置（对于第一个单词）自注意力层的输出。

That concludes the self-attention calculation. The resulting vector is one we can send along to the feed-forward neural network. In the actual implementation, however, this calculation is done in matrix form for faster processing. So let’s look at that now that we’ve seen the intuition of the calculation on the word level.自注意力计算到此结束。得到的向量可以发送到前馈神经网络。然而，在实际实现中，为了加快处理速度，这种计算是以矩阵形式完成的。既然我们已经看到了单词级别的计算直觉，那么让我们来看看矩阵形式。

Matrix Calculation of Self-Attention自注意力的矩阵计算

The first step is to calculate the Query, Key, and Value matrices. We do that by packing our embeddings into a matrix X, and multiplying it by the weight matrices we’ve trained (WQ, WK, WV).第一步是计算查询、键和值矩阵。我们通过将嵌入打包成矩阵X，并将其乘以我们训练过的权重矩阵（WQ、WK、WV）来实现。

Finally, since we’re dealing with matrices, we can condense steps two through six in one formula to calculate the outputs of the self-attention layer.最后，由于我们处理的是矩阵，我们可以将第二步到第六步浓缩为一个公式来计算自注意力层的输出。

The Beast With Many Heads多头注意力

The paper further refined the self-attention layer by adding a mechanism called “multi-headed” attention. This improves the performance of the attention layer in two ways:论文进一步改进了自注意力层，增加了一种称为“多头”注意力的机制。这从两个方面提高了注意力层的性能：

1. It expands the model’s ability to focus on different positions. Yes, in the example above, z1 contains a little bit of every other encoding, but it could be dominated by the actual word itself. If we’re translating a sentence like “The animal didn’t cross the street because it was too tired”, it would be useful to know which word “it” refers to.它扩展了模型关注不同位置的能力。是的，在上面的例子中，z1包含了其他编码的一小部分，但它可能被实际单词本身主导。如果我们翻译一个像“那只动物没有过马路，因为它太累了”这样的句子，知道“它”指的是哪个词会很有用。

2. It gives the attention layer multiple “representation subspaces”. As we’ll see next, with multi-headed attention we have not only one, but multiple sets of Query/Key/Value weight matrices (the Transformer uses eight attention heads, so we end up with eight sets for each encoder/decoder). Each of these sets is randomly initialized. Then, after training, each set is used to project the input embeddings (or vectors from lower encoders/decoders) into a different representation subspace.它为注意力层提供了多个“表示子空间”。接下来我们将看到，使用多头注意力，我们不仅有一组，而是多组查询/键/值权重矩阵（Transformer使用八个注意力头，因此每个编码器/解码器最终有八组）。每组都是随机初始化的。然后，在训练之后，每组用于将输入嵌入（或来自较低编码器/解码器的向量）投影到不同的表示子空间中。

If we do the same self-attention calculation we outlined above, just eight different times with different weight matrices, we end up with eight different Z matrices如果我们执行上面概述的相同自注意力计算，只是使用不同的权重矩阵进行八次不同的计算，我们最终得到八个不同的Z矩阵。

This leaves us with a bit of a challenge. The feed-forward layer is not expecting eight matrices – it’s expecting a single matrix (a vector for each word). So we need a way to condense these eight down into a single matrix.这给我们留下了一个小挑战。前馈层期望的不是八个矩阵，而是一个单一的矩阵（每个单词一个向量）。因此，我们需要一种方法将这八个矩阵压缩成一个单一的矩阵。

How do we do that? We concat the matrices then multiply them by an additional weights matrix WO.我们如何做到这一点？我们将这些矩阵连接起来，然后乘以一个额外的权重矩阵WO。

That’s pretty much all there is to multi-headed self-attention. It’s quite a handful of matrices, I realize. Let me try to put them all in one visual so we can look at them in one place这就是多头自注意力的全部内容。我意识到这涉及很多矩阵。让我尝试将它们全部放在一个可视化中，以便我们可以在一个地方查看它们。

Now that we have touched upon attention heads, let’s revisit our example from before to see where the different attention heads are focusing as we encode the word “it” in our example sentence:既然我们已经接触了注意力头，让我们重新审视之前的例子，看看在编码示例句子中的单词“它”时，不同的注意力头关注哪里：

If we add all the attention heads to the picture, however, things can be harder to interpret:然而，如果我们将所有注意力头都添加到图中，事情可能更难解释：

Representing The Order of The Sequence Using Positional Encoding使用位置编码表示序列顺序

One thing that’s missing from the model as we have described it so far is a way to account for the order of the words in the input sequence.到目前为止，我们描述的模型中缺少的一点是考虑输入序列中单词顺序的方法。

To address this, the transformer adds a vector to each input embedding. These vectors follow a specific pattern that the model learns, which helps it determine the position of each word, or the distance between different words in the sequence. The intuition here is that adding these values to the embeddings provides meaningful distances between the embedding vectors once they’re projected into Q/K/V vectors and during dot-product attention.为了解决这个问题，Transformer向每个输入嵌入添加了一个向量。这些向量遵循模型学习的特定模式，有助于确定每个单词的位置或序列中不同单词之间的距离。这里的直觉是，将这些值添加到嵌入中，可以在它们被投影到Q/K/V向量以及点积注意力期间，为嵌入向量提供有意义的距离。

If we assumed the embedding has a dimensionality of 4, the actual positional encodings would look like this:如果我们假设嵌入的维度为4，实际的位置编码将如下所示：

What might this pattern look like?这种模式可能是什么样子？

In the following figure, each row corresponds to a positional encoding of a vector. So the first row would be the vector we’d add to the embedding of the first word in an input sequence. Each row contains 512 values – each with a value between 1 and -1. We’ve color-coded them so the pattern is visible.在下图中，每一行对应一个向量的位置编码。因此，第一行将是我们要添加到输入序列中第一个单词嵌入的向量。每行包含512个值——每个值在1到-1之间。我们对它们进行了颜色编码，以便模式可见。

The formula for positional encoding is described in the paper (section 3.5). You can see the code for generating positional encodings in get_timing_signal_1d(). This is not the only possible method for positional encoding. It, however, gives the advantage of being able to scale to unseen lengths of sequences (e.g. if our trained model is asked to translate a sentence longer than any of those in our training set).位置编码的公式在论文中描述（第3.5节）。您可以在get_timing_signal_1d()中看到生成位置编码的代码。这不是位置编码的唯一可能方法。然而，它提供了能够扩展到未见过的序列长度的优势（例如，如果我们的训练模型被要求翻译比训练集中任何句子都长的句子）。

July 2020 Update: The positional encoding shown above is from the Tensor2Tensor implementation of the Transformer. The method shown in the paper is slightly different in that it doesn’t directly concatenate, but interweaves the two signals. The following figure shows what that looks like. Here’s the code to generate it:2020年7月更新： 上面显示的位置编码来自Transformer的Tensor2Tensor实现。论文中显示的方法略有不同，它不是直接连接，而是交织两个信号。下图显示了它的样子。以下是生成它的代码：

The Residuals残差连接

One detail in the architecture of the encoder that we need to mention before moving on, is that each sub-layer (self-attention, ffnn) in each encoder has a residual connection around it, and is followed by a layer-normalization step.在继续之前，我们需要提到编码器架构中的一个细节：每个编码器中的每个子层（自注意力、前馈神经网络）都有一个残差连接，并且后面跟着一个层归一化步骤。

If we’re to visualize the vectors and the layer-norm operation associated with self attention, it would look like this:如果我们可视化与自注意力相关的向量和层归一化操作，它将如下所示：

This goes for the sub-layers of the decoder as well. If we’re to think of a Transformer of 2 stacked encoders and decoders, it would look something like this:解码器的子层也是如此。如果我们考虑一个由两个堆叠的编码器和解码器组成的Transformer，它看起来像这样：

The Decoder Side解码器端

Now that we’ve covered most of the concepts on the encoder side, we basically know how the components of decoders work as well. But let’s take a look at how they work together.既然我们已经涵盖了编码器端的大部分概念，我们基本上也知道解码器组件的工作原理。但让我们看看它们如何协同工作。

The encoder start by processing the input sequence. The output of the top encoder is then transformed into a set of attention vectors K and V. These are to be used by each decoder in its “encoder-decoder attention” layer which helps the decoder focus on appropriate places in the input sequence:编码器首先处理输入序列。顶层编码器的输出然后被转换为一组注意力向量K和V。这些将被每个解码器在其“编码器-解码器注意力”层中使用，该层帮助解码器关注输入序列中的适当位置：

The following steps repeat the process until a special symbol is reached indicating the transformer decoder has completed its output. The output of each step is fed to the bottom decoder in the next time step, and the decoders bubble up their decoding results just like the encoders did. And just like we did with the encoder inputs, we embed and add positional encoding to those decoder inputs to indicate the position of each word.解码器中的自注意力层与编码器中的略有不同：以下步骤重复该过程，直到达到一个特殊的<end of sentence>符号，表示Transformer解码器已完成其输出。每一步的输出被馈送到下一个时间步的底层解码器，解码器像编码器一样向上传递其解码结果。就像我们对编码器输入所做的那样，我们嵌入这些解码器输入并添加位置编码以指示每个单词的位置。

The self attention layers in the decoder operate in a slightly different way than the one in the encoder:在解码器中，自注意力层只允许关注输出序列中较早的位置。这是通过在自注意力计算的softmax步骤之前屏蔽未来位置（将它们设置为-inf）来实现的。

In the decoder, the self-attention layer is only allowed to attend to earlier positions in the output sequence. This is done by masking future positions (setting them to -inf) before the softmax step in the self-attention calculation.“编码器-解码器注意力”层的工作原理与多头自注意力类似，只是它从其下方的层创建查询矩阵，并从编码器堆栈的输出中获取键和值矩阵。

The “Encoder-Decoder Attention” layer works just like multiheaded self-attention, except it creates its Queries matrix from the layer below it, and takes the Keys and Values matrix from the output of the encoder stack.最后的线性层和Softmax层

The Final Linear and Softmax Layer解码器堆栈输出一个浮点数向量。我们如何将其转换为一个单词？这是最后的线性层的工作，其后跟着一个Softmax层。

The decoder stack outputs a vector of floats. How do we turn that into a word? That’s the job of the final Linear layer which is followed by a Softmax Layer.线性层是一个简单的全连接神经网络，它将解码器堆栈产生的向量投影到一个大得多的向量中，称为logits向量。

The Linear layer is a simple fully connected neural network that projects the vector produced by the stack of decoders, into a much, much larger vector called a logits vector.假设我们的模型知道10,000个独特的英语单词（我们模型的“输出词汇表”），这些单词是从训练数据集中学习到的。这将使logits向量有10,000个单元格宽——每个单元格对应一个独特单词的分数。这就是我们解释模型输出后跟线性层的方式。

Let’s assume that our model knows 10,000 unique English words (our model’s “output vocabulary”) that it’s learned from its training dataset. This would make the logits vector 10,000 cells wide – each cell corresponding to the score of a unique word. That is how we interpret the output of the model followed by the Linear layer.然后softmax层将这些分数转换为概率（全部为正，总和为1.0）。选择概率最高的单元格，并输出与该单元格关联的单词作为该时间步的输出。

The softmax layer then turns those scores into probabilities (all positive, all add up to 1.0). The cell with the highest probability is chosen, and the word associated with it is produced as the output for this time step.此图从底部开始，显示解码器堆栈输出的向量。然后将其转换为输出单词。

Recap Of Training既然我们已经涵盖了训练好的Transformer的整个前向传播过程，那么简要了解一下训练模型的直觉会很有用。

Now that we’ve covered the entire forward-pass process through a trained Transformer, it would be useful to glance at the intuition of training the model.在训练期间，未训练的模型将经历完全相同的前向传播。但由于我们在标记的训练数据集上训练它，我们可以将其输出与实际正确输出进行比较。

During training, an untrained model would go through the exact same forward pass. But since we are training it on a labeled training dataset, we can compare its output with the actual correct output.为了可视化这一点，假设我们的输出词汇表只包含六个单词（“a”、“am”、“i”、“thanks”、“student”和“<eos>”（表示“句子结束”））。

To visualize this, let’s assume our output vocabulary only contains six words(“a”, “am”, “i”, “thanks”, “student”, and “<eos>” (short for ‘end of sentence’)).我们模型的输出词汇表是在预处理阶段创建的，甚至在我们开始训练之前。

Once we define our output vocabulary, we can use a vector of the same width to indicate each word in our vocabulary. This also known as one-hot encoding. So for example, we can indicate the word “am” using the following vector:示例：输出词汇表的独热编码

Following this recap, let’s discuss the model’s loss function – the metric we are optimizing during the training phase to lead up to a trained and hopefully amazingly accurate model.损失函数

The Loss Function假设我们正在训练我们的模型。假设这是训练阶段的第一步，我们在一个简单的例子上训练它——将“merci”翻译成“thanks”。

Say we are training our model. Say it’s our first step in the training phase, and we’re training it on a simple example – translating “merci” into “thanks”.这意味着，我们希望输出是一个指示单词“thanks”的概率分布。但由于这个模型尚未训练，这不太可能发生。

What this means, is that we want the output to be a probability distribution indicating the word “thanks”. But since this model is not yet trained, that’s unlikely to happen just yet.由于模型的参数（权重）都是随机初始化的，（未训练的）模型会为每个单元格/单词生成一个具有任意值的概率分布。我们可以将其与实际输出进行比较，然后使用反向传播调整所有模型的权重，使输出更接近期望输出。

How do you compare two probability distributions? We simply subtract one from the other. For more details, look at cross-entropy and Kullback–Leibler divergence.但请注意，这是一个过于简化的例子。更现实的情况是，我们会使用一个比一个单词更长的句子。例如——输入：“je suis étudiant”和期望输出：“i am a student”。这实际上意味着，我们希望我们的模型依次输出概率分布，其中：

But note that this is an oversimplified example. More realistically, we’ll use a sentence longer than one word. For example – input: “je suis étudiant” and expected output: “i am a student”. What this really means, is that we want our model to successively output probability distributions where:每个概率分布由一个宽度为vocab_size的向量表示（在我们的玩具示例中为6，但更现实的是像30,000或50,000这样的数字）

• Each probability distribution is represented by a vector of width vocab_size (6 in our toy example, but more realistically a number like 30,000 or 50,000)第一个概率分布在单词“i”对应的单元格处具有最高概率
• The first probability distribution has the highest probability at the cell associated with the word “i”第二个概率分布在单词“am”对应的单元格处具有最高概率
• The second probability distribution has the highest probability at the cell associated with the word “am”依此类推，直到第五个输出分布指示“<end of sentence>”符号，该符号在10,000个元素的词汇表中也有一个对应的单元格。
• And so on, until the fifth output distribution indicates ‘<end of sentence>’ symbol, which also has a cell associated with it from the 10,000 element vocabulary.在训练示例中，针对一个样本句子，我们将训练模型的目标概率分布。

After training the model for enough time on a large enough dataset, we would hope the produced probability distributions would look like this:希望在训练后，模型会输出我们期望的正确翻译。当然，如果这个短语是训练数据集的一部分，这并不能真正说明问题（参见：交叉验证）。请注意，每个位置都有一点概率，即使它不太可能是该时间步的输出——这是softmax的一个非常有用的属性，有助于训练过程。

Now, because the model produces the outputs one at a time, we can assume that the model is selecting the word with the highest probability from that probability distribution and throwing away the rest. That’s one way to do it (called greedy decoding). Another way to do it would be to hold on to, say, the top two words (say, ‘I’ and ‘a’ for example), then in the next step, run the model twice: once assuming the first output position was the word ‘I’, and another time assuming the first output position was the word ‘a’, and whichever version produced less error considering both positions #1 and #2 is kept. We repeat this for positions #2 and #3…etc. This method is called “beam search”, where in our example, beam_size was two (meaning that at all times, two partial hypotheses (unfinished translations) are kept in memory), and top_beams is also two (meaning we’ll return two translations). These are both hyperparameters that you can experiment with.开始变形吧

Go Forth And Transform我希望您发现这是一个有用的起点，可以打破Transformer主要概念的僵局。如果您想更深入地了解，我建议以下步骤：

I hope you’ve found this a useful place to start to break the ice with the major concepts of the Transformer. If you want to go deeper, I’d suggest these next steps:阅读《Attention Is All You Need》论文、Transformer博客文章（Transformer：一种用于语言理解的新型神经网络架构）和Tensor2Tensor公告。

• Read the Attention Is All You Need paper, the Transformer blog post (Transformer: A Novel Neural Network Architecture for Language Understanding), and the Tensor2Tensor announcement.观看Łukasz Kaiser的演讲，了解模型及其细节
• Watch Łukasz Kaiser’s talk walking through the model and its details使用Tensor2Tensor仓库中提供的Jupyter Notebook进行实验
• Play with the Jupyter Notebook provided as part of the Tensor2Tensor repo探索Tensor2Tensor仓库。
• Explore the Tensor2Tensor repo.后续工作：

Follow-up works:深度可分离卷积用于神经机器翻译

• Depthwise Separable Convolutions for Neural Machine Translation一个模型学习所有
• One Model To Learn Them All序列模型的离散自编码器
• Discrete Autoencoders for Sequence Models通过总结长序列生成维基百科
• Generating Wikipedia by Summarizing Long Sequences图像Transformer
• Image TransformerTransformer模型的训练技巧
• Training Tips for the Transformer Model具有相对位置表示的自注意力
• Self-Attention with Relative Position Representations使用离散潜在变量进行序列模型的快速解码
• Fast Decoding in Sequence Models using Discrete Latent VariablesAdafactor：具有次线性内存成本的自适应学习率
• Adafactor: Adaptive Learning Rates with Sublinear Memory Cost致谢

Acknowledgements感谢Illia Polosukhin、Jakob Uszkoreit、Llion Jones、Lukasz Kaiser、Niki Parmar和Noam Shazeer对本文早期版本提供的反馈。

Thanks to Illia Polosukhin, Jakob Uszkoreit, Llion Jones , Lukasz Kaiser, Niki Parmar, and Noam Shazeer for providing feedback on earlier versions of this post.如有任何更正或反馈，请在Twitter上联系我。

Please hit me up on Twitter for any corrections or feedback.写于2018年6月27日