Peter Sideris [Thu, 30 Apr 2026 05:18:25 +0000 (08:18 +0300)]
spec : fix vocab compat checks in spec example (#22426)
* port #22358 PR to examples/speculative/speculative.cpp
* use vocab_[tgt,dft] instead of ctx_[tgt,dft] when logging on draft
model / target model vocabulary mismatch
ggml-cpu : disable tiled matmul on AIX to fix page boundary segfault (#22293)
* ggml-cpu : disable tiled matmul on AIX to fix page boundary segfault
vec_xst operations in the tiled path crash on AIX when writing
near 4KB page boundaries due to strict memory protection. Fall
back to mnpack implementation on AIX for stable execution.
ggml-cpu: cmake: append xsmtvdotii march for SpacemiT IME (#22317)
* ggml-cpu: cmake: append xsmtvdotii march for SpacemiT IME
When GGML_CPU_RISCV64_SPACEMIT=ON is set, ime1_kernels.cpp contains
inline asm for the vmadot family which requires the xsmtvdotii custom
extension.(problem can see in some blogs and make sure in K3 platform)
The current CMakeLists does not include xsmtvdotii, so any toolchain
that honours the explicit -march (tested with SpacemiT GCC 15.2) fails
at the assembler stage:
Append _xsmtvdotii to MARCH_STR when GGML_CPU_RISCV64_SPACEMIT is
enabled so the IME path can actually build with a capable toolchain.
No effect on builds that leave GGML_CPU_RISCV64_SPACEMIT off.
toolchain from https://www.spacemit.com/community/resources-download/Tools
ggml-cuda: add flash-attn support for DKQ=320/DV=256 with ncols2=32 (… (#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 <redacted>
* 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 <redacted>
* Update ggml/src/ggml-cuda/template-instances/generate_cu_files.py
Co-authored-by: Johannes Gäßler <redacted>
---------
common : re-arm reasoning budget after DONE on new <think> (#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).
Matt Corallo [Tue, 28 Apr 2026 15:31:04 +0000 (15:31 +0000)]
vulkan: Coalesce Q4_K/Q5_K scale loads (#21751)
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
https://github.com/KhronosGroup/glslang/issues/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
ggml : revert to -lm linking instead of find_library (#22355)
* 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 <redacted>
* ggml : use MATH_LIBRARY only if defined
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
server: (router) Forward form-data to model server (Fixes #22044) (#22118)
* This commit enables the router to forward form-data to model server.
Fixes #22044 (enabling to use the /v1/audio/transcriptions in router mode)
* * Applied the suggestion from Copilots first comment: using the non-throwing json::parse overload.
* Addressed Copilots third comment by extending the files representation to also include filename and content-type
* Addressed Copilots fourth comment by making the RNG thread_local
* Changed variable body from std::string to std::ostringstream in build_multipart_body
as suggested by ngxson in https://github.com/ggml-org/llama.cpp/pull/22118#discussion_r3127099053
* Added sanitize_field lambda in build_multipart_body for key, filename and content_type
as suggested by ngxson in https://github.com/ggml-org/llama.cpp/pull/22118#discussion_r3127104647
* explicitly checking if value/item is string before calling value/item.get<std::string>()
as requested by ngxson in https://github.com/ggml-org/llama.cpp/pull/22118#discussion_r3127111279
* Added double quote to the sanitize lambda and throw on json parse failure
Max Krasnyansky [Mon, 27 Apr 2026 05:06:39 +0000 (22:06 -0700)]
common: fix missing exports in llama-common (#22340)
* common: refactor common/debug to move abort_on_nan into base_callback_data
Passing bool abort_on_nan as template parameter for common_debug_cb_eval is unnecessary and creates an issue with LTO.
It should just be a member of the base_callback_data instead.
* cont : cleanup
* common : use pimpl in debug.h to reduce header dependencies
Move common_debug_cb_user_data's data members (std::regex,
std::vector<uint8_t>) into a private impl struct in debug.cpp.
This removes the includes of common.h and <regex> from debug.h,
reducing transitive dependencies for any translation unit that
includes the header.
Fix recurrent state serialization for partial reads and writes (#22362)
The previous code worked only for full tensor reads and writes and was hitting `GGML_ASSERT(size == ggml_nbytes(tensor)); ` assert when tested with llama-server.
metal : optimize Metal Tensor API usage for GGML_OP_MUL_MAT (#20962)
* Optimize Metal Tensor API usage for matmul2d
Separates the Metal Tensor API (matmul2d) path in kernel_mul_mm into its own standalone kernel, gated by GGML_METAL_HAS_TENSOR.
The legacy simdgroup_matrix kernel is preserved under #else.
Previously both paths were interleaved via #ifdef blocks within a single kernel, forcing the tensor path to share the legacy kernel's data layout and threadgroup memory scheme. Splitting the kernel enabled memory and dispatch optimizations that weren't possible when the two paths shared code structure.
Change the default `ftype` in `llama_model_quantize_params` from
`LLAMA_FTYPE_MOSTLY_Q5_1` to `LLAMA_FTYPE_MOSTLY_Q8_0`.
In case some external program naively uses the default quantization
params, we should probably default to a known-good type like Q8_0 rather
than Q5_1, which is rather old.
cli: Remove redundant local sampling variables (#20429) (#22264)
This change implements the third requested change in issue 20429.
Because defaults.sampling contains the reasoning budget token count and
the reasoning budget message, it's not necessary to assign them to
struct variables.
Song Li [Thu, 23 Apr 2026 16:39:07 +0000 (12:39 -0400)]
server: fix heap-buffer-overflow from negative n_discard (CVE-2026-21869) (#22267)
* server: clamp n_discard to non-negative at JSON parse boundary (CVE-2026-21869)
A negative n_discard from client JSON causes heap-buffer-overflow in
update_slots() context-shift loop (CWE-787, CVSS 8.8). Clamp to 0 at
ingress; n_discard=0 already triggers auto-discard (n_left/2).
When testing claude code against llama.cpp, I noticed that only
n_past 18577 was used even when context was 60k or more. The log
in llama-server says:
```
slot update_slots: id 3 | task 10342 | old: ... ; cch= | defa0;You are
slot update_slots: id 3 | task 10342 | new: ... ; cch= | 1c8b4;
```
I observed that the cch value changed every time. Reading about that,
the x-anthropic-billing-header system message seems to be specially
handled inside of the anthropic api. I could remove it, but there
is a meaningful string sometimes included at the end. So instead,
I just replace the changing cch checksum with fffff.
I'm treating this as an anthropic message body API detail - I think this
is the right way to do this, but by all means please correct me!
It's always 5 hexadecimal characters, but I've written the replacement
defensively in case they change the protocol.
Daniel Bevenius [Thu, 23 Apr 2026 13:07:38 +0000 (15:07 +0200)]
model-conversion : fix mmproj output file name [no ci] (#22274)
* model-conversion : fix mmproj output file name [no ci]
This commit updates the convert-model.sh script to properly handle
mmproj output files.
The motivation for this that currently the same name as the original
model is used as the mmproj file, which causes the original model to
be overwritten and no mmproj-<model_name>.gguf to be created.
Create an MMVQ kernel so ggml_sycl_mul_mat_id can consolidate
n_experts_used matmuls in a single kernel launch. The kernel
also reads expert IDs directly, removing a per-call host sync.
This is similar to the CUDA backend's ggml_cuda_mul_mat_vec_q*
paths.
All types supported in the current MMVQ are supported here as well:
Q2_K, Q3_K, Q4_K, Q5_K, Q6_K, Q4_0, Q4_1, Q5_0, Q5_1, Q8_0
It will fall back to the existing per-expert path when src0 has been rewritten
by opt_for_reorder(), and for any shape the fused path doesn't handle.
test-backend-ops passes for supported type/shape combos.
overview / pkg / ggml / sources / llama.cpp / log