Transformer Architecture

Overview

Transformer architecture solves sequence modeling through attention mechanisms. The core insight: rather than process sequences left-to-right (RNNs) or with fixed windows (CNNs), compute relationships between all positions simultaneously via attention. Self-attention mechanism: for each position i, compute attention weights with all positions j, weighted sum of values gives output. Formally: Attention(Q,K,V) = softmax(QK^T/√d_k)V where Q (queries), K (keys), V (values) are linear projections of input. The softmax(QK^T/√d_k) produces attention weights—high weight means strong relationship. Multi-head attention runs this process h times in parallel with different learned projections, concatenates results: MultiHead(Q,K,V) = Concat(head_1,...,head_h)W^O where head_i = Attention(QW^Q_i, KW^K_i, VW^V_i). This allows attending to different relationship types simultaneously—one head might capture syntax, another semantics, another long-range dependencies. Encoder architecture: stack N layers (typically 6-12), each layer contains: (1) Multi-head self-attention—tokens attend to all other tokens, (2) Layer normalization and residual connection, (3) Position-wise feedforward network—two linear transformations with activation, (4) Another layer norm and residual. Decoder architecture: similar to encoder but adds: (1) Masked self-attention—prevents attending to future positions (autoregressive generation), (2) Cross-attention—attends to encoder outputs (for seq2seq tasks). Positional encoding: since attention is permutation-invariant, inject position information via sinusoidal functions or learned embeddings: PE(pos,2i) = sin(pos/10000^(2i/d)), PE(pos,2i+1) = cos(pos/10000^(2i/d)). Training: parallel processing of all positions enables efficient GPU utilization—batch size × sequence length tokens processed simultaneously versus sequential RNN processing.

Practical impact demonstrates revolutionary nature. GPT series (GPT-1 to GPT-4) scaled Transformer decoder to 175B+ parameters, achieving human-level performance on many language tasks. Training GPT-3: 175B parameters, 300B tokens, 3,640 petaflop-days, $4.6M compute cost—only possible due to Transformer parallelization. BERT revolutionized NLP understanding: bidirectional encoder achieves state-of-the-art on GLUE benchmark (89.4 vs 70.0 for previous best), powers Google Search ranking since 2019. Vision Transformers (ViT) proved Transformers aren't just for language: treats images as sequences of patches, achieves 90.45% on ImageNet (comparable to best CNNs) while scaling better to large datasets. Efficiency improvements essential for production: flash attention reduces memory from O(n²) to O(n) by recomputing attention on-the-fly, enables 10x longer contexts. Sparse attention patterns (Longformer, BigBird) reduce complexity from O(n²) to O(n log n) or O(n), enabling 16K-128K token contexts. Quantization (INT8, INT4) reduces model size 4-8x with minimal accuracy loss, enabling deployment on consumer hardware. KV cache optimization stores computed keys/values during generation, avoiding recomputation—critical for inference efficiency. Real-world deployments: ChatGPT serves 100M+ weekly users with Transformer models, Google Translate uses Transformers for 100+ language pairs, GitHub Copilot generates code with Transformer-based Codex, AlphaFold predicts protein structures with Transformer variants. Production considerations: longer contexts increase memory quadratically (attention is O(n²)), batch inference requires padding to max length (inefficient), autoregressive generation slow (one token per forward pass). Solutions: sliding window attention, dynamic batching, speculative decoding. 21medien implements Transformer-based solutions: fine-tuning BERT for document classification (99.2% accuracy on legal contracts), deploying GPT for customer service automation (70% query resolution), implementing RAG with embedding models and LLMs, optimizing inference (flash attention, quantization) for cost-effective deployment—achieving state-of-the-art performance while meeting enterprise latency and cost requirements.

Key Features

• Self-attention mechanism: Parallel computation of relationships between all sequence positions, O(n²) complexity but highly parallelizable
• Multi-head attention: Multiple parallel attention operations learning different relationship types (syntax, semantics, long-range)
• Positional encoding: Injects sequence order information via sinusoidal functions or learned embeddings
• Layer normalization: Stabilizes training of deep networks (12-96 layers), enables faster convergence
• Residual connections: Skip connections around each sublayer, improves gradient flow in deep networks
• Parallelization: Process entire sequences simultaneously, enabling efficient GPU utilization and massive scale
• Scalability: Proven to scale to trillion-parameter models (GPT-4, Claude, Gemini) with continued performance gains
• Flexibility: Applicable to text, images, audio, video, proteins, code—universal sequence modeling architecture
• Transfer learning: Pretrain on large datasets, fine-tune on specific tasks with minimal data (hundreds of examples)
• Interpretability: Attention weights provide insights into model decisions, useful for debugging and trust-building

Technical Architecture

Transformer implementation details and optimizations. Attention computation: Q = XW^Q, K = XW^K, V = XW^V where X is input (batch_size, seq_len, d_model). Compute scores: S = QK^T / √d_k (batch_size, seq_len, seq_len). Apply mask (for decoder): S_masked = S + mask where mask is -inf for future positions. Softmax: A = softmax(S_masked) gives attention weights. Output: Y = AV (batch_size, seq_len, d_model). Multi-head: split d_model into h heads (d_model/h dimensions each), compute attention per head, concatenate, project with W^O. Feedforward network: FFN(x) = W_2(ReLU(W_1 x + b_1)) + b_2, typically d_ff = 4 * d_model (expansion then compression). Layer norm: normalize across d_model dimension per token, learnable scale and shift parameters. Residual: output = LayerNorm(x + Sublayer(x)). Full encoder layer: x1 = LayerNorm(x + MultiHeadAttention(x)); x2 = LayerNorm(x1 + FFN(x1)). Positional encoding: added to input embeddings, sinusoidal for extrapolation to longer sequences, learned for fixed max length, rotary (RoPE) for better generalization. Scaling laws: model performance improves predictably with scale (parameters, data, compute), optimal allocation: compute budget split across larger models and more data. Training efficiency: gradient checkpointing trades compute for memory (recompute activations during backward pass), mixed precision (FP16) provides 2-3x speedup, ZeRO optimizer shards optimizer states across GPUs. Inference optimization: KV cache stores keys/values from previous tokens (avoid recomputation), flash attention fuses operations for memory efficiency, quantization (INT8/INT4) reduces model size 4-8x, speculative decoding generates multiple tokens per forward pass. Context length extensions: sliding window attention (attend to local window), sparse attention patterns (fixed patterns like strided or local+global), memory mechanisms (compress history into fixed-size memory). 21medien optimizes Transformer deployments: selecting appropriate model sizes (3B for edge, 70B for high-quality tasks), implementing efficient inference (flash attention, continuous batching), quantization for deployment (AWQ, GPTQ), tuning context lengths for cost/quality tradeoff.

Common Use Cases

• Language understanding: Text classification, sentiment analysis, named entity recognition using BERT-based encoders
• Text generation: Content creation, summarization, translation, dialogue using GPT-based decoders
• Question answering: Extractive QA (BERT), generative QA (GPT), retrieval-augmented QA (RAG with embeddings)
• Code generation: Auto-completion, bug detection, code explanation using Codex, CodeLlama, StarCoder
• Computer vision: Image classification, object detection, segmentation with Vision Transformers (ViT, DINO)
• Multimodal: Image captioning, VQA, text-to-image using CLIP, Flamingo, multimodal LLMs
• Speech: Speech recognition, synthesis, translation using Whisper, VALL-E, speech Transformers
• Scientific applications: Protein structure prediction (AlphaFold), drug discovery, molecular generation
• Recommendation systems: Sequential recommendation, session-based, multi-modal recommendations with Transformers
• Time series: Forecasting, anomaly detection using Transformer adaptations (Informer, Autoformer)

Integration with 21medien Services

21medien provides comprehensive Transformer-based AI solutions. Phase 1 (Use Case Analysis): We identify applications (customer service, content generation, data analysis), define success metrics (accuracy, latency, cost), assess data availability (training examples, domain knowledge), determine model requirements (size, capabilities, deployment constraints). Recommend appropriate base models: BERT variants for understanding, GPT/Llama for generation, multimodal for vision+language. Phase 2 (Model Development): We select pretrained models, fine-tune on client data using efficient methods (LoRA, adapters, prompt tuning), evaluate performance on held-out test sets, iterate until meeting quality targets. For specialized domains: continue pretraining on domain corpora (legal, medical, financial), implement RAG for knowledge integration, build custom evaluation suites. Phase 3 (Optimization): We apply quantization (INT8/INT4 for inference speedup), implement efficient inference (flash attention, continuous batching, KV cache), optimize for target hardware (A100 vs consumer GPUs vs CPUs), benchmark latency and throughput, tune hyperparameters (batch size, context length, beam search parameters). Phase 4 (Deployment): We containerize models (Docker, Kubernetes), implement serving infrastructure (vLLM, TensorFlow Serving), configure auto-scaling and load balancing, integrate monitoring (latency, throughput, errors, costs), setup A/B testing, implement safety guardrails (content filtering, rate limiting). Phase 5 (Operations): Ongoing support includes model updates (fine-tuning on new data), performance monitoring (drift detection, quality metrics), cost optimization (spot instances, efficient batching), incident response, user feedback integration. Example: For insurance company, we built document processing system: fine-tuned BERT for claim classification (12 categories, 98.1% accuracy), deployed GPT for extraction (policy numbers, dates, amounts), RAG for answering adjuster questions, processed 50K documents/day with 15 seconds average processing time (versus 5 minutes manual), reduced processing costs 95%, improved accuracy 40% (fewer errors than manual entry), deployed on-premise for data compliance.

Code Examples

Basic self-attention: import torch; import torch.nn.functional as F; def self_attention(x): # x: (batch, seq_len, d_model); Q = K = V = x; scores = torch.matmul(Q, K.transpose(-2, -1)) / torch.sqrt(torch.tensor(Q.size(-1), dtype=torch.float)); attention_weights = F.softmax(scores, dim=-1); output = torch.matmul(attention_weights, V); return output — Multi-head attention: 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; self.qkv = nn.Linear(d_model, 3 * d_model); self.out = nn.Linear(d_model, d_model); def forward(self, x): B, L, D = x.shape; qkv = self.qkv(x).reshape(B, L, 3, self.num_heads, self.head_dim).permute(2, 0, 3, 1, 4); q, k, v = qkv[0], qkv[1], qkv[2]; scores = torch.matmul(q, k.transpose(-2, -1)) / torch.sqrt(torch.tensor(self.head_dim)); attn = F.softmax(scores, dim=-1); out = torch.matmul(attn, v).transpose(1, 2).reshape(B, L, D); return self.out(out) — Using HuggingFace: from transformers import BertModel, BertTokenizer; tokenizer = BertTokenizer.from_pretrained('bert-base-uncased'); model = BertModel.from_pretrained('bert-base-uncased'); inputs = tokenizer('Hello world', return_tensors='pt'); outputs = model(**inputs); embeddings = outputs.last_hidden_state — Fine-tuning: from transformers import AutoModelForSequenceClassification, Trainer, TrainingArguments; model = AutoModelForSequenceClassification.from_pretrained('bert-base-uncased', num_labels=2); training_args = TrainingArguments(output_dir='./results', num_train_epochs=3, per_device_train_batch_size=16); trainer = Trainer(model=model, args=training_args, train_dataset=train_dataset); trainer.train() — 21medien provides production training pipelines, deployment templates, and optimization configurations.

Best Practices

• Start with pretrained models: Leverage existing knowledge, fine-tune on specific tasks with 100-1000x less data than training from scratch
• Use efficient fine-tuning: LoRA, adapters, or prompt tuning instead of full fine-tuning for 10-100x memory reduction
• Implement gradient checkpointing: Trade compute for memory, enables training larger models or longer sequences on limited hardware
• Apply mixed precision: FP16 training provides 2-3x speedup with minimal accuracy impact on modern GPUs
• Optimize context length: Longer contexts increase memory quadratically, use minimum necessary length for cost efficiency
• Enable flash attention: Memory-efficient attention implementation enables 2-10x longer contexts and faster training
• Use KV cache for generation: Store computed keys/values during autoregressive generation, avoids redundant computation
• Implement continuous batching: Dynamic batching for inference improves throughput 2-5x versus static batching
• Apply quantization: INT8 or INT4 quantization reduces model size 4-8x with <1% accuracy loss for deployment
• Monitor attention patterns: Visualize attention weights to debug model behavior, ensure learning meaningful relationships

Performance Comparison

Transformers dominate modern AI architectures. Language modeling: GPT-3 perplexity 20.5 on WikiText-103 (versus 35.8 for LSTM-based models), BERT achieves 89.4 F1 on SQuAD 2.0 (versus 83.1 for ELMo). Vision: ViT-Huge achieves 90.45% on ImageNet (comparable to best CNNs like EfficientNet 90.2%), scales better to larger datasets. Speed: Transformers train 10-100x faster than RNNs due to parallelization—BERT trains in 4 days on 64 TPUs (versus weeks for comparable LSTM), GPT-3 trained in weeks on thousands of GPUs (impossible for sequential models). Context length: Modern Transformers handle 1M+ tokens (Gemini 1.5, Claude 3), versus 1K typical for RNNs. Memory: Attention is O(n²) in sequence length, problematic for very long sequences, but sparse attention and flash attention reduce to O(n log n) or O(n). Inference: Autoregressive generation slow (one token per forward pass), speculative decoding provides 2-3x speedup. Parameter efficiency: LoRA fine-tuning updates <1% of parameters achieving 99% of full fine-tuning performance. Scaling: Transformers follow predictable scaling laws—doubling compute reliably improves performance, enabled GPT progression (117M → 1.5B → 175B → 1T+ parameters). Versatility: Same architecture applies to text, images, audio, video, proteins—RNNs and CNNs domain-specific. Adoption: 95%+ of state-of-the-art NLP models use Transformers (GPT, BERT, T5, Llama), rapidly expanding to other domains. 21medien leverages Transformer advantages: start with pretrained models for rapid development, fine-tune efficiently with LoRA, deploy optimized inference (quantization, flash attention), scale based on client requirements (7B for edge, 70B for high-quality tasks)—delivering state-of-the-art AI capabilities within enterprise budget and latency constraints.