ggml_rope: Rotary Position Embedding (Graph)

In ggmlR: 'GGML' Tensor Operations for Machine Learning

Rotary Position Embedding (Graph)

Description

Creates a graph node for RoPE (Rotary Position Embedding). RoPE is the dominant position encoding method in modern LLMs like LLaMA, Mistral, and many others.

Usage

ggml_rope(ctx, a, b, n_dims, mode = 0L)

Arguments

ctx	GGML context
a	Input tensor of shape [head_dim, n_head, seq_len, batch]
b	Position tensor (int32) of shape [seq_len] containing position indices
n_dims	Number of dimensions to apply rotation to (usually head_dim)
mode	RoPE mode: GGML_ROPE_TYPE_NORM (0), GGML_ROPE_TYPE_NEOX (2), etc.

Details

RoPE encodes position information by rotating pairs of dimensions in the embedding space. The rotation angle depends on position and dimension index.

Key benefits of RoPE: - Relative position information emerges naturally from rotation - Better extrapolation to longer sequences than absolute embeddings - No additional parameters needed

Value

Tensor with same shape as input, with rotary embeddings applied

Examples

ctx <- ggml_init(16 * 1024 * 1024)
# Query tensor: head_dim=8, n_head=4, seq_len=16, batch=1
q <- ggml_new_tensor_4d(ctx, GGML_TYPE_F32, 8, 4, 16, 1)
ggml_set_f32(q, rnorm(8 * 4 * 16))
# Position indices
pos <- ggml_new_tensor_1d(ctx, GGML_TYPE_I32, 16)
ggml_set_i32(pos, 0:15)
# Apply RoPE
q_rope <- ggml_rope(ctx, q, pos, 8, GGML_ROPE_TYPE_NORM)
graph <- ggml_build_forward_expand(ctx, q_rope)
ggml_graph_compute(ctx, graph)
ggml_free(ctx)

ggmlR documentation built on July 14, 2026, 1:08 a.m.