Transformers Models

Transformer and its variants already became the most important backbone of the AI industry. It is the core of the most popular LLMs, and also the important part of lots of sequence and generative models. In this blog, we will focus on the fundamental implementation of vanilla transformer, from the basic building blocks to the complete transformer encoder and decoder. We will also cover commonly used optimization techniques for the transformer, especially for common hardwares like GPU and TPU.

Core Knowledge

Core Attention Mechanisms

• Multi-head attention implementation patterns:

Positional Encoding Schemes

Layer Components & Architecture

• Encoder/Decoder layer composition:

• Layer connection:

• Encoder-Decoder Architecture

Optimization

• Batch matrix operation optimizations:

• Memory constraints management:

• Initialization schemes:

Implementation Considerations

• Masking strategies:

• Dimension validation:

• Stability patterns:

Key Questions

Status	Question	Category
	Implement Sinusoidal Positional Encoding	Transformers Models
	Implement Learned Positional Embedding	Transformers Models
	Implement Single-Head Self-Attention	Transformers Models
	Implement Multi-Head Attention with Tensor Reshaping	Transformers Models
	Complete Transformer Encoder Layer	Transformers Models
	Assemble a Multi-Layer Encoder	Transformers Models
	Implement Padding Mask Handling in Attention Calculations	Transformers Models
	Implement Causal Masking for Autoregressive Decoding	Transformers Models
	Implement a Multi-Layer Transformer Decoder with Causal Masking	Transformers Models

Common Pitfalls

Extended Questions

Status	Question	Category
	Implement Fused Operations for QKV Transformations	Efficiency & Optimization
	Efficient Attention Mechanism with Einsum Operations	Efficiency & Optimization
	Implement Mixed-Precision Self-Attention	Efficiency & Optimization
	Implement Cross-Attention Mechanism between Encoder and Decoder	Architectural Variations
	Implement Pre-LN vs Post-LN Architecture Comparison	Architectural Variations
	Dimension Validation Checks for Head/Embedding Divisions	Architectural Variations

Transformers Models

Core Knowledge

Core Attention Mechanisms

Positional Encoding Schemes

Layer Components & Architecture

Optimization

Implementation Considerations

Key Questions

Common Pitfalls

Not scaling attention scores by 1/√(d_k) before softmax

Incorrect tensor reshaping for multi-head attention outputs

Dimension mismatches when concatenating multi-head outputs

Leaky attention in decoder implementations

Mishandling of residual connections (element-wise add without normalization)

Naive matrix multiplications causing O(n²) memory usage

Extended Questions

Transformers Models

Core Knowledge

Core Attention Mechanisms

• Mathematical formulation of QKV transformations

• Batch matrix operations for attention scores

• Scaled dot-product attention (1/√d_k scaling factor)

◦ Dimensional split/merge operations

◦ Parallel vs sequential head computation

Positional Encoding Schemes

• Sinusoidal encoding: even/odd index alternation

• Learned positional embeddings integration

• Relative position encoding variants

Layer Components & Architecture

◦ Self-attention vs cross-attention

◦ Position-wise FFN dimensional expansion

◦ LayerNorm placement (Pre-LN vs Post-LN)

◦ Residual connection

◦ Cross-attention between encoder and decoder

Optimization

◦ Einsum-based matrix operations

◦ Tensor reshaping without copying

◦ Kernel fusion for QKV projections

◦ Gradient checkpointing strategies

◦ Xavier/Glorot for linear layers

◦ Zero-initialized residual branches

Implementation Considerations

◦ Padding masks for variable-length sequences

◦ Causal masking strategies for decoder implementations

◦ Head dimension divisibility checks

◦ Embedding vs FFN dimension matching

◦ Attention score clipping

◦ FP16/FP32 mixed precision casting

◦ Epsilon handling in LayerNorm

◦ Gradient scaling

Key Questions

Common Pitfalls

Not scaling attention scores by 1/√(d_k) before softmax

Incorrect tensor reshaping for multi-head attention outputs

Dimension mismatches when concatenating multi-head outputs

Leaky attention in decoder implementations

Mishandling of residual connections (element-wise add without normalization)

Naive matrix multiplications causing O(n²) memory usage

Extended Questions