Description
• Parallel Layers – We use a “parallel” formulation in each Transformer block (Wang & Komatsuzaki, 2021), rather than the standard “serialized” formulation. Specifically, the standard formulation can be written as:
y = x + MLP(LayerNorm(x + Attention(LayerNorm(x)))
Whereas the parallel formulation can be written as:
y = x + MLP(LayerNorm(x)) + Attention(LayerNorm(x))
The parallel formulation results in roughly 15% faster training speed at large scales, since the MLP and Attention input matrix multiplications can be fused. Ablation experiments showed a small quality degradation at 8B scale but no quality degradation at 62B scale, so we extrapolated that the effect of parallel layers should be quality neutral at the 540B scale.