– What are transformers? Discuss the major breakthroughs in transformer models
– What are the primary advantages of transformer models?
– What are the limitations of transformer models?
– What is Natural Language Processing (NLP) ? List the different types of NLP tasks
Transformers architecture is a deep learning model introduced in the paper “Attention Is All You Need” by Vaswani et al. in 2017. It has revolutionized the field of natural language processing (NLP) and has since been used in various other machine learning tasks due to its remarkable ability to capture long-range dependencies in data and its parallelizable nature. Here’s an overview of the key components and concepts of the Transformer architecture:
1. Self-Attention Mechanism
Related questions: Explain Attention, and Masked Self-Attention as used in Transformers
– Self-attention computes a weighted sum of all input elements, where the weights are determined dynamically based on their inter-relationships. This allows capturing dependencies between distant words in a sentence.
– In order to determine the weights dynamically, the self-attention mechanism computes three vectors for each input element: Key (k), Query (q), and Value (v). These vectors are then used to compute the weights of different words in the input sequence as explained in the diagram below:
2. Multi-Head Attention
– The role of multi-head attention is to capture different types of relationships in the data. This is accomplished by using multiple self-attention heads in parallel, whereby each head learns different aspects of the input, allowing the model to attend to various patterns simultaneously.
Head_size for each head is computed as
d_embed / num_of_heads. Thereafter, output from each head is concatenated to generate an attention vector of dimension
d_embed. This vector is then passed through another projection matrix of dimension (
d_embed x d_embed) to generate the final multi-head attention vector as explained below:
3. Positional Encoding
Related Question: Explain the need for Positional Encoding and how it is implemented in Transformers?
– Unlike recurrent neural networks (RNNs) and convolutional neural networks (CNNs), the Transformer does not have built-in notions of sequence order.
– Positional encoding is added to the input embeddings to provide information about the position of each word in the sequence, enabling the model to consider the order of elements.
4. Stacked Attention layers with Dropout, Residual Connections, and Layer Normalization
Related Questions: Explain how Dropout helps with better training of Deep Learning models?
Explain the need for Residual Connections?
Explain Layer Normalization and how it helps with stabilizing model training?
– The Transformer consists of multiple identical layers stacked on top of each other. Each layer contains a multi-head self-attention sub-layer followed by a feedforward neural network sub-layer.
– The output of each sub-layer is passed through a dropout layer, a layer normalization step, and residual connections are employed to facilitate gradient flow during training.
– Residual connections (skip connections) help mitigate the vanishing gradient problem and facilitate the training of deep networks.
– Layer normalization is applied before and after the sub-layers to stabilize training.
5. Position-wise Feedforward Layer
– After multiple attention layers, in the final layer transformer applies a position-wise feedforward layer to each position independently.
Encoder-Decoder Architecture (for sequence-to-sequence tasks)
Related Questions: What are Encoder models?
What are Decoder models?
What are Encoder-Decoder models?
– In tasks like machine translation, where the input and output sequences can be of different lengths, the Transformer uses an encoder-decoder architecture.
– The encoder processes the input sequence, and the decoder generates the output sequence.
– The encoder and decoder each have their own stacks of layers, and the decoder additionally employs masked self-attention to ensure that each position only looks at previous positions during generation.
The Transformer architecture has had a profound impact on various NLP tasks, including machine translation, text generation, question answering, and sentiment analysis. Its parallelizable nature and ability to capture long-range dependencies make it highly versatile and efficient for many sequence-based applications. Variants of the Transformer architecture, such as BERT, GPT, and T5, have further improved performance on a wide range of NLP benchmarks.
In the following video Andrej Karpathy implements the full GPT-2 model from scratch in numpy. In the process, he explains Attention, Self-attention, Multi-headed Attention, Layer Normalization, Residual connections, dropout, and Encoder-Decoder architecture in detail.
Even though the video’s runtime is 2 hrs, this video is “All you need to understand Transformers”. The whole GPT-2 model is implemented in less than 250 lines of python code, and the associated code is shared on github.
YouTube Link: https://www.youtube.com/watch?v=kCc8FmEb1nY (Runtime: 2 hrs)
Other Video Recommendations:
The following two video lectures from Prof. Pascal Poupart:
- In this video lecture, Prof. Poupart explains the origins of “Attention“, which was introduced by Bahdanau et al. and was used with Recurrent Neural Networks for machine translation task: https://youtu.be/lClNhXVNZ-0?t=4050 (Watch from 1:07:30 – 1:30:00)
- In the next video lecture, Prof. Poupart dives into the Transformers Architecture in detail, and explains the associate math behind it: https://www.youtube.com/watch?v=OyFJWRnt_AY