Diagnosing a Transformer Block Refactor: Attention/FFN Shapes and Norm Placement
You are reviewing a teammate’s refactor of a Transformer block used in an internal LLM service. The block should contain (1) a multi-head self-attention sub-layer and (2) a two-layer position-wise FFN, each with a residual connection and layer normalization. The model uses sequence length m and model width d, and the attention module uses h heads.
During integration testing, you see two symptoms: (A) training becomes unstable when stacking many blocks (loss spikes and occasional divergence), and (B) a shape error appears in the FFN path when d=512 and the hidden width is set to d_h=2048.
Write a technical diagnosis that:
- Identifies the most likely normalization/residual ordering mistake that would explain symptom (A), explicitly contrasting pre-norm vs post-norm computation for a sub-layer and why one tends to be more stable in very deep stacks.
- Explains, using correct tensor/matrix dimensions, how multi-head self-attention can take an input H ∈ R^{m×d} and still return an output in R^{m×d} (include what happens across heads conceptually, and why concatenation must be followed by a projection).
- Pinpoints the FFN dimension mismatch that could cause symptom (B) and states the required shapes of W_h, b_h, W_f, and b_f so that the FFN maps an input of width d back to width d.
Your answer should connect these three parts into a single coherent explanation of how a correct Transformer block maintains consistent dimensions while using residuals and layer normalization, and how an incorrect norm placement can interact with deep stacking to destabilize training.
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Ch.1 Pre-training - Foundations of Large Language Models
Foundations of Large Language Models
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Ch.2 Generative Models - Foundations of Large Language Models
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