Sync master with upstream release b8966#500
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jan-service-account merged 13 commits intodevfrom Apr 29, 2026
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* spec : refactor params * cont : fix * cont : rename "sparam" to "sampling" * cont : add spec params category * cont : add info about removed arguments * cont : skip param length check for spec params * cont : adapt server tests
New operators: - GGML_OP_SET: implement via aclnnInplaceCopy on target region - GGML_OP_CUMSUM: implement via aclnnCumsum - GGML_OP_FILL: implement via aclnnInplaceFillScalar - GGML_OP_DIAG: implement via aclnnInplaceCopy on diagonal strides - GGML_OP_TRI (lower/lower_diag/upper_diag/upper): implement via aclnnTril(-1/0) and aclnnTriu(0/1) with appropriate diagonal offsets - GGML_OP_SOLVE_TRI: implement via aclnnTriangularSolve - GGML_UNARY_OP_SOFTPLUS: implement via aclnnSoftplus Optimizations: - GLU (SwiGLU/GeGLU/GeGLU_ERF/GeGLU_QUICK): fuse with aclnnSwiGlu / aclnnGeGluV3 when applicable; fallback conditions now checked inside each function rather than at the call site - CROSS_ENTROPY_LOSS: replace 5-kernel sequence (LogSoftmax→Mul→ ReduceSum×2→Muls) with single aclnnSoftmaxCrossEntropyWithLogits call - L2_NORM: fix in-place ClampMin on norm result (was clamping wrong tensor); add eps clamping before division to avoid divide-by-zero - PAD_REFLECT_1D: eliminate per-ne[3] loop; assert contiguity and call ReflectionPad1d once on the full 4-D view; remove redundant nb copies - GET_ROWS: replace IndexSelect with GatherV2 per batch slice; refactor helper into gather_batched lambda with batch loop inlined - SET_ROWS: replace IndexCopy with InplaceIndexCopy per batch slice; refactor helper into scatter_batched lambda with batch loop inlined - OUT_PROD: replace O(ne[3]*ne[2]*ne[1]) Ger+InplaceAdd loop with per-slice Matmul loop (src0 @ src1^T); handles strided-broadcast batch dims where ne02/ne03 may differ from ne2/ne3 - backend memset_tensor: implement via aclrtMemset (was NULL) Bug fixes: - COUNT_EQUAL: use non-inplace EqTensor into a same-type temporary buffer instead of InplaceEqTensor, avoiding corruption of src0 - ACL graph cache (USE_ACL_GRAPH): restore node_type and src_type[] fields in ggml_graph_node_properties; has_matching_properties() was missing type checks, causing F16 and BF16 tensors (same nb[0]=2) to incorrectly share cached graphs and produce wrong results (ERR≈679) - graph cache op_params matching: compare full GGML_MAX_OP_PARAMS bytes so that ops differing only in parameters are not incorrectly replayed from cache
* ggml : revert to -lm linking instead of find_library `find_library(MATH_LIBRARY m)` was introduced recently, but it breaks CUDA compilation with GGML_STATIC. I could not find any valid use case where we would prefer `find_library` over the standard `-lm` approach. This commit is also meant to start a discussion if there is a valid reason to keep `find_library(MATH_LIBRARY m)`, we should clarify what problem it was solving and find an alternative fix that does not break CUDA with GGML_STATIC. Signed-off-by: Adrien Gallouët <angt@huggingface.co> * ggml : use MATH_LIBRARY only if defined Signed-off-by: Adrien Gallouët <angt@huggingface.co> * ggml : fix initial broken condition Signed-off-by: Adrien Gallouët <angt@huggingface.co> * ggml : always respect MATH_LIBRARY when defined Signed-off-by: Adrien Gallouët <angt@huggingface.co> --------- Signed-off-by: Adrien Gallouët <angt@huggingface.co>
Signed-off-by: Adrien Gallouët <angt@huggingface.co>
…1918) * ggml: improve SPIR-V headers detection with __has_include while preserving original _WIN32 logic * Address review comments: fix fallback logic and add FreeBSD support * Remove spirv_cross fallback as per review * Remove redundant __has_include check
* wip: server_tools * feat: Integrate with `/tools` endpoint * feat: Builtin + MCP + JSON Schema Tools WIP * refactor * displayName -> display_name * snake_case everywhere * rm redundant field * feat: Improvements * chore: update webui build output * refactor: Updates after server updates * chore: update webui build output * change arg to --tools all * feat: UI improvements * chore: update webui build output * add readme mention * llama-gen-docs * chore: update webui build output * chore: update webui build output * chore: update webui build output * feat: Reorganize settings sections * feat: Separate dialogs for MCP Servers Settings and Import/Export * feat: WIP * feat: WIP * feat: WIP * feat: WIP * feat: WIP * feat: WIP * WIP on allozaur/20677-webui-server-tools * feat: UI improvements * chore: Update package lock * chore: Run `npm audit fix` * feat: UI WIP * feat: UI * refactor: Desktop Icon Strip DRY * feat: Cleaner rendering and transition for ChatScreen * feat: UI improvements * feat: UI improvement * feat: Remove MCP Server "enable" switch from Tools submenu * chore: Run `npm audit fix` * feat: WIP * feat: Logic improvements * refactor: Cleanup * refactor: DRY * test: Fix Chat Sidebar UI Tests * chore: Update package lock * refactor: Cleanup * feat: Chat Message Action Card with Continue and Permission flow implementations * feat: Add agentic steering messages, draft messages and improve chat UX * fix: Search results UI * test: Fix unit test * feat: UI/UX improvements * refactor: Simplify `useToolsPanel` access in components * feat: Implement Processing Info Context API * feat: Implement 'Go back to chat' functionality for settings * feat: Enhance MCP Server management in Chat Form Attachments * style: Minor UI and branding adjustments * chore: Update webui static build output * chore: Formatting, linting & type checks * feat: Draft messages logic * feat: UI improvements * feat: Steering Messages improvements * refactor: Cleanup * refactor: Cleanup * feat: Improve UI * refactor: Settings navigation hook * refactor: DRY code * refactor: DRY ChatMessageUser UI components * refactor: Desktop Icon Strip DRY * refactor: Tools & permissions * fix: Navigation condition * refactor: Cleanup * refactor: Cleanup * refactor: Cleanup * fix: preserve reasoning_content in agentic flow --------- Co-authored-by: Xuan Son Nguyen <son@huggingface.co>
…ogic (ggml-org#22456) * Refactor buffer aliasing to be part of shader lib decisions * cleanup * formatting
Some SPIR-V compilers (notably mesa) don't handle the current vulkan Q4_K/Q5_K scale load pattern in mul_mat particularly well. While reading three `u8`s from the 12-byte scale array should (at least on some hardware) result in loading the full 12 bytes in a single LOAD followed by whatever extraction is needed, at least the ANV Intel driver really can't practically perform this optimization. `mesa`'s unsigned upper bound logic doesn't handle tracking bounds through ternary, resulting in the `(is < 4) ? ... : is - 4` having an infinite upper bound (as it cannot prove `is - 4` doesn't underflow). While this could still be rectified if mesa looked at the array bounds, it currently doesn't and `glslc` currently emits SPIR-V that doesn't allow for this optimization anyway (though maybe it will at some point, see KhronosGroup/glslang#4206). In mul_mat_vecq we took a different approach to loading the same fields. We read the first two bytes we needed from `scale` then took a branch before deciding whether we needed to read a third byte. In mesa this did, indeed, lead to a top-level branch with conditional loads. As such these loads ended up not being coalesced either (at least in the ANV driver) resulting in additional instructions in our hot loop. Instead, here, we go ahead and force loading the full 12 bytes and extract the bits we need from the packed-u32s instead. In mul_mat there's a few less ternaries and only one extra shift, so even on drivers that did optimize the previous loads properly the only material change should be pulling a few extra bytes into registers (which on most hardware won't cost anything anyway, though ironically on Intel it theoretically could). In mul_mat_vecq this requires a bit of extra math and may read bytes from the u32 that weren't needed, but it seems likely avoiding the branch is a win on most platforms. On Intel Xe2/mesa 26.0.4 with the optimizations from https://gitlab.freedesktop.org/mesa/mesa/-/work_items/15162, for shader matmul_id_subgroup_q4_k_f32_f16acc_aligned_l: * Instruction Count: 2753 -> 2688 * SEND Count: 269 -> 261 * Cycle Count: 273976 -> 266138 * Max live registers: 248 -> 246 * Non SSA regs after NIR: 381 -> 382 for shader matmul_id_subgroup_q5_k_f32_f16acc_aligned_l: * Instruction Count: 2767 -> 2702 * SEND Count: 271 -> 263 * Cycle Count: 274140 -> 268144 * Max live registers: 248 -> 246 * Non SSA regs after NIR: 381 -> 382 for shader mul_mat_vec_id_q4_k_q8_1_f32: * Instruction Count: 1930 -> 1646 * SEND Count: 116 -> 71 * Cycle Count: 1348306 -> 843350 * Max live registers: 78 -> 84 * Non SSA regs after NIR: 300 -> 135 for shader mul_mat_vec_id_q5_k_q8_1_f32: * Instruction Count: 2207 -> 1922 * SEND Count: 131 -> 86 * Cycle Count: 1392012 -> 1037836 * Max live registers: 90 -> 90 * Non SSA regs after NIR: 300 -> 135 for shader mul_mat_vec_q4_k_q8_1_f32: * Instruction Count: 2029 -> 1749 * SEND Count: 111 -> 66 * Cycle Count: 1347278 -> 840118 * Max live registers: 74 -> 80 * Non SSA regs after NIR: 299 -> 134 for shader mul_mat_vec_q5_k_q8_1_f32: * Instruction Count: 2307 -> 2022 * SEND Count: 126 -> 81 * Cycle Count: 1379820 -> 954042 * Max live registers: 86 -> 86 * Non SSA regs after NIR: 299 -> 134 On one Arc Pro B60, unsloth/Qwen3.5-35B-A3B-GGUF:UD-Q4_K_XL: * pp512: 907.34 ± 9.28 -> 941.94 ± 10.53 (+4%) * pp2048: 897.95 ± 1.82 -> 931.55 ± 1.79 (+4%) * tg128: 49.49 ± 0.02 -> 49.86 ± 0.05 (+ <1%) On one Arc Pro B60, unsloth/Qwen3.5-27B-GGUF:Q4_K_S: * pp512: 324.13 ± 10.52 -> 354.33 ± 6.81 (+9%) * pp2048: 329.80 ± 0.25 -> 357.10 ± 0.06 (+8%) * tg128: 17.11 ± 0.01 -> 18.11 ± 0.01 (+6%) On four Arc Pro B60s, unsloth/Qwen3.5-122B-A10B-GGUF:Q5_K_S with -sm layer (note that -sm tensor improvements will naturally be less): * pp512: 264.55 ± 2.81 -> 280.45 ± 3.94 (+6%) * pp2048: 319.32 ± 2.72 -> 335.70 ± 3.48 (+5%) * tg128: 26.39 ± 0.01 -> 26.67 ± 0.01 (+1%)
…22323) DONE state absorbs all tokens including a new start tag, causing any think blocks after the first to run unbudgeted. Observed on unsloth/Qwen3.6-27B-GGUF which interleaves multiple <think> blocks per response. Fixed by advancing start_matcher in DONE branch and re-arming to COUNTING with a fresh budget on match. Adds regression test (test-reasoning-budget: test 6).
This commit adds support for NVIDIA Nemotron Nano 3 Omni model enabling this model to be converted to GGUF.
ggml-org#22286) * ggml-cuda: add flash-attn support for DKQ=320/DV=256 with ncols2=32 (GQA=32) Adds MMA-f16 and tile kernel configs, dispatch logic, template instances, and tile .cu file for Mistral Small 4 (head sizes 320/256), restricting to ncols2=32 to support GQA ratio 32 only. * Adding check to return BEST_FATTN_KERNEL_NONE in case GQA!=32 * Apply suggestions from code review Address review comments Co-authored-by: Johannes Gäßler <johannesg@5d6.de> * Address review comments and making kernel config default to DQK=512, DV=512 instead of DQK=256,DV=256 * Fixed bug with sinks=1, with ncols=32, there are two warp-groups created but sinks index is same(0,...,15) for both the groups hence with sinks=1, output is not matching with CPU output. Added sink_base which will be base index for each warp_group (threadIdx.y / np) * Apply suggestions from code review Co-authored-by: Johannes Gäßler <johannesg@5d6.de> * Update ggml/src/ggml-cuda/template-instances/generate_cu_files.py Co-authored-by: Johannes Gäßler <johannesg@5d6.de> --------- Co-authored-by: Johannes Gäßler <johannesg@5d6.de>
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Updates dev branch with latest release (b8966) from ggml-org/llama.cpp