🔥 Drawing Transformer Networks From Scratch In Python Secrets That Will Boost Your!
Hey there! Ready to dive into Drawing Transformer Networks From Scratch In Python? This friendly guide will walk you through everything step-by-step with easy-to-follow examples. Perfect for beginners and pros alike!
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💡 Pro tip: This is one of those techniques that will make you look like a data science wizard! Introduction to Transformer Networks - Made Simple!
The Transformer architecture, introduced in the “Attention Is All You Need” paper, revolutionized natural language processing. This slideshow will guide you through building a Transformer from scratch using Python.
Let’s make this super clear! Here’s how we can tackle this:
import torch
import torch.nn as nn
import math
class Transformer(nn.Module):
def __init__(self):
super(Transformer, self).__init__()
# We'll fill this in as we progress🚀
🎉 You’re doing great! This concept might seem tricky at first, but you’ve got this! Embedding Layer - Made Simple!
The embedding layer converts input tokens into dense vector representations. It also adds positional encoding to incorporate sequence order information.
Let’s make this super clear! Here’s how we can tackle this:
class Embedding(nn.Module):
def __init__(self, vocab_size, d_model):
super(Embedding, self).__init__()
self.embed = nn.Embedding(vocab_size, d_model)
self.d_model = d_model
def forward(self, x):
return self.embed(x) * math.sqrt(self.d_model)🚀
✨ Cool fact: Many professional data scientists use this exact approach in their daily work! Positional Encoding - Made Simple!
Positional encoding adds information about the position of tokens in the sequence. We use sine and cosine functions of different frequencies.
Let’s make this super clear! Here’s how we can tackle this:
def positional_encoding(seq_len, d_model):
position = torch.arange(seq_len).unsqueeze(1)
div_term = torch.exp(torch.arange(0, d_model, 2) * -(math.log(10000.0) / d_model))
pos_encoding = torch.zeros(seq_len, d_model)
pos_encoding[:, 0::2] = torch.sin(position * div_term)
pos_encoding[:, 1::2] = torch.cos(position * div_term)
return pos_encoding🚀
🔥 Level up: Once you master this, you’ll be solving problems like a pro! Multi-Head Attention Mechanism - Made Simple!
Multi-head attention allows the model to jointly attend to information from different representation subspaces. It’s a key component of the Transformer.
Don’t worry, this is easier than it looks! Here’s how we can tackle this:
class MultiHeadAttention(nn.Module):
def __init__(self, d_model, num_heads):
super(MultiHeadAttention, self).__init__()
self.num_heads = num_heads
self.d_model = d_model
assert d_model % num_heads == 0, "d_model must be divisible by num_heads"
self.d_k = d_model // num_heads
self.W_q = nn.Linear(d_model, d_model)
self.W_k = nn.Linear(d_model, d_model)
self.W_v = nn.Linear(d_model, d_model)
self.W_o = nn.Linear(d_model, d_model)🚀 Scaled Dot-Product Attention - Made Simple!
The core of the attention mechanism is the scaled dot-product attention. It computes the compatibility between queries and keys, then applies softmax and multiplies with values.
Here’s where it gets exciting! Here’s how we can tackle this:
def scaled_dot_product_attention(query, key, value, mask=None):
d_k = query.size(-1)
scores = torch.matmul(query, key.transpose(-2, -1)) / math.sqrt(d_k)
if mask is not None:
scores = scores.masked_fill(mask == 0, -1e9)
attention_weights = torch.softmax(scores, dim=-1)
return torch.matmul(attention_weights, value), attention_weights🚀 Multi-Head Attention Forward Pass - Made Simple!
The forward pass of multi-head attention splits the input into multiple heads, applies attention, and concatenates the results.
Let’s make this super clear! Here’s how we can tackle this:
def forward(self, query, key, value, mask=None):
batch_size = query.size(0)
Q = self.W_q(query).view(batch_size, -1, self.num_heads, self.d_k).transpose(1, 2)
K = self.W_k(key).view(batch_size, -1, self.num_heads, self.d_k).transpose(1, 2)
V = self.W_v(value).view(batch_size, -1, self.num_heads, self.d_k).transpose(1, 2)
x, attention = scaled_dot_product_attention(Q, K, V, mask)
x = x.transpose(1, 2).contiguous().view(batch_size, -1, self.d_model)
return self.W_o(x)🚀 Feed-Forward Network - Made Simple!
The feed-forward network is applied to each position separately and identically. It consists of two linear transformations with a ReLU activation in between.
Here’s where it gets exciting! Here’s how we can tackle this:
class FeedForward(nn.Module):
def __init__(self, d_model, d_ff):
super(FeedForward, self).__init__()
self.linear1 = nn.Linear(d_model, d_ff)
self.linear2 = nn.Linear(d_ff, d_model)
self.relu = nn.ReLU()
def forward(self, x):
return self.linear2(self.relu(self.linear1(x)))🚀 Layer Normalization - Made Simple!
Layer normalization helps stabilize the learning process. It’s applied after each sub-layer in the encoder and decoder.
Let me walk you through this step by step! Here’s how we can tackle this:
class LayerNorm(nn.Module):
def __init__(self, features, eps=1e-6):
super(LayerNorm, self).__init__()
self.gamma = nn.Parameter(torch.ones(features))
self.beta = nn.Parameter(torch.zeros(features))
self.eps = eps
def forward(self, x):
mean = x.mean(-1, keepdim=True)
std = x.std(-1, keepdim=True)
return self.gamma * (x - mean) / (std + self.eps) + self.beta🚀 Encoder Layer - Made Simple!
The encoder layer consists of multi-head attention followed by a feed-forward network, with residual connections and layer normalization.
Here’s a handy trick you’ll love! Here’s how we can tackle this:
class EncoderLayer(nn.Module):
def __init__(self, d_model, num_heads, d_ff, dropout=0.1):
super(EncoderLayer, self).__init__()
self.self_attn = MultiHeadAttention(d_model, num_heads)
self.feed_forward = FeedForward(d_model, d_ff)
self.norm1 = LayerNorm(d_model)
self.norm2 = LayerNorm(d_model)
self.dropout = nn.Dropout(dropout)
def forward(self, x, mask):
attn_output = self.self_attn(x, x, x, mask)
x = self.norm1(x + self.dropout(attn_output))
ff_output = self.feed_forward(x)
x = self.norm2(x + self.dropout(ff_output))
return x🚀 Decoder Layer - Made Simple!
The decoder layer is similar to the encoder but includes an additional multi-head attention layer that attends to the encoder output.
Ready for some cool stuff? Here’s how we can tackle this:
class DecoderLayer(nn.Module):
def __init__(self, d_model, num_heads, d_ff, dropout=0.1):
super(DecoderLayer, self).__init__()
self.self_attn = MultiHeadAttention(d_model, num_heads)
self.cross_attn = MultiHeadAttention(d_model, num_heads)
self.feed_forward = FeedForward(d_model, d_ff)
self.norm1 = LayerNorm(d_model)
self.norm2 = LayerNorm(d_model)
self.norm3 = LayerNorm(d_model)
self.dropout = nn.Dropout(dropout)
def forward(self, x, enc_output, src_mask, tgt_mask):
attn_output = self.self_attn(x, x, x, tgt_mask)
x = self.norm1(x + self.dropout(attn_output))
attn_output = self.cross_attn(x, enc_output, enc_output, src_mask)
x = self.norm2(x + self.dropout(attn_output))
ff_output = self.feed_forward(x)
x = self.norm3(x + self.dropout(ff_output))
return x🚀 Encoder - Made Simple!
The encoder consists of a stack of identical layers. It processes the input sequence and produces representations for the decoder.
This next part is really neat! Here’s how we can tackle this:
class Encoder(nn.Module):
def __init__(self, vocab_size, d_model, num_heads, d_ff, num_layers, dropout=0.1):
super(Encoder, self).__init__()
self.embedding = Embedding(vocab_size, d_model)
self.pos_encoding = positional_encoding(5000, d_model)
self.layers = nn.ModuleList([EncoderLayer(d_model, num_heads, d_ff, dropout) for _ in range(num_layers)])
self.dropout = nn.Dropout(dropout)
def forward(self, x, mask):
x = self.embedding(x) + self.pos_encoding[:x.size(1), :].to(x.device)
x = self.dropout(x)
for layer in self.layers:
x = layer(x, mask)
return x🚀 Decoder - Made Simple!
The decoder also consists of a stack of identical layers. It takes both the encoder output and the target sequence as input.
Ready for some cool stuff? Here’s how we can tackle this:
class Decoder(nn.Module):
def __init__(self, vocab_size, d_model, num_heads, d_ff, num_layers, dropout=0.1):
super(Decoder, self).__init__()
self.embedding = Embedding(vocab_size, d_model)
self.pos_encoding = positional_encoding(5000, d_model)
self.layers = nn.ModuleList([DecoderLayer(d_model, num_heads, d_ff, dropout) for _ in range(num_layers)])
self.dropout = nn.Dropout(dropout)
def forward(self, x, enc_output, src_mask, tgt_mask):
x = self.embedding(x) + self.pos_encoding[:x.size(1), :].to(x.device)
x = self.dropout(x)
for layer in self.layers:
x = layer(x, enc_output, src_mask, tgt_mask)
return x🚀 Complete Transformer Model - Made Simple!
Now we can put all the pieces together to create the complete Transformer model.
Let me walk you through this step by step! Here’s how we can tackle this:
class Transformer(nn.Module):
def __init__(self, src_vocab_size, tgt_vocab_size, d_model, num_heads, d_ff, num_layers, dropout=0.1):
super(Transformer, self).__init__()
self.encoder = Encoder(src_vocab_size, d_model, num_heads, d_ff, num_layers, dropout)
self.decoder = Decoder(tgt_vocab_size, d_model, num_heads, d_ff, num_layers, dropout)
self.linear = nn.Linear(d_model, tgt_vocab_size)
def forward(self, src, tgt, src_mask, tgt_mask):
enc_output = self.encoder(src, src_mask)
dec_output = self.decoder(tgt, enc_output, src_mask, tgt_mask)
return self.linear(dec_output)🚀 Creating Masks - Made Simple!
Masks are crucial for the Transformer. They prevent attending to padding tokens and future tokens in the target sequence.
Ready for some cool stuff? Here’s how we can tackle this:
def create_padding_mask(seq):
return (seq != 0).unsqueeze(1).unsqueeze(2)
def create_look_ahead_mask(size):
mask = torch.triu(torch.ones(size, size), diagonal=1).float()
return mask == 0🚀 Initializing and Using the Transformer - Made Simple!
Finally, we can initialize our Transformer model and use it for a simple forward pass.
Here’s where it gets exciting! Here’s how we can tackle this:
# Initialize the model
src_vocab_size = 10000
tgt_vocab_size = 10000
d_model = 512
num_heads = 8
d_ff = 2048
num_layers = 6
model = Transformer(src_vocab_size, tgt_vocab_size, d_model, num_heads, d_ff, num_layers)
# Example forward pass
src = torch.randint(1, src_vocab_size, (64, 20)) # (batch_size, seq_len)
tgt = torch.randint(1, tgt_vocab_size, (64, 20)) # (batch_size, seq_len)
src_mask = create_padding_mask(src)
tgt_mask = create_look_ahead_mask(tgt.size(1))
output = model(src, tgt, src_mask, tgt_mask)
print(output.shape) # Should be (64, 20, tgt_vocab_size)🚀 Additional Resources - Made Simple!
For a deeper understanding of Transformer networks, consider these resources:
- “Attention Is All You Need” paper: https://arxiv.org/abs/1706.03762
- “The Annotated Transformer” blog post: http://nlp.seas.harvard.edu/2018/04/03/attention.html
- “Transformers from Scratch” tutorial: https://e2eml.school/transformers.html
These resources provide in-depth explanations and implementations of Transformer networks.
🎊 Awesome Work!
You’ve just learned some really powerful techniques! Don’t worry if everything doesn’t click immediately - that’s totally normal. The best way to master these concepts is to practice with your own data.
What’s next? Try implementing these examples with your own datasets. Start small, experiment, and most importantly, have fun with it! Remember, every data science expert started exactly where you are right now.
Keep coding, keep learning, and keep being awesome! 🚀