ggmlR 0.6.3

ONNX model import

• onnx_load(path, device, input_shapes) — load an ONNX model file, build a ggml computation graph, and allocate tensors on Vulkan GPU or CPU. Weights are loaded via memory-mapped file (zero-copy where possible).
• onnx_run(model, inputs) — run inference on a loaded ONNX model with named input data.
• onnx_inputs(model) — list expected input tensor names and shapes.
• onnx_summary(model) — return model metadata (IR version, opset, producer, ops used).
• print.onnx_model() — formatted summary of a loaded ONNX model.
• Built-in zero-dependency protobuf parser: no external libraries or Python required.
• input_shapes parameter for models with dynamic dimensions: specify fixed shapes at load time (e.g. input_shapes = list(image = c(1L, 3L, 224L, 224L))).
• 40+ supported ONNX ops: Add, Sub, Mul, Div, MatMul, Gemm, Conv (1D/2D), ConvTranspose (1D/2D), Relu, Sigmoid, Tanh, GELU, SiLU, LeakyRelu, Elu, Softmax, MaxPool, AveragePool, GlobalAveragePool, BatchNormalization, LayerNormalization, GroupNormalization, RMSNormalization, Reshape, Transpose, Concat, Flatten, Squeeze, Unsqueeze, Gather, Pad, Clip, Cast, Constant, ConstantOfShape, Shape, Expand, Slice, Split, Where, Erf, Pow, Sqrt, Exp, Log, Abs, Neg, Floor, Ceil, ReduceMean, ReduceSum, Resize/Upsample, Identity, Dropout.
• auto_pad attribute (SAME_UPPER, SAME_LOWER) supported for Conv and pooling ops.
• Numpy-style broadcast for binary ops (Add/Sub/Mul/Div): handles mismatched ranks and dimensions, with left-align, right-align, and greedy dim-matching strategies.
• Scalar Constant tensors (0-dimensional TensorProto) correctly handled.

Tested real-world ONNX models (9/15 from ONNX Model Zoo)

• mnist-8 — OK (12 nodes)
• squeezenet1.0-8 — OK (66 nodes: Conv, Relu, MaxPool, Concat, Dropout, GlobalAveragePool, Softmax)
• adv_inception_v3 Opset 17/18 — OK (215 nodes)
• super-resolution-10 — OK with input_shapes (Conv, Reshape, Transpose)
• bert Opset 17 — OK (533 nodes: MatMul, Add, LayerNorm, GELU/Erf, Softmax, Shape, Gather, Cast, Where, ConstantOfShape)
• emotion-ferplus-8 — OK (52 nodes: Conv, Relu, MaxPool, Reshape, Gemm, Constant)
• sageconv Opset 16 — OK (24 nodes: MatMul, Add, Mul, Sigmoid, ReduceSum)
• roberta-sequence-classification-9 — OK with input_shapes (1180 nodes)
• Remaining failures: bat_resnext26ts (MatMul 3D broadcast), botnet26t_256 (MatMul dims), cait_xs24 (Concat mismatch), gptneox (shape propagation), MaskRCNN (quantized ops), xcit_tiny (Concat mismatch).

ggmlR 0.6.2

• Fixed Windows cleanup script that removed inst/lib/libggml.a, breaking static linking from dependent packages (e.g. llamaR).

ggmlR 0.6.1

• dp_train(make_model, data, loss_fn, forward_fn, target_fn, n_gpu, n_iter, lr, max_norm, verbose) — data-parallel training across multiple replicas. Weights are broadcast from replica 0 before the first step; gradients are averaged across replicas each iteration; weights are re-broadcast after each optimizer update. Returns list(params, loss_history, model).
• ag_mul and ag_sub now support CPU broadcast: [d×s] * [1×s] and [d×s] * [d×1] shapes work correctly with proper gradient reduction.
• ag_softmax_cross_entropy_loss accepts integer target vectors (0-based class indices) and converts them to one-hot automatically.
• ggml_sum_rows f16 on Vulkan: F16→F16 dispatch now supported natively (no CPU fallback).

ggmlR 0.6.0

Dynamic autograd engine (PyTorch-style training)

• ag_tensor() / ag_param() — environment-backed tensors with reference semantics; in-place optimizer updates visible to all references.
• with_grad_tape({ ... }) — enables the global gradient tape for the enclosed forward pass.
• backward(loss) — reverse-mode automatic differentiation; returns a gradient environment keyed by tensor id.
• Differentiable ops: ag_matmul, ag_add (with bias broadcast), ag_sub, ag_mul, ag_scale.
• Activations: ag_relu, ag_sigmoid, ag_tanh, ag_softmax.
• Reduction / math ops: ag_sum, ag_mean, ag_log, ag_exp, ag_pow, ag_clamp.
• Shape ops: ag_reshape, ag_transpose.
• Loss functions: ag_mse_loss, ag_cross_entropy_loss, ag_softmax_cross_entropy_loss (numerically-stable fused).
• optimizer_sgd() — SGD with optional momentum.
• optimizer_adam() — Adam with bias-corrected moment estimates.
• ag_linear() — Glorot-initialised dense layer (closure-based, returns $forward, $params()).
• ag_gradcheck() — central finite-difference gradient checker (like torch.autograd.gradcheck).

Layer objects (environment-based, train/eval modes)

• ag_sequential(...) — ordered layer container; collects all parameters for the optimizer.
• ag_dropout(rate) — inverted dropout; identity in eval mode.
• ag_batch_norm(num_features) — batch normalisation with running statistics and learnable γ/β.
• ag_embedding(vocab_size, dim) — token lookup with scatter-add backward.
• ag_train(model) / ag_eval(model) — switch all sub-layers between train and eval mode.

Training utilities

• ag_dataloader(x, y, batch_size, shuffle, col_major) — mini-batch iterator with shuffle and $epoch() helper.
• lr_scheduler_step(optimizer, step_size, gamma) — step-decay learning rate.
• lr_scheduler_cosine(optimizer, T_max, lr_min, restart) — cosine-annealing (with optional SGDR warm restarts).
• clip_grad_norm(params, grads, max_norm) — clips all gradients by global L2 norm in-place.

ggmlR 0.5.9

• ggml_layer_lstm() — LSTM recurrent layer (unrolled BPTT).
• ggml_layer_gru() — GRU recurrent layer (unrolled BPTT).
• ggml_layer_global_max_pooling_2d() — reduces [H,W,C] to [C] via max pooling.
• ggml_layer_global_average_pooling_2d() — reduces [H,W,C] to [C] via average pooling.
• ggml_save_model() — saves full model (architecture + weights) to RDS file.
• ggml_load_model() — restores a model saved with ggml_save_model().
• ggml_dense(), ggml_conv_2d(), ggml_conv_1d(), ggml_batch_norm(), ggml_embedding(), ggml_lstm(), ggml_gru() — layer object constructors returning a reusable ggml_layer object.
• ggml_apply(tensor, layer) — applies a ggml_layer object to a tensor node; shared weights by object identity.

ggmlR 0.5.7

• ggml_layer_dropout() — dropout with deterministic or stochastic (per-epoch Bernoulli mask) mode.
• ggml_layer_embedding() — token embedding lookup for integer inputs.
• ggml_input() gains dtype argument ("float32" or "int32").
• Multi-output support in ggml_model() and ggml_predict().

ggmlR 0.5.6

• ggml_input() — declare a symbolic input tensor node (Functional API).
• ggml_model() — assemble a ggml_functional_model from input/output nodes.
• ggml_layer_add() — element-wise addition of tensor nodes (residual connections).
• ggml_layer_concatenate() — concatenate tensor nodes along an axis.
• All ggml_layer_*() functions now accept a ggml_tensor_node as first argument (Functional API mode).
• ggml_compile(), ggml_fit(), ggml_evaluate(), ggml_predict() are now S3 generics with methods for ggml_functional_model.

ggmlR 0.5.5

• ggml_fit_opt() — low-level optimizer loop with callbacks and learning-rate control.
• ggml_callback_early_stopping() — stops training when a metric stagnates.
• ggml_schedule_step_decay() — step learning-rate decay.
• ggml_schedule_cosine_decay() — cosine learning-rate annealing.
• ggml_schedule_reduce_on_plateau() — reduces LR when metric stops improving.
• ggml_opt_init_for_fit(), ggml_opt_set_lr(), ggml_opt_get_lr() — learning-rate control without recreating the optimizer context.

ggmlR 0.5.4

• Vulkan GPU backend support on Windows via configure.win.
• Vulkan auto-detected at build time on Linux and Windows.

ggmlR 0.5.3

• ggml_layer_conv_1d() — 1D convolution layer.
• ggml_layer_batch_norm() — batch normalization layer.
• ggml_predict_classes() — argmax wrapper returning 1-based class indices.
• summary.ggml_sequential_model() — detailed model summary with parameter counts.
• ggml_fit() now returns model$history (class ggml_history) with print and plot methods.
• Sequential API: ggml_model_sequential(), ggml_layer_dense(), ggml_layer_conv_2d(), ggml_layer_max_pooling_2d(), ggml_layer_flatten(), ggml_compile(), ggml_fit(), ggml_evaluate(), ggml_predict(), ggml_save_weights(), ggml_load_weights().
• Vulkan GPU backend covering 90%+ of ML operations.

ggmlR 0.5.2

• ggml_timestep_embedding() — sinusoidal timestep embeddings.
• N-D tensor access: ggml_set_f32_nd(), ggml_get_f32_nd(), ggml_set_i32_nd(), ggml_get_i32_nd().
• Tensor utilities: ggml_tensor_nb(), ggml_tensor_num(), ggml_tensor_copy(), ggml_tensor_set_f32_scalar(), ggml_get_first_tensor(), ggml_get_next_tensor().

ggmlR 0.5.1

• Static library libggml.a exported for linking by dependent packages.
• gguf.cpp added for GGUF file format support.
• Headers exported via inst/include/ for LinkingTo.

ggmlR 0.5.0

• Full optimization/training API: ggml_opt_init(), ggml_opt_free(), ggml_opt_fit(), ggml_opt_epoch(), ggml_opt_eval().
• Dataset management: ggml_opt_dataset_init(), ggml_opt_dataset_data(), ggml_opt_dataset_labels(), ggml_opt_dataset_shuffle().
• Training results: ggml_opt_result_init(), ggml_opt_result_loss(), ggml_opt_result_accuracy(), ggml_opt_result_pred().
• Extended backend API: device management, registry, async operations, graph planning, buffer management (~50 new functions).
• Loss functions: MSE, cross-entropy. Optimizers: AdamW, SGD.

ggmlR 0.4.0

• Multi-GPU backend scheduler API.
• Vulkan GPU backend support.

ggmlR 0.2.0

• Initial release: R bindings for GGML tensor library.
• Core tensor operations, neural network ops, activation functions, quantization (Q4_0, Q4_1, Q8_0), OpenMP parallelization, computation graph API.

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