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

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).

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.

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.

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.

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().

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.

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.

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.

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().

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.

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.