ggml_custom: Custom Operation Node

In ggmlR: 'GGML' Tensor Operations for Machine Learning

Custom Operation Node

Description

Creates a graph node whose value is computed by a C kernel registered under name. The kernel is looked up in ggmlR's custom-op registry; supplying an unregistered name is an error, listing the names that are available.

Usage

ggml_custom(
  ctx,
  name,
  args = NULL,
  ne,
  type = GGML_TYPE_F32,
  n_tasks = GGML_N_TASKS_MAX
)

Arguments

ctx	GGML context
name	Name of a registered custom op (character scalar)
args	List of input tensors, or NULL for none. At most 9 tensors.
ne	Output shape as a numeric vector of up to 4 dimensions, innermost first (ggml column-major order). Missing dimensions default to 1.
type	Output data type (default GGML_TYPE_F32)
n_tasks	Number of threads the kernel may be split across, or GGML_N_TASKS_MAX (-1) to use as many as the backend allows. Use 1 for a kernel that is not thread-safe.

Details

Kernels are registered from C, not from R. A package that links against ggmlR registers its kernels at load time via the ggmlR_register_custom_op callable; see system.file("include", "ggmlR.h", package = "ggmlR") for the C-side contract and a worked example. R callbacks are not supported: the kernel may run off the main thread, where calling into R is unsafe.

The output tensor is newly allocated with the given type and shape. Input tensors passed in args become the node's sources and are visible to the kernel as dst->src[0], dst->src[1], and so on.

Value

Tensor pointer for the custom node

Backend

Custom ops run on the CPU backend only, because the kernel is a host function pointer that a GPU backend cannot execute. Computing a graph that contains a custom node raises an error when the target backend is not the CPU, or when a scheduler has no CPU backend to fall back to. Keep custom nodes out of graphs you intend to run entirely on Vulkan.

See Also

ggml_custom_inplace, ggml_custom_ops

Examples

# "row_median" is one of ggmlR's built-in kernels; see ggml_custom_ops().
ggml_set_n_threads(1L)
ctx <- ggml_init(16 * 1024 * 1024)
x <- ggml_new_tensor_2d(ctx, GGML_TYPE_F32, 5, 2)
ggml_set_f32(x, c(5, 1, 3, 2, 4,
                  9, 7, 8, 6, 10))

# One median per row: the output collapses ne[1] to 1.
med <- ggml_custom(ctx, "row_median", args = list(x), ne = c(1, 2))

graph <- ggml_build_forward_expand(ctx, med)
ggml_graph_compute(ctx, graph)
ggml_get_f32(med)   # 3, 8
ggml_free(ctx)

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