---
title: "Using ggmlR as a Backend in Your Package"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Using ggmlR as a Backend in Your Package}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(eval = TRUE)
```

ggmlR exports a static library (`libggml.a`) and C headers so downstream
packages can link against ggml directly — the same pattern used by `llamaR`
(LLM inference) and `sd2R` (Stable Diffusion).

---

## 1. What ggmlR exports

After installing ggmlR you will find:

```
$(R_HOME_DIR)/library/ggmlR/lib/libggml.a
$(R_HOME_DIR)/library/ggmlR/include/ggml.h
$(R_HOME_DIR)/library/ggmlR/include/ggml-backend.h
$(R_HOME_DIR)/library/ggmlR/include/ggml-alloc.h
$(R_HOME_DIR)/library/ggmlR/include/ggml-opt.h
$(R_HOME_DIR)/library/ggmlR/include/ggml-quants.h
$(R_HOME_DIR)/library/ggmlR/include/ggml-vulkan.h
$(R_HOME_DIR)/library/ggmlR/include/r_ggml_compat.h
... (full list in inst/include/)
```

`r_ggml_compat.h` redirects `printf`/`fprintf`/`abort` to R-safe equivalents
(`Rprintf`, `Rf_error`).  Always include it (or use `-include r_ggml_compat.h`)
in your C/C++ sources.

---

## 2. DESCRIPTION

Add ggmlR to `LinkingTo` and `Imports`:

```
Package: myPackage
...
Imports:   ggmlR
LinkingTo: ggmlR
```

`LinkingTo` makes R add `ggmlR/include` to the compiler include path
automatically.

---

## 3. `src/Makevars`

Link against the static library:

```makefile
GGMLR_LIB = $(shell Rscript -e "cat(system.file('lib', package='ggmlR'))")
GGMLR_INC = $(shell Rscript -e "cat(system.file('include', package='ggmlR'))")

PKG_CPPFLAGS = -I$(GGMLR_INC) -include r_ggml_compat.h
PKG_LIBS     = $(GGMLR_LIB)/libggml.a
```

If ggmlR was built with Vulkan you also need to link Vulkan:

```makefile
# detect Vulkan (same logic as ggmlR's own configure)
VULKAN_LIBS = $(shell pkg-config --libs vulkan 2>/dev/null)
PKG_LIBS    = $(GGMLR_LIB)/libggml.a $(VULKAN_LIBS)
```

---

## 4. `configure` — detect ggmlR at build time

```sh
#!/bin/sh
# configure

GGMLR_INC=$(Rscript -e "cat(system.file('include', package='ggmlR'))" 2>/dev/null)
GGMLR_LIB=$(Rscript -e "cat(system.file('lib',     package='ggmlR'))" 2>/dev/null)

if [ -z "$GGMLR_INC" ] || [ ! -f "$GGMLR_LIB/libggml.a" ]; then
  echo "ERROR: ggmlR not found. Install it first: install.packages('ggmlR')" >&2
  exit 1
fi

sed -e "s|@GGMLR_INC@|$GGMLR_INC|g" \
    -e "s|@GGMLR_LIB@|$GGMLR_LIB|g" \
    src/Makevars.in > src/Makevars
```

`src/Makevars.in`:

```makefile
PKG_CPPFLAGS = -I@GGMLR_INC@ -include r_ggml_compat.h
PKG_LIBS     = @GGMLR_LIB@/libggml.a
```

---

## 5. C code — minimal example

```c
/* src/my_model.c */
#include "ggml.h"
#include "ggml-backend.h"
#include <R.h>
#include <Rinternals.h>

SEXP R_my_inference(SEXP r_input) {
    struct ggml_init_params params = {
        .mem_size   = 256 * 1024 * 1024,  /* 256 MB */
        .mem_buffer = NULL,
        .no_alloc   = false,
    };
    struct ggml_context *ctx = ggml_init(params);

    int n = length(r_input);
    struct ggml_tensor *x = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n);
    memcpy(x->data, REAL(r_input), n * sizeof(float));

    /* ... build graph, compute ... */

    ggml_free(ctx);
    return R_NilValue;
}
```

Register in the `.Call` table (R's standard routine registration):

```c
/* src/init.c */
#include <R.h>
#include <Rinternals.h>
#include <R_ext/Rdynload.h>

extern SEXP R_my_inference(SEXP);

static const R_CallMethodDef CallEntries[] = {
    {"R_my_inference", (DL_FUNC) &R_my_inference, 1},
    {NULL, NULL, 0}
};

void R_init_myPackage(DllInfo *dll) {
    R_registerRoutines(dll, NULL, CallEntries, NULL, NULL);
    R_useDynamicSymbols(dll, FALSE);
}
```

---

## 6. R wrapper

```{r}
my_inference <- function(input) {
  .Call("R_my_inference", as.numeric(input))
}
```

---

## 7. Thread count

ggmlR manages the CPU thread count via `ggmlR_get_n_threads()` (exported from
`r_interface.c`).  If your package calls `ggml_backend_cpu_init()` directly,
set the thread count to match:

```c
#include "ggml-backend.h"
/* ggmlR_get_n_threads() is exported by ggmlR — link against libggml.a */
extern int ggmlR_get_n_threads(void);

ggml_backend_t cpu = ggml_backend_cpu_init();
ggml_backend_cpu_set_n_threads(cpu, ggmlR_get_n_threads());
```

This ensures your package respects the same thread limit as ggmlR (important
for CRAN compliance — tests must not exceed 2 threads).

---

## 8. Vulkan in downstream packages

If your package needs Vulkan, the Vulkan backend is already compiled into
`libggml.a` when ggmlR was built with Vulkan support.  You only need to link
`-lvulkan`:

```makefile
VULKAN_LIBS = $(shell pkg-config --libs vulkan 2>/dev/null)
PKG_LIBS    = @GGMLR_LIB@/libggml.a $(VULKAN_LIBS)
```

Do **not** vendor ggml source again — link the pre-built `libggml.a` from
ggmlR to avoid symbol collisions.

---

## 9. Real-world references

- `llamaR` — LLM inference (LLaMA, Mistral, …) using ggmlR as backend
- `sd2R` — Stable Diffusion image generation using ggmlR as backend

Both follow the pattern above: `LinkingTo: ggmlR`, `configure` script to
locate headers and `libggml.a`, thin C wrappers, R `.Call` interface.
