{\displaystyle i} In some architectures, there are multiple "heads" of attention (termed 'multi-head attention'), each operating independently with their own queries, keys, and values. DocQA adds an additional self-attention calculation in its attention mechanism. In other words, in this attention mechanism, the context vector 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 (this is a slightly modified sentence from [Attention Is All You Need] https://arxiv.org/pdf/1706.03762.pdf ). In general, the feature responsible for this uptake is the multi-head attention mechanism. If both arguments are 2-dimensional, the matrix-matrix product is returned. {\displaystyle j} Is Koestler's The Sleepwalkers still well regarded? Why must a product of symmetric random variables be symmetric? The present study tested the intrinsic ERP features of the effects of acute psychological stress on speed perception. On the first pass through the decoder, 94% of the attention weight is on the first English word "I", so the network offers the word "je". Your home for data science. Indeed, the authors used the names query, key and value to indicate that what they propose is similar to what is done in information retrieval. Sign up for a free GitHub account to open an issue and contact its maintainers and the community. To illustrate why the dot products get large, assume that the components of. Any insight on this would be highly appreciated. Can the Spiritual Weapon spell be used as cover? In that paper, the attention vector is calculated through a feed-forward network, using the hidden states of the encoder and decoder as input (this is called "additive attention"). In the multi-head attention mechanism of the transformer, why do we need both $W_i^Q$ and ${W_i^K}^T$? scale parameters, so my point above about the vector norms still holds. However, in this case the decoding part differs vividly. Of course, here, the situation is not exactly the same, but the guy who did the video you linked did a great job in explaining what happened during the attention computation (the two equations you wrote are exactly the same in vector and matrix notation and represent these passages): In the paper, the authors explain the attention mechanisms saying that the purpose is to determine which words of a sentence the transformer should focus on. Do EMC test houses typically accept copper foil in EUT? While existing methods based on deep learning models have overcome the limitations of traditional methods and achieved intelligent image classification, they still suffer . How can the mass of an unstable composite particle become complex? rev2023.3.1.43269. Attention-like mechanisms were introduced in the 1990s under names like multiplicative modules, sigma pi units, . This is exactly how we would implement it in code. i The computations involved can be summarised as follows. In TensorFlow, what is the difference between Session.run() and Tensor.eval()? If we fix $i$ such that we are focusing on only one time step in the decoder, then that factor is only dependent on $j$. 1. Thus, in stead of just passing the hidden state from the previous layer, we also pass a calculated context vector that manages decoders attention. Thank you. I believe that a short mention / clarification would be of benefit here. Is there a more recent similar source? What is the difference between Attention Gate and CNN filters? The two most commonly used attention functions are additive attention , and dot-product (multiplicative) attention. What is difference between attention mechanism and cognitive function? q Papers With Code is a free resource with all data licensed under, methods/Screen_Shot_2020-05-25_at_12.32.09_PM_yYfmHYZ.png, Effective Approaches to Attention-based Neural Machine Translation. Dot-product attention is identical to our algorithm, except for the scaling factor of [math]1/\sqrt{d_k}[/math]. {\textstyle \sum _{i}w_{i}=1} Attention Mechanism. multi-head self attention mechanism position-wise feed-forward network (fully-connected layer) Decoder: multi-head self attention mechanism multi-head context-attention mechanism position-wise feed-forward network Attention: Weighted + Avg. k What is the difference between softmax and softmax_cross_entropy_with_logits? where d is the dimensionality of the query/key vectors. It only takes a minute to sign up. Finally, concat looks very similar to Bahdanau attention but as the name suggests it concatenates encoders hidden states with the current hidden state. The process of comparing one "query" with "keys" is done with simple multiplication of a vector and a matrix, as you can see in the figure below. Dot-product (multiplicative) attention Step 2: Calculate score Say we're calculating the self-attention for the first word "Thinking". The above work (Jupiter Notebook) can be easily found on my GitHub. Is Koestler's The Sleepwalkers still well regarded? Planned Maintenance scheduled March 2nd, 2023 at 01:00 AM UTC (March 1st, Why is dot product attention faster than additive attention? Pre-trained models and datasets built by Google and the community Lets see how it looks: As we can see the first and the forth hidden states receives higher attention for the current timestep. The best answers are voted up and rise to the top, Start here for a quick overview of the site, Detailed answers to any questions you might have, Discuss the workings and policies of this site. The h heads are then concatenated and transformed using an output weight matrix. It only takes a minute to sign up. t How to get the closed form solution from DSolve[]? 100-long vector attention weight. Notes In practice, a bias vector may be added to the product of matrix multiplication. The query-key mechanism computes the soft weights. The final h can be viewed as a "sentence" vector, or a. What are logits? The Bandanau variant uses a concatenative (or additive) instead of the dot product/multiplicative forms. Partner is not responding when their writing is needed in European project application. Scaled Dot-Product Attention is defined as: How to understand Scaled Dot-Product Attention? I hope it will help you get the concept and understand other available options. Bahdanau attention). Additive attention computes the compatibility function using a feed-forward network with a single hidden layer. In this example the encoder is RNN. Additive and multiplicative attention are similar in complexity, although multiplicative attention is faster and more space-efficient in practice as it can be implemented more efficiently using matrix multiplication. s dot t W ah s general v a tanh W a[h t;h s] concat Besides, in our early attempts to build attention-based models, we use a location-basedfunction in which the alignment scores are computed from solely the target hidden state h as follows: a t =softmax(W ah t) location (8) Given the alignment vector as weights, the context vector c We need to calculate the attn_hidden for each source words. H, encoder hidden state; X, input word embeddings. Scaled Dot-Product Attention vs. Multi-Head Attention From "Attention is All You Need" . 2014: Neural machine translation by jointly learning to align and translate" (figure). th token. The first option, which is dot, is basically a dot product of hidden states of the encoder (h_s) and the hidden state of the decoder (h_t). In the section 3.1 They have mentioned the difference between two attentions as follows. Finally, we can pass our hidden states to the decoding phase. Dot Product Attention (Multiplicative) We will cover this more in Transformer tutorial. As we might have noticed the encoding phase is not really different from the conventional forward pass. Dot product of vector with camera's local positive x-axis? S, decoder hidden state; T, target word embedding. additive attention. The dot products yield values anywhere between negative and positive infinity, so a softmax is applied to map the values to [0,1] and to ensure that they sum to 1 over the whole sequence. From the word embedding of each token, it computes its corresponding query vector Can I use a vintage derailleur adapter claw on a modern derailleur. I believe that a short mention / clarification would be of benefit here. {\displaystyle q_{i}} What is the weight matrix in self-attention? Since it doesn't need parameters, it is faster and more efficient. The latter one is built on top of the former one which differs by 1 intermediate operation. As it can be seen the task was to translate Orlando Bloom and Miranda Kerr still love each other into German. In the Pytorch Tutorial variant training phase, T alternates between 2 sources depending on the level of. Site design / logo 2023 Stack Exchange Inc; user contributions licensed under CC BY-SA. However, the model also uses the standard softmax classifier over a vocabulary V so that it can predict output words that are not present in the input in addition to reproducing words from the recent context. j These values are then concatenated and projected to yield the final values as can be seen in 8.9. This view of the attention weights addresses the "explainability" problem that neural networks are criticized for. additive attention dot-product attention attentionattentionfunction, additive attention sigmoidsoftmaxattention Keyword Arguments: out ( Tensor, optional) - the output tensor. The text was updated successfully, but these errors were encountered: You signed in with another tab or window. Thanks. Scaled Dot-Product Attention contains three part: 1. The query determines which values to focus on; we can say that the query attends to the values. Find a vector in the null space of a large dense matrix, where elements in the matrix are not directly accessible. Here s is the query while the decoder hidden states s to s represent both the keys and the values. What's more, is that in Attention is All you Need they introduce the scaled dot product where they divide by a constant factor (square root of size of encoder hidden vector) to avoid vanishing gradients in the softmax. $\mathbf{Q}$ refers to the query vectors matrix, $q_i$ being a single query vector associated with a single input word. {\displaystyle i} They are very well explained in a PyTorch seq2seq tutorial. Encoder-decoder with attention. This mechanism refers to Dzmitry Bahdanaus work titled Neural Machine Translation by Jointly Learning to Align and Translate. other ( Tensor) - second tensor in the dot product, must be 1D. {\displaystyle v_{i}} The mechanism of scaled dot-product attention is just a matter of how to concretely calculate those attentions and reweight the "values". head Q(64), K(64), V(64) Self-Attention . However, the mainstream toolkits (Marian, OpenNMT, Nematus, Neural Monkey) use the Bahdanau's version.more details: The computing of the attention score can be seen as computing similarity of the decoder state h t with all . How can the mass of an unstable composite particle become complex? where In . i Next the new scaled dot-product attention is used on each of these to yield a \(d_v\)-dim. Attention module this can be a dot product of recurrent states, or the query-key-value fully-connected layers. Scaled Dot Product Attention Self-Attention . Luong attention used top hidden layer states in both of encoder and decoder. However, dot-product attention is relatively faster and more space-efficient in practice due to the highly optimized matrix multiplication code. By clicking Accept all cookies, you agree Stack Exchange can store cookies on your device and disclose information in accordance with our Cookie Policy. Well occasionally send you account related emails. {\displaystyle t_{i}} Dot-product attention layer, a.k.a. The first option, which is dot, is basically a dot product of hidden states of the encoder (h_s) and the hidden state of the decoder (h_t). FC is a fully-connected weight matrix. @Zimeo the first one dot, measures the similarity directly using dot product. where h_j is j-th hidden state we derive from our encoder, s_i-1 is a hidden state of the previous timestep (i-1th), and W, U and V are all weight matrices that are learnt during the training. (2 points) Explain one advantage and one disadvantage of additive attention compared to mul-tiplicative attention. Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX AVX2, Could not find a version that satisfies the requirement tensorflow. What does meta-philosophy have to say about the (presumably) philosophical work of non professional philosophers? By clicking Accept all cookies, you agree Stack Exchange can store cookies on your device and disclose information in accordance with our Cookie Policy. We can pick and choose the one we want, There are some minor changes like Luong concatenates the context and the decoder hidden state and uses one weight instead of 2 separate ones, Last and the most important one is that Luong feeds the attentional vector to the next time-step as they believe that past attention weight history is important and helps predict better values. Difference between constituency parser and dependency parser. Traditional rock image classification methods mainly rely on manual operation, resulting in high costs and unstable accuracy. Thus, the . The context vector c can also be used to compute the decoder output y. Below is the diagram of the complete Transformer model along with some notes with additional details. U+22C5 DOT OPERATOR. 2-layer decoder. Viewed as a matrix, the attention weights show how the network adjusts its focus according to context. Already on GitHub? Multiplicative Attention is an attention mechanism where the alignment score function is calculated as: $$f_{att}\left(\textbf{h}_{i}, \textbf{s}_{j}\right) = \mathbf{h}_{i}^{T}\textbf{W}_{a}\mathbf{s}_{j}$$. Data Types: single | double | char | string Papers With Code is a free resource with all data licensed under, methods/Screen_Shot_2020-05-25_at_12.32.09_PM.png, Effective Approaches to Attention-based Neural Machine Translation. In Computer Vision, what is the difference between a transformer and attention? If you are a bit confused a I will provide a very simple visualization of dot scoring function. w Asking for help, clarification, or responding to other answers. The base case is a prediction that was derived from a model based on only RNNs, whereas the model that uses attention mechanism could easily identify key points of the sentence and translate it effectively. The paper A Deep Reinforced Model for Abstractive Summarization[3] introduces a neural network model with a novel self-attention that attends over the input and continuously generated output separately. In the previous computation, the query was the previous hidden state s while the set of encoder hidden states h to h represented both the keys and the values. Multiplicative Attention. So it's only the score function that different in the Luong attention. Performing multiple attention steps on the same sentence produces different results, because, for each attention 'head', new $\mathbf{W_q}$, $\mathbf{W_v}$, $\mathbf{W_k}$ are randomly initialised. This image shows basically the result of the attention computation (at a specific layer that they don't mention). - Attention Is All You Need, 2017. Connect and share knowledge within a single location that is structured and easy to search. Why is dot product attention faster than additive attention? Update: I am a passionate student. vegan) just to try it, does this inconvenience the caterers and staff? The off-diagonal dominance shows that the attention mechanism is more nuanced. Attention as a concept is so powerful that any basic implementation suffices. The basic idea is that the output of the cell 'points' to the previously encountered word with the highest attention score. Here is the amount of attention the ith output should pay to the jth input and h is the encoder state for the jth input. Multiplicative attention as implemented by the Transformer, is computed like the following: Where: Sqrt(dk) is used for scaling: It is suspected that the bigger the values of dk (the dimension of Q and K), the bigger the dot product. How to combine multiple named patterns into one Cases? For convolutional neural networks, the attention mechanisms can also be distinguished by the dimension on which they operate, namely: spatial attention,[10] channel attention,[11] or combinations of both.[12][13]. What is the difference between additive and multiplicative attention? [3][4][5][6] Listed in the Variants section below are the many schemes to implement the soft-weight mechanisms. This is the simplest of the functions; to produce the alignment score we only need to take the . Connect and share knowledge within a single location that is structured and easy to search. I enjoy studying and sharing my knowledge. The rest dont influence the output in a big way. Thus, this technique is also known as Bahdanau attention. ii. torch.matmul(input, other, *, out=None) Tensor. [1] D. Bahdanau, K. Cho, and Y. Bengio, Neural Machine Translation by Jointly Learning to Align and Translate (2014), [2] S. Merity, C. Xiong, J. Bradbury, and R. Socher, Pointer Sentinel Mixture Models (2016), [3] R. Paulus, C. Xiong, and R. Socher, A Deep Reinforced Model for Abstractive Summarization (2017), [4] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser, and I. Polosukhin, Attention Is All You Need by (2017). Additive attention computes the compatibility function using a feed-forward network with a single hidden layer. More from Artificial Intelligence in Plain English. . (2) LayerNorm and (3) your question about normalization in the attention matrix multiplication . matrix multiplication code. The paper Pointer Sentinel Mixture Models[2] uses self-attention for language modelling. 08 Multiplicative Attention V2. I encourage you to study further and get familiar with the paper. Stack Exchange network consists of 181 Q&A communities including Stack Overflow, the largest, most trusted online community for developers to learn, share their knowledge, and build their careers. Each Finally, our context vector looks as above. w The Wa matrix in the "general" equations can be thought of as some sort of weighted similarity or a more general notion of similarity where setting Wa to the diagonal matrix gives you the dot similarity. $\mathbf{K}$ refers to the keys vectors matrix, $k_i$ being a single key vector associated with a single input word. This method is proposed by Thang Luong in the work titled Effective Approaches to Attention-based Neural Machine Translation. Is email scraping still a thing for spammers. for each The core idea of attention is to focus on the most relevant parts of the input sequence for each output. The weight matrices here are an arbitrary choice of a linear operation that you make BEFORE applying the raw dot product self attention mechanism. (2 points) Explain one advantage and one disadvantage of dot product attention compared to multiplicative attention. It'd be a great help for everyone. Luong-style attention. I didn't see a good reason anywhere on why they do this but a paper by Pascanu et al throws a clue..maybe they are looking to make the RNN deeper. To me, it seems like these are only different by a factor. Jordan's line about intimate parties in The Great Gatsby? -------. dot-product attention is much faster and more space-efficient in practice since it can be implemented using highly optimized matrix multiplication code. There are no weights in it. 2 3 or u v Would that that be correct or is there an more proper alternative? What problems does each other solve that the other can't? Is variance swap long volatility of volatility? Scaled dot product self-attention The math in steps. It only takes a minute to sign up. Want to improve this question? If you order a special airline meal (e.g. Here $\mathbf{h}$ refers to the hidden states for the encoder/source, and $\mathbf{s}$ is the hidden states for the decoder/target. AlphaFold2 Evoformer block, as its name suggests, is a special cases of transformer (actually, structure module is a transformer as well). The scaled dot-product attention computes the attention scores based on the following mathematical formulation: Source publication Incorporating Inner-word and Out-word Features for Mongolian . . i we don't really know why the BatchNorm works, We've added a "Necessary cookies only" option to the cookie consent popup. So, the coloured boxes represent our vectors, where each colour represents a certain value. Also, the first paper mentions additive attention is more computationally expensive, but I am having trouble understanding how. Attention mechanism is formulated in terms of fuzzy search in a key-value database. Unlike NumPy's dot, torch.dot intentionally only supports computing the dot product of two 1D tensors with the same number of elements. AttentionCompatibility function TransformerScaled Dot-Product Attention Dot-Product AttentionKeysoftmax i What's the motivation behind making such a minor adjustment? In the simplest case, the attention unit consists of dot products of the recurrent encoder states and does not need training. Why people always say the Transformer is parallelizable while the self-attention layer still depends on outputs of all time steps to calculate? is the output of the attention mechanism. Part II deals with motor control. If you are new to this area, lets imagine that the input sentence is tokenized breaking down the input sentence into something similar: [, orlando, bloom, and, miranda, kerr, still, love, each, other, ]. Given a set of vector values, and a vector query, attention is a technique to compute a weighted sum of values dependent on the query. Can say that the other ca n't function TransformerScaled dot-product attention is all you need & quot ; the presumably. Normalization in the Great Gatsby attention from & quot ; attention is more nuanced in high costs unstable... Head q ( 64 ), k ( 64 ), k 64! Computes the attention weights addresses the `` explainability '' problem that Neural networks are criticized for i what 's Sleepwalkers. Colour represents a certain value easily found on my GitHub matrix, attention. Will provide a very simple visualization of dot products get large, assume that the query which. Responding when their writing is needed in European project application Vision, what is the between! S is the query while the self-attention layer still depends on outputs of time. Form solution from DSolve [ ] layer that they do n't mention ) transformed using an output weight matrix vector... Is much faster and more space-efficient in practice due to the decoding phase attention layer, a.k.a follows... Linear operation that you make BEFORE applying the raw dot product attention compared to mul-tiplicative attention try it does. Familiar with the current hidden state ; t, target word embedding to calculate between a Transformer and?! ) your question about normalization in the simplest case, the matrix-matrix product is returned the space... Are then concatenated and projected to yield the final h can be seen in 8.9 a factor single location is! For help, clarification, or the query-key-value fully-connected layers ) your question about normalization in the case. An arbitrary choice of a large dense matrix, where each colour a... Much faster and more efficient very similar to Bahdanau attention but as the name suggests it encoders! The 1990s under names like multiplicative modules, sigma pi units, the section 3.1 they have mentioned the between! Knowledge within a single location that is structured and easy to search here s is the dot product attention vs multiplicative attention! To calculate order a special airline meal ( e.g be correct or is there an more proper?. Weights addresses the `` explainability '' problem that Neural networks are criticized for products. Image shows basically the result of the attention scores based on deep models... Target word embedding in the Pytorch tutorial variant training phase, t alternates between sources! Work titled Neural Machine Translation is formulated in terms of fuzzy search in a big way doesn... Responding when their writing is needed in European project application contact its maintainers and the values the most relevant of... Can say that the components of user contributions licensed under CC BY-SA was updated successfully, but i AM trouble! This image shows basically the result of the Transformer is parallelizable while the decoder output y computes the compatibility using... Conventional forward pass { \displaystyle t_ { i } =1 } attention mechanism formulated. Partner is not responding when their writing is needed in European project application view of the attention multiplication... Spiritual Weapon spell be used as cover they are very well explained in a way! Line about intimate parties in the 1990s under names like multiplicative modules, sigma pi,... Our hidden states s to s represent both the keys and the values between 2 depending... Are an arbitrary choice of a linear operation that you make BEFORE applying the dot! Of attention is more nuanced, *, out=None ) Tensor a value... Which differs by 1 intermediate operation query while the self-attention layer still depends on outputs of all time steps calculate! Which differs by 1 intermediate operation attention-like mechanisms were introduced in the multi-head attention mechanism understand other options! Between attention mechanism their writing is needed in European project application translate '' ( figure ) why dot... The intrinsic ERP features of the effects of acute psychological stress on speed perception the Transformer is parallelizable the! And does not need training not really different from the conventional forward.! Dominance shows that the other ca n't is so powerful that any basic implementation suffices task was translate... Further and get familiar with the current hidden state ; X, input word embeddings they have the... ; X, input word embeddings be seen in 8.9 short mention / clarification would be benefit... Key-Value database w Asking for help, clarification, or the query-key-value fully-connected layers score function that different the... Luong attention an unstable composite particle become complex Spiritual Weapon spell be used to compute the decoder y. Q_ { i } w_ { i } } dot-product attention attentionattentionfunction, additive attention of attention. Resource with all data licensed under CC BY-SA states in both of encoder and decoder fully-connected layers concat very... 3.1 they have mentioned the difference between two attentions as follows computationally expensive, but i having! To take the dot products get large, assume that the components of it concatenates encoders hidden states the. \Displaystyle t_ { i } } dot-product attention vs. multi-head attention from & quot ; attention to... Bahdanaus work titled Effective Approaches to Attention-based Neural Machine Translation by jointly learning to align and translate '' figure... Of dot scoring function consists of dot products of the effects of psychological... Named patterns into one Cases is there an more proper alternative elements in the dot of. Exchange Inc ; user contributions licensed under CC BY-SA a feed-forward network with a single layer... Is needed in European project application another tab or window a certain value ) instead of the query/key vectors their. \Displaystyle i } } what is the difference between attention Gate and CNN filters only need to the. Into German its maintainers and the values variant training phase, t alternates between 2 sources depending on the mathematical! Share knowledge within a single hidden layer as the name suggests it concatenates encoders states! Known as Bahdanau attention sigmoidsoftmaxattention Keyword arguments: out ( Tensor ) - second Tensor in the simplest of complete., the first paper mentions additive attention Bahdanau attention but as the dot product attention vs multiplicative attention suggests it concatenates encoders hidden s! Hidden states s to s represent both the keys and the community the decoder hidden states with the.... Be seen the task was to translate Orlando Bloom and Miranda Kerr still love each other solve that query. On outputs of all time steps to calculate function using a feed-forward with! Weights addresses the `` explainability '' problem that Neural networks are criticized for (., what is the difference between two attentions as follows about normalization in the multi-head attention dot product attention vs multiplicative attention... A certain value is returned on my GitHub alternates between 2 sources depending the. $ W_i^Q $ and $ { W_i^K } ^T $ about normalization in the Great Gatsby this! Text was updated successfully, but i AM having trouble understanding how t, target word.. With code is a free GitHub account to open an issue and contact its maintainers the. Are very well explained in a big way as Bahdanau attention but as the name suggests it concatenates encoders states! / clarification would be of benefit here might have noticed the encoding phase is not when. As can be implemented using highly optimized matrix multiplication code you get the closed solution. Clarification, or responding to other answers speed perception i AM having trouble understanding how 2 uses... Responding to other answers does not need training single location that is and... Attends to the highly optimized matrix multiplication used top hidden layer states in both of and... Matrix in self-attention q Papers with code is a free GitHub account to open an and! The dot product attention vs multiplicative attention most commonly used attention functions are additive attention computes the attention weights show the. Account to open an issue and contact its maintainers and the community do EMC test houses typically accept copper in! Differs by 1 intermediate operation an additional self-attention calculation in its attention mechanism and! High costs and unstable accuracy available options input word embeddings methods mainly rely on manual operation resulting! Which values to focus on ; we can say that the attention addresses... The closed form solution from DSolve [ ] practice, a bias vector may be added the! Believe that a short mention / clarification would be of benefit here that! Name suggests it concatenates encoders hidden states s to s represent both the and. Be added to the decoding part differs vividly it seems like these are only by... Basically the result of the dot product bit confused a i will provide very... Bahdanaus work titled Effective Approaches to Attention-based Neural Machine Translation find a vector in the space. Mentioned the difference between a Transformer and attention Dzmitry Bahdanaus work titled Neural Machine Translation by factor. T alternates between 2 sources depending on the following mathematical formulation: Source publication Inner-word! Product self attention mechanism like these are only different by a factor into German March 1st, why we... Bit confused a i will provide a very simple visualization of dot product of recurrent states, or.... Relatively faster and more space-efficient in practice since it doesn & # x27 ;,. As Bahdanau attention but as the name suggests it concatenates encoders hidden s... Pointer Sentinel Mixture models [ 2 ] uses self-attention for language modelling, target word embedding to Attention-based Machine... S to s represent both the keys and the values non professional philosophers `` explainability '' problem that Neural are... Directly using dot product attention faster than additive attention compared to multiplicative attention elements in multi-head. Cnn filters be correct or is there an more proper alternative solution from DSolve [ ] phase not. You are a bit confused a i will provide a very simple visualization dot! Diagram of the attention mechanism is more computationally expensive, but i AM having trouble how! - second Tensor in the Pytorch dot product attention vs multiplicative attention variant training phase, t between! Product attention faster than additive attention rest dont influence the output Tensor { \displaystyle t_ { i } } is.