ggml_mul_mat_id: Matrix Multiplication with Expert Selection (Graph)

In ggmlR: 'GGML' Tensor Operations for Machine Learning

Matrix Multiplication with Expert Selection (Graph)

Description

Indirect matrix multiplication for Mixture of Experts architectures. Selects expert weights based on indices and performs batched matmul.

Usage

ggml_mul_mat_id(ctx, as, b, ids)

Arguments

ctx	GGML context
as	Stacked expert weight matrices [n_embd, n_ff, n_experts]
b	Input tensor
ids	Expert selection indices tensor (I32)

Value

Output tensor after expert-selected matrix multiplication

Examples

ctx <- ggml_init(64 * 1024 * 1024)
# 4 experts, each with 8x16 weights (small for example)
experts <- ggml_new_tensor_3d(ctx, GGML_TYPE_F32, 8, 16, 4)
ggml_set_f32(experts, rnorm(8 * 16 * 4))
input <- ggml_new_tensor_2d(ctx, GGML_TYPE_F32, 8, 2)
ggml_set_f32(input, rnorm(16))
# Select expert 0 for token 0, expert 2 for token 1
ids <- ggml_new_tensor_1d(ctx, GGML_TYPE_I32, 2)
ggml_set_i32(ids, c(0L, 2L))
output <- ggml_mul_mat_id(ctx, experts, input, ids)
graph <- ggml_build_forward_expand(ctx, output)
ggml_graph_compute(ctx, graph)
ggml_free(ctx)

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