ggml_layer_layer_norm: Add Layer Normalization Layer

In ggmlR: 'GGML' Tensor Operations for Machine Learning

Add Layer Normalization Layer

Description

Normalizes each sample over its own features – subtract the mean, divide by the standard deviation – then scales by gamma and shifts by beta (both learnable). Like ggml_layer_rms_norm it keeps no running statistics and does not couple the samples in a batch, but unlike it, it does centre the input. This is the normalization of the original transformer.

Usage

ggml_layer_layer_norm(model, eps = 1e-05, name = NULL, trainable = TRUE)

Arguments

model	A ggml_sequential_model or a ggml_tensor_node (functional API).
eps	Small constant for numerical stability (default 1e-5)
name	Optional character name for the layer.
trainable	Logical; whether the layer weights are updated during training.

Value

The model with the layer appended, or a new ggml_tensor_node in the functional API.

See Also

ggml_layer_rms_norm, ggml_layer_batch_norm

Examples

x <- ggml_input(shape = c(10L, 32L))
h <- x |> ggml_layer_layer_norm()
h <- h |> ggml_layer_attention(d_model = 32L, n_heads = 4L)

ggmlR documentation built on Oct. 23, 2026, 1:07 a.m.