LLaMA-Activation-FFN
author: Deepseek Chat created: 2025-04-06
Both DeepSeek and LLaMA (Large Language Model Meta AI) models primarily use the SwiGLU (Swish-Gated Linear Unit) activation function in their feed-forward networks (FFN).
The Swish activation function is a smooth, non-linear function that has gained popularity in deep learning, particularly in large neural networks like LLaMA and DeepSeek-V3. It was introduced by researchers at Google in 2017 and has since been used as an alternative to ReLU, LeakyReLU, and GELU.
The Swish function is defined as:
Swish(x) = x . sigma(beta . x)
where:
| Activation | Formula | Pros | Cons |
|---|---|---|---|
| ReLU | Simple, fast computation | "Dying ReLU" problem | |
| LeakyReLU | Fixes dying ReLU issue | Not smooth | |
| GELU | Used in GPT models, smooth | Computationally expensive | |
| Swish | Smooth, better performance | Slightly slower than ReLU |
LLaMA (both v1 and v2) uses only the SwiGLU activation function in its feed-forward networks (FFN), not standalone Swish.
| Feature | Swish | SwiGLU |
|---|---|---|
| Type | Single activation function | Gated variant (Swish + Linear) |
| Usage | Rare in LLMs alone | Standard in LLaMA, PaLM, etc. |
| Formula |
The Gated Linear Unit (GLU) is an activation function that introduces a gating mechanism to control information flow in neural networks. It was introduced in the 2016 paper Language Modeling with Gated Convolutional Networks by Dauphin et al. and has since been widely used in transformers (like LLaMA, GPT, etc.).
The standard GLU is defined as:
where:
Let’s say:
GLU helps neural networks:
✅ Control information flow (like an "attention" mechanism inside a layer).
✅ Avoid vanishing gradients (better than sigmoid alone).
✅ Improve expressiveness compared to ReLU/GELU.
| Activation | Formula | Pros | Cons |
|---|---|---|---|
| ReLU | Simple, fast | "Dying ReLU" problem | |
| GELU | Smooth, used in GPT | Expensive compute | |
| Swish | Smooth, better gradients | Slower than ReLU | |
| GLU | Dynamic gating | Doubles parameters |
Different gating mechanisms can be used in place of the sigmoid:
Would you like a PyTorch implementation of GLU/SwiGLU? 🚀
So, GLU is not a constant function across the given layer since it has associated weight? Let us consider computation at the end of each neuron; the constant activation function like RELU takes a single scalar value and produces a scalar value; However how does GLU work -- does it have single unique weight associated per neuron or multiple weights? does it produce single scalar value?
Great question! Let's break this down step by step to understand how GLU (Gated Linear Unit) operates at the neuron level compared to traditional activation functions like ReLU.
GLU is not a constant function —it involves learnable weights and operates on entire layers, not individual scalars. Here’s how it works:
| Property | ReLU | GLU |
|---|---|---|
| Input | Scalar ( |
Vector ( |
| Weights | None (static) | Two matrices ( |
| Output | Scalar ( |
Vector ( |
| Per-Neuron | Independent | Coupled (all inputs contribute) |
Is it not a misnomer to call GLU as activation function? Is it possible to design a constant activation function + hidden layer that performs like an equivalent GLU?
You've touched on a subtle and important distinction! GLU (and its variants like SwiGLU) indeed behaves more like a "hidden layer with a gating mechanism" than a traditional activation function. Let's break this down:
Classical activation functions (e.g., ReLU, Sigmoid, Tanh):
GLU breaks these rules:
Yes, arguably. GLU is better described as:
The term "activation function" likely stuck because:
Yes, but inefficiently. Here’s how:
To mimic GLU
This is identical to GLU, but requires:
GLU’s gating mechanism has unique advantages:
Example:
pythonCopy
import torch
import torch.nn as nn
# GLU splits the input into two parts (gate + value)
glu = nn.GLU(dim=-1) # Built-in GLU (splits input channels)
x = torch.randn(1, 10, 20) # (batch, seq_len, 2*dim)
output = glu(x) # Output shape: (1, 10, 10)
pythonCopy
class ManualGLU(nn.Module):
def __init__(self, dim):
super().__init__()
self.W = nn.Linear(dim, dim) # Gate weights
self.V = nn.Linear(dim, dim) # Value weights
def forward(self, x):
gate = torch.sigmoid(self.W(x))
value = self.V(x)
return gate * value
# Usage:
manual_glu = ManualGLU(dim=10)
x = torch.randn(1, 10, 10) # (batch, seq_len, dim)
output = manual_glu(x) # Same as GLU
The Feed-Forward Network (FFN), also called a Multi-Layer Perceptron (MLP), is a fundamental building block in modern neural networks, including transformers like LLaMA, GPT, and BERT.
An FFN is a stack of fully connected (dense) layers with non-linear activations. It processes input data sequentially, passing it through:
In transformer models (e.g., LLaMA, GPT), the FFN is applied independently to each token after the self-attention layer. The standard FFN has:
512, the hidden dim might be 2048 (4x expansion).| Type | Formula | Used in Models |
|---|---|---|
| Standard FFN | ReLU(xW₁ + b₁)W₂ + b₂ |
Original Transformer |
| GLU FFN | (σ(xW) ⊙ xV)W₂ + b₂ |
Older ConvNets |
| SwiGLU FFN | (Swish(xW) ⊙ xV)W₂ + b₂ |
LLaMA, GPT-4 |
Why GLU/SwiGLU?
pythonCopy
import torch.nn as nn
class FFN(nn.Module):
def __init__(self, dim, hidden_dim):
super().__init__()
self.w1 = nn.Linear(dim, hidden_dim) # Expansion (e.g., dim=512 → 2048)
self.w2 = nn.Linear(hidden_dim, dim) # Compression back to original dim
self.act = nn.GELU() # or SwiGLU, ReLU, etc.
def forward(self, x):
return self.w2(self.act(self.w1(x)))
✅ Core Component: FFNs introduce non-linearity in transformers.
✅ GLU/SwiGLU: Modern models use gated versions for better performance.
✅ Computes Per Token: Unlike attention, FFN processes each token independently.
| Model Type | Key Components | Example Architectures |
|---|---|---|
| FFN/MLP | Dense layers + activations | Basic classifiers |
| RNN | Recurrent loops + hidden state | LSTM, GRU, Bi-RNN |
| Transformer | Self-attention + FFN blocks | GPT, BERT, LLaMA |
✅ FFN: Basic neural net with ≥2 linear layers + activation (used in both RNNs and Transformers).
✅ RNN: Handles sequences via recurrence (LSTM/GRU fix its limitations).
✅ Transformer: Uses attention + FFNs (no recurrence, better parallelism).