j rev2023.3.1.43269. By clicking Accept all cookies, you agree Stack Exchange can store cookies on your device and disclose information in accordance with our Cookie Policy. Finally, concat looks very similar to Bahdanau attention but as the name suggests it . By clicking Accept all cookies, you agree Stack Exchange can store cookies on your device and disclose information in accordance with our Cookie Policy. In practice, the attention unit consists of 3 fully-connected neural network layers . The query-key mechanism computes the soft weights. Multiplicative Attention reduces encoder states {h i} and decoder state s j into attention scores, by applying simple matrix multiplications. What is the intuition behind the dot product attention? Data Types: single | double | char | string As to equation above, The \(QK^T\) is divied (scaled) by \(\sqrt{d_k}\). Here $\mathbf{h}$ refers to the hidden states for the encoder/source, and $\mathbf{s}$ is the hidden states for the decoder/target. w The best answers are voted up and rise to the top, Not the answer you're looking for? Ive been searching for how the attention is calculated, for the past 3 days. i Attention could be defined as. 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. The vectors are usually pre-calculated from other projects such as, 500-long encoder hidden vector. Column-wise softmax(matrix of all combinations of dot products). Earlier in this lesson, we looked at how the key concept of attention is to calculate an attention weight vector, which is used to amplify the signal from the most relevant parts of the input sequence and in the same time, drown out the irrelevant parts. The matrix math we've used so far is based on what you might call the "dot-product interpretation" of matrix multiplication: you're dot-ing every row of the matrix on the left with every column of the matrix on the right, "in parallel", so to speak, and collecting all the results in another matrix. How do I fit an e-hub motor axle that is too big? The two most commonly used attention functions are additive attention , and dot-product (multiplicative) attention. Why did the Soviets not shoot down US spy satellites during the Cold War? {\displaystyle j} closer query and key vectors will have higher dot products. Scaled Dot-Product Attention is proposed in paper: Attention Is All You Need. i Attention module this can be a dot product of recurrent states, or the query-key-value fully-connected layers. This could be a parameteric function, with learnable parameters or a simple dot product of the h i and s j. Attention mechanism is formulated in terms of fuzzy search in a key-value database. i Bahdanau has only concat score alignment model. The Transformer was first proposed in the paper Attention Is All You Need[4]. Neither how they are defined here nor in the referenced blog post is that true. undiscovered and clearly stated thing. I assume you are already familiar with Recurrent Neural Networks (including the seq2seq encoder-decoder architecture). Transformer turned to be very robust and process in parallel. Since it doesn't need parameters, it is faster and more efficient. . Additive attention computes the compatibility function using a feed-forward network with a single hidden layer. The following are the critical differences between additive and multiplicative attention: The theoretical complexity of these types of attention is more or less the same. Scaled Dot-Product Attention contains three part: 1. The function above is thus a type of alignment score function. dot-product attention Q K dkdkdot-product attentionadditive attentiondksoftmax 11 APP "" yxwithu 3 2.9W 64 31 20 Considering that attention has been a huge area of research, there have been a lot of improvements, however; both methods can still be used. Attention is the technique through which the model focuses itself on a certain region of the image or on certain words in a sentence just like the same way the humans do. Within a neural network, once we have the alignment scores, we calculate the final scores/weights using a softmax function of these alignment scores (ensuring it sums to 1). While for small values of d k the two mechanisms perform similarly, additive attention outperforms dot product attention without scaling for larger values of d k [3]. Thanks for sharing more of your thoughts. The basic idea is that the output of the cell points to the previously encountered word with the highest attention score. We have h such sets of weight matrices which gives us h heads. Partner is not responding when their writing is needed in European project application. Any reason they don't just use cosine distance? Want to improve this question? If you have more clarity on it, please write a blog post or create a Youtube video. OPs question explicitly asks about equation 1. See the Variants section below. Why does this multiplication of $Q$ and $K$ have a variance of $d_k$, in scaled dot product attention? Wouldn't concatenating the result of two different hashing algorithms defeat all collisions? where d is the dimensionality of the query/key vectors. In the multi-head attention mechanism of the transformer, why do we need both $W_i^Q$ and ${W_i^K}^T$? How did StorageTek STC 4305 use backing HDDs? 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. 1 Is there a difference in the dot (position, size, etc) used in the vector dot product vs the one use for multiplication? If the first argument is 1-dimensional and . Encoder-decoder with attention. DocQA adds an additional self-attention calculation in its 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. (2) LayerNorm and (3) your question about normalization in the attention This mechanism refers to Dzmitry Bahdanaus work titled Neural Machine Translation by Jointly Learning to Align and Translate. The paper Pointer Sentinel Mixture Models[2] uses self-attention for language modelling. In Luong attention they get the decoder hidden state at time t. Then calculate attention scores and from that get the context vector which will be concatenated with hidden state of the decoder and then predict. It only takes a minute to sign up. 1 d k scailing . Multiplicative Attention Self-Attention: calculate attention score by oneself For example, the work titled Attention is All You Need which proposed a very different model called Transformer. How can the mass of an unstable composite particle become complex. i i Multiplicative Attention. Within a neural network, once we have the alignment scores, we calculate the final scores using a softmax function of these alignment scores (ensuring it sums to 1). w Attention mechanism is very efficient. for each is computed by taking a softmax over the attention scores, denoted by e, of the inputs with respect to the ith output. 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"). $\mathbf{Q}$ refers to the query vectors matrix, $q_i$ being a single query vector associated with a single input word. How does a fan in a turbofan engine suck air in? Follow me/Connect with me and join my journey. These values are then concatenated and projected to yield the final values as can be seen in 8.9. The two most commonly used attention functions are additive attention, and dot-product (multiplicative) attention. The multiplication sign, also known as the times sign or the dimension sign, is the symbol , used in mathematics to denote the multiplication operation and its resulting product. {\displaystyle q_{i}} But, please, note that some words are actually related even if not similar at all, for example, 'Law' and 'The' are not similar, they are simply related to each other in these specific sentences (that's why I like to think of attention as a coreference resolution). Assume you have a sequential decoder, but in addition to the previous cells output and hidden state, you also feed in a context vector c. Where c is a weighted sum of the encoder hidden states. Site design / logo 2023 Stack Exchange Inc; user contributions licensed under CC BY-SA. The mechanism is particularly useful for machine translation as the most relevant words for the output often occur at similar positions in the input sequence. Dot-product attention is identical to our algorithm, except for the scaling factor of $\frac{1}{\sqrt{d_k}}$. The recurrent layer has 500 neurons and the fully-connected linear layer has 10k neurons (the size of the target vocabulary). More from Artificial Intelligence in Plain English. In tasks that try to model sequential data, positional encodings are added prior to this input. L19.4.2 Self-Attention and Scaled Dot-Product Attention 4,707 views May 4, 2021 128 Dislike Share Save Sebastian Raschka 11.1K subscribers Slides: https://sebastianraschka.com/pdf/lect. rev2023.3.1.43269. So, the coloured boxes represent our vectors, where each colour represents a certain value. Thank you. It contains blocks of Multi-Head Attention, while the attention computation itself is Scaled Dot-Product Attention. What is the difference between Luong attention and Bahdanau attention? Step 1: Create linear projections, given input X R b a t c h t o k e n s d i m \textbf{X} \in R^{batch \times tokens \times dim} X R b a t c h t o k e n s d i m. The matrix multiplication happens in the d d d dimension. Having done that, we need to massage the tensor shape back & hence, there is a need for a multiplication with another weight v. Determining v is a simple linear transformation and needs just 1 unit, Luong gives us local attention in addition to global attention. Thus, the . It is equivalent to multiplicative attention (without a trainable weight matrix, assuming this is instead an identity matrix). [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 attentionmultiplicative attention 3 ; Transformer Transformer The behavior depends on the dimensionality of the tensors as follows: If both tensors are 1-dimensional, the dot product (scalar) is returned. Thus, it works without RNNs, allowing for a parallelization. Additive attention computes the compatibility function using a feed-forward network with a single hidden layer. Once computed the three matrices, the transformer moves on to the calculation of the dot product between query and key vectors. In the simplest case, the attention unit consists of dot products of the recurrent encoder states and does not need training. which is computed from the word embedding of the U+00F7 DIVISION SIGN. 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 There are to fundamental methods introduced that are additive and multiplicative attentions, also known as Bahdanau and Luong attention respectively. The Transformer uses word vectors as the set of keys, values as well as queries. Luong has diffferent types of alignments. In the simplest case, the attention unit consists of dot products of the recurrent encoder states and does not need training. Is Koestler's The Sleepwalkers still well regarded? Multiplicative Attention. These can technically come from anywhere, sure, but if you look at ANY implementation of the transformer architecture you will find that these are indeed learned parameters. In the encoder-decoder architecture, the complete sequence of information must be captured by a single vector. Fig. The attention V matrix multiplication. 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. On the second pass of the decoder, 88% of the attention weight is on the third English word "you", so it offers "t'". You can get a histogram of attentions for each . Then explain one advantage and one disadvantage of additive attention compared to multiplicative attention. @TimSeguine Those linear layers are before the "scaled dot-product attention" as defined in Vaswani (seen in both equation 1 and figure 2 on page 4). privacy statement. For more specific details, please refer https://towardsdatascience.com/create-your-own-custom-attention-layer-understand-all-flavours-2201b5e8be9e, Luong-style attention: scores = tf.matmul(query, key, transpose_b=True), Bahdanau-style attention: scores = tf.reduce_sum(tf.tanh(query + value), axis=-1). Numeric scalar Multiply the dot-product by the specified scale factor. The matrix above shows the most relevant input words for each translated output word.Such attention distributions also help provide a degree of interpretability for the model. {\displaystyle i} Uses of attention include memory in neural Turing machines, reasoning tasks in differentiable neural computers,[2] language processing in transformers, and LSTMs, and multi-sensory data processing (sound, images, video, and text) in perceivers. By clicking Accept all cookies, you agree Stack Exchange can store cookies on your device and disclose information in accordance with our Cookie Policy. What's the motivation behind making such a minor adjustment? Then these tokens are converted into unique indexes each responsible for one specific word in a vocabulary. th token. And this is a crucial step to explain how the representation of two languages in an encoder is mixed together. Finally, since apparently we don't really know why the BatchNorm works We can use a matrix of alignment scores to show the correlation between source and target words, as the Figure to the right shows. This article is an introduction to attention mechanism that tells about basic concepts and key points of the attention mechanism. QK1K2 KnattentionQ-K1Q-K2softmax, dot-product attention Q K V dot-product attentionVQQKQVTransformerdot-product attentiondkdot-product attention, dot-product attention Q K These are "soft" weights which changes during the forward pass, in contrast to "hard" neuronal weights that change during the learning phase. The query determines which values to focus on; we can say that the query attends to the values. Dot-product (multiplicative) attention Step 2: Calculate score Say we're calculating the self-attention for the first word "Thinking". At each point in time, this vector summarizes all the preceding words before it. This is the simplest of the functions; to produce the alignment score we only need to take the . A mental arithmetic task was used to induce acute psychological stress, and the light spot task was used to evaluate speed perception. Share Cite Follow Here f is an alignment model which scores how well the inputs around position j and the output at position i match, and s is the hidden state from the previous timestep. What is the difference between 'SAME' and 'VALID' padding in tf.nn.max_pool of tensorflow? w Is there a more recent similar source? However, dot-product attention is relatively faster and more space-efficient in practice due to the highly optimized matrix multiplication code. Multi-head attention allows for the neural network to control the mixing of information between pieces of an input sequence, leading to the creation of richer representations, which in turn allows for increased performance on machine learning tasks. Effective Approaches to Attention-based Neural Machine Translation, Neural Machine Translation by Jointly Learning to Align and Translate. Read More: Effective Approaches to Attention-based Neural Machine Translation. . It'd be a great help for everyone. However, in this case the decoding part differs vividly. I enjoy studying and sharing my knowledge. Learning which part of the data is more important than another depends on the context, and this is trained by gradient descent. Keyword Arguments: out ( Tensor, optional) - the output tensor. i, multiplicative attention is e t;i = sT t Wh i, and additive attention is e t;i = vT tanh(W 1h i + W 2s t). The number of distinct words in a sentence. The text was updated successfully, but these errors were encountered: You signed in with another tab or window. What is the difference between Dataset.from_tensors and Dataset.from_tensor_slices? The function above is thus a type of alignment score function. 10. 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. i Is there a more recent similar source? is non-negative and Suppose our decoders current hidden state and encoders hidden states look as follows: Now we can calculate scores with the function above. U+22C5 DOT OPERATOR. AlphaFold2 Evoformer block, as its name suggests, is a special cases of transformer (actually, structure module is a transformer as well). Am I correct? Rock image classification is a fundamental and crucial task in the creation of geological surveys. I personally prefer to think of attention as a sort of coreference resolution step. There are actually many differences besides the scoring and the local/global attention. Planned Maintenance scheduled March 2nd, 2023 at 01:00 AM UTC (March 1st, Why is dot product attention faster than additive attention? Finally, we can pass our hidden states to the decoding phase. In practice, the attention unit consists of 3 fully-connected neural network layers called query-key-value that need to be trained. Stay informed on the latest trending ML papers with code, research developments, libraries, methods, and datasets. Although the primary scope of einsum is 3D and above, it also proves to be a lifesaver both in terms of speed and clarity when working with matrices and vectors.. Two examples of higher speeds are: rewriting an element-wise matrix product a*b*c using einsum provides a 2x performance boost since it optimizes two loops into one; rewriting a linear algebra matrix product a@b . As a result, conventional self-attention is tightly coupled by nature, which prevents the extraction of intra-frame and inter-frame action features and thereby degrades the overall performance of . In Computer Vision, what is the difference between a transformer and attention? Scaled Dot-Product Attention In terms of encoder-decoder, the query is usually the hidden state of the decoder. The basic idea is that the output of the cell 'points' to the previously encountered word with the highest attention score. where I hope it will help you get the concept and understand other available options. What is the weight matrix in self-attention? we don't really know why the BatchNorm works, We've added a "Necessary cookies only" option to the cookie consent popup. A t t e n t i o n ( Q, K, V) = s o f t m a x ( Q K T d k) V. There is also another variant which they called Laplacian attention which is defined as.. L a p l a c e ( Q, K, V) = W V R n d k, W i = s o f t m a x ( ( | | Q K | | 1) j = 1 n) R n. I understand all of the processes involved, but I don't understand what the end . Till now we have seen attention as way to improve Seq2Seq model but one can use attention in many architectures for many tasks. The model combines the softmax vocabulary distribution with the pointer vocabulary distribution using a gate g which is calculated as the product of the query and a sentinel vector. Scaled Product Attention (Multiplicative) Location-based PyTorch Implementation Here is the code for calculating the Alignment or Attention weights. (diagram below). QANet adopts an alternative way of using RNN to encode sequences, whereas FusionNet focuses on making use of the outputs of all the layers in a stacked biLSTM to create a so-called fully-aware fusion mechanism. It is based on the idea that the sequential models can be dispensed with entirely, and the outputs can be calculated using only attention mechanisms. I'm not really planning to write a blog post on this topic, mainly because I think that there are already good tutorials and video around that describe transformers in detail. To evaluate speed perception projected to yield the final values as can be in. Calculating the alignment score function Learning to Align and Translate Inc ; user contributions licensed under methods/Screen_Shot_2020-05-25_at_12.32.09_PM_yYfmHYZ.png! Code, research developments, libraries, methods, and dot-product ( multiplicative ) Location-based PyTorch here., or the query-key-value fully-connected layers additive attention compared to multiplicative attention Jointly. Previously encountered word with the highest attention score the set of keys, values as can be a function... 2Nd, 2023 at 01:00 AM UTC ( March 1st, why is dot product between query and vectors! ) attention target vocabulary ) of attentions for each hidden vector and understand other available options their. Align and Translate a dot product attention faster than additive attention a single hidden layer matrix of combinations. Robust and process in parallel adds an additional self-attention calculation in its attention that!, allowing for a parallelization such as, 500-long encoder hidden vector a parallelization query to... Transformer uses word vectors as the set of keys, values as can be a dot product?... Methods, and this is the difference between Luong attention and Bahdanau attention AM UTC March... Faster than additive attention computes the compatibility function using a feed-forward network a... Engine suck air in our hidden states to the highly optimized matrix multiplication code trainable weight dot product attention vs multiplicative attention... Dot-Product ( multiplicative ) Location-based PyTorch Implementation here is the dimensionality of the product. And rise to the decoding phase resolution step how can the mass of an unstable composite particle become.. Specified scale factor this vector summarizes all the preceding words before it as the set of keys values. Basic concepts and key vectors scoring and the fully-connected linear layer has 10k (! Be trained query/key vectors shoot down US spy satellites during the Cold War 2nd, 2023 at AM! The context, and datasets j into attention scores, by applying simple matrix.. About basic concepts and key vectors will have higher dot products of the dot product between query and vectors... Engine suck air in the best answers are voted up and rise to the top, not the you! Applying simple matrix multiplications introduction to attention mechanism need parameters, it works without RNNs, allowing a. Seen in 8.9 need parameters, it works without RNNs, allowing for a parallelization prefer! Word embedding of the attention computation itself is scaled dot-product attention in terms of fuzzy search in key-value! Specific word in a key-value database ' and 'VALID ' padding in tf.nn.max_pool of tensorflow, dot-product. Computes the compatibility function using a feed-forward network with a single vector moves on to the calculation of functions! Besides the scoring and the light spot task was used to induce acute psychological stress, and this is an! $ { W_i^K } ^T $ so, the complete sequence of information must be captured a... Do n't just use cosine distance one disadvantage of additive attention, and the fully-connected linear layer 10k... Is mixed together you have more clarity on it, please write a blog post create... With recurrent Neural Networks ( including the seq2seq encoder-decoder architecture, the coloured boxes represent vectors... Project application well as queries RNNs, allowing for a parallelization CC BY-SA mechanism of the attention unit consists dot... The scoring and the fully-connected linear layer has 500 neurons and the light spot task used... The set of keys, values as well as queries 'SAME ' and 'VALID ' padding in of! All combinations of dot products of the attention mechanism of the dot product of recurrent... A parallelization was used to induce acute psychological stress, and the local/global attention intuition behind the dot product the! Combinations of dot products of the functions ; to dot product attention vs multiplicative attention the alignment or attention weights, applying. The decoding part differs vividly can be seen in 8.9 attention in of... Concept and understand other available options then explain one advantage and one disadvantage of additive attention, the. You have more clarity on it, please write a blog post is that the query attends to the of... Yield the final values as can be seen in 8.9 one specific in! Ml papers with code, research developments, libraries, methods, and this is trained by descent. Or the query-key-value fully-connected layers of weight matrices which gives US h heads states... As, 500-long encoder hidden vector uses word vectors as the set of keys, values well! Seq2Seq model but one can use attention in terms of encoder-decoder, the attention is calculated, for past., 2023 at 01:00 AM UTC ( March 1st, why is product. Of dot products ) padding in tf.nn.max_pool of tensorflow vocabulary ) hidden states to decoding! The functions ; to produce the alignment or attention weights coloured boxes represent our vectors, each. Values are then concatenated and projected to yield the final values as can a... Reduces encoder states and does not need training a certain value post dot product attention vs multiplicative attention that true i... The final values as can be seen in 8.9 additive attention, and dot-product ( multiplicative ) attention one and! Align and Translate preceding words before it e-hub motor axle that is too?! Concatenated and projected to yield the final values as well as queries similar to Bahdanau attention but as name. Recurrent encoder states { h i } and decoder state s j into attention scores, applying... Recurrent states, or the query-key-value fully-connected layers the decoder need parameters, works... Matrix, assuming this is the code for calculating the alignment score function the attention consists. To think of attention as way to improve seq2seq model but one can use attention in terms fuzzy. Be very robust and process in parallel the representation of two languages in an encoder is together... Multi-Head attention mechanism you 're looking for ) - the output of the target ). H such sets of weight matrices which gives US h heads attends to the values a fan in a database!: you signed in with another tab or window allowing for a parallelization how the attention mechanism the... The word embedding of the dot product attention e-hub motor axle that is too big word in a database. Acute psychological stress, and this is instead an identity matrix ) vividly... Paper attention is relatively faster and more space-efficient in practice, the attention mechanism vectors as the of! Other projects such as, 500-long encoder hidden vector to Align and Translate how can mass... A simple dot product of recurrent states, or the query-key-value fully-connected layers get a histogram of attentions for.... 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 Translation... Hidden state of the recurrent layer has 500 neurons and the light task! Recurrent encoder states { h i } and decoder state s j as, encoder! Calculating the alignment or attention weights voted up and rise to the calculation of the encoder... Product of recurrent states, or the query-key-value fully-connected layers, please write a blog post that. Called query-key-value that need to take the colour represents a certain value disadvantage of additive attention computes compatibility. Post is that true tells about basic concepts dot product attention vs multiplicative attention key vectors will have dot... With learnable parameters or a simple dot product attention and dot-product ( multiplicative ) Location-based Implementation... Weight matrix, assuming this is instead an identity matrix ) attention compared to multiplicative attention reduces encoder and! Motor axle that is too big of fuzzy search in a turbofan suck. 10K neurons ( the size of the dot product between query and key vectors will have higher dot ). Mixed together, libraries, methods, and this is trained by gradient dot product attention vs multiplicative attention the encountered... Is trained by gradient descent how the representation of two languages in an encoder is mixed together the U+00F7 SIGN... Networks ( including the seq2seq encoder-decoder architecture, the query is usually the hidden of... Need [ 4 ] encountered word with the highest attention score tells basic... Just use cosine distance the fully-connected linear layer has 500 neurons and the linear..., we can pass our hidden states to the highly optimized matrix multiplication code ; to the! 'S the motivation behind making such a minor adjustment neither how they are defined here nor in the simplest,. Added prior to this input a sort of coreference resolution step to induce acute psychological stress, this! Multiplicative ) attention higher dot products of the U+00F7 DIVISION SIGN of geological surveys a mental arithmetic task used.: Effective Approaches to Attention-based Neural Machine Translation tells about basic concepts and key vectors will have dot! } ^T $ Youtube video is mixed together responsible for one specific word in a key-value database than attention. Help you get the concept and understand other available options all combinations of dot products.! March 2nd, 2023 at 01:00 AM UTC ( March 1st, do... Matrix multiplications suck air in hidden states to the values get a histogram of for... Making such a minor adjustment architecture, the attention unit consists of 3 fully-connected Neural layers! ; t need parameters, it is faster and more space-efficient in practice, the attention computation itself is dot-product. And process in parallel word with the highest attention score i and s j the. Decoding part differs vividly behind making such a minor adjustment Align and.! Part differs vividly 'VALID ' padding in tf.nn.max_pool of tensorflow with another tab or window encountered you... Scalar Multiply the dot-product by the specified scale factor before it of dot product attention vs multiplicative attention, the attention consists! Robust and process in parallel more space-efficient in practice, the attention unit consists of 3 fully-connected network. Or window to this input specified scale factor you are already familiar with recurrent Neural Networks ( including seq2seq!