Transformer (deep learning)

In deep learning, the transformer is an artificial neural network architecture based on the multi-head attention mechanism, in which text is converted to numerical representations called tokens, and each token is converted into a vector via lookup from a word embedding table. At each layer, each token is then contextualized within the scope of the context window with other (unmasked) tokens via a parallel multi-head attention mechanism, allowing the signal for key tokens to be amplified and less important tokens to be diminished. Transformers have the advantage of having no recurrent units, therefore requiring less training time than earlier recurrent neural architectures (RNNs) such as long short-term memory (LSTM). Later variations have been widely adopted for training large language models (LLMs) on large (language) datasets.

The modern version of the transformer was proposed in the 2017 paper "Attention Is All You Need" by researchers at Google. The predecessors of transformers were developed as an improvement over previous architectures for machine translation, but have found many applications since. They are used in large-scale natural language processing, computer vision (vision transformers), reinforcement learning, audio, multimodal learning, robotics, and even playing chess. It has also led to the development of pre-trained systems, such as generative pre-trained transformers (GPTs) and BERT (bidirectional encoder representations from transformers).

== History ==

=== Predecessors === For many years, sequence modelling and generation was done by using plain recurrent neural networks (RNNs). A well-cited early example was the Elman network (1990). In theory, the information from one token can propagate arbitrarily far down the sequence, but in practice the vanishing-gradient problem leaves the model's state at the end of a long sentence without precise, extractable information about preceding tokens. A key breakthrough was LSTM (1995), an RNN which used various innovations to overcome the vanishing gradient problem, allowing efficient learning of long-sequence modelling. One key innovation was the use of an attention mechanism which used neurons that multiply the outputs of other neurons, so-called multiplicative units. Neural networks using multiplicative units were later called sigma-pi networks or higher-order networks. LSTM became the standard architecture for long sequence modelling until the 2017 publication of transformers. However, LSTM still used sequential processing, like most other RNNs. Specifically, RNNs operate one token at a time from first to last; they cannot operate in parallel over all tokens in a sequence. Modern transformers overcome this problem, but unlike RNNs, they require computation time that is quadratic in the size of the context window. The linearly scaling fast weight controller (1992) learns to compute a weight matrix for further processing depending on the input. One of its two networks has "fast weights" or "dynamic links" (1981). A slow neural network learns by gradient descent to generate keys and values for computing the weight changes of the fast neural network which computes answers to queries. This was later shown to be equivalent to the unnormalized linear transformer.

=== Attention with seq2seq ===

The idea of encoder–decoder sequence transduction had been developed in the early 2010s; commonly cited as the originators that produced seq2seq are two concurrently published papers from 2014. A 380M-parameter model for machine translation uses two long short-term memories (LSTM). Its architecture consists of two parts. The encoder is an LSTM that takes in a sequence of tokens and turns it into a vector. The decoder is another LSTM that converts the vector into a sequence of tokens. Similarly, another 130M-parameter model used gated recurrent units (GRU) instead of LSTM. Later research showed that GRUs are neither better nor worse than LSTMs for seq2seq. These early seq2seq models had no attention mechanism, and the state vector is accessible only after the last word of the source text was processed. Although in theory such a vector retains the information about the whole original sentence, in practice the information is poorly preserved. This is because the input is processed sequentially by one recurrent network into a fixed-size output vector, which is then processed by another recurrent network into an output. If the input is long, then the output vector would not be able to contain all relevant information, degrading the output. As evidence, reversing the input sentence improved seq2seq translation. The RNN search model introduced an attention mechanism to seq2seq for machine translation to solve the bottleneck problem (of the fixed-size output vector), allowing the model to process long-distance dependencies more easily. The name is because it "emulates searching through a source sentence during decoding a translation". The relative performances were compared between global (that of RNN search) and local (sliding window) attention model architectures for machine translation, finding that mixed attention had higher quality than global attention, while local attention reduced translation time. In 2016, Google Translate was revamped to Google Neural Machine Translation, which replaced the previous model based on statistical machine translation. The new model was a seq2seq model where the encoder and the decoder were both 8 layers of bidirectional LSTM. It took nine months to develop, and it outperformed the statistical approach, which took ten years to develop.

=== Parallelizing attention ===

Seq2seq models with attention (including self-attention) still suffered from the same issue with recurrent networks, which is that they are hard to parallelize, which prevented them from being accelerated on GPUs. In 2016, decomposable attention applied a self-attention mechanism to feedforward networks, which are easy to parallelize, and achieved SOTA result in textual entailment with an order of magnitude fewer parameters than LSTMs. One of its authors, Jakob Uszkoreit, suspected that attention without recurrence would be sufficient for language translation, thus the title "attention is all you need". That hypothesis was against conventional wisdom at the time, and even his father Hans Uszkoreit, a well-known computational linguist, was skeptical. In the same year, self-attention (called intra-attention or intra-sentence attention) was proposed for LSTMs. In 2017, the original (100M-sized) encoder–decoder transformer model was proposed in the "Attention is all you need" paper. At the time, the focus of the research was on improving seq2seq for machine translation, by removing its recurrence to process all tokens in parallel, but preserving its dot-product attention mechanism to keep its text processing performance. This led to the introduction of a multi-head attention model that was easier to parallelize due to the use of independent heads and the lack of recurrence. Its parallelizability was an important factor to its widespread use in large neural networks.

=== AI boom era === As early as spring 2017, even before the "Attention is all you need" preprint was published, one of the co-authors applied the "decoder-only" variation of the architecture to generate fictitious Wikipedia articles. Transformer architecture is now used alongside many generative models that contribute to the ongoing AI boom. In language modelling, ELMo (2018) was a bi-directional LSTM that produces contextualized word embeddings, improving upon the line of research from bag of words and word2vec. It was followed by BERT (2018), an encoder-only transformer model. In 2019 October, Google started using BERT to process search queries. In 2020, Google Translate replaced the previous RNN-encoder–RNN-decoder model by a transformer-encoder–RNN-decoder model. Starting in 2018, the OpenAI GPT series of decoder-only transformers became state of the art in natural language generation. In 2022, a chatbot based on GPT-3, ChatGPT, became unexpectedly popular, triggering a boom around large language models. Since 2020, transformers have been applied in modalities beyond text, including the vision transformer, speech recognition, robotics, and multimodal. The vision transformer, in turn, stimulated new developments in convolutional neural networks. Image and video generators like DALL-E (2021), Stable Diffusion 3 (2024), and Sora (2024), use transformers to analyse input data (like text prompts) by breaking it down into "tokens" and then calculating the relevance between each token using self-attention, which helps the model understand the context and relationships within the data.

== Training ==

=== Methods for stabilizing training === The plain transformer architecture had difficulty in converging. In the original paper, the authors recommended using learning rate warmup. That is, the learning rate should linearly scale up from 0 to maximal value for the first part of the training (usually recommended to be 2% of the total number of training steps), before decaying again. A 2020 paper found that using layer normalization before (instead of after) multihead attention and feedforward layers stabilizes training, not requiring learning rate warmup.

=== Pretrain-finetune === Transformers typically are first pretrained by self-supervised learning on a large generic dataset, followed by supervised fine-tuning on a small task-specific dataset. The pretrain dataset is typically an unlabeled large corpus, such as The Pile. Tasks for pretraining and fine-tuning commonly include:

language modeling next-sentence prediction question answering reading comprehension sentiment analysis paraphrasing The T5 transformer report documents a large number of natural language pretraining tasks. Some examples are:

restoring or repairing incomplete or corrupted text. For example, the input, "Thank you ~~ me to your party ~~ week", might generate the output, "Thank you for inviting me to your party last week". translation between natural languages (machine translation) judging the pragmatic acceptability of natural language. For example, the following sentence might be judged "not acceptable", because even though it is syntactically well-formed, it is improbable in ordinary human usage: The course is jumping well. Note that while each of these tasks is trivial or obvious for human native speakers of the language (or languages), they have typically proved challenging for previous generations of machine learning architecture.

=== Tasks ===

In general, there are 3 classes of language modelling tasks: "masked", "autoregressive", and "prefixLM". These classes are independent of a specific modeling architecture such as transformer, but they are often discussed in the context of transformer. In a masked task, one or more of the tokens is masked out, and the model would produce a probability distribution predicting what the masked-out tokens are based on the context. The loss function for the task is typically sum of log-perplexities for the masked-out tokens: ${\text{Loss}}=-\sum _{t\in {\text{masked tokens}}}\ln({\text{probability of }}t{\text{ conditional on its context}})$
and the model is trained to minimize this loss function. The BERT series of models are trained for masked token prediction and another task. In an autoregressive task, the entire sequence is masked at first, and the model produces a probability distribution for the first token. Then the first token is revealed and the model predicts the second token, and so on. The loss function for the task is still typically the same. The GPT series of models are trained by autoregressive tasks. In a prefixLM task, the sequence is divided into two parts. The first part is presented as context, and the model predicts the first token of the second part. Then that would be revealed, and the model predicts the second token, and so on. The loss function for the task is still typically the same. The T5 series of models are trained by prefixLM tasks. Note that "masked" as in "masked language modelling" is not "masked" as in "masked attention", and "prefixLM" as in "prefix language modeling" is not "prefixLM" as in " prefix language model".

== Architecture == All transformers have the same primary components:

Tokenizers, which convert text into tokens. Embedding layer, which converts tokens and positions of the tokens into vector representations. Transformer layers, which carry out repeated transformations on the vector representations, extracting more and more linguistic information. These consist of alternating attention and feedforward layers. There are two major types of transformer layers: encoder layers and decoder layers, with further variants. Un-embedding layer, which converts the final vector representations back to a probability distribution over the tokens. The following description follows exactly the transformer as described in the original paper. There are variants, described in the following section. By convention, we write all vectors as row vectors. For example, pushing a vector through a linear layer means multiplying it by a weight matrix on the right, as $xW$
.

=== Tokenization === As the transformer architecture natively consists of operations over numbers (matrix multiplications, dot products, activation functions) rather than over text, there must first be a mapping from any input text to some numerical representation. This happens in three steps. First, the input text is treated by a preprocessor, which performs both textual transformations and splits the text into coarse-grained segments called pretokens. The latter is referred to as pretokenization. Second, each pretoken is segmented further into tokens by a tokenizer that expects to only see pretokens output by its preprocessor. Each token it produces is a string of one or more characters belonging to a finite set of strings called the vocabulary $V$
. Third, because the vocabulary is finite and known beforehand, each token can be assigned an integer identifier, and this mapping is applied to the sequence of tokens to represent any input text as a numerical sequence. Since this mapping is bijective, the output side can produce a sequence of integer identifiers which can then be turned back into tokens. After undoing some of the preprocessing, the result is again legible text. Training a tokenizer (sometimes referred to as vocabularization) means finding a suitable vocabulary $V$
, but also learning how to use it, since any given string $|V|$
: when it is small, the learned vocabulary generally consists of characters and smaller strings, and words will be segmented into many tokens. At larger sizes, it becomes affordable to dedicate tokens to full words, although depending on the preprocessor and tokenizer, it is not necessarily the case that large vocabularies will always use the largest token(s) available to segment a word. Because tokens are not always full words, they may also be referred to as subwords and tokenization algorithms may be referred to as subword tokenizers. This is also to differentiate these systems from traditional terminology used in older information retrieval and natural language processing systems, where "tokenization" was used to denote what is today called "pretokenization" (very crudely: splitting into words). In tokenizers that produce tokens that are not part of the vocabulary, a special token that does belong to the vocabulary is used as a generic stand-in, written as "[UNK]" for "unknown". In principle, any string could be hidden by such an [UNK]. Indeed, in information retrieval, pretokenizers were themselves used as tokenizers (and also called "tokenizers") with a word-level vocabulary that contained an [UNK]. Commonly used subword tokenization algorithms are byte pair encoding (BPE) and the unigram language model (ULM), which each include a vocabularization algorithm and a dedicated segmentation algorithm. There also exist several segmentation algorithms that require no learning and can be applied given a vocabulary (produced by BPE or ULM, for example), like greedily recognising tokens in a pretoken by moving through it left-to-right. Well-known software implementations of subword tokenizers are Hugging Face's tokenizers Python package implemented in Rust, and the sentencepiece Python package implemented in C++. The latter package is named as such because one of its configuration options allows disabling the built-in pretokenizer, hence effectively making entire sentences a pretoken and thus having the tokenizer see entire sentences, rather than individual words.

=== Embedding ===

Each integer token identifier is converted into an embedding vector via a lookup table. Equivalently stated, it multiplies a one-hot representation of the token identifier by an embedding matrix $M$
. For example, if the input token's identifier is $3$
, then the one-hot representation is $[0,0,0,1,0,0,\dots ]$
, and its embedding vector is $\mathrm {Embed} (3)=[0,0,0,1,0,0,\dots ]M$
The token embedding vectors are added to their respective positional encoding vectors (see below), producing the sequence of input vectors. The dimension of an embedding vector is called hidden size or embedding size and written as $d_{\text{emb}}$
. This size is written as $d_{\text{model}}$

=== Un-embedding === An un-embedding layer is almost the reverse of an embedding layer. Whereas an embedding layer converts a token identifier into a vector, an un-embedding layer converts a vector into a probability distribution over tokens. The un-embedding layer is a linear-softmax layer:

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