# ggmlR — Neural Networks for R

[![R-hub check on the R Consortium cluster](https://github.com/r-hub2/separate-jaguar-ggmlR/actions/workflows/rhub-rc.yaml/badge.svg)](https://github.com/r-hub2/separate-jaguar-ggmlR/actions/workflows/rhub-rc.yaml)

A native R package for building, training, and deploying neural networks. Backed by the [ggml](https://github.com/ggml-org/ggml) C library, designed primarily for **Vulkan GPU acceleration** with full CPU fallback — no Python, no TensorFlow, everything runs inside your R session.

> **GPU-first design**: when a Vulkan-capable GPU is available (NVIDIA, AMD, Intel, ARM Mali, Qualcomm Adreno), all operations run on GPU automatically. On machines without a GPU the package falls back to CPU transparently — no code changes needed.

Two complementary APIs:

| API | Style | When to use |
|---|---|---|
| Sequential / Functional | Keras-like, static graph | Production models, CRAN-standard workflow |
| Dynamic autograd (`ag_*`) | PyTorch-like, eager | Research, custom architectures, Transformers |

Also serves as the backend engine for [llamaR](https://github.com/Zabis13/llamaR) (LLM inference) and [sd2R](https://github.com/Zabis13/sd2R) (Stable Diffusion).

## Installation

```r
install.packages("ggmlR")
```

GPU (Vulkan) support is auto-detected at build time.

**Ubuntu / Debian** — to enable GPU:
```bash
sudo apt install libvulkan-dev glslc
```

**Windows** — install [Rtools](https://cran.r-project.org/bin/windows/Rtools/) and optionally the [Vulkan SDK](https://vulkan.lunarg.com/sdk/home) for GPU support.

### Build options

Force-enable or disable Vulkan GPU backend:
```r
install.packages("ggmlR", configure.args = "--with-vulkan")
install.packages("ggmlR", configure.args = "--without-vulkan")
```

Enable CPU SIMD acceleration (AVX2, SSE4, etc.) for faster inference on your machine:
```r
install.packages("ggmlR", configure.args = "--with-simd")
```

Options can be combined:
```r
install.packages("ggmlR", configure.args = "--with-vulkan --with-simd")
```

## Sequential API

The fastest way to get a model running — stack layers with the pipe, compile, train.

```r
library(ggmlR)

model <- ggml_model_sequential() |>
  ggml_layer_dense(128L, activation = "relu",    input_shape = 784L) |>
  ggml_layer_dropout(rate = 0.3) |>
  ggml_layer_dense(10L,  activation = "softmax")

model <- ggml_compile(model,
                      optimizer = "adam",
                      loss      = "categorical_crossentropy",
                      metrics   = "accuracy")

model <- ggml_fit(model, x_train, y_train,
                  epochs           = 10L,
                  batch_size       = 32L,
                  validation_split = 0.1,
                  verbose          = 1L)
# Important: always assign the return value back to model —
# ggml_fit() returns the model with updated weights.

plot(model$history)

ggml_evaluate(model, x_test, y_test)
preds <- ggml_predict(model, x_new)
```

### Available layers (Sequential)

| Layer | Function |
|---|---|
| Dense | `ggml_layer_dense(units, activation)` |
| Conv1D | `ggml_layer_conv_1d(filters, kernel_size)` |
| Conv2D | `ggml_layer_conv_2d(filters, kernel_size, padding)` |
| MaxPooling2D | `ggml_layer_max_pooling_2d(pool_size)` |
| GlobalAvgPool2D | `ggml_layer_global_average_pooling_2d()` |
| BatchNorm | `ggml_layer_batch_norm()` |
| Flatten | `ggml_layer_flatten()` |
| Dropout | `ggml_layer_dropout(rate)` |
| Embedding | `ggml_layer_embedding(vocab_size, dim)` |
| LSTM | `ggml_layer_lstm(units, return_sequences)` |
| GRU | `ggml_layer_gru(units, return_sequences)` |

### CNN example (MNIST)

```r
model <- ggml_model_sequential() |>
  ggml_layer_conv_2d(32L, kernel_size = c(3L, 3L), activation = "relu",
                     input_shape = c(28L, 28L, 1L)) |>
  ggml_layer_max_pooling_2d(pool_size = c(2L, 2L)) |>
  ggml_layer_conv_2d(64L, kernel_size = c(3L, 3L), activation = "relu") |>
  ggml_layer_global_average_pooling_2d() |>
  ggml_layer_dense(10L, activation = "softmax")
```

## Functional API

Wire layers into arbitrary graphs — residual connections, multi-input/output, shared weights.

### Residual (skip) connection

```r
inp <- ggml_input(shape = 64L)
x   <- inp |> ggml_layer_dense(64L, activation = "relu")
res <- ggml_layer_add(list(inp, x))        # element-wise add
out <- res |> ggml_layer_dense(10L, activation = "softmax")

m <- ggml_model(inputs = inp, outputs = out)
m <- ggml_compile(m, optimizer = "adam", loss = "categorical_crossentropy")
m <- ggml_fit(m, x_train, y_train, epochs = 5L, batch_size = 32L)
```

### Embedding + GRU + skip connection (NLP)

```r
inp <- ggml_input(shape = 30L, dtype = "int32", name = "tokens")
emb <- inp |> ggml_layer_embedding(vocab_size = 500L, dim = 32L)

# Branch A: GRU path
proj_a <- emb |>
  ggml_layer_gru(32L, return_sequences = FALSE) |>
  ggml_layer_dense(32L)

# Branch B: flatten + projection
proj_b <- emb |>
  ggml_layer_flatten() |>
  ggml_layer_dense(32L, activation = "relu") |>
  ggml_layer_dense(32L)

# Residual merge
out <- ggml_layer_add(list(proj_a, proj_b)) |>
  ggml_layer_dropout(rate = 0.3) |>
  ggml_layer_dense(2L, activation = "softmax")

m <- ggml_model(inputs = inp, outputs = out)
```

> Token values must be 0-based integers in `[0, vocab_size - 1]`.

### Multi-input model

```r
inp1 <- ggml_input(shape = 20L, name = "timeseries")
inp2 <- ggml_input(shape = 3L,  name = "metadata")

br1 <- inp1 |> ggml_layer_dense(16L, activation = "relu")
br2 <- inp2 |> ggml_layer_dense(8L,  activation = "relu")

out <- ggml_layer_concatenate(list(br1, br2), axis = 0L) |>
  ggml_layer_dense(2L, activation = "softmax")

m <- ggml_model(inputs = list(inp1, inp2), outputs = out)
m <- ggml_compile(m, optimizer = "adam", loss = "categorical_crossentropy")

# Pass x as a list — one matrix per input
m <- ggml_fit(m, x = list(x_ts, x_meta), y = y,
              epochs = 10L, batch_size = 32L)
preds <- ggml_predict(m, list(x_ts, x_meta))
```

### Multi-output model

```r
inp    <- ggml_input(shape = 64L)
hidden <- inp    |> ggml_layer_dense(64L, activation = "relu")
out    <- hidden |> ggml_layer_dense(10L, activation = "softmax")

m     <- ggml_model(inputs = inp, outputs = list(hidden, out))
preds <- ggml_predict(m, x)
# preds[[1]] — hidden activations  [n × 64]
# preds[[2]] — class probabilities [n × 10]
```

### ResNet-like image classifier

```r
residual_block <- function(x, filters, name) {
  main     <- x |> ggml_layer_conv_2d(filters, c(3L, 3L), padding = "same",
                                       name = paste0(name, "_conv"))
  shortcut <- x |> ggml_layer_conv_2d(filters, c(1L, 1L), padding = "same",
                                       name = paste0(name, "_proj"))
  ggml_layer_add(list(main, shortcut), name = paste0(name, "_add"))
}

inp <- ggml_input(shape = c(32L, 32L, 3L))
x   <- inp |> ggml_layer_conv_2d(16L, c(3L, 3L), activation = "relu",
                                  padding = "same")
x   <- residual_block(x, 16L, "res1")
x   <- residual_block(x, 32L, "res2")
out <- x |>
  ggml_layer_global_average_pooling_2d() |>
  ggml_layer_dropout(rate = 0.4) |>
  ggml_layer_dense(3L, activation = "softmax")

m <- ggml_model(inputs = inp, outputs = out)
```

### Shared layers (Siamese / weight sharing)

```r
enc <- ggml_dense(32L, activation = "relu", name = "encoder")

x1 <- ggml_input(shape = 16L, name = "left")
x2 <- ggml_input(shape = 16L, name = "right")

h1 <- ggml_apply(x1, enc)   # identical weights
h2 <- ggml_apply(x2, enc)

out <- ggml_layer_add(list(h1, h2)) |>
  ggml_layer_dense(2L, activation = "softmax")

m <- ggml_model(inputs = list(x1, x2), outputs = out)
```

### Differences from Keras

| Feature | Keras (Python) | ggmlR |
|---|---|---|
| Batch dimension | part of `input_shape` | excluded from `shape` |
| Merge layers | `add([a, b])` | `ggml_layer_add(list(a, b))` |
| Shared layers | reuse layer object | `ggml_dense()` + `ggml_apply()` |
| Multi-input data | list of arrays | `list()` of R matrices |
| Multi-output predict | list of numpy arrays | R list of matrices |
| Backend | TensorFlow / JAX / PyTorch | ggml (Vulkan GPU, CPU fallback) |

## Dynamic Autograd Engine (PyTorch-style)

Build and train arbitrary architectures with eager execution and automatic differentiation.

```r
library(ggmlR)

# Define parameters
W <- ag_param(matrix(rnorm(4 * 8) * 0.1, 8, 4))
b <- ag_param(matrix(0, 8, 1))

# Forward + backward
with_grad_tape({
  h    <- ag_add(ag_matmul(W, x_batch), b)
  h    <- ag_relu(h)
  loss <- ag_mse_loss(h, y_batch)
})
grads <- backward(loss)

opt <- optimizer_adam(list(W = W, b = b), lr = 1e-3)
opt$step(grads)
opt$zero_grad()
```

### Transformer encoder block

```r
model <- ag_sequential(
  ag_linear(64L, 128L, activation = "relu"),
  ag_batch_norm(128L),
  ag_dropout(0.1),
  ag_linear(128L, 10L)
)

params <- model$parameters()
opt    <- optimizer_adam(params, lr = 1e-3)
sch    <- lr_scheduler_cosine(opt, T_max = 50L, lr_min = 1e-5)

dl <- ag_dataloader(x_train, y_train, batch_size = 32L, shuffle = TRUE)

for (epoch in 1:50) {
  for (batch in dl$epoch()) {
    with_grad_tape({
      out  <- model$forward(batch$x)
      loss <- ag_softmax_cross_entropy_loss(out, batch$y)
    })
    grads <- backward(loss)
    clip_grad_norm(params, grads, max_norm = 1.0)
    opt$step(grads)
    opt$zero_grad()
  }
  sch$step()
}
```

### Data-parallel training

`dp_train()` splits data across N replicas, averages gradients, and keeps weights in sync automatically.

```r
make_model <- function() {
  W <- ag_param(matrix(rnorm(4 * 2) * 0.1, 2, 4))
  b <- ag_param(matrix(0, 2, 1))
  list(
    forward    = function(x) ag_add(ag_matmul(W, x), b),
    parameters = function() list(W = W, b = b)
  )
}

result <- dp_train(
  make_model  = make_model,
  data        = my_dataset,           # list of samples
  loss_fn     = function(out, tgt) ag_mse_loss(out, tgt),
  forward_fn  = function(model, s) model$forward(s$x),
  target_fn   = function(s) s$y,
  n_gpu       = 2L,                   # number of replicas
  n_iter      = 100L,
  lr          = 1e-3,
  max_norm    = 5.0
)

result$loss_history   # numeric vector, one value per iteration
result$model          # trained replica 0
```

### Autograd op reference

| Category | Functions |
|---|---|
| Linear | `ag_matmul`, `ag_add`, `ag_sub`, `ag_mul`, `ag_scale` |
| Activations | `ag_relu`, `ag_sigmoid`, `ag_tanh`, `ag_softmax` |
| Reductions | `ag_sum`, `ag_mean` (with `dim`, `keepdim`) |
| Math | `ag_log`, `ag_exp`, `ag_pow`, `ag_clamp` |
| Shape | `ag_reshape`, `ag_transpose` |
| Attention | `ag_multihead_attention` |
| Loss | `ag_mse_loss`, `ag_cross_entropy_loss`, `ag_softmax_cross_entropy_loss` |
| Layers | `ag_linear`, `ag_batch_norm`, `ag_dropout`, `ag_embedding` |
| Containers | `ag_sequential` |
| Optimizers | `optimizer_sgd`, `optimizer_adam` |
| Schedulers | `lr_scheduler_step`, `lr_scheduler_cosine` |
| Utilities | `clip_grad_norm`, `ag_gradcheck`, `dp_train` |

## mlr3 Integration

ggmlR ships with [mlr3](https://mlr3.mlr-org.com/) learners for tabular classification and regression. After `library(ggmlR)` (with `mlr3` installed), sequential and functional ggmlR networks become available as first-class learners:

```r
library(mlr3)
library(ggmlR)

# Classification on iris (GPU auto-detected via backend = "auto")
task <- tsk("iris")

learner <- lrn("classif.ggml",
               epochs     = 50L,
               batch_size = 16L,
               backend    = "auto")      # "auto" | "cpu" | "gpu"
learner$predict_type <- "prob"

learner$train(task)
pred <- learner$predict(task)
pred$score(msr("classif.logloss"))
```

### Features

- **Both ggmlR APIs** — `model_fn` can return a `ggml_sequential_model` or `ggml_functional_model`. The default builder is `ggml_default_mlp()`, an exported MLP builder you can also use directly.
- **Vulkan GPU** — set `backend = "gpu"` (or leave `"auto"`) and the learner trains and predicts on GPU.
- **Parallel tuning** — the learners declare `properties = "marshal"` and implement in-memory marshalling (SHA-256-checksummed container), so trained models can be shipped to `future` / `callr` workers without file-system round-trips.
- **Weighted training** — `classif.ggml` honours `task$weights_learner`, mapping them to `sample_weight` in `ggml_fit()`.
- **Callbacks for tuning** — pass `ggml_callback_early_stopping()` etc. via the `callbacks` parameter to drive early stopping inside `mlr3` tuning runs.
- **Custom architectures** — set `learner$model_fn <- function(task, n_features, n_out, pars) { ... }` to build any ggmlR network you like; the learner handles task → matrix conversion, compilation, training, and prediction.

```r
# Regression with a custom architecture
library(mlr3)
library(ggmlR)

learner <- lrn("regr.ggml", epochs = 100L)
learner$model_fn <- function(task, n_features, n_out, pars) {
  ggml_model_sequential() |>
    ggml_layer_dense(256L, activation = "gelu", input_shape = n_features) |>
    ggml_layer_dropout(0.2) |>
    ggml_layer_dense( 64L, activation = "gelu") |>
    ggml_layer_dense(n_out, activation = "linear")
}

learner$train(tsk("mtcars"))
```

Only numeric features are supported: wrap the learner in a pipeline
(`po("encode") %>>% po("scale") %>>% lrn("classif.ggml")`) when the task has
factor columns. `mlr3`, `paradox`, `R6`, and `mlr3pipelines` are `Suggests`;
ggmlR only wires up the integration when they are installed.

## tidymodels / parsnip Integration

ggmlR registers a `"ggml"` engine for [`parsnip::mlp()`](https://parsnip.tidymodels.org/reference/mlp.html), letting you use ggmlR networks inside the tidymodels ecosystem.

```r
library(ggmlR)
library(parsnip)

spec <- mlp(
  hidden_units = 64,
  epochs       = 100,
  dropout      = 0.2,
  learn_rate   = 0.001
) |>
  set_engine("ggml",
             batch_size = 32,
             backend    = "auto",   # "auto" | "cpu" | "gpu"
             verbose    = 0) |>
  set_mode("classification")

fit_obj <- fit(spec, Species ~ ., data = iris)
predict(fit_obj, new_data = iris)
predict(fit_obj, new_data = iris, type = "prob")
```

Regression works the same way:

```r
spec_reg <- mlp(hidden_units = 32, epochs = 200) |>
  set_engine("ggml", batch_size = 8, backend = "gpu") |>
  set_mode("regression")

fit_reg <- fit(spec_reg, mpg ~ ., data = mtcars)
predict(fit_reg, new_data = mtcars)
```

`parsnip`, `tibble`, `rlang`, and `dials` are in `Suggests` — ggmlR only wires up the engine when they are installed.

## ONNX Model Import

Load pre-trained ONNX models from PyTorch, TensorFlow, or other frameworks and run inference on Vulkan GPU or CPU. No Python or external libraries required — ggmlR includes a built-in zero-dependency protobuf parser.

### Quick start

```r
library(ggmlR)

# 1. Load the model
model <- onnx_load("squeezenet.onnx")
model
#> ONNX Model: torch_jit
#>   Producer: pytorch 2.0.1
#>   IR version: 8 / Opset: 18
#>   Nodes: 66 / Weights: 26

# 2. Check expected inputs
onnx_inputs(model)
#> $x.1
#> [1]   1   3 224 224

# 3. Prepare input data (flat numeric vector, row-major NCHW order)
img <- runif(1 * 3 * 224 * 224)

# 4. Run inference — pass a named list matching input names
result <- onnx_run(model, list(x.1 = img))

# 5. Get predictions
scores <- result[[1]]
cat("Predicted class:", which.max(scores) - 1L, "\n")
```

### Loading models

`onnx_load()` parses the .onnx file, builds a ggml computation graph, and allocates tensors on the specified device. Weights are loaded from the file via memory-mapping (zero-copy).

```r
# Auto-detect device (Vulkan GPU if available, else CPU)
model <- onnx_load("model.onnx")

# Force CPU
model <- onnx_load("model.onnx", device = "cpu")

# Force Vulkan GPU
model <- onnx_load("model.onnx", device = "vulkan")
```

### Dynamic shapes

Some models (BERT, RoBERTa, etc.) have dynamic dimensions for batch size or sequence length. Specify fixed shapes at load time:

```r
model <- onnx_load("bert.onnx",
                    input_shapes = list(
                      input_ids      = c(1L, 128L),
                      attention_mask = c(1L, 128L)
                    ))
```

If you forget, `onnx_load()` will tell you which inputs need shapes:

```
Error: Input 'input_ids' has dynamic shape [?x?].
  Specify fixed shape via input_shapes parameter.
```

### Inspecting the model

```r
# Print overview
model
#> ONNX Model: torch_jit
#>   Nodes: 533 / Weights: 199
#>   Ops: MatMul, Add, LayerNormalization, Softmax, ...

# Detailed metadata
onnx_summary(model)

# Input names and shapes (what to pass to onnx_run)
onnx_inputs(model)

# Backend placement: GPU vs CPU split, scheduler info
onnx_device_info(model)
```

### Running inference

```r
# Single input model
result <- onnx_run(model, list(x = my_data))

# Multiple inputs
result <- onnx_run(model, list(
  input_ids      = as.numeric(token_ids),
  attention_mask = rep(1, 128)
))

# Result is a named list of output tensors
str(result)
#> List of 1
#>  $ output: num [1:1000] 0.00123 0.00045 ...
```

### Preparing input data

ONNX models expect inputs in **row-major** order (batch, channels, height, width for images). R matrices are column-major, so you may need to transpose:

```r
# Image classification: model expects [1, 3, 224, 224]
# If you have a 224x224x3 R array:
img_array <- array(runif(224 * 224 * 3), dim = c(224, 224, 3))

# Rearrange to NCHW: [1, 3, 224, 224] — channel first
img_chw <- aperm(img_array, c(3, 1, 2))  # [3, 224, 224]
input <- as.numeric(img_chw)              # flat vector, row-major

result <- onnx_run(model, list(x = input))
```

For NLP models, inputs are typically 1D integer sequences:

```r
# BERT-style: token IDs as numeric vector
tokens <- c(101, 2023, 2003, 1037, 3231, 102, rep(0, 122))  # pad to 128
result <- onnx_run(model, list(input_ids = as.numeric(tokens)))
```

### Interpreting outputs

```r
# Classification: get top-5 predictions
scores <- result[[1]]
top5 <- order(scores, decreasing = TRUE)[1:5]
cat("Top-5 classes:", top5 - 1L, "\n")  # 0-based class indices
cat("Top-5 scores:", scores[top5], "\n")

# Apply softmax if model outputs logits (not probabilities)
probs <- exp(scores) / sum(exp(scores))
```

### Repeated inference

Models can be run multiple times with zero overhead — weights live on GPU permanently and are never re-transferred:

```r
model <- onnx_load("classifier.onnx")

for (batch in data_batches) {
  result <- onnx_run(model, list(x = batch))
  # process result...
}
```

### Tested models

13 out of 15 ONNX Model Zoo models load and run successfully (native 5D tensor support):

| Model | Nodes | Key ops |
|---|---|---|
| mnist-8 | 12 | Conv, Relu, MaxPool, Reshape, MatMul |
| squeezenet1.0-8 | 66 | Conv, Relu, MaxPool, Concat, GlobalAveragePool, Softmax |
| adv_inception_v3 (Opset 17/18) | 215 | Conv, BatchNorm, Relu, Concat, AveragePool |
| emotion-ferplus-8 | 52 | Conv, Relu, MaxPool, Gemm, Constant |
| bat_resnext26ts (Opset 18) | 570 | Conv, BatchNorm, SiLU, Concat, Expand, Split |
| bert (Opset 17) | 533 | MatMul, LayerNorm, GELU/Erf, Softmax, Shape, Gather, Where |
| gptneox (Opset 18) | 482 | MatMul, LayerNorm, GELU, Softmax, Shape, Gather |
| MaskRCNN-12-int8 | 937 | QLinearConv, DequantizeLinear, Resize, Concat, Reshape |
| roberta-9 | 1180 | MatMul, LayerNorm, Erf, Softmax, Shape, Gather, Cast |
| sageconv (Opset 16) | 24 | MatMul, Add, Mul, Sigmoid, ScatterElements |
| super-resolution-10 | 12 | Conv, Reshape, Transpose |
| botnet26t_256 (Opset 16) | 530 | Conv, BatchNorm, RelPosBias2D (fused custom op), Softmax |
| xcit_tiny | 436 | MatMul, LayerNorm, Softmax, Concat, Transpose |

### Supported ONNX ops (50+)

Arithmetic: Add, Sub, Mul, Div, Pow, Sqrt, Exp, Log, Abs, Neg, Floor, Ceil, Clip, Erf, Equal.
Linear: MatMul (batched), Gemm.
Convolution: Conv (1D/2D, grouped, depthwise), ConvTranspose (1D/2D), with `auto_pad` (SAME_UPPER, SAME_LOWER).
Pooling: MaxPool, AveragePool, GlobalAveragePool, Resize/Upsample (nearest, bilinear).
Normalization: BatchNorm, LayerNorm, GroupNorm, RMSNorm.
Activations: Relu, Sigmoid, Tanh, GELU, SiLU, Softmax, LeakyRelu, Elu.
Shape: Reshape, Transpose, Concat, Flatten, Squeeze, Unsqueeze, Expand, Slice, Split, Gather, Pad, Shape, Cast, Identity, EyeLike.
Constants: Constant (TensorProto + scalar), ConstantOfShape (INT64/INT32/DOUBLE/FLOAT value).
Scatter/Gather: ScatterElements (axis=0, reduction=none/add, Vulkan atomicAdd), Gather (axis=0 on rank>2 via reshape).
Logic: Where, Equal.
Reduction: ReduceMean, ReduceSum.
Quantization: DequantizeLinear, QuantizeLinear, QLinearConv, QLinearAdd, QLinearMatMul, QLinearSigmoid, QLinearConcat.
Fused custom ops: RelPosBias2D (BoTNet-style 2D relative position bias).
Pass-through: Dropout.

## GGUF Pre-trained Weights

Load pre-trained weights from GGUF files (llama.cpp, Hugging Face, etc.) with automatic dequantization. Supports all ggml quantization types (F32, F16, Q4_0, Q8_0, K-quants, IQ, etc.).

```r
library(ggmlR)

# Load a GGUF file
g <- gguf_load("model.gguf")
g
#> GGUF file: model.gguf
#>   Version:  3
#>   Tensors:  291
#>   Metadata: 24 key-value pairs

# Inspect metadata (architecture, tokenizer, quant info)
meta <- gguf_metadata(g)
meta[["general.architecture"]]

# List all tensor names
gguf_tensor_names(g)

# Get shape and type for a specific tensor
gguf_tensor_info(g, "blk.0.attn_q.weight")
#> $name:  "blk.0.attn_q.weight"
#> $shape: 4096 4096
#> $type:  "Q4_0"

# Extract dequantized weights as R numeric array
w <- gguf_tensor_data(g, "blk.0.attn_q.weight")
dim(w)
#> [1] 4096 4096

# Free when done (also freed by GC)
gguf_free(g)
```

## Examples

Ready-to-run example scripts in `inst/examples/`:

| Script | Description |
|---|---|
| `titanic_classification.R` | Binary classification with feature engineering, dropout, stratified split, manual metrics (~82% val accuracy) |
| `mnist_cnn.R` | CNN image classifier on MNIST |
| `functional_resnet_cifar.R` | ResNet-style model with skip connections (Functional API) |
| `functional_text_gru.R` | Text classification with GRU + embedding (Functional API) |
| `transformer_encoder_demo.R` | Transformer encoder with multi-head attention (autograd) |
| `dp_train_demo.R` | Data-parallel training across multiple replicas |
| `benchmark_onnx.R` | GPU vs CPU inference benchmark for ONNX models |
| `benchmark_ops.R` | Per-op micro-benchmark: every ggml op on CPU and GPU with auto-batching |
| `profile_onnx_superres_gpu.R` | GPU profiler for SuperResolution ONNX model across input sizes |
| `mlr3_integration.R` | mlr3 learners: CPU vs GPU comparison, iris + mtcars, 3-fold CV |
| `tidymodels_integration.R` | parsnip engine: CPU vs GPU comparison, iris + mtcars |

## Save / Load

```r
ggml_save_model(model, "my_model.rds")
model <- ggml_load_model("my_model.rds")
```

## ONNX Benchmark: GPU (Vulkan) vs CPU

Measured on AMD Ryzen 5 5600 + AMD RX 9070, single-image inference:

| Model | CPU (ms) | GPU (ms) | Speedup | CPU FPS | GPU FPS |
|---|---:|---:|---:|---:|---:|
| Inception V3 | 204.3 | 7.3 | 27.9x | 4.9 | 136.4 |
| MNIST | 0.0 | 0.3 | — | Inf | 3000.0 |
| SqueezeNet 1.0 | 21.7 | 2.0 | 10.8x | 46.2 | 500.0 |
| SuperResolution | 87.3 | 3.0 | 29.1x | 11.5 | 333.3 |
| EmotionFerPlus | 29.7 | 1.7 | 17.8x | 33.7 | 600.0 |
| Inception V3 Op18 | 186.0 | 8.7 | 21.5x | 5.4 | 115.4 |
| BAT-ResNeXt26ts | 87.0 | 6.3 | 13.7x | 11.5 | 157.9 |
| BERT (Opset17) | 243.0 | 8.0 | 30.4x | 4.1 | 125.0 |
| GPT-NeoX | 2.0 | 2.7 | 0.7x | 500.0 | 375.0 |

SuperResolution speedup improved from 31.8x to 29.1x while absolute GPU time dropped from 7.3 ms to 3.0 ms — the result of replacing IM2COL+GEMM with a direct `GGML_OP_CONV_2D` kernel.

Benchmark scripts: `inst/examples/benchmark_onnx.R`, `inst/examples/profile_onnx_superres_gpu.R`

## GPU Acceleration

ggmlR is designed GPU-first: Vulkan is auto-detected at build time and, when available, 90%+ of operations run on GPU with up to 78x speedup over CPU. On machines without a Vulkan-capable GPU the package falls back to CPU transparently — no code changes required.

```r
ggml_vulkan_available()   # TRUE if a Vulkan GPU was detected
ggml_vulkan_status()      # device name, memory, capabilities

# Dynamic autograd: switch device at runtime
ag_device("gpu")   # move subsequent ops to GPU (f16 by default)
ag_device("cpu")   # fall back to CPU
```

Supported GPUs: NVIDIA, AMD, Intel, ARM Mali, Qualcomm Adreno.

### Vulkan optimizations

- **Vulkan 1.4** — push constants limit raised to 256 bytes, enabling full 5D tensor parameter blocks in compute shaders without staging buffers.
- **Push Descriptors** (`VK_KHR_push_descriptor`) — when available, descriptors are pushed directly into the command buffer, eliminating descriptor pool allocation overhead. Falls back to descriptor pools on older hardware.
- **Q4_K flash attention** — `GGML_OP_FLASH_ATTN_EXT` with Q4_K key/value tensors now runs fully on GPU (FA_SCALAR and FA_COOPMAT1 paths). Previously Q4_K attention fell back to CPU. Relevant for llamaR with quantized LLMs on AMD/Intel GPU (KHR cooperative matrix).
- **Subgroup-shuffle mmq** (`USE_SUBGROUP_NO_SHMEM`) — on wavefront-64 devices (RDNA4, subgroup_size=64) Q4_K / Q5_K / Q6_K weight tiles are loaded directly into registers via `subgroupShuffle`, eliminating the shared-memory staging round-trip. ~10-15% token-generation throughput gain on LLaMA 3.x models.

## System Requirements

- R ≥ 4.1.0, C++17 compiler
- **Optional GPU**: Vulkan 1.2+, `libvulkan-dev` + `glslc` (Linux) or Vulkan SDK (Windows)
- Platforms: Linux, macOS, Windows (x86-64, ARM64)

## See Also

- [llamaR](https://github.com/Zabis13/llamaR) — LLM inference in R
- [sd2R](https://github.com/Zabis13/sd2R) — Stable Diffusion in R
- [ggml](https://github.com/ggml-org/ggml) — underlying C library

## License

MIT
