Engineers Guide to AI - Decoding Transformer Models Part 2
Embeddings
With all the excitement around vector databases you've certainly heard the word embeddings or sentence embeddings.
Embeddings are what is called a learned layer. This learned embedding layer is a fully connected layer that maps the input tokens (which have been assigned integer values) to dense vectors (embeddings). These embeddings are then used throughout the model to encode the semantic information of the words. The key point here is that these embeddings are learned from the data during training rather than being pre-computed as in the Word2Vec case.
A learned layer, such as the embedding layer in a transformer model, is a part of the neural network that is trained on the input data and improves its performance over time.
To illustrate, let's think about an embedding layer for a moment. In the context of natural language processing, an embedding layer's job is to convert each word (or token) in the input data to a dense vector of fixed size. Initially, the embeddings for all words are random. However, during the training process, these embeddings get adjusted or "learned" based on the task at hand, such as predicting the next word in a sentence, or classifying the sentiment of a review.
import torch
from torch import nn
# Define the size of your vocabulary and the number of dimensions in your embeddings
vocab_size = 10000? # For example, 10,000 unique words in your vocabulary
embedding_dim = 300? # For example, each word will be represented as a 300-dimensional vector
# Create an embedding layer
embedding = nn.Embedding(vocab_size, embedding_dim)
# The weights of your embedding layer are initialized randomly
print(embedding.weight)
# During training, these weights get updated to minimize your loss
# Here is a very simplified example of what training might look like:
optimizer = torch.optim.Adam(embedding.parameters())
for epoch in range(100): # train for 100 epochs
input_data = ... # your input data here
target_data = ... # your target data here
# Forward pass: compute the embeddings for your input data
embeddings = embedding(input_data)
# Compute your loss
loss = ... # depends on your specific task
# Backward pass: compute the gradients
loss.backward()
# Update the weights
optimizer.step()
# Zero the gradients for the next iteration
optimizer.zero_grad()
# Suppose you have an input batch of size 2 (i.e., two sentences) and each sentence has 8 words
# These would have been previously tokenized and mapped to their integer values
input_batch = torch.tensor([[1, 2, 3, 4, 5, 6, 7, 8], [1, 9, 10, 11, 12, 13, 14, 15]])
# You can pass this input batch to your embedding layer to get the word embeddings
word_embeddings = embedding(input_batch)
print(word_embeddings.shape)? # Output: torch.Size([2, 8, 300])
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Feed-Forward Layers
In a Transformer model, the feed-forward neural network (FFNN) is a critical component in both the encoder and decoder segments. However, unlike the typical usage of a FFNN, where it acts as the main architecture of the model, in a Transformer model, the FFNN acts as one of the several components working together.
Each layer of the Transformer model contains two main components: the self-attention mechanism and a feed-forward neural network. These components are connected using residual connections and followed by layer normalization.
Let's break down the role of the FFNN within a Transformer layer:
Here's a simplified Python example of a single Transformer layer:
import torch
from torch import nn
class TransformerLayer(nn.Module):
def __init__(self, d_model, ff_dim):
? ? ? ? super().__init__()
# Multi-head self-attention mechanism
self.self_attn = MultiheadAttention(d_model)
# Position-wise feed-forward network
self.feed_forward = nn.Sequential(
nn.Linear(d_model, ff_dim),
nn.ReLU(),
nn.Linear(ff_dim, d_model)
)
# Layer normalization layers
self.norm1 = nn.LayerNorm(d_model)
self.norm2 = nn.LayerNorm(d_model)
def forward(self, x):
# Self-attention with residual connection and layer normalization
attn_output = self.self_attn(x)
x = self.norm1(x + attn_output)
# Feed-forward network with residual connection and layer normalization
ff_output = self.feed_forward(x)
output = self.norm2(x + ff_output)
return output
In this code, d_model is the dimension of the input (and output), and ff_dim is the dimension of the hidden layer in the feed-forward network. The actual Transformer model contains multiple such layers stacked together.
Multi-Head Attention
In the context of Transformer models, Multi-Head Attention is a mechanism that allows the model to focus on different positions of the input sequence when producing a particular output in the sequence. This is especially crucial in tasks like machine translation where the order of words can be very different between languages.
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Let's take a closer look at what happens inside a Multi-Head Attention block:
Here's a simple code snippet for a multi-head attention mechanism:
import torch
from torch import nn
class MultiheadAttention(nn.Module):
def __init__(self, d_model, num_heads):
super().__init__()
self.d_model = d_model
self.num_heads = num_heads
self.head_dim = d_model // num_heads
# These are the "learned" matrices used to transform the input into queries, keys, and values.
self.query_linear = nn.Linear(d_model, d_model)
self.key_linear = nn.Linear(d_model, d_model)
self.value_linear = nn.Linear(d_model, d_model)
# This is the "learned" matrix used to transform the concatenated output of all attention heads.
self.out = nn.Linear(d_model, d_model)
def forward(self, query, key, value):
# Get batch size
batch_size = query.shape[0]
# Generate the queries, keys, and values.
query = self.query_linear(query)
key = self.key_linear(key)
value = self.value_linear(value)
# Split into multiple heads
query = query.view(batch_size, -1, self.num_heads, self.head_dim).transpose(1, 2)
key = key.view(batch_size, -1, self.num_heads, self.head_dim).transpose(1, 2)
value = value.view(batch_size, -1, self.num_heads, self.head_dim).transpose(1, 2)
# Calculate the attention scores
attention_scores = torch.matmul(query, key.transpose(-2, -1)) / math.sqrt(self.head_dim)
# Apply softmax to get the weights
attention_weights = torch.softmax(attention_scores, dim=-1)
# Multiply the weights by the value vectors
output = torch.matmul(attention_weights, value)
# Concatenate the heads back together and pass through a linear layer
output = output.transpose(1, 2).contiguous().view(batch_size, -1, self.d_model)
output = self.out(output)
return output
Architecture Variants GPT, Bert, T5, etc
Transformers have been exceptionally influential in the field of Natural Language Processing, with various architectural variants being proposed for different tasks. The variants are based on the type of attention mechanism used (encoder-decoder or self-attention), the way they process text (unidirectionally or bidirectionally), or the specific problem they aim to solve.
1. GPT (Generative Pretrained Transformer): Designed by OpenAI, GPT is a transformer variant that uses a decoder-only architecture with masked self-attention. It processes text from left-to-right and is primarily used for tasks that involve generating text, like language translation or writing articles.
2. BERT (Bidirectional Encoder Representations from Transformers): Developed by Google, BERT uses a transformer encoder with self-attention to process text in both directions. BERT has achieved state-of-the-art results in several NLP tasks, including text classification, sentiment analysis, and question answering.
3. T5 (Text-to-Text Transfer Transformer): Also developed by Google, T5 models every NLP task as a text generation problem. It's designed with an encoder-decoder architecture and has been successful in a wide range of tasks.
4. RoBERTa (A Robustly Optimized BERT Pretraining Approach): This is a variant of BERT developed by Facebook AI that changes key hyperparameters in BERT, including removing the next-sentence pretraining objective, and training with much larger mini-batches and learning rates.
5. ALBERT (A Lite BERT): Another variant of BERT, ALBERT, modifies BERT to reduce parameters, increase training speed, and improve scalability. It introduces two parameter-reduction techniques: factorized embedding parameterization and cross-layer parameter sharing.
6. XLNet: XLNet integrates the idea of autoregressive and autoencoding models by using a permutation-based training objective. It outperforms BERT on 20 tasks.
7. Transformer-XL: It introduces a recurrence mechanism to the Transformer model to tackle long-term dependencies, thus capturing information from much earlier in the sequence, which traditional transformers can't.
8. DistilBERT: It is a smaller, faster, and cheaper version of BERT that retains most of BERT's performance while being much more efficient.
The best choice of Transformer variant often depends on the specific requirements of the task at hand, including the size and nature of the available data, computational resources, and the particularities of the problem domain.
For example, BERT and its derivatives like RoBERTa and ALBERT have achieved state-of-the-art performance on a number of benchmark tasks, especially those that require a deep understanding of sentence context from both directions, such as question answering and named entity recognition.
On the other hand, GPT-2 and GPT-3, which process text in a unidirectional manner, have shown remarkable capabilities in generating human-like text and are often used in language generation tasks.
The T5 model has been successful across a wide range of tasks by modeling all NLP tasks as a text-to-text problem, which simplifies the process of applying the model to various tasks.
Lastly, lighter models like DistilBERT have gained popularity in scenarios where computational resources are a limiting factor, as they offer a good trade-off between performance and efficiency.Remember, this is not an exhaustive list. New variants continue to be developed as researchers find new ways to improve transformer models.
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