Attention Variants & Norms

Grouped-Query Attention (GQA), Multi-Query (MQA), RoPE embeddings, and RMSNorm topology.

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Explanation

A focused 3-minute explanation using the topic's authored material.

Learning goals and prerequisites

After this lesson

Compare MHA, MQA, and GQA precisely
Implement RoPE and normalization variants
Choose a serving-aware attention block

Helpful before starting

Transformer attention equations
Tensor shapes and GPU memory basics
Normalization and residual networks

Start here

Attention Variants & Norms, in plain language

Grouped-Query Attention (GQA), Multi-Query (MQA), RoPE embeddings, and RMSNorm topology. Attention and normalization variants determine context quality, KV-cache cost, training stability, and serving throughput in large Transformers.

For a small example, one sentence contains subject, action, and location relationships. Let separate heads emphasize each relation, then concatenate their outputs and inspect why identical heads add little. This is the mechanism to keep in view as the lesson becomes more technical. Before moving on, identify the input, transformation, output, and one observation that would falsify your conclusion.

Key points

Head dimensions and Q/K/V projection shapes.
KV-cache growth during autoregressive generation.
LayerNorm, RMSNorm, residual paths, and positional encodings.

Use it well

When Attention Variants & Norms helps—and where it breaks

A document model must process 50,000 tokens. Compare full, local, sparse, and linear attention by receptive field, memory, accuracy, and implementation support. A useful result still depends on checking the assumptions and evidence below rather than treating one successful output as proof.

Key points

Comparing variants with unequal parameter budgets. Better approach: Control dimensions and training conditions before drawing conclusions.
Ignoring decode concurrency. Better approach: Measure KV memory per request and aggregate capacity.
Changing position encoding only at inference. Better approach: Use a validated scaling method and long-context evaluation.

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