🤖 Transformer Architecture
Transformers revolutionized natural language processing and form the foundation of modern large language models. They introduced the self-attention mechanism, enabling parallel processing and better capture of long-range dependencies in sequences.
Self-Attention
Core mechanism for parallel sequence processing
Parallelization
Efficient training on modern GPU hardware
Scalability
Powers models from BERT to GPT-4
Key Features & Impact
2017
Year Introduced
Attention Is All You Need paper
Self-Attention
Key Innovation
Parallel sequence processing
GPT, BERT
Popular Models
Claude, ChatGPT, Gemini
175B+ params
Scale
Modern LLMs have billions of parameters
Architecture Components
Self-Attention Mechanism
Core mechanism that allows models to focus on relevant parts of input
# Simplified Self-Attention Implementation
import torch
import torch.nn as nn
import torch.nn.functional as F
import math
class SelfAttention(nn.Module):
def __init__(self, embed_dim, num_heads):
super().__init__()
self.embed_dim = embed_dim
self.num_heads = num_heads
self.head_dim = embed_dim // num_heads
assert self.head_dim * num_heads == embed_dim
self.query = nn.Linear(embed_dim, embed_dim)
self.key = nn.Linear(embed_dim, embed_dim)
self.value = nn.Linear(embed_dim, embed_dim)
self.out_proj = nn.Linear(embed_dim, embed_dim)
def forward(self, x):
batch_size, seq_len, embed_dim = x.size()
# Generate Q, K, V matrices
Q = self.query(x) # (batch, seq_len, embed_dim)
K = self.key(x)
V = self.value(x)
# Reshape for multi-head attention
Q = Q.view(batch_size, seq_len, self.num_heads, self.head_dim).transpose(1, 2)
K = K.view(batch_size, seq_len, self.num_heads, self.head_dim).transpose(1, 2)
V = V.view(batch_size, seq_len, self.num_heads, self.head_dim).transpose(1, 2)
# Scaled dot-product attention
scores = torch.matmul(Q, K.transpose(-2, -1)) / math.sqrt(self.head_dim)
attention_weights = F.softmax(scores, dim=-1)
# Apply attention to values
attention_output = torch.matmul(attention_weights, V)
# Concatenate heads and project
attention_output = attention_output.transpose(1, 2).contiguous().view(
batch_size, seq_len, embed_dim
)
return self.out_proj(attention_output)Key Points
- Allows parallel processing of sequences
- Captures long-range dependencies effectively
- Forms the foundation of modern LLMs
- Scales quadratically with sequence length
🚀 Why Transformers Revolutionized AI
Before Transformers
- • RNNs processed sequences sequentially
- • Limited context window
- • Vanishing gradient problems
- • Slow training and inference
After Transformers
- • Parallel processing of entire sequences
- • Long-range dependency modeling
- • Stable training at massive scale
- • Foundation for modern LLMs
🎯 Common Applications
Language Models
GPT, Claude, Gemini for text generation
- • Chatbots
- • Content creation
- • Code generation
Understanding Tasks
BERT-style models for comprehension
- • Question answering
- • Sentiment analysis
- • Text classification
Multimodal AI
Vision transformers and multimodal models
- • Image captioning
- • Visual question answering
- • DALL-E style generation
📝 Transformers Architecture Quiz
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