Model Releases
b11569
metal : fix mul_mat_id with duplicate ids (#30373) metal : fix mul_mat_id limits and kernel name metal : fix mul_mat_id with duplicate ids kernel_mul_mm_id_map0 assumed at most one match per token and
metal : fix mul_mat_id with duplicate ids (#30373) metal : fix mul_mat_id limits and kernel name metal : fix mul_mat_id with duplicate ids kernel_mul_mm_id_map0 assumed at most one match per token and a fixed capacity of ne21 entries per expert, which produced wrong or dropped output rows and could overflow into the slice of the next expert when the ids contain duplicates. build a compact per-expert list of the (token, slot) entries instead: pass 1 counts the entries of every expert, thread 0 computes the offsets of the expert slices (tpe now holds [base, count]) pass 2 writes the entries with one unconditional store per token (conditional stores of divergent lanes are very slow), the dummy value of the tokens without a match lands on a padding slot of the slice that is never read - duplicates are handled by a rare branch kernel_mul_mm_id reads the base/count of its expert and loops over the entry tiles that do not fit into the grid The ids buffer is sized as ne20*ne21 + ne02 (entries + padding). 883/883 test-backend-ops MUL_MAT_ID tests pass, llama-bench is within run-to-run variance of the previous behavior. Assisted-by: pi:llama.cpp/MiMo-V2.6-Flash-MOPD metal : make mul_mat_id map0 ne02 a function constant use FOR_UNROLL for the id loops of kernel_mul_mm_id_map0 and turn ne02 into the function constant FC_mul_mm_id_map0_ne02 (FC_MUL_MM + 7), so that the threadgroup offsets of cnt/bas/sids and the prefix-sum bound fold at compile time. the pipeline name now encodes ne02 (same convention as the ssm_conv nc/silu constants) and the map0 pipeline is compiled with the constant value set. ne02 is no longer part of ggml_metal_kargs_mul_mm_id_map0 - the dispatch grid still comes from the tensor ne[2] on the host. no measurable perf change: 883/883 test-backend-ops MUL_MAT_ID tests pass, op-level and llama-bench results are within run variance. Assisted-by: pi:llama.cpp/MiMo-V2.6-Flash-MOPD cont : clean-up + TODO metal : dispatch mul_mat_id entry tiles from the prepass follow the approach of the vulkan implementation (#29998): instead of launching a grid over experts x ne21 entry tiles and letting most of the threadgroups early exit, kernel_mul_mm_id_map0 now also emits a row map - the total tile count plus one (expert, first entry, valid rows) descriptor per 32-entry tile that has work. kernel_mul_mm_id is launched with a static upper bound on the number of tiles (full tiles over all entries plus at most one partial tile per expert), reads its descriptor and processes a single tile - the tail early exits. This removes the round loop that was needed to cover duplicate ids past the grid (register pressure: th_max 768 -> back to the plain mul_mm level) and duplicates are now handled implicitly, since they only produce more descriptors within the bound. The grid shrinks by ~8-16x on typical shapes (e.g. 128 experts, 8 used, 512 tokens: 128 tiles instead of 2048 entry tiles, of which 94% only launched to exit) and ne21 is no longer needed by the mm kernel kargs. 883/883 test-backend-ops MUL_MAT_ID tests pass. llama-bench (Qwen3.6-35B-A3B Q8_0) is now faster than master on all pp sizes (pp2048/4096/8192 +2%, pp512 +0.5%), tg32 unchanged. Assisted-by: pi:llama.cpp/MiMo-V2.6-Flash-MOPD cont : add smem assert Website: https://llama.app Attestations: https://github.com/ggml-org/llama.cpp/attestations/55057952 macOS/iOS: macOS Apple Silicon (arm64) macOS Apple Silicon (arm64, KleidiAI enabled) DISABLED macOS Intel (x64) iOS XCFramework Linux: Ubuntu x64 (CPU) Ubuntu arm64 (CPU) Ubuntu s390x (CPU) Ubuntu x64 (Vulkan) Ubuntu arm64 (Vulkan) Ubuntu x64 (CUDA 12) - CUDA 12.8 libraries Ubuntu x64 (CUDA 13) - CUDA 13.4 libraries Ubuntu arm64 (CUDA 13) - CUDA 13.4 libraries Ubuntu x64 (ROCm 10.0) Ubuntu x64 (OpenVINO) Ubuntu x64 (SYCL FP32) Ubuntu x64 (SYCL FP16) Linux arm64 (Snapdragon: CPU, Adreno GPU, Hexagon NPU) - setup guide Android: Android arm64 (CPU) Android arm64 (Snapdragon: CPU, Adreno GPU, Hexagon NPU) - setup guide Windows: Windows x64 (CPU) Windows arm64 (CPU) Windows arm64 (OpenCL Adreno) Windows x64 (CUDA 12) - CUDA 12.4 DLLs Windows x64 (CUDA 13) - CUDA 13.4 DLLs Windows arm64 (CUDA 13) - CUDA 13.4 DLLs Windows x64 (Vulkan) Windows arm64 (Vulkan) Windows x64 (OpenVINO) Windows x64 (SYCL) Windows x64 (ROCm 10.0) openEuler: DISABLED openEuler x86 (310p) openEuler x86 (910b, ACL Graph) openEuler aarch64 (310p) openEuler aarch64 (910b, ACL Graph) UI: UI
Related
Source: llama.cpp Releases | 2026-10-12