attention is all you need explained

Results for English-to-German translation and English-to-French translation: The Transformer achieves better BLUE scores than previous state-of-the-art models at a fraction of the training cost. 184K views. The Transformer paper, "Attention is All You Need" is the #1 all-time paper on Arxiv Sanity Preserver as of this writing (Aug 14, 2019). Extensions to Attention If we only computed a single attention weighted sum of the values, it would be difficult to capture various different aspects of the input. In addition to attention, the Transformer uses layer normalization and residual connections to make optimization easier. Now, you may be wondering, didn't LSTMs handle the long-range dependency problem in RNNs? Think of attention as a highlighter. Out of all these noises, you find yourself able to tune out the irrelevant sounds … However, in those CNN-based approaches, the number of calculations in the parallel computation of the hidden representation, for input → output position in the sequence, grows with the distance between those positions (architecture grows in height). As an alternative to convolutions, a new approach is presented by the Transformer. Previously, RNNs were regarded as the go-to architecture for translation. They could even be the same! For those unfamiliar with neural machine translation, I'll provide a quick overview in this section that should be enough to understand the paper "Attention is All You Need". These models are trained to maximize the likelihood of generating the correct output sequence: at each step, the decoder is rewarded for predicting the next word correctly and penalized for making mistakes. awesome transformer attention attention-is-all-you-need multihead-attention reformer self-attention transformer-network longformer linformer Updated Nov 12, 2020 Python Traditionally, the attention weights were the relevance of the encoder hidden states (values) in processing the decoder state (query) and were calculated based on the encoder hidden states (keys) and the decoder hidden state (query). The overall Transformer looks like this (don't be intimidated, we'll dissect this diagram piece by piece): As you can see, the Transformer still uses the basic encoder-decoder design of traditional neural machine translation systems. Here's some code to implement the positional encodings: And this basically finishes our discussion of the Transformer! For instance, the word "than" as in "She is taller than me" and "I have no choice other than to write this blog post" use "than" in different ways. https://ai.googleblog.com/2017/08/transformer-novel-neural-network.html, https://ai.googleblog.com/2017/06/accelerating-deep-learning-research.html, http://nlp.seas.harvard.edu/2018/04/03/attention.html, https://mchromiak.github.io/articles/2017/Sep/12/Transformer-Attention-is-all-you-need/#.W6EvHRMza-p, https://www.cloudsek.com/announcements/blog/hierarchical-attention-text-classification/, https://medium.com/syncedreview/a-brief-overview-of-attention-mechanism-13c578ba9129, http://mlexplained.com/2017/12/29/attention-is-all-you-need-explained/, Understanding Baseline Techniques for REINFORCE, Classification of sounds using android mobile phone and the YAMNet ML model, Let’s Talk Reinforcement Learning — The Fundamentals - Part 1, Position-Encoding and Position-Wise Feed Forward NNs, In a regular encoder-decoder architecture, we fact the problem of long-term dependencies (whether it be LSTM/GRUs or CNNs), To eliminate this, for every input word’s representation we learn the attention distribution with every other word (as pairs) and use said distribution with every pair of words as weights of a linear layer and compute a newer representation for each input representation, This way, not only at the connection between the encoder and the decoder (the end of the sequence) but even at the starting, each input representation has global level information on every other token in the said sequence, Encoder Input is created by adding the Input Embedding and the Positional Encodings, ’N’ layers of Multi-Head Attention and Position-Wise Feed Forward with Residual Connections employed around each of the 2 sub-layers followed by a layer of Normalization. Yannic Kilcher. In contrast, after it outputs the word "dog" (which is "犬" in Japanese), it needs to know what the dog was being compared against, but no longer needs to remember about the dog. This sequentiality is an obstacle toward parallelization of the process. Given what we just learned above, it would seem like attention solves all the problems with RNNs and encoder-decoder architectures. When we do this, if we give the decoder access to the entire target sentence, the model can just repeat the target sentence (in other words, it doesn't need to learn anything). In other words, when we train the network to map the sentence "I like cats more than dogs" to "私は犬よりも猫が好き", we train the network to predict the word "犬" comes after "私は" when the source sentence is "I like cats more than dogs". This network attends over the previous decoder states, so plays a similar role to the decoder hidden state in traditional machine translation architectures. The output is computed as a weighted sum of the values, where the weight assigned to each value is computed by a compatibility function of the query with the corresponding key. To prevent the leftward information flow in the decoder, masking support is implemented inside of the scaled dot-product attention by masking out all values in the input of the softmax of the multi-head attention which corresponds to illegal connections (masking of future/subsequent words). Whether attention really is all you need, this paper is a huge milestone in neural NLP, and this post is an attempt to dissect and explain it. Instead of going from left to right using RNNs, why don't we just allow the encoder and decoder to see the entire input sequence all at once, directly modeling these dependencies using attention? For instance, in the sentence "I like cats more than dogs", you might want to capture the fact that the sentence compares two entities, while also retaining the actual entities being compared. The paper also introduces Masked-LM which makes Bidirectional training possible. Now, let's delve into the details with some PyTorch code: As you can see, a single attention head has a very simple structure: it applies a unique linear transformation to its input queries, keys, and values, computes the attention score between each query and key, then uses it to weight the values and sum them up. Instead of fixing said positional encodings, a learned set of representation is also providing the same result as the above. Here, 2 sinusoids (sine, cosine functions) of different frequencies are used: Where pos is the position of the token and i is the dimension. Deep learning, python, data wrangling and other machine learning related topics explained for practitioners. When RNN’s (or CNN) takes a sequence as an input, it handles sentences word by word. (Attention is all you need) Video unavailable. Though the authors attempted to use learned positional encodings, they found that these pre-set encodings performed just as well. ;) Attention is one of the most complex processes in our brain. Attention allows you to "tune out" information, sensations, and perceptions that are not relevant at the moment … Again, once we have the DecoderBlock implemented, the Decoder is very simple. They also applied dropout to the sum of the embeddings and to the positional encodings. This involves a few steps: MatMul: This is a matrix dot-product operation. But attention is not just about centering your focus on one particular thing; it also involves ignoring a great deal of competing for information and stimuli. We'll talk about the positional encodings later. 5. If you want to replicate the results or learn about the evaluation in more detail, I highly recommend you go and read it! 1:51:03. For each word, self-attention aggregates information from all other words (pairwise) in the context of the sentence, thus creating a new representation for each word — which is an attended representation of all other words in the sequence. The network displayed catastrophic results on removing the Residual Connections. This paper showed that using attention mechanisms alone, it's possible to achieve state-of-the-art results on language translation. For every single target decoder output ( say, t_j ), all hidden state source inputs (say s_i’s) are taken into account to compute the cosine similarity with the source inputs s_i, to generate the theta_i’s (attention weights) for every s_i. This is illustrated in the following figure: This image captures the overall idea fairly well. This paper demonstrates that attention is a powerful and efficient way to replace recurrent networks as a method of modeling dependencies. The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration. Attention is all you need. Let's look at this component in detail. The attention mechanism in the Transformer is interpreted as a way of computing the relevance of a set of values(information)based on some keys and queries. Subsequent models built on the Transformer (e.g. Whenever long-term dependencies (natural language processing problems) are involved, we know that RNNs (even with using hacks like bi-directional, multi-layer, memory-based gates — LSTMs/GRUs) suffer from vanishing gradient problem. represents the dimensionality of the queries and keys. The reason this is called the masked multi-head attention block is that we need to mask the inputs to the decoder from future time-steps. In addition to attention, the Transformer uses layer normalization and residual connections to make optimization easier. Attention basically gives the decoder access to all of the original information instead of just a summary and allows the decoder to pick and choose what information to use. The problem with the encoder-decoder approach above is that the decoder needs different information at different timesteps.For instance, in the example of translating the sentence "I like cats more than dogs" to "私は犬より猫が好き", the second token in the input ("like") corresponds to the last token in the output ("好き"), creating a long-term dependency that the RNN has to carry all while reading the source sentence and generating the target sentence. The point is that by stacking these transformations on top of each other, we can create a very powerful network. The Transformer seems very intimidating at first glance, but when we pick it apart it isn't that complex! First the Query and Key undergo this operation. When we think of attention this way, we can see that the keys, values, and queries could be anything. The Transformer reduces the number of sequential operations to relate two symbols from input/output sequences to a constant O(1) number of operations. The attention weight can be computed in many ways, but the original attention mechanism used a simple feed-forward neural network. The attention weights are the relevance scores of the input encoder hidden states (values), in processing the decoder state (query). They fundamentally share the same concept and many common mathematical operations. The entire encoder is very simple to implement once we have the EncoderBlock: See? The initial inputs to the encoder are the embeddings of the input sequence, and the initial inputs to the decoder are the embeddings of the outputs up to that point. However, there are a few shortcomings of RNNs that the Transformer tries to address. The Multi-Head Attention block just applies multiple blocks in parallel, concatenates their outputs, then applies one single linear transformation. The decoder is very similar to the encoder but has one Multi-Head Attention layer labeled the "masked multi-head attention" network. The most important part here is the “Residual Connections” around the layers. To solve this problem the Transformer uses the Multi-Head Attention block. They were in the process of doing said experiments, but their initial results seem to say that the residual connections there can be mainly applied to the concatenated positional encoding section to propagate it through. Layer normalization is a normalization method in deep learning that is similar to batch normalization (for a more detailed explanation, please refer to this blog post). As discussed previously these intermediate encoder states store the local information of the input sequence. Update: I've heavily updated this post to include code and better explanations regarding the intuition behind how the Transformer works. The context vector (out — refer to the above equation) is now computed for every source input s_i and theta_i (generated for the corresponding target decoder word t_j). And didn't we introduce attention to handle this problem a few paragraphs ago? The best performing models also connect the encoder and decoder through an attention mechanism. Here, we see that the dependencies are learned between the inputs and outputs. If you don't use CNN/RNN, it's a clean stream, but take a closer look, essentially a bunch of vectors to calculate the attention. In layman’s terms, the self-attention mechanism allows the inputs to interact with each other (“self”) and find out who they should pay more attention to (“attention”). The Transformer uses Multi-Head Attention in three different ways: Types of problems the algorithm well suited? Encoder-Decoder Model 2. Imagine that you are at a party for a friend hosted at a bustling restaurant. These new architectures rely on a common paradigm called enco… But, in the Transformer architecture this idea is extended to learn intra-input and intra-output dependencies as well (we’ll get to that soon!). The traditional attention mechanism largely solved the first dependency by giving the decoder access to the entire input sequence. The Transformer models all these dependencies using attention 3. Attention is a concept that helped improve the performance of neural machine translation applications. So what attention does is it asks the decoder to choose which hidden states to use and which to ignore by weighting the hidden states. This makes it more difficult to l… The core of this is the attention mechanism which modifies and attends over a wide range of information. Here, I will present the most impressive results as well as some practical insights that were inferred from the experiments. Attention mechanism solves this problem by allowing the decoder to “look-back” at the encoder’s hidden states based on its current state. At time step 7, the attention mechanism enables the decoder to focus on the word "étudiant" ("student" in french) before it generates the English translation. The paper uses the following equation to compute the positional encodings: where represents the position, and is the dimension. Can still have short-term memory problems Transformer seems very intimidating at first,. The left and the decoder is on the GPU sequence to another sentence of Transformer ( a purely attention-based to! Which you can view here evaluation in more detail, I highly recommend you go and read!. Using an encoder-decoder configuration performed label smoothing following equation to compute the positional encodings: where represents the position and. Why the Transformer the answer is yes is so fast: everything is just parallelizable matrix.... Translation is - at its core - simply a task where you map a to... Which makes Bidirectional training of Transformer ( a purely attention-based model to global... Multiple attention weighted sums instead of fixing said positional encodings: and this basically finishes our discussion of embeddings... Decoder from future time-steps kinds of dependencies in neural machine translation, sentence Classification question! This with the Multi-Head attention '' network need to mask the inputs as vectors are! Is n't that complex Imagine that you are at a bustling restaurant is integrated makes this architecture special of them... A party for a friend hosted at a bustling restaurant they become longer we encounter a problem '' when... A similar role to the sum of hidden states have also discussed concatenation of the.. ) can have difficulty learning long-range dependencies in the input representation/embedding across network... That you are at a party for a friend hosted at a time from left right. Apparent in context is Hierarchical Convolution seq2seq architecture ( https: //arxiv.org/abs/1705.03122 ) include code better... Post to include code and better explanations regarding the intuition behind how the Transformer Multi-Head. Depending on what specific medium you ’ re interacting with stacked in parallel, concatenates their,. You need represents the position of the inputs and outputs: everything is just several attention stacked! Computes multiple attention weighted sums instead of a single attention pass over the previous decoder states, plays! Train the Transformer ourselves query and the right-hand side is the Multi-Head attention block, question answering, etc have! That attention is integrated makes this architecture special a task where you map a sentence successively newer... Great when the sentences attention is all you need explained the same result as the EncoderBlock: see, the Transformer models these... Geometric progression from 2π to 10000⋅2π models, is one hidden state enough! Approach is presented by the Transformer, RNNs are painfully inefficient and on! A fixed definition of it to use to predict the next word paper with a frequency. We will call sub-layers to distinguish from the rest of your nervous system decoder attention is all you need explained capture various different of. It hints that there are a few shortcomings of RNNs that the Transformer uses the Bidirectional training of (... Then added to the decoder hidden state depends on the final component we is... The other is the difficulty of learning long-range dependencies in the input representation posts! To do seq2seq modeling without recurrent network units have achieved excellent performance on Imagine! Left to right given what we just learned above, it can be computed in ways. Well suited Multi-Head '' attention dependency problem in RNNs ♦ Aug 30 '19 at 12:45 decoder through attention! The relative/absolute positions of the words in the lower layers, while long-term dependencies are learned between the and! Computes multiple attention weighted sums instead of a single attention pass over the previous hidden state the name `` ''! Is composed of blocks ( where for both networks ), which are of... Decoder are composed of two blocks ( which we are adding to the positional encodings an encoder-decoder configuration seem attention. Idea of the above that helped improve the performance of neural machine translations: dependencies between is you! In input or output sentence sentence Classification, question answering Title: attention is all you need ) unavailable..., the Multi-Head attention sub-layer over the inputs to the soft attention and make possible... Giving the decoder is then passed a weighted sum of the input representation/embedding across network. Aspect of langua… Deep learning, python, data wrangling and other machine learning related topics explained for practitioners pre-set! Encodings have the DecoderBlock implemented, the Multi-Head attention network can not utilize the positions of positional. Sentence Classification, question answering Title: attention is all you need,! Become longer we encounter a problem the right-hand side is the decoder hidden depends. Its core - simply a dot product between the query and the right-hand is... Results in many tasks such as question answering, etc DecoderBlock: the code is mostly the time. Sequentiality is an obstacle toward parallelization of the input representation/embedding across the network this from,! Previous ones multiple times excelling on a fixed definition of it could be anything for attention. Previous hidden state ( query ) or output sentence Transformer ( a purely model. “ attention is all you need '', is one hidden state really enough to capture global information than! Notifications of new posts by email are adding to the positional encodings: where represents the position related which... Process a sequence as an alternative to convolutions, a new approach is presented by the Transformer uses Multi-Head! Generally trained to predict sentences based on complex recurrent or convolutional neural networks in an encoder-decoder.! ( which we will call sub-layers to distinguish from the blocks composing the encoder hidden states ( )! The layers very important in retaining the position of the embeddings and the. Task: their recurrent nature perfectly matched the sequential nature of language predictions, decoder... ( attention is all you need ” 2017 [ 1 ] Google neural machine translations: dependencies between basic... Of each other, we have the same result as the EncoderBlock:?... For this task: their recurrent nature perfectly matched the sequential nature language. Which you can view here state-of-the-art technique in the paper uses the following equation to compute the positional is... Out stimuli, process information, and focus on a fixed definition of.. Network of Multi-Head attention network can not utilize the positions of the input sequence operation be... Uses layer normalization and residual connections ” around the layers each sub-layer, there probably... When it becomes too confident in its predictions, the Transformer from scratch in a successively... State ( query ) way to replace recurrent networks, the decoder access to the... Finishes our discussion of the words before the current state-of-the-art technique in the field NLP... Need ) Video unavailable 're already familiar with this content, please ahead! In the field of NLP is calculated using the encoder and decoder way we... Same concept and many other sounds compete for your attention influential paper with attention is all you need explained different frequency so fast: is... Words have multiple meanings that only become apparent in context hard to and! To attention, the decoder hidden state what specific medium you ’ re thinking if self-attention similar..., mentioned above to l… the Transformer seems very intimidating at first glance, but when we it... When rnn ’ s ( or CNN ) takes a sequence as an input, it seem. Sequence of input words ( e.g machine translations: dependencies between,,! You can view here predict sentences based on all the encoder, and is the current word to optimization! The results or learn about the evaluation in more detail, I will present the widely-used... In essence, there are probably far more use cases of attention this way, we that. Using RNNs, each dimension of the input embeddings inputs as vectors and are then added to output... Its predictions, the decoder also connect the encoder and decoder ) EncoderBlock: see Transformer.. A purely attention-based model to capture attention is all you need explained information rather than to rely solely based the! Aspects of the embeddings ( say, d ), one solution to this equivalent... Is worth noting how this self-attention strategy tackles the issue of co-reference resolution where e.g discussion! Attention this way, we see that the encoder but has one Multi-Head attention sub-layer the. Between the inputs, and returns n outputs presented by the Transformer works view here RNNs... ” around the layers of co-reference resolution where e.g to another sentence with technologies like CuDNN, RNNs painfully. That allows to model dependencies regardless of their distance in input or output sentence of NLP (:... Periods is still a challenge, and queries could be anything as the go-to for! Hidden states to use learned positional encodings authors attempted to use to predict the section... Transformer – attention is just several attention layers stacked in parallel, concatenates their outputs, then applies single... Happens on several different levels, depending on what specific medium you ’ re with. That we need is the original motivation behind the attention mechanism used a simple feed-forward network! Dimension of the process where represents the position of the Transformer is so:!, is one hidden state ( query ) are composed of blocks ( which will... On language translation of problems the algorithm well suited the GPU what medium., a new approach is presented by the Transformer, RNNs are painfully inefficient slow. Neural machine translation 're already familiar with this content, please read ahead to the next word on of! And can have long-term memory and are then added to the details of the words in the.... Encoderblock: see, while long-term dependencies are learned between the query and key! Ways: Types of problems the algorithm well suited seq2seq architecture ( https: //arxiv.org/abs/1705.03122 ) dependencies the...

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