Summary Transformer (deep learning architecture) - Wikipedia en.wikipedia.org
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Google's transformer model uses multi-head attention to efficiently convert text to numerical tokens, enabling its use in various applications.
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Key Points
- The transformer is a deep learning architecture developed by Google, based on the multi-head attention mechanism, and used in natural language processing, computer vision, audio, and multi-modal processing
- Transformers typically undergo self-supervised learning involving unsupervised pretraining followed by supervised fine-tuning, and have had great success in various NLP tasks such as machine translation, document summarization, and named entity recognition
- The transformer architecture has been implemented in standard deep learning frameworks like TensorFlow and PyTorch, and consists of key components like tokenizers, embedding layers, transformer layers, and un-embedding layers
- Advancements in the transformer architecture include the development of FlashAttention-2 for efficient GPU implementation, Random Feature Attention and Performer for linear-time attention computation, and speculative decoding for more efficient token generation
- Transformers can be adapted for modalities beyond text, such as computer vision and speech recognition, by treating input data as a sequence of tokens, and have seen significant advancements in terms of efficiency, adaptability, and performance
Summaries
19 word summary
Google's transformer uses multi-head attention to convert text to numerical tokens, reducing training time. It's used in various applications.
60 word summary
The transformer, developed by Google, uses multi-head attention to convert text into numerical tokens and has no recurrent units, reducing training time. It is used in natural language processing, computer vision, audio, and multi-modal processing, leading to the development of pre-trained systems like GPTs and BERT. Predecessors of the attention mechanism were added to gated recurrent neural networks before transformers.
138 word summary
The transformer, developed by Google, uses the multi-head attention mechanism to convert text into numerical tokens and has no recurrent units, reducing training time. It is used in natural language processing, computer vision, audio, and multi-modal processing, leading to the development of pre-trained systems like GPTs and BERT. Predecessors of the attention mechanism were added to gated recurrent neural networks before transformers. The plain transformer architecture had convergence issues, but normalizing layers before multiheaded attention solved this. Transformers typically undergo self-supervised learning and have been successful in NLP, machine translation, document summarization, and video understanding. They have also been implemented in standard deep learning frameworks and can be adapted for modalities beyond text, such as computer vision and speech recognition. Key advancements include FlashAttention-2, Random Feature Attention, Performer, speculative decoding, Vision transformers, Conformer, Whisper, Perceivers, and diffusion transformers.
403 word summary
The transformer is a deep learning architecture developed by Google and based on the multi-head attention mechanism. It converts text to numerical representations called tokens, contextualizes each token within a context window with other tokens, and has no recurrent units, requiring less training time than previous architectures. The transformer architecture is used in natural language processing, computer vision, audio, and multi-modal processing, and has led to the development of pre-trained systems such as generative pre-trained transformers (GPTs) and BERT.
Before transformers, predecessors of the attention mechanism were added to gated recurrent neural networks, such as LSTMs and GRUs. In 1992, the fast weight controller was proposed as an alternative to recurrent neural networks that can learn “internal spotlights of attention”. In 2016, highly parallelizable decomposable attention was successfully combined with a feedforward network. The plain transformer architecture had difficulty converging, but this was solved by normalizing layers before (instead of after) multiheaded attention by Xiong et al.
Transformers typically undergo self-supervised learning involving unsupervised pretraining followed by supervised fine-tuning. The transformer has had great success in natural language processing (NLP), machine translation, document summarization, and video understanding. It has also been successful in other fields, such as computer vision and protein folding applications.
The transformer model has been implemented in standard deep learning frameworks such as TensorFlow and PyTorch. All transformers have the same primary components: Tokenizers, a single embedding layer, transformer layers, and an un-embedding layer. Transformers may use other positional encoding methods than sinusoidal, such as RoPE (rotary positional embedding). ALiBi (Attention with Linear Biases) is an additional positional encoder that is directly plugged into the attention mechanism.
Key advancements in the field include the development of FlashAttention-2, which achieves up to 230 TFLOPs/s on A100 GPUs, and the introduction of Random Feature Attention and Performer. Additionally, speculative decoding has been proposed as a method to use spare compute power by computing several tokens in parallel, allowing for more efficient generation of tokens.
Transformers can be adapted for modalities beyond text, such as computer vision and speech recognition. Vision transformers break down input images into patches and treat them like tokens in a standard transformer. Conformer and Whisper follow a similar pattern for speech recognition, turning the speech signal into a spectrogram and treating it like an image. Perceivers can learn from large amounts of heterogeneous data, while diffusion transformers facilitate the use of the transformer architecture for diffusion-based image production.
700 word summary
The transformer is a deep learning architecture developed by Google and based on the multi-head attention mechanism. It converts text to numerical representations called tokens, contextualizes each token within a context window with other tokens, and has no recurrent units, requiring less training time than previous architectures. The transformer architecture is used in natural language processing, computer vision, audio, and multi-modal processing, and has led to the development of pre-trained systems such as generative pre-trained transformers (GPTs) and BERT.
Before transformers, predecessors of the attention mechanism were added to gated recurrent neural networks, such as LSTMs and GRUs. In 1992, the fast weight controller was proposed as an alternative to recurrent neural networks that can learn “internal spotlights of attention”. In 2016, highly parallelizable decomposable attention was successfully combined with a feedforward network. The plain transformer architecture had difficulty converging, but this was solved by normalizing layers before (instead of after) multiheaded attention by Xiong et al.
Transformers typically undergo self-supervised learning involving unsupervised pretraining followed by supervised fine-tuning. Pretraining is typically done on a larger dataset than fine-tuning, due to the limited availability of labeled training data. The transformer has had great success in natural language processing (NLP), for example in machine translation, document summarization, document generation, named entity recognition (NER), biological sequence analysis, writing computer code based on requirements expressed in natural language, and video understanding. It has also been successful in other fields, such as computer vision and protein folding applications.
The transformer model has been implemented in standard deep learning frameworks such as TensorFlow and PyTorch. All transformers have the same primary components: Tokenizers, a single embedding layer, transformer layers, and an un-embedding layer. Transformers may use other positional encoding methods than sinusoidal, such as RoPE (rotary positional embedding). ALiBi (Attention with Linear Biases) is an additional positional encoder that is directly plugged into the attention mechanism.
In conclusion, the transformer architecture has revolutionized deep learning by providing a more efficient and effective way to process and analyze data in various fields such as natural language processing, computer vision, and audio processing. It has paved the way for the development of pre-trained systems and has been implemented in standard deep learning frameworks. The transformer's success in various applications demonstrates its potential for real-world use and its ability to perform a wide variety of tasks in natural language processing.
The Attention softmax formula is a key aspect of the Transformer architecture, allowing for pretraining on short context windows and finetuning on longer context windows. It can be combined with any positional encoder and is directly plugged into the attention mechanism. Relative Position Encodings is similar to ALiBi, but more generic, using a Toeplitz matrix. FlashAttention is an algorithm that implements the transformer attention mechanism efficiently on a GPU, performing matrix multiplications in blocks to minimize data copying between GPU caches. FlashAttention-2 offers enhancements in work partitioning and parallelism, achieving up to 230 TFLOPs/s on A100 GPUs. Multi-Query Attention changes the multiheaded attention mechanism, increasing inference speed without affecting model quality or training speed.
Key advancements in the field include the development of FlashAttention-2, which achieves up to 230 TFLOPs/s on A100 GPUs, and the introduction of Random Feature Attention and Performer, which use Fourier random features to compute attention matrices in linear time. Additionally, speculative decoding has been proposed as a method to use spare compute power by computing several tokens in parallel, allowing for more efficient generation of tokens.
Transformers can be adapted for modalities beyond text, such as computer vision and speech recognition. Vision transformers break down input images into patches and treat them like tokens in a standard transformer. Conformer and Whisper follow a similar pattern for speech recognition, turning the speech signal into a spectrogram and treating it like an image. Perceivers can learn from large amounts of heterogeneous data, while diffusion transformers facilitate the use of the transformer architecture for diffusion-based image production.
In conclusion, the Transformer architecture has seen significant advancements in recent years, particularly in terms of efficiency, adaptability to different modalities, and improvements in performance. These advancements have the potential to further enhance the capabilities of transformers in various applications, including natural language processing, computer vision, and speech recognition.
837 word summary
The transformer is a deep learning architecture developed by Google and based on the multi-head attention mechanism. It converts text to numerical representations called tokens, contextualizes each token within a context window with other tokens, and has no recurrent units, requiring less training time than previous architectures. The transformer architecture is used in natural language processing, computer vision, audio, and multi-modal processing, and has led to the development of pre-trained systems such as generative pre-trained transformers (GPTs) and BERT.
Before transformers, predecessors of the attention mechanism were added to gated recurrent neural networks, such as LSTMs and GRUs. In 1992, the fast weight controller was proposed as an alternative to recurrent neural networks that can learn “internal spotlights of attention”. In 2016, highly parallelizable decomposable attention was successfully combined with a feedforward network. The plain transformer architecture had difficulty converging, but this was solved by normalizing layers before (instead of after) multiheaded attention by Xiong et al.
Transformers typically undergo self-supervised learning involving unsupervised pretraining followed by supervised fine-tuning. Pretraining is typically done on a larger dataset than fine-tuning, due to the limited availability of labeled training data. The transformer has had great success in natural language processing (NLP), for example in machine translation, document summarization, document generation, named entity recognition (NER), biological sequence analysis, writing computer code based on requirements expressed in natural language, and video understanding. It has also been successful in other fields, such as computer vision and protein folding applications.
The transformer model has been implemented in standard deep learning frameworks such as TensorFlow and PyTorch. All transformers have the same primary components: Tokenizers, a single embedding layer, transformer layers, and an un-embedding layer. Transformers may use other positional encoding methods than sinusoidal, such as RoPE (rotary positional embedding). ALiBi (Attention with Linear Biases) is an additional positional encoder that is directly plugged into the attention mechanism.
The original transformer uses ReLU activation function. Other activation functions were developed, such as SwiGLU. In large language models, the terminology is somewhat different than the terminology used in the original Transformer paper: “encoder only” refers to full encoder, full decoder; “encoder-decoder” refers to full encoder, autoregressive decoder; and “decoder only” refers to autoregressive encoder, autoregressive decoder.
In conclusion, the transformer architecture has revolutionized deep learning by providing a more efficient and effective way to process and analyze data in various fields such as natural language processing, computer vision, and audio processing. It has paved the way for the development of pre-trained systems and has been implemented in standard deep learning frameworks. The transformer's success in various applications demonstrates its potential for real-world use and its ability to perform a wide variety of tasks in natural language processing.
The Attention softmax formula is a key aspect of the Transformer architecture, allowing for pretraining on short context windows and finetuning on longer context windows. It can be combined with any positional encoder and is directly plugged into the attention mechanism. Relative Position Encodings is similar to ALiBi, but more generic, using a Toeplitz matrix. FlashAttention is an algorithm that implements the transformer attention mechanism efficiently on a GPU, performing matrix multiplications in blocks to minimize data copying between GPU caches. FlashAttention-2 offers enhancements in work partitioning and parallelism, achieving up to 230 TFLOPs/s on A100 GPUs. Multi-Query Attention changes the multiheaded attention mechanism, increasing inference speed without affecting model quality or training speed.
Speculative decoding uses spare compute power by computing several tokens in parallel, allowing for a more efficient generation of tokens. Sub-quadratic transformers reduce the computational load for long inputs, with models like Reformer and ETC/BigBird using locality-sensitive hashing and reversible layers. Attention-free transformers reduce memory size while retaining the advantages of a transformer. Random Feature Attention uses Fourier random features to compute the attention matrix in linear time. Performer uses the same Random Feature Attention but with Gram-Schmidt processed parameters.
Transformers can be adapted for modalities beyond text, such as computer vision and speech recognition. Vision transformers break down input images into patches and treat them like tokens in a standard transformer. Conformer and Whisper follow a similar pattern for speech recognition, turning the speech signal into a spectrogram and treating it like an image. Perceivers can learn from large amounts of heterogeneous data, while diffusion transformers facilitate the use of the transformer architecture for diffusion-based image production.
Key advancements in the field include the development of FlashAttention-2, which achieves up to 230 TFLOPs/s on A100 GPUs, and the introduction of Random Feature Attention and Performer, which use Fourier random features to compute attention matrices in linear time. Additionally, speculative decoding has been proposed as a method to use spare compute power by computing several tokens in parallel, allowing for more efficient generation of tokens.
In conclusion, the Transformer architecture has seen significant advancements in recent years, particularly in terms of efficiency, adaptability to different modalities, and improvements in performance. These advancements have the potential to further enhance the capabilities of transformers in various applications, including natural language processing, computer vision, and speech recognition.