Introduction to Transformers
Transformers are a type of deep learning model that have been widely used in natural language processing (NLP) tasks such as machine translation, language modeling, and text classification. The Transformer architecture was introduced in the paper "Attention is All You Need" by Vaswani et al. in 2017, and has since become one of the most popular models in NLP.
The key innovation of the Transformer architecture is its self-attention mechanism, which allows the model to weigh the importance of different parts of the input when making predictions. This is in contrast to traditional recurrent neural networks (RNNs) and convolutional neural networks (CNNs), which process the input sequentially or hierarchically, respectively.
The Transformer architecture consists of two main components: an encoder and a decoder. The encoder takes in an input sequence and generates a set of hidden representations, while the decoder takes in these representations and generates an output sequence. Each component is composed of multiple layers of self-attention and feed-forward neural networks.
The self-attention mechanism in the Transformer allows the model to attend to different parts of the input sequence to generate the hidden representations. Specifically, each input token is associated with three vectors: a query vector, a key vector, and a value vector. The model computes a score for each pair of query and key vectors, which represents the similarity between them. These scores are then used to compute a weighted sum of the value vectors, which is the output of the self-attention mechanism.
Here's some code that shows how to implement a basic Transformer model in PyTorch:
import torch
import torch.nn as nn
class Transformer(nn.Module):
def __init__(self, input_dim, hidden_dim, num_layers, num_heads):
super().__init__()
self.embedding = nn.Embedding(input_dim, hidden_dim)
self.encoder_layers = nn.ModuleList([
nn.TransformerEncoderLayer(hidden_dim, num_heads)
for _ in range(num_layers)
])
self.encoder = nn.TransformerEncoder(self.encoder_layers)
self.decoder_layers = nn.ModuleList([
nn.TransformerDecoderLayer(hidden_dim, num_heads)
for _ in range(num_layers)
])
self.decoder = nn.TransformerDecoder(self.decoder_layers)
self.linear = nn.Linear(hidden_dim, input_dim)
def forward(self, src, trg):
src_emb = self.embedding(src)
trg_emb = self.embedding(trg)
enc_out = self.encoder(src_emb)
dec_out = self.decoder(trg_emb, enc_out)
out = self.linear(dec_out)
return out
In this code, we define a Transformer class that takes in an input dimension, a hidden dimension, the number of layers, and the number of attention heads. We create an embedding layer for the input and output sequences, and then create the encoder and decoder layers using the nn.TransformerEncoderLayer and nn.TransformerDecoderLayer classes. We then stack these layers together to create the encoder and decoder using the nn.TransformerEncoder and nn.TransformerDecoder classes. Finally, we pass the output of the decoder through a linear layer to generate the final output sequence.
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