'GGML' Tensor Operations for Machine Learning

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Documentation for package ‘ggmlR’ version 0.6.1

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A B C D G I L N O P Q R S W

-- A --

ag_add Element-wise addition with broadcasting
ag_batch_norm Create a Batch Normalisation layer
ag_clamp Element-wise clamp
ag_cross_entropy_loss Categorical Cross-Entropy loss
ag_dataloader Create a mini-batch data loader
ag_default_device Return the current default compute device
ag_default_dtype Return the current default dtype for GPU operations
ag_device Set the default compute device for ag_* operations
ag_dropout Create a Dropout layer
ag_dtype Set the default floating-point precision for ag_* GPU operations
ag_embedding Create an Embedding layer
ag_eval Switch a layer or sequential model to eval mode
ag_exp Element-wise exponential
ag_gradcheck Numerical gradient check (like torch.autograd.gradcheck)
ag_linear Create a dense layer with learnable parameters
ag_log Element-wise natural logarithm
ag_matmul Matrix multiplication
ag_mean Mean of elements (or along a dim)
ag_mse_loss Mean Squared Error loss
ag_mul Element-wise multiplication
ag_multihead_attention Create a Multi-Head Attention layer
ag_param Create a parameter tensor (gradient tracked)
ag_pow Element-wise power
ag_relu ReLU activation
ag_reshape Reshape tensor
ag_scale Scale tensor by a scalar constant
ag_sequential Create a sequential container of layers
ag_sigmoid Sigmoid activation
ag_softmax Softmax activation (column-wise)
ag_softmax_cross_entropy_loss Fused softmax + cross-entropy loss (numerically stable)
ag_sub Element-wise subtraction
ag_sum Sum all elements (or along a dim): out = sum(x)
ag_tanh Tanh activation
ag_tensor Create a dynamic tensor (no gradient tracking)
ag_to_device Move a tensor to the specified device
ag_train Switch a layer or sequential model to training mode
ag_transpose Transpose a tensor

-- B --

backward Run backward pass from a scalar loss tensor

-- C --

clip_grad_norm Clip gradients by global L2 norm

-- D --

dequantize_row_iq1_m Dequantize Row (IQ)
dequantize_row_iq1_s Dequantize Row (IQ)
dequantize_row_iq2_s Dequantize Row (IQ)
dequantize_row_iq2_xs Dequantize Row (IQ)
dequantize_row_iq2_xxs Dequantize Row (IQ)
dequantize_row_iq3_s Dequantize Row (IQ)
dequantize_row_iq3_xxs Dequantize Row (IQ)
dequantize_row_iq4_nl Dequantize Row (IQ)
dequantize_row_iq4_xs Dequantize Row (IQ)
dequantize_row_mxfp4 Dequantize Row (MXFP4)
dequantize_row_q2_K Dequantize Row (K-quants)
dequantize_row_q3_K Dequantize Row (K-quants)
dequantize_row_q4_0 Dequantize Row (Q4_0)
dequantize_row_q4_1 Dequantize Row (Q4_0)
dequantize_row_q4_K Dequantize Row (K-quants)
dequantize_row_q5_0 Dequantize Row (Q4_0)
dequantize_row_q5_1 Dequantize Row (Q4_0)
dequantize_row_q5_K Dequantize Row (K-quants)
dequantize_row_q6_K Dequantize Row (K-quants)
dequantize_row_q8_0 Dequantize Row (Q4_0)
dequantize_row_q8_K Dequantize Row (K-quants)
dequantize_row_tq1_0 Dequantize Row (Ternary)
dequantize_row_tq2_0 Dequantize Row (Ternary)
dp_train Data-parallel training across multiple GPUs

-- G --

ggml_abort_is_r_enabled Check if R Abort Handler is Enabled
ggml_abs Absolute Value (Graph)
ggml_abs_inplace Absolute Value In-place (Graph)
ggml_add Add tensors
ggml_add1 Add Scalar to Tensor (Graph)
ggml_add_inplace Element-wise Addition In-place (Graph)
ggml_apply Apply a Layer Object to a Tensor Node
ggml_are_same_layout Check if Two Tensors Have the Same Layout
ggml_are_same_shape Compare Tensor Shapes
ggml_are_same_stride Compare Tensor Strides
ggml_argmax Argmax (Graph)
ggml_argsort Argsort - Get Sorting Indices (Graph)
ggml_backend_alloc_ctx_tensors Allocate Context Tensors to Backend
ggml_backend_buffer_clear Clear buffer memory
ggml_backend_buffer_free Free Backend Buffer
ggml_backend_buffer_get_size Get Backend Buffer Size
ggml_backend_buffer_get_usage Get buffer usage
ggml_backend_buffer_is_host Check if buffer is host memory
ggml_backend_buffer_is_multi_buffer Check if buffer is a multi-buffer
ggml_backend_buffer_name Get Backend Buffer Name
ggml_backend_buffer_reset Reset buffer
ggml_backend_buffer_set_usage Set buffer usage hint
ggml_backend_buffer_usage_any Buffer usage: Any
ggml_backend_buffer_usage_compute Buffer usage: Compute
ggml_backend_buffer_usage_weights Buffer usage: Weights
ggml_backend_cpu_init Initialize CPU Backend
ggml_backend_cpu_set_n_threads Set CPU Backend Threads
ggml_backend_device_register Register a device
ggml_backend_device_type_accel Device type: Accelerator
ggml_backend_device_type_cpu Device type: CPU
ggml_backend_device_type_gpu Device type: GPU
ggml_backend_device_type_igpu Device type: Integrated GPU
ggml_backend_dev_by_name Get device by name
ggml_backend_dev_by_type Get device by type
ggml_backend_dev_count Get number of available devices
ggml_backend_dev_description Get device description
ggml_backend_dev_get Get device by index
ggml_backend_dev_get_props Get device properties
ggml_backend_dev_init Initialize backend from device
ggml_backend_dev_memory Get device memory
ggml_backend_dev_name Get device name
ggml_backend_dev_offload_op Check if device should offload operation
ggml_backend_dev_supports_buft Check if device supports buffer type
ggml_backend_dev_supports_op Check if device supports operation
ggml_backend_dev_type Get device type
ggml_backend_event_free Free event
ggml_backend_event_new Create new event
ggml_backend_event_record Record event
ggml_backend_event_synchronize Synchronize event
ggml_backend_event_wait Wait for event
ggml_backend_free Free Backend
ggml_backend_get_device Get device from backend
ggml_backend_graph_compute Compute Graph with Backend
ggml_backend_graph_compute_async Compute graph asynchronously
ggml_backend_graph_plan_compute Execute graph plan
ggml_backend_graph_plan_create Create graph execution plan
ggml_backend_graph_plan_free Free graph execution plan
ggml_backend_init_best Initialize best available backend
ggml_backend_init_by_name Initialize backend by name
ggml_backend_init_by_type Initialize backend by type
ggml_backend_load Load backend from dynamic library
ggml_backend_load_all Load all available backends
ggml_backend_multi_buffer_alloc_buffer Allocate multi-buffer
ggml_backend_multi_buffer_set_usage Set usage for all buffers in a multi-buffer
ggml_backend_name Get Backend Name
ggml_backend_register Register a backend
ggml_backend_reg_by_name Get backend registry by name
ggml_backend_reg_count Get number of registered backends
ggml_backend_reg_dev_count Get number of devices in registry
ggml_backend_reg_dev_get Get device from registry
ggml_backend_reg_get Get backend registry by index
ggml_backend_reg_name Get registry name
ggml_backend_sched_alloc_graph Allocate graph on scheduler
ggml_backend_sched_free Free backend scheduler
ggml_backend_sched_get_backend Get backend from scheduler
ggml_backend_sched_get_n_backends Get number of backends in scheduler
ggml_backend_sched_get_n_copies Get number of tensor copies
ggml_backend_sched_get_n_splits Get number of graph splits
ggml_backend_sched_get_tensor_backend Get tensor backend assignment
ggml_backend_sched_graph_compute Compute graph using scheduler
ggml_backend_sched_graph_compute_async Compute graph asynchronously
ggml_backend_sched_new Create a new backend scheduler
ggml_backend_sched_reserve Reserve memory for scheduler
ggml_backend_sched_reset Reset scheduler
ggml_backend_sched_set_tensor_backend Set tensor backend assignment
ggml_backend_sched_synchronize Synchronize scheduler
ggml_backend_synchronize Synchronize backend
ggml_backend_tensor_copy_async Copy tensor asynchronously between backends
ggml_backend_tensor_get_and_sync Backend Tensor Get and Sync
ggml_backend_tensor_get_async Get tensor data asynchronously
ggml_backend_tensor_get_data Get Tensor Data via Backend
ggml_backend_tensor_get_f32_first Get First Float from Backend Tensor
ggml_backend_tensor_set_async Set tensor data asynchronously
ggml_backend_tensor_set_data Set Tensor Data via Backend
ggml_backend_unload Unload backend
ggml_batch_norm Create a Batch Normalization Layer Object
ggml_blck_size Get Block Size
ggml_build_forward_expand Build forward expand
ggml_callback_early_stopping Early stopping callback
ggml_can_repeat Check If Tensor Can Be Repeated
ggml_ceil Ceiling (Graph)
ggml_ceil_inplace Ceiling In-place (Graph)
ggml_clamp Clamp (Graph)
ggml_compile Compile a Sequential Model
ggml_compile.ggml_functional_model Compile a Sequential Model
ggml_compile.ggml_sequential_model Compile a Sequential Model
ggml_concat Concatenate Tensors (Graph)
ggml_cont Make Contiguous (Graph)
ggml_conv_1d 1D Convolution (Graph)
ggml_conv_2d 2D Convolution (Graph)
ggml_conv_transpose_1d Transposed 1D Convolution (Graph)
ggml_cos Cosine (Graph)
ggml_count_equal Count Equal Elements (Graph)
ggml_cpu_add Element-wise Addition (CPU Direct)
ggml_cpu_features Get All CPU Features
ggml_cpu_get_rvv_vlen Get RISC-V Vector Length
ggml_cpu_get_sve_cnt Get SVE Vector Length (ARM)
ggml_cpu_has_amx_int8 CPU Feature Detection - AMX INT8
ggml_cpu_has_arm_fma CPU Feature Detection - ARM FMA
ggml_cpu_has_avx CPU Feature Detection - AVX
ggml_cpu_has_avx2 CPU Feature Detection - AVX2
ggml_cpu_has_avx512 CPU Feature Detection - AVX-512
ggml_cpu_has_avx512_bf16 CPU Feature Detection - AVX-512 BF16
ggml_cpu_has_avx512_vbmi CPU Feature Detection - AVX-512 VBMI
ggml_cpu_has_avx512_vnni CPU Feature Detection - AVX-512 VNNI
ggml_cpu_has_avx_vnni CPU Feature Detection - AVX-VNNI
ggml_cpu_has_bmi2 CPU Feature Detection - BMI2
ggml_cpu_has_dotprod CPU Feature Detection - Dot Product (ARM)
ggml_cpu_has_f16c CPU Feature Detection - F16C
ggml_cpu_has_fma CPU Feature Detection - FMA
ggml_cpu_has_fp16_va CPU Feature Detection - FP16 Vector Arithmetic (ARM)
ggml_cpu_has_llamafile CPU Feature Detection - Llamafile
ggml_cpu_has_matmul_int8 CPU Feature Detection - INT8 Matrix Multiply (ARM)
ggml_cpu_has_neon CPU Feature Detection - NEON (ARM)
ggml_cpu_has_riscv_v CPU Feature Detection - RISC-V Vector
ggml_cpu_has_sme CPU Feature Detection - SME (ARM)
ggml_cpu_has_sse3 CPU Feature Detection - SSE3
ggml_cpu_has_ssse3 CPU Feature Detection - SSSE3
ggml_cpu_has_sve CPU Feature Detection - SVE (ARM)
ggml_cpu_has_vsx CPU Feature Detection - VSX (PowerPC)
ggml_cpu_has_vxe CPU Feature Detection - VXE (IBM z/Architecture)
ggml_cpu_has_wasm_simd CPU Feature Detection - WebAssembly SIMD
ggml_cpu_mul Element-wise Multiplication (CPU Direct)
ggml_cpy Copy Tensor with Type Conversion (Graph)
ggml_cycles Get CPU Cycles
ggml_cycles_per_ms Get CPU Cycles per Millisecond
ggml_dense Create a Dense Layer Object
ggml_diag Diagonal Matrix (Graph)
ggml_diag_mask_inf Diagonal Mask with -Inf (Graph)
ggml_diag_mask_inf_inplace Diagonal Mask with -Inf In-place (Graph)
ggml_diag_mask_zero Diagonal Mask with Zero (Graph)
ggml_div Element-wise Division (Graph)
ggml_div_inplace Element-wise Division In-place (Graph)
ggml_dup Duplicate Tensor (Graph)
ggml_dup_inplace Duplicate Tensor In-place (Graph)
ggml_dup_tensor Duplicate Tensor
ggml_element_size Get Element Size
ggml_elu ELU Activation (Graph)
ggml_elu_inplace ELU Activation In-place (Graph)
ggml_embedding Create an Embedding Layer Object
ggml_estimate_memory Estimate Required Memory
ggml_evaluate Evaluate a Trained Model
ggml_evaluate.ggml_functional_model Evaluate a Trained Model
ggml_evaluate.ggml_sequential_model Evaluate a Trained Model
ggml_exp Exponential (Graph)
ggml_exp_inplace Exponential In-place (Graph)
ggml_fit Train a Model (dispatcher)
ggml_fit.default Train a Model (dispatcher)
ggml_fit.ggml_functional_model Train a Model (dispatcher)
ggml_fit.ggml_sequential_model Train a Model (dispatcher)
ggml_fit_opt Fit model with R-side epoch loop and callbacks
ggml_flash_attn_back Flash Attention Backward (Graph)
ggml_flash_attn_ext Flash Attention (Graph)
ggml_floor Floor (Graph)
ggml_floor_inplace Floor In-place (Graph)
ggml_free Free GGML context
ggml_freeze_weights Freeze Layer Weights
ggml_ftype_to_ggml_type Convert ftype to ggml_type
ggml_gallocr_alloc_graph Allocate Memory for Graph
ggml_gallocr_free Free Graph Allocator
ggml_gallocr_get_buffer_size Get Graph Allocator Buffer Size
ggml_gallocr_new Create Graph Allocator
ggml_gallocr_reserve Reserve Memory for Graph
ggml_geglu GeGLU (GELU Gated Linear Unit) (Graph)
ggml_geglu_quick GeGLU Quick (Fast GeGLU) (Graph)
ggml_geglu_split GeGLU Split (Graph)
ggml_gelu GELU Activation (Graph)
ggml_gelu_erf Exact GELU Activation (Graph)
ggml_gelu_inplace GELU Activation In-place (Graph)
ggml_gelu_quick GELU Quick Activation (Graph)
ggml_get_f32 Get F32 data
ggml_get_f32_nd Get Single Float Value by N-D Index
ggml_get_first_tensor Get First Tensor from Context
ggml_get_i32 Get I32 Data
ggml_get_i32_nd Get Single Int32 Value by N-D Index
ggml_get_layer Get a Layer from a Sequential Model
ggml_get_max_tensor_size Get Maximum Tensor Size
ggml_get_mem_size Get Context Memory Size
ggml_get_name Get Tensor Name
ggml_get_next_tensor Get Next Tensor from Context
ggml_get_no_alloc Get No Allocation Mode
ggml_get_n_threads Get Number of Threads
ggml_get_op_params Get Tensor Operation Parameters
ggml_get_op_params_f32 Get Float Op Parameter
ggml_get_op_params_i32 Get Integer Op Parameter
ggml_get_rows Get Rows by Indices (Graph)
ggml_get_rows_back Get Rows Backward (Graph)
ggml_get_unary_op Get Unary Operation from Tensor
ggml_glu Generic GLU (Gated Linear Unit) (Graph)
GGML_GLU_OP_GEGLU GLU Operation Types
GGML_GLU_OP_GEGLU_ERF GLU Operation Types
GGML_GLU_OP_GEGLU_QUICK GLU Operation Types
GGML_GLU_OP_REGLU GLU Operation Types
GGML_GLU_OP_SWIGLU GLU Operation Types
GGML_GLU_OP_SWIGLU_OAI GLU Operation Types
ggml_glu_split Generic GLU Split (Graph)
ggml_graph_compute Compute graph
ggml_graph_compute_with_ctx Compute Graph with Context (Alternative Method)
ggml_graph_dump_dot Export Graph to DOT Format
ggml_graph_get_tensor Get Tensor from Graph by Name
ggml_graph_node Get Graph Node
ggml_graph_n_nodes Get Number of Nodes in Graph
ggml_graph_overhead Get Graph Overhead
ggml_graph_print Print Graph Information
ggml_graph_reset Reset Graph (for backpropagation)
ggml_graph_view Create a View of a Subgraph
ggml_group_norm Group Normalization (Graph)
ggml_group_norm_inplace Group Normalization In-place (Graph)
ggml_gru Create a GRU Layer Object
ggml_hardsigmoid Hard Sigmoid Activation (Graph)
ggml_hardswish Hard Swish Activation (Graph)
ggml_im2col Image to Column (Graph)
ggml_init Initialize GGML context
ggml_init_auto Create Context with Auto-sizing
ggml_input Declare a Functional API Input Tensor
ggml_is_available Check if GGML is available
ggml_is_contiguous Check if Tensor is Contiguous
ggml_is_contiguously_allocated Check If Tensor is Contiguously Allocated
ggml_is_contiguous_0 Check Tensor Contiguity (Dimension 0)
ggml_is_contiguous_1 Check Tensor Contiguity (Dimensions >= 1)
ggml_is_contiguous_2 Check Tensor Contiguity (Dimensions >= 2)
ggml_is_contiguous_channels Check Channel-wise Contiguity
ggml_is_contiguous_rows Check Row-wise Contiguity
ggml_is_permuted Check if Tensor is Permuted
ggml_is_quantized Check If Type is Quantized
ggml_is_transposed Check if Tensor is Transposed
ggml_l2_norm L2 Normalization (Graph)
ggml_l2_norm_inplace L2 Normalization In-place (Graph)
ggml_layer_add Element-wise Addition of Two Tensor Nodes
ggml_layer_batch_norm Add Batch Normalization Layer
ggml_layer_concatenate Concatenate Tensor Nodes Along an Axis
ggml_layer_conv_1d Create a Conv1D Layer Object
ggml_layer_conv_2d Create a Conv2D Layer Object
ggml_layer_dense Add Dense (Fully Connected) Layer
ggml_layer_dropout Add Dropout Layer
ggml_layer_embedding Add Embedding Layer
ggml_layer_flatten Add Flatten Layer
ggml_layer_global_average_pooling_2d Global Average Pooling for 2D Feature Maps
ggml_layer_global_max_pooling_2d Global Max Pooling for 2D Feature Maps
ggml_layer_gru Add a GRU Layer
ggml_layer_lstm Add an LSTM Layer
ggml_layer_max_pooling_2d Add 2D Max Pooling Layer
ggml_leaky_relu Leaky ReLU Activation (Graph)
ggml_load_model Load a Full Model (Architecture + Weights)
ggml_load_weights Load Model Weights from File
ggml_log Natural Logarithm (Graph)
ggml_log_inplace Natural Logarithm In-place (Graph)
ggml_log_is_r_enabled Check if R Logging is Enabled
ggml_log_set_default Restore Default GGML Logging
ggml_log_set_r Enable R-compatible GGML Logging
ggml_lstm Create an LSTM Layer Object
ggml_mean Mean (Graph)
ggml_model Create a Functional Model
ggml_model_sequential Create a Sequential Neural Network Model
ggml_mul Multiply tensors
ggml_mul_inplace Element-wise Multiplication In-place (Graph)
ggml_mul_mat Matrix Multiplication (Graph)
ggml_mul_mat_id Matrix Multiplication with Expert Selection (Graph)
ggml_nbytes Get number of bytes
ggml_neg Negation (Graph)
ggml_neg_inplace Negation In-place (Graph)
ggml_nelements Get number of elements
ggml_new_f32 Create Scalar F32 Tensor
ggml_new_i32 Create Scalar I32 Tensor
ggml_new_tensor Create Tensor with Arbitrary Dimensions
ggml_new_tensor_1d Create 1D tensor
ggml_new_tensor_2d Create 2D tensor
ggml_new_tensor_3d Create 3D Tensor
ggml_new_tensor_4d Create 4D Tensor
ggml_norm Layer Normalization (Graph)
ggml_norm_inplace Layer Normalization In-place (Graph)
ggml_nrows Get Number of Rows
ggml_n_dims Get Number of Dimensions
ggml_opt_alloc Allocate graph for evaluation
ggml_opt_context_optimizer_type Get optimizer type from context
ggml_opt_dataset_data Get data tensor from dataset
ggml_opt_dataset_free Free optimization dataset
ggml_opt_dataset_get_batch Get batch from dataset
ggml_opt_dataset_init Create a new optimization dataset
ggml_opt_dataset_labels Get labels tensor from dataset
ggml_opt_dataset_ndata Get number of datapoints in dataset
ggml_opt_dataset_shuffle Shuffle dataset
ggml_opt_default_params Get default optimizer parameters
ggml_opt_epoch Run one training epoch
ggml_opt_eval Evaluate model
ggml_opt_fit Fit model to dataset
ggml_opt_free Free optimizer context
ggml_opt_get_lr Get current learning rate from optimizer context
ggml_opt_grad_acc Get gradient accumulator for a tensor
ggml_opt_init Initialize optimizer context
ggml_opt_init_for_fit Initialize optimizer context for R-side epoch loop
ggml_opt_inputs Get inputs tensor from optimizer context
ggml_opt_labels Get labels tensor from optimizer context
ggml_opt_loss Get loss tensor from optimizer context
ggml_opt_loss_type_cross_entropy Loss type: Cross Entropy
ggml_opt_loss_type_mean Loss type: Mean
ggml_opt_loss_type_mse Loss type: Mean Squared Error
ggml_opt_loss_type_sum Loss type: Sum
ggml_opt_ncorrect Get number of correct predictions tensor
ggml_opt_optimizer_name Get optimizer name
ggml_opt_optimizer_type_adamw Optimizer type: AdamW
ggml_opt_optimizer_type_sgd Optimizer type: SGD
ggml_opt_outputs Get outputs tensor from optimizer context
ggml_opt_pred Get predictions tensor from optimizer context
ggml_opt_prepare_alloc Prepare allocation for non-static graphs
ggml_opt_reset Reset optimizer context
ggml_opt_result_accuracy Get accuracy from result
ggml_opt_result_free Free optimization result
ggml_opt_result_init Initialize optimization result
ggml_opt_result_loss Get loss from result
ggml_opt_result_ndata Get number of datapoints from result
ggml_opt_result_pred Get predictions from result
ggml_opt_result_reset Reset optimization result
ggml_opt_set_lr Set learning rate in optimizer context
ggml_opt_static_graphs Check if using static graphs
ggml_op_can_inplace Check if Operation Can Be Done In-place
ggml_op_desc Get Operation Description from Tensor
ggml_op_name Get Operation Name
GGML_OP_POOL_AVG 1D Pooling (Graph)
GGML_OP_POOL_MAX 1D Pooling (Graph)
ggml_op_symbol Get Operation Symbol
ggml_out_prod Outer Product (Graph)
ggml_pad Pad Tensor with Zeros (Graph)
ggml_permute Permute Tensor Dimensions (Graph)
ggml_pool_1d 1D Pooling (Graph)
ggml_pool_2d 2D Pooling (Graph)
ggml_pop_layer Remove the Last Layer from a Sequential Model
ggml_predict Get Predictions from a Trained Model
ggml_predict.ggml_functional_model Get Predictions from a Trained Model
ggml_predict.ggml_sequential_model Get Predictions from a Trained Model
ggml_predict_classes Predict Classes from a Trained Model
ggml_print_mem_status Print Context Memory Status
ggml_print_objects Print Objects in Context
ggml_quantize_chunk Quantize Data Chunk
ggml_quantize_free Free Quantization Resources
ggml_quantize_init Initialize Quantization Tables
ggml_quantize_requires_imatrix Check if Quantization Requires Importance Matrix
ggml_quant_block_info Get Quantization Block Info
ggml_reglu ReGLU (ReLU Gated Linear Unit) (Graph)
ggml_reglu_split ReGLU Split (Graph)
ggml_relu ReLU Activation (Graph)
ggml_relu_inplace ReLU Activation In-place (Graph)
ggml_repeat Repeat (Graph)
ggml_repeat_back Repeat Backward (Graph)
ggml_reset Reset GGML Context
ggml_reshape_1d Reshape to 1D (Graph)
ggml_reshape_2d Reshape to 2D (Graph)
ggml_reshape_3d Reshape to 3D (Graph)
ggml_reshape_4d Reshape to 4D (Graph)
ggml_rms_norm RMS Normalization (Graph)
ggml_rms_norm_back RMS Norm Backward (Graph)
ggml_rms_norm_inplace RMS Normalization In-place (Graph)
ggml_rope Rotary Position Embedding (Graph)
ggml_rope_ext Extended RoPE with Frequency Scaling (Graph)
ggml_rope_ext_back RoPE Extended Backward (Graph)
ggml_rope_ext_inplace Extended RoPE Inplace (Graph)
ggml_rope_inplace Rotary Position Embedding In-place (Graph)
ggml_rope_multi Multi-RoPE for Vision Models (Graph)
ggml_rope_multi_inplace Multi-RoPE Inplace (Graph)
GGML_ROPE_TYPE_MROPE RoPE Mode Constants
GGML_ROPE_TYPE_NEOX RoPE Mode Constants
GGML_ROPE_TYPE_NORM RoPE Mode Constants
GGML_ROPE_TYPE_VISION RoPE Mode Constants
ggml_round Round (Graph)
ggml_round_inplace Round In-place (Graph)
ggml_save_model Save a Full Model (Architecture + Weights)
ggml_save_weights Save Model Weights to File
ggml_scale Scale (Graph)
ggml_scale_inplace Scale Tensor In-place (Graph)
GGML_SCALE_MODE_BICUBIC Upscale Tensor (Graph)
GGML_SCALE_MODE_BILINEAR Upscale Tensor (Graph)
GGML_SCALE_MODE_NEAREST Upscale Tensor (Graph)
ggml_schedule_cosine_decay Cosine annealing LR scheduler
ggml_schedule_reduce_on_plateau Reduce on plateau LR scheduler
ggml_schedule_step_decay Step decay LR scheduler
ggml_set Set Tensor Region (Graph)
ggml_set_1d Set 1D Tensor Region (Graph)
ggml_set_2d Set 2D Tensor Region (Graph)
ggml_set_abort_callback_default Restore Default Abort Behavior
ggml_set_abort_callback_r Enable R-compatible Abort Handling
ggml_set_f32 Set F32 data
ggml_set_f32_nd Set Single Float Value by N-D Index
ggml_set_i32 Set I32 Data
ggml_set_i32_nd Set Single Int32 Value by N-D Index
ggml_set_input Mark Tensor as Input
ggml_set_name Set Tensor Name
ggml_set_no_alloc Set No Allocation Mode
ggml_set_n_threads Set Number of Threads
ggml_set_op_params Set Tensor Operation Parameters
ggml_set_op_params_f32 Set Float Op Parameter
ggml_set_op_params_i32 Set Integer Op Parameter
ggml_set_output Mark Tensor as Output
ggml_set_param Set Tensor as Trainable Parameter
ggml_set_zero Set Tensor to Zero
ggml_sgn Sign Function (Graph)
ggml_sigmoid Sigmoid Activation (Graph)
ggml_sigmoid_inplace Sigmoid Activation In-place (Graph)
ggml_silu SiLU Activation (Graph)
ggml_silu_back SiLU Backward (Graph)
ggml_silu_inplace SiLU Activation In-place (Graph)
ggml_sin Sine (Graph)
ggml_softplus Softplus Activation (Graph)
ggml_softplus_inplace Softplus Activation In-place (Graph)
ggml_soft_max Softmax (Graph)
ggml_soft_max_ext Extended Softmax with Masking and Scaling (Graph)
ggml_soft_max_ext_back Softmax Backward Extended (Graph)
ggml_soft_max_ext_back_inplace Extended Softmax Backward Inplace (Graph)
ggml_soft_max_ext_inplace Extended Softmax Inplace (Graph)
ggml_soft_max_inplace Softmax In-place (Graph)
GGML_SORT_ORDER_ASC Sort Order Constants
GGML_SORT_ORDER_DESC Sort Order Constants
ggml_sqr Square (Graph)
ggml_sqrt Square Root (Graph)
ggml_sqrt_inplace Square Root In-place (Graph)
ggml_sqr_inplace Square In-place (Graph)
ggml_step Step Function (Graph)
ggml_sub Element-wise Subtraction (Graph)
ggml_sub_inplace Element-wise Subtraction In-place (Graph)
ggml_sum Sum (Graph)
ggml_sum_rows Sum Rows (Graph)
ggml_swiglu SwiGLU (Swish/SiLU Gated Linear Unit) (Graph)
ggml_swiglu_split SwiGLU Split (Graph)
ggml_tanh Tanh Activation (Graph)
ggml_tanh_inplace Tanh Activation In-place (Graph)
ggml_tensor_copy Copy Tensor Data
ggml_tensor_nb Get Tensor Strides (nb)
ggml_tensor_num Count Tensors in Context
ggml_tensor_overhead Get Tensor Overhead
ggml_tensor_set_f32_scalar Fill Tensor with Scalar
ggml_tensor_shape Get Tensor Shape
ggml_tensor_type Get Tensor Type
ggml_test Test GGML
ggml_timestep_embedding Timestep Embedding (Graph Operation)
ggml_time_init Initialize GGML Timer
ggml_time_ms Get Time in Milliseconds
ggml_time_us Get Time in Microseconds
ggml_top_k Top-K Indices (Graph)
ggml_transpose Transpose (Graph)
GGML_TYPE_BF16 GGML Data Types
GGML_TYPE_F16 GGML Data Types
GGML_TYPE_F32 GGML Data Types
GGML_TYPE_I32 GGML Data Types
ggml_type_name Get Type Name
GGML_TYPE_Q4_0 GGML Data Types
GGML_TYPE_Q4_1 GGML Data Types
GGML_TYPE_Q8_0 GGML Data Types
ggml_type_size Get Type Size in Bytes
ggml_type_sizef Get Type Size as Float
ggml_unary_op_name Get Unary Operation Name
ggml_unfreeze_weights Unfreeze Layer Weights
ggml_upscale Upscale Tensor (Graph)
ggml_used_mem Get Used Memory
ggml_version Get GGML version
ggml_view_1d 1D View with Byte Offset (Graph)
ggml_view_2d 2D View with Byte Offset (Graph)
ggml_view_3d 3D View with Byte Offset (Graph)
ggml_view_4d 4D View with Byte Offset (Graph)
ggml_view_tensor View Tensor
ggml_vulkan_available Check if Vulkan support is available
ggml_vulkan_backend_name Get Vulkan backend name
ggml_vulkan_device_count Get number of Vulkan devices
ggml_vulkan_device_description Get Vulkan device description
ggml_vulkan_device_memory Get Vulkan device memory
ggml_vulkan_free Free Vulkan backend
ggml_vulkan_init Initialize Vulkan backend
ggml_vulkan_is_backend Check if backend is Vulkan
ggml_vulkan_list_devices List all Vulkan devices
ggml_vulkan_status Print Vulkan status
ggml_with_temp_ctx Execute with Temporary Context

-- I --

iq2xs_free_impl Free IQ2 Quantization Tables
iq2xs_init_impl Initialize IQ2 Quantization Tables
iq3xs_free_impl Free IQ3 Quantization Tables
iq3xs_init_impl Initialize IQ3 Quantization Tables

-- L --

lr_scheduler_cosine Cosine-annealing learning rate scheduler
lr_scheduler_step Step-decay learning rate scheduler

-- N --

nn_topo_sort Topologically sort nodes reachable from output nodes

-- O --

optimizer_adam Create an Adam optimizer
optimizer_sgd Create an SGD optimizer

-- P --

plot.ggml_history Plot training history
print.ag_tensor Print method for ag_tensor
print.ggml_functional_model Print method for ggml_functional_model
print.ggml_history Print method for ggml_history
print.ggml_sequential_model Print method for ggml_sequential_model

-- Q --

quantize_iq1_m Quantize Data (IQ)
quantize_iq1_s Quantize Data (IQ)
quantize_iq2_s Quantize Data (IQ)
quantize_iq2_xs Quantize Data (IQ)
quantize_iq2_xxs Quantize Data (IQ)
quantize_iq3_s Quantize Data (IQ)
quantize_iq3_xxs Quantize Data (IQ)
quantize_iq4_nl Quantize Data (IQ)
quantize_iq4_xs Quantize Data (IQ)
quantize_mxfp4 Quantize Data (MXFP4)
quantize_q2_K Quantize Data (K-quants)
quantize_q3_K Quantize Data (K-quants)
quantize_q4_0 Quantize Data (Q4_0)
quantize_q4_1 Quantize Data (Q4_0)
quantize_q4_K Quantize Data (K-quants)
quantize_q5_0 Quantize Data (Q4_0)
quantize_q5_1 Quantize Data (Q4_0)
quantize_q5_K Quantize Data (K-quants)
quantize_q6_K Quantize Data (K-quants)
quantize_q8_0 Quantize Data (Q4_0)
quantize_row_iq2_s_ref Quantize Row Reference (IQ)
quantize_row_iq3_s_ref Quantize Row Reference (IQ)
quantize_row_iq3_xxs_ref Quantize Row Reference (IQ)
quantize_row_iq4_nl_ref Quantize Row Reference (IQ)
quantize_row_iq4_xs_ref Quantize Row Reference (IQ)
quantize_row_mxfp4_ref Quantize Row Reference (MXFP4)
quantize_row_q2_K_ref Quantize Row Reference (K-quants)
quantize_row_q3_K_ref Quantize Row Reference (K-quants)
quantize_row_q4_0_ref Quantize Row Reference (Basic)
quantize_row_q4_1_ref Quantize Row Reference (Basic)
quantize_row_q4_K_ref Quantize Row Reference (K-quants)
quantize_row_q5_0_ref Quantize Row Reference (Basic)
quantize_row_q5_1_ref Quantize Row Reference (Basic)
quantize_row_q5_K_ref Quantize Row Reference (K-quants)
quantize_row_q6_K_ref Quantize Row Reference (K-quants)
quantize_row_q8_0_ref Quantize Row Reference (Basic)
quantize_row_q8_1_ref Quantize Row Reference (Basic)
quantize_row_q8_K_ref Quantize Row Reference (K-quants)
quantize_row_tq1_0_ref Quantize Row Reference (Ternary)
quantize_row_tq2_0_ref Quantize Row Reference (Ternary)
quantize_tq1_0 Quantize Data (Ternary)
quantize_tq2_0 Quantize Data (Ternary)

-- R --

rope_types RoPE Mode Constants

-- S --

summary.ggml_sequential_model Summary method for ggml_sequential_model

-- W --

with_grad_tape Run code with gradient tape enabled