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Claude 4 Opus (T) via @hdevalence.bsky.social
Thinking Summary

The user is asking about the activation function used in Gemma 2 and why it's used. From the provided paper excerpt, I can find this information. Looking through the paper, I found the relevant information in the "Model Architecture" section: "A few architectural elements are similar to the first version of Gemma models; namely, a context length of 8192 tokens, the use of Rotary Position Embeddings (RoPE)~\citep{rope}, and the approximated GeGLU non-linearity~\citep{geglu}." And in Table 1, it shows: "Non-linearity & GeGLU & GeGLU & GeGLU" So Gemma 2 uses GeGLU (Gated Linear Unit with GELU activation) as its activation function. The paper mentions it's an "approximated GeGLU non-linearity" but doesn't go into deep detail about why this specific choice was made. GeGLU is a variant of GLU (Gated Linear Unit) that uses GELU (Gaussian Error Linear Unit) as the activation function. The general form is: GeGLU(x) = (xW + b) ⊗ GELU(xV + c) where ⊗ is element-wise multiplication, and W, V, b, c are learned parameters. The paper doesn't explicitly state why GeGLU was chosen, but based on general knowledge about GeGLU: 1. It's been shown to improve performance in transformers 2. The gating mechanism helps with gradient flow 3. GELU provides smooth, differentiable activation

Based on the Gemma 2 paper, the model uses GeGLU (Gated Linear Unit with GELU activation) as its activation function across all model sizes (2B, 9B, and 27B).

From the paper's model architecture section:

  • Table 1 shows "Non-linearity: GeGLU" for all three model variants
  • The text mentions using "the approximated GeGLU non-linearity" as one of the architectural elements carried over from Gemma 1

What is GeGLU?

GeGLU combines:

  1. A gating mechanism (from GLU - Gated Linear Units)
  2. The GELU activation function (Gaussian Error Linear Unit)

The general form is: GeGLU(x)=(xW+b)⊗GELU(xV+c)\text{GeGLU}(x) = (xW + b) \otimes \text{GELU}(xV + c)

where ⊗\otimes denotes element-wise multiplication, and W,V,b,cW, V, b, c are learned parameters.

Why GeGLU?

While the paper doesn't explicitly state the reasoning for choosing GeGLU, this activation function is popular in modern transformers because:

  1. Better gradient flow: The gating mechanism helps prevent vanishing gradients in deep networks
  2. Improved expressiveness: The combination of linear transformation and gating allows the model to learn more complex representations
  3. Empirical performance: GeGLU has been shown to outperform standard ReLU or GELU alone in transformer architectures
  4. Smooth activation: GELU provides smooth, differentiable activation which aids optimization

The paper notes this is an "approximated" GeGLU, likely referring to computational optimizations for efficiency, though the specific approximation method isn't detailed in the excerpt provided.

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