Gemma 2
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I see that the paper says that Gemma 2 uses GeGLU activation but I don't see any exploration of why?
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Claude 4 Sonnet (T) via @hdevalence.bsky.social
Thinking Summary
The user is asking about why Gemma 2 uses GeGLU activation, noting that the paper mentions this but doesn't explore the reasoning behind it. Looking at the Gemma 2 paper, I can see that it mentions they use the "approximated GeGLU non-linearity" and that this is similar to previous Gemma models. The user has also embedded a quote that references the GeGLU paper, which is about "GLU Variants Improve Transformer" by Noam Shazeer. This paper explores different variants of Gated Linear Units (GLU) and shows that GEGLU and SwiGLU perform better than standard ReLU or GELU activations in transformer models. From the GLU variants paper, I can see that: 1. GEGLU showed the best perplexity results in pre-training (1.942 vs 1.997 for ReLU baseline) 2. GLU variants generally outperformed standard activations on downstream tasks 3. The paper tested various GLU variants including GEGLU, SwiGLU, ReGLU, etc. The Gemma 2 paper doesn't provide detailed ablation studies on why they chose GeGLU specifically, but it's likely based on the empirical evidence from the Shazeer paper and potentially internal experiments at Google. The fact that they mention it's an "approximated" GeGLU suggests they might be using an efficient implementation. I should explain this context and note that while the Gemma 2 paper doesn't dive deep into the activation function choice, the referenced GLU variants paper provides strong empirical evidence for why GeGLU would be preferred over traditional activations.
You're absolutely right to notice this! The Gemma 2 paper indeed doesn't provide detailed exploration of why they chose GeGLU, but the embedded GLU variants paper you've shared provides the key insights.
From Noam Shazeer's "GLU Variants Improve Transformer" paper, we can see the empirical justification:
Performance Results:
- GEGLU achieved the best perplexity (1.942) compared to ReLU baseline (1.997) after full training
- GLU variants consistently outperformed standard activations across downstream tasks
- The improvements were consistent across GLUE, SuperGLUE, and SQuAD benchmarks
Key Advantages of GeGLU:
- Gating mechanism - Uses component-wise product of two linear projections, where one path is gated by GELU activation
- Better gradient flow - The gating allows for more selective information processing
- Empirically superior - Consistently better results across multiple benchmarks
Mathematical Form:
GEGLU(x, W, V) = GELU(xW) ⊗ (xV)
The Gemma 2 paper likely inherited this choice from earlier research and internal experiments at Google, where they found GeGLU to be empirically superior. The fact that they mention "approximated GeGLU" suggests they're using an efficient implementation that maintains the performance benefits while being computationally practical.
It's a common pattern in large-scale model papers to build on established architectural choices without re-deriving the justification - the GLU variants paper provides that foundation showing why GeGLU outperforms traditional activations like ReLU and GELU.