Transformer Coding: Building AI Language Models from Scratch

Coding a Transformer from scratch is one of the most rewarding journeys in modern artificial intelligence development. By implementing every component yourself—from attention mechanisms to positional encodings—you gain deep insights into how systems like ChatGPT, BERT, and other revolutionary language models actually work under the hood.youtubegithub+3

Why Code Transformers from Scratch?

Deep Understanding vs. Black Box Usage

While using pre-built libraries like Hugging Face Transformers is convenient for production, building from scratch provides irreplaceable understanding. When you code every matrix multiplication, every attention head, and every layer normalization, you develop an intuitive grasp of:githubyoutube

• How attention really works in practice, not just theory

• Why certain design choices matter (layer normalization placement, residual connections)

• How to debug and optimize transformer models effectively

• What causes common training problems and how to solve them

Career Development Benefits

Transformer coding skills are highly valued in the AI industry. Companies building cutting-edge AI systems need developers who can:reddityoutube

• Implement custom architectures beyond standard libraries

• Debug complex training issues at the model level

• Optimize performance for specific use cases

• Innovate new transformer variants for specialized applications

  
  

Core Components: Building Blocks of Intelligence

1. Input Embeddings: Converting Words to Numbers

Input embeddings transform discrete tokens (words, subwords) into continuous vector representations that neural networks can process:pureai.substackyoutube

class InputEmbedding(nn.Module):

def __init__(self, d_model, vocab_size):

super().__init__()

self.d_model = d_model

self.vocab_size = vocab_size

self.embedding = nn.Embedding(vocab_size, d_model)

def forward(self, x):

# Scale embeddings by sqrt(d_model) for stability

return self.embedding(x) * math.sqrt(self.d_model)

Key Concepts:

• Token IDs → Dense Vectors: Each word becomes a learnable d_model-dimensional vector

• Embedding Scaling: Multiply by √d_model to balance with positional encodings

• Learnable Parameters: The embedding matrix learns optimal representations during training

2. Positional Encoding: Teaching Order to Transformers

Unlike RNNs, Transformers process all positions simultaneously, so they need explicit position information:towardsdatascience+1youtube

class PositionalEncoding(nn.Module):

def __init__(self, d_model, max_len=5000):

super().__init__()

pe = torch.zeros(max_len, d_model)

position = torch.arange(0, max_len).unsqueeze(1).float()

# Create sinusoidal patterns

div_term = torch.exp(torch.arange(0, d_model, 2).float() *

-(math.log(10000.0) / d_model))

pe[:, 0::2] = torch.sin(position * div_term) # Even positions

pe[:, 1::2] = torch.cos(position * div_term) # Odd positions

self.register_buffer('pe', pe.unsqueeze(0))

def forward(self, x):

return x + self.pe[:, :x.size(1)]

The Mathematics Behind Positional Encoding:

• Sinusoidal Functions: Different frequencies for different positions

• Even/Odd Pattern: sin for even dimensions, cos for odd dimensions

• Unique Signatures: Each position gets a unique, mathematically consistent encoding

3. Multi-Head Attention: The Heart of Understanding

Multi-head attention enables the transformer to focus on different aspects of the input simultaneously:geeksforgeeks+1youtube

class MultiHeadAttention(nn.Module):

def __init__(self, d_model, num_heads, dropout=0.1):

super().__init__()

assert d_model % num_heads == 0

self.d_model = d_model

self.num_heads = num_heads

self.d_k = d_model // num_heads

# Linear projections for Q, K, V

self.W_q = nn.Linear(d_model, d_model, bias=False)

self.W_k = nn.Linear(d_model, d_model, bias=False)

self.W_v = nn.Linear(d_model, d_model, bias=False)

self.W_o = nn.Linear(d_model, d_model)

self.dropout = nn.Dropout(dropout)

def scaled_dot_product_attention(self, Q, K, V, mask=None):

"""The famous attention formula: softmax(QK^T/√d_k)V"""

d_k = Q.size(-1)

scores = torch.matmul(Q, K.transpose(-2, -1)) / math.sqrt(d_k)

if mask is not None:

scores = scores.masked_fill(mask == 0, -1e9)

attention_weights = F.softmax(scores, dim=-1)

attention_weights = self.dropout(attention_weights)

output = torch.matmul(attention_weights, V)

return output, attention_weights

def forward(self, query, key, value, mask=None):

batch_size = query.size(0)

# 1. Linear projections

Q = self.W_q(query)

K = self.W_k(key)

V = self.W_v(value)

# 2. Reshape for multi-head attention

Q = Q.view(batch_size, -1, self.num_heads, self.d_k).transpose(1, 2)

K = K.view(batch_size, -1, self.num_heads, self.d_k).transpose(1, 2)

V = V.view(batch_size, -1, self.num_heads, self.d_k).transpose(1, 2)

# 3. Apply attention

attn_output, _ = self.scaled_dot_product_attention(Q, K, V, mask)

# 4. Concatenate heads

attn_output = attn_output.transpose(1, 2).contiguous().view(

batch_size, -1, self.d_model)

# 5. Final projection

return self.W_o(attn_output)

Multi-Head Attention Steps:

1. Project to Q, K, V: Each input gets three different representations

2. Split into Heads: Divide attention across multiple "heads" for diversity

3. Compute Attention: Apply the scaled dot-product attention formula

4. Concatenate: Combine all head outputs

5. Final Projection: Transform concatenated output

   
   

4. Feed-Forward Networks: Adding Computational Depth

Position-wise feed-forward networks provide the transformer with non-linear transformation capacity:github+1youtube

class FeedForward(nn.Module):

def __init__(self, d_model, d_ff, dropout=0.1):

super().__init__()

self.linear1 = nn.Linear(d_model, d_ff)

self.linear2 = nn.Linear(d_ff, d_model)

self.dropout = nn.Dropout(dropout)

self.relu = nn.ReLU()

def forward(self, x):

# FFN(x) = max(0, xW1 + b1)W2 + b2

return self.linear2(self.dropout(self.relu(self.linear1(x))))

5. Layer Normalization and Residual Connections

These components ensure stable training in deep networks:youtubegithub

class TransformerEncoderLayer(nn.Module):

def __init__(self, d_model, num_heads, d_ff, dropout=0.1):

super().__init__()

self.self_attn = MultiHeadAttention(d_model, num_heads, dropout)

self.feed_forward = FeedForward(d_model, d_ff, dropout)

self.norm1 = nn.LayerNorm(d_model)

self.norm2 = nn.LayerNorm(d_model)

self.dropout = nn.Dropout(dropout)

def forward(self, x, mask=None):

# Self-attention with residual connection

attn_output = self.self_attn(x, x, x, mask)

x = self.norm1(x + self.dropout(attn_output))

# Feed-forward with residual connection

ff_output = self.feed_forward(x)

x = self.norm2(x + self.dropout(ff_output))

return x

Residual Connections + Layer Norm:

• Information Flow: Direct paths for gradients to flow backward

• Training Stability: Prevents vanishing gradient problems

• Normalization: Keeps activation values in reasonable ranges

Complete Transformer Implementation

Here's a complete, runnable transformer implementation you can use and modify:

import torch

import torch.nn as nn

import torch.nn.functional as F

import math

class SimpleTransformer(nn.Module):

"""Complete transformer model for language tasks"""

def __init__(self, vocab_size, d_model=512, num_heads=8, num_layers=6,

d_ff=2048, max_len=5000, dropout=0.1):

super().__init__()

self.d_model = d_model

self.embedding = nn.Embedding(vocab_size, d_model)

self.positional_encoding = PositionalEncoding(d_model, max_len)

self.transformer_layers = nn.ModuleList([

TransformerEncoderLayer(d_model, num_heads, d_ff, dropout)

for _ in range(num_layers)

])

self.norm = nn.LayerNorm(d_model)

self.fc_out = nn.Linear(d_model, vocab_size)

self.dropout = nn.Dropout(dropout)

def create_padding_mask(self, x, pad_idx=0):

"""Create mask to ignore padding tokens"""

return (x != pad_idx).unsqueeze(1).unsqueeze(2)

def forward(self, src, src_mask=None):

# Embedding with scaling

x = self.embedding(src) * math.sqrt(self.d_model)

# Add positional encoding

x = x.transpose(0, 1)

x = self.positional_encoding(x)

x = x.transpose(0, 1)

x = self.dropout(x)

# Pass through transformer layers

for layer in self.transformer_layers:

x = layer(x, src_mask)

# Final normalization and projection

x = self.norm(x)

output = self.fc_out(x)

return output

# Usage example

if __name__ == "__main__":

# Model parameters

vocab_size = 10000

model = SimpleTransformer(vocab_size=vocab_size)

# Sample input

batch_size, seq_len = 4, 50

src = torch.randint(1, vocab_size, (batch_size, seq_len))

# Forward pass

output = model(src)

print(f"Input shape: {src.shape}")

print(f"Output shape: {output.shape}")

print("Model created successfully!")

Training Your Transformer

Training Loop Implementation

def train_transformer(model, train_loader, num_epochs=10):

device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

model = model.to(device)

# Loss function and optimizer

criterion = nn.CrossEntropyLoss(ignore_index=0) # Ignore padding

optimizer = torch.optim.Adam(

model.parameters(),

lr=0.0001,

betas=(0.9, 0.98),

eps=1e-9

)

model.train()

for epoch in range(num_epochs):

total_loss = 0

for batch_idx, (src, tgt) in enumerate(train_loader):

src, tgt = src.to(device), tgt.to(device)

optimizer.zero_grad()

# Create source mask

src_mask = model.create_padding_mask(src)

# Forward pass

output = model(src, src_mask)

# Compute loss

loss = criterion(output.view(-1, output.size(-1)), tgt.view(-1))

loss.backward()

# Gradient clipping (important!)

torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)

optimizer.step()

total_loss += loss.item()

if batch_idx % 100 == 0:

print(f'Epoch: {epoch+1}, Batch: {batch_idx}, Loss: {loss.item():.4f}')

avg_loss = total_loss / len(train_loader)

print(f'Epoch: {epoch+1}, Average Loss: {avg_loss:.4f}')

Key Training Considerations

Learning Rate Scheduling:

scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(

optimizer, 'min', patience=3, factor=0.5

)

Mixed Precision Training (For Efficiency)

scaler = torch.cuda.amp.GradScaler()

with torch.cuda.amp.autocast():

output = model(src, src_mask)

loss = criterion(output.view(-1, output.size(-1)), tgt.view(-1))

scaler.scale(loss).backward()

scaler.step(optimizer)

scaler.update()

Advanced Implementation Tips

1. Memory Optimization

Gradient Checkpointing for large models:

import torch.utils.checkpoint as checkpoint

def forward(self, x, mask=None):

# Use checkpointing for memory efficiency

x = checkpoint.checkpoint(self.attention_layer, x, mask)

return x

Dynamic Padding to reduce memory usages,

def collate_fn(batch):

# Pad only to the longest sequence in the batch, not max_len

src_seqs, tgt_seqs = zip(*batch)

src_lens = [len(seq) for seq in src_seqs]

max_src_len = max(src_lens)

padded_src = torch.zeros(len(batch), max_src_len, dtype=torch.long)

for i, seq in enumerate(src_seqs):

padded_src[i, :len(seq)] = torch.tensor(seq)

return padded_src, torch.tensor(tgt_seqs)

2. Debugging Techniques

Attention Visualization:

def visualize_attention(model, src, layer_idx=0, head_idx=0):

model.eval()

with torch.no_grad():

# Get attention weights from specific layer and head

x = model.embedding(src) * math.sqrt(model.d_model)

x = model.positional_encoding(x)

for i, layer in enumerate(model.transformer_layers):

if i == layer_idx:

# Extract attention weights

attn_weights = layer.self_attn.attention_weights[head_idx]

return attn_weights

return None

Gradient Monitoring:

def check_gradients(model):

total_norm = 0

for p in model.parameters():

if p.grad is not None:

param_norm = p.grad.data.norm(2)

total_norm += param_norm.item() ** 2

total_norm = total_norm ** (1. / 2)

print(f'Gradient norm: {total_norm}')

3. Model Variations

Encoder-Only (BERT-style):

class BERTStyleTransformer(SimpleTransformer):

def __init__(self, *args, **kwargs):

super().__init__(*args, **kwargs)

# Only encoder, no decoder needed

def forward(self, src, src_mask=None):

# Same as SimpleTransformer - already encoder-only

return super().forward(src, src_mask)

Decoder-Only (GPT-style):

class GPTStyleTransformer(nn.Module):

def __init__(self, vocab_size, d_model=512, num_heads=8, num_layers=6):

super().__init__()

# Similar structure but with causal masking

self.causal_mask = self.create_causal_mask()

def create_causal_mask(self, seq_len):

"""Prevent attention to future positions"""

mask = torch.tril(torch.ones(seq_len, seq_len))

return mask.unsqueeze(0).unsqueeze(0)

Project Structure for Production

transformer_project/

├── models/

│ ├── __init__.py

│ ├── transformer.py # Main model

│ ├── attention.py # Attention modules

│ └── embeddings.py # Embedding layers

├── data/

│ ├── __init__.py

│ ├── dataset.py # Dataset classes

│ └── preprocessing.py # Data processing

├── training/

│ ├── __init__.py

│ ├── trainer.py # Training logic

│ └── utils.py # Training utilities

├── config.py # Configuration

├── train.py # Training script

└── requirements.txt # Dependencies

Common Pitfalls and Solutions

Training Issues

Problem: Loss not decreasing

# Solutions:

1. Check learning rate (try 1e-4 to 1e-3)

2. Verify data preprocessing

3. Monitor gradient norms

4. Start with smaller model

Problem: Memory errors

# Solutions:

1. Reduce batch size

2. Use gradient checkpointing

3. Implement gradient accumulation

4. Use mixed precision training

Problem: Attention collapse

# Solutions:

1. Check attention weight distributions

2. Adjust initialization

3. Verify mask implementation

4. Monitor attention entropy

Performance Optimization

GPU Utilization

# Efficient data loading

train_loader = DataLoader(

dataset,

batch_size=32,

num_workers=4,

pin_memory=True,

persistent_workers=True

)

# Model parallelism for large models

model = nn.DataParallel(model)

# Faster attention computation

torch.backends.cuda.matmul.allow_tf32 = True

torch.backends.cudnn.allow_tf32 = True

Inference Optimization

@torch.no_grad()

def generate(model, src, max_length=100):

model.eval()

for _ in range(max_length):

# Use KV-caching for efficiency in autoregressive generation

output = model(src)

next_token = output[:, -1:].argmax(dim=-1)

src = torch.cat([src, next_token], dim=1)

if next_token.item() == EOS_TOKEN:

break

return src

Building Your AI Career Through Transformer Coding

Skill Progression Path

1. Start Simple: Implement basic attention mechanism

2. Add Complexity: Build complete encoder-decoder

3. Optimize: Add training efficiency improvements

4. Specialize: Create domain-specific variants

5. Scale: Handle large models and distributed training

   
   

Portfolio Projects

Language Model: Build a mini-GPT for text generationTranslation Model: Create English-to-French translatorSummarization: Develop document summarization systemQuestion Answering: Build BERT-style QA modelCode Generation: Create programming assistant

Industry Applications

• Custom LLMs for specific domains (legal, medical, finance)

• Efficient Inference for mobile and edge devices

• Novel Architectures for research and innovation

• MLOps Integration for production AI systems

Beyond Basic Implementation

Once you've mastered basic transformer coding, explore advanced topics:

• Sparse Attention Patterns for longer sequences

• Memory-Efficient Training for larger models

• Model Compression and quantization techniques

• Distributed Training across multiple GPUs

• Custom CUDA Kernels for maximum performance

Conclusion: From Code to Intelligence

Coding transformers from scratch transforms you from an AI user to an AI builder. Every line of code you write—every attention head you implement, every layer normalization you add—deepens your understanding of how artificial intelligence actually works.

The journey from basic matrix multiplications to a working language model mirrors the evolution of AI itself: complex intelligence emerging from simple, well-orchestrated components. When your transformer generates its first coherent sentence or correctly translates text, you'll experience the profound satisfaction of building intelligence with your own hands.

This hands-on experience becomes the foundation for innovation. Understanding how transformers work at the code level enables you to push boundaries, create novel architectures, and solve problems that existing solutions can't handle. In a field where the next breakthrough might come from anyone with deep understanding and creative insight, transformer coding skills are your key to contributing to the AI revolution.

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