NEWS

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Rggml 0.1.0

• Added view and copy C-callables (API version 6): Rggml_view_1d/ _2d/_3d (byte offsets/strides, as in ggml) and Rggml_cpy - the building blocks of a KV cache: cpy nodes write new keys/values into views of a persistent cache tensor, expanded into the graph ahead of the attention nodes that read other views of the same cache (see Rllm's incremental decoding).
• Added the transformer graph-op C-callables (API version 5): the ops a forward pass composes - Rggml_get_rows, Rggml_rms_norm, Rggml_mul, Rggml_add, Rggml_silu, Rggml_scale, Rggml_soft_max, Rggml_diag_mask_inf, Rggml_rope (wrapping ggml_rope_ext with YaRN off), and the shape ops Rggml_reshape_2d/_3d, Rggml_permute, Rggml_cont, Rggml_transpose. Downstream packages can now assemble and compute full transformer graphs (see Rllm's llama forward pass) without linking GGML.
• Added quantization C-callables (API version 4): Rggml_quantize wraps ggml_quantize_chunk() so downstream packages can encode f32 rows into any GGUF block format (the output is byte-compatible with GGUF tensor payloads), and Rggml_dequantize wraps GGML's type-traits to_float - the authoritative reference dequantizer, used by the Rfmalloc ecosystem to cross-validate its codec decoders (this cross-validation caught and pinned a Q4_K decode bug in Rgguf's vendored gguflib; see that package's NEWS).
• Verified the full quantized-weight compute path over an external payload: a Q4_K tensor pointed zero-copy at a heap buffer standing in for an mmap'd GGUF payload, multiplied by dense F32 activations via ggml_mul_mat() on the CPU backend, routes each weight row through the runtime-SIMD-dispatched vec_dot (activations quantized to Q8_K on the fly, as at llama.cpp inference) and tracks the true product to q4_K accuracy (test_mul_mat_q4k.R).
• Initial release. Rggml is a carrier package: it vendors a CPU-only, architecture-generic build of the 'GGML' tensor library as a static library (inst/ggml/libggml.a), installs its headers, and exposes GGML's tensor-context/matrix-multiply compute path through R_RegisterCCallable C-callable entry points, declared for downstream use in inst/include/Rggml.h.
• Registered C-callables: context lifecycle (Rggml_context_create, Rggml_context_free, plus Rggml_used_mem/Rggml_tensor_overhead/ Rggml_graph_overhead for sizing), tensor creation with an optional zero-copy external data pointer (Rggml_new_tensor, Rggml_tensor_data, Rggml_tensor_set_data, Rggml_tensor_ne, Rggml_tensor_nb), the CPU backend (Rggml_backend_cpu_init, Rggml_backend_free, Rggml_backend_graph_compute), graph building (Rggml_new_graph, Rggml_build_forward_expand), matrix multiply (Rggml_mul_mat, and the canned single-op Rggml_compute_mul_mat that also copies the F32 result into a caller-provided float*/double* buffer), and type/size introspection (Rggml_type_size, Rggml_row_size, Rggml_blck_size, Rggml_nbytes, Rggml_nelements, Rggml_type_name).
• Added runtime SIMD dispatch for GGML's hot quantized vec-dot kernels, starting with q4_K x q8_K. configure compiles the kernel into ISA-specific variants (-mavx2 -mfma -O3 on x86 incl. Intel macOS; -O3 NEON on aarch64 incl. Apple Silicon) staged under tools/simd/, and a CPUID dispatcher (tools/simd/rggml_simd_dispatch.c, using a vendored copy of RsimdDispatch's cpu_features) selects the best at runtime, falling back to GGML's scalar reference. The ISA flags live in configure, not in R's recorded package flags, so R CMD check raises no non-portable-flags NOTE; a variant is only called after its ISA is confirmed at runtime (single .so, no dlopen, unlike GGML's own GGML_CPU_ALL_VARIANTS). The AVX2 q4_K dot measures ~6-7x faster than the scalar reference on x86.
• Enabled GGML's BLAS backend (Rggml_backend_blas_init, Rggml_backend_blas_set_n_threads): dense F32 mul_mat offloads to whatever BLAS the R build links against, since BLAS is universal in R. GGML's backend calls the C cblas_sgemm, which R does not guarantee, so Rggml bridges it with a small portable shim (inst/ggml/cblas.h + rggml_cblas.c) forwarding to Fortran sgemm_ via F77_NAME(), linking $(BLAS_LIBS) $(FLIBS). The BLAS backend is a drop-in backend for the existing Rggml_compute_mul_mat/Rggml_backend_graph_compute path.
• Added ggml_version(), returning the vendored GGML library's own runtime version string.
• Verified and documented the ggml_mul_mat() convention: loading two R matrices directly into GGML tensors with ne = dim(matrix) (the raw column-major bytes, no copy or transpose), ggml_mul_mat(ctx, A, B) produces a result tensor of dim (ncol(A), ncol(B)) equal to crossprod(A, B) (t(A) %*% B). Covered by the tinytest suite with both hand-checkable small cases and larger random matrices, through both the ggml-managed and the zero-copy (externally-owned buffer) tensor creation paths, exercised via the registered C-callables (not the internal implementation functions directly).
• CPU kernels are architecture-generic (no -march SIMD flags, no OpenMP, no hand-written x86 SIMD kernel files) for CRAN-facing build portability, plus the BLAS backend above for dense products. No Vulkan/CUDA/Metal GPU backends. Runtime-SIMD-dispatched quantized kernels (GGML_CPU_ALL_VARIANTS) and a Vulkan backend are planned; see README.md.