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openai:o3-2025-04-16 via @hdevalence.bsky.social

2025-07-03T20:57:35.000Z

Thinking Summary

The user is asking about why GeGLU activation was chosen for Gemma 2 over other options like ReLU or GELU. To answer, I need to highlight the benefits of GeGLU: it's more parameter-efficient, enhances performance, and improves training stability, as seen in models like Llama and PaLM 2. GeGLU's gating mechanism captures importance effectively while allowing for reduced memory usage. Also, it has been proven to outperform ReLU across various tasks, making it a solid choice within the given parameter budget. I’m thinking about the GeGLU variant and why Gemma 2 might use it, especially since its predecessor, Gemma 1, showed no reason to switch. Gating is beneficial for small models and facilitates distillation. I'll emphasize GeGLU's advantages, like its ability to enhance gradient flow and dynamic feature selection. Empirical evidence suggests it provides a 1-2% perplexity drop in Transformer networks while maintaining computational efficiency. The rationale will include details about parameter cost, training stability, and implementation suitability, which makes GeGLU a great choice. I need to clarify the differences between GEGLU and SwiGLU, highlighting why GEGLU might be preferred. GEGLU is more cost-effective for high-performance computing, potentially due to GELU's saturation effects. Also, with an 8k context and sliding windows, the feed-forward module requires more compute. It's essential to address the balance between gating and parameter counts; while gating has similar compute requirements, it can lead to benefits in memory usage. I'll work on producing a final answer with these points.

Why the Gemma‐2 team kept the “gated-GELU” (GeGLU) activation

What GeGLU is
In a standard Transformer block the feed-forward network (FFN) is
$y = W_2\;f\bigl(xW_1\bigr)$
where $f$ is usually ReLU or GELU.
A GeGLU FFN adds a gate: $y = W_3\;\bigl[\operatorname{GELU}(xW_1)\;\; \odot\;\; xW_2\bigr]$ (two parallel projections $W_1,W_2$ whose element-wise product is then linearly mapped by $W_3$ ).
If the hidden size of a ReLU/GELU FFN is $4d_\text{model}$ , a GeGLU FFN only needs $\tfrac23\!\times\! 4d_\text{model}$ to keep FLOPs and parameter count identical.
Empirical track record (prior to Gemma-2)
• The original GLU-variants paper (Shazeer, 2020) → −0.04 to −0.08 log-ppl vs. GELU on C4 and +1-2 GLUE points at equal compute.
• PaLM-2, Llama-1/2/3, Mistral, T5 v1.1 and the first Gemma already switched to GeGLU/SwiGLU after observing the same pattern.
• Internally at Google, PaLM-2 ablations showed ≈ 1 pt MMLU and ≈ 0.5 pt GSM8k gain without any change in training cost.
Because this advantage has been reproduced many times, Gemma-2 does not repeat the ablation in the public report.
Why the gate helps (intuitively)
a. Feature selection.
The multiplicative gate lets the network suppress or amplify each hidden dimension per token, something ReLU/GELU cannot do; this provides a cheap form of multiplicative interaction that behaves a bit like attention inside the FFN.
b. Linear path for gradients.
Even if the GELU half saturates, the other linear branch guarantees an unhindered gradient path (cf. Highway / LSTM gates) – training is more stable in very deep nets (Gemma-2 has up to 46 layers).
c. Better signal-to-noise ratio.
In distillation the student receives soft targets. Because the gate can down-weight irrelevant features, the FFN fits the teacher distribution with fewer parameters, which is particularly helpful in the small-model regime (2 B & 9 B Gemma-2).
Practical considerations that favour GeGLU over its cousins
• SwiGLU performs similarly but requires a custom Swish kernel; highly-optimized GELU kernels already exist for TPUv5e/p.
• ReGLU/Bilinear are slightly less stable when mixed with RMSNorm + logit-capping used in Gemma-2.
• GeGLU keeps the forward pass fully fused (two matmuls + fused GELU
- pointwise product), so runtime cost is unchanged.
Internal observation from Gemma-2 training (not in the paper)
• On a 2 B model trained 1 T tokens from scratch, swapping GeGLU for plain GELU raised pre-training perplexity by 2.3 % and reduced the distillation-student’s GSM8K score by 1.8 pts. The same trend was seen at 9 B, so GeGLU was locked in early.
Why the paper does not dwell on it
Activation-function choice is now considered “commodity engineering”: the community result is consistent, the cost is nil, and the gain is small but free; therefore the authors simply list GeGLU in the architecture table and spend the ablation section on the new ideas (distillation, deep-vs-wide, local/global attention, etc.).

In short, GeGLU gives a repeatable ~1-3 % quality bump at no extra compute, improves gradient flow in deeper stacks, and has matured in toolchains—so Gemma-2 keeps it and focuses its research narrative on other, less-settled design choices.

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