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qwen4exp : halve the indexer score memory (#29825) qwen4exp : halve the indexer score memory The indexer scored all heads in one product and rectified a copy of it, so two [n_pool, n_idx_h, n_tokens] f32 tensors were live at once, the largest buffers of the graph at long context. Each head now gets its own product, rectified and summed in place into one [n_pool, n_tokens] score. qwen4exp: let the allocator reuse the indexer score buffers Address review from CISC: use plain ggml_add and ggml_relu in the indexer head loop. The graph allocator already runs them in place when their source has no other consumer, so the _inplace variants are not needed. The compute buffer and the speed are unchanged. cuda: support 4 heads in the lightning indexer Dispatch 4 heads to the vector kernel, too few for a wmma tile, and accept them in supports_op. test-backend-ops covers 4 heads. metal: take the lightning indexer head count as a function constant The kernel reads the head count from a function constant and zero fills the last head tile, so any head count runs and 64 heads is unchanged. qwen4exp: compute the indexer score with the lightning indexer Address review from am17an: the unweighted sum of the rectified head scores scaled by 1/sqrt(head_dim) is the lightning indexer with every head weight set to that scale, so the indexer calls ggml_lightning_indexer on the pooled keys with an f16 pool mask. The keys are read once for all heads and no per head score is materialized. vulkan: tile the lightning indexer over keys and tokens A workgroup scores 64 keys against 8 tokens: the keys are staged once in shared memory, the queries one head at a time, and each invocation owns one key for two tokens, so no dot product needs a cross invocation reduction. The subgroup variant and the flat dispatch are gone, the grid is keys x tokens x streams. vectorize vulkan loads and use fp16 dot product Co-authored-by: Ruben Ortlam rortlam@redhat.com Website: https://llama.app Attestations: https://github.com/ggml-org/llama.cpp/attestations/52404802 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 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

Source: llama.cpp Releases | 2026-10-03

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