- adapt ggml-zendnn.cpp to the new lowoha::matmul interface
- update the ZenDNN git tag in CMake to the latest release (ZenDNN‑2026‑WW08)
- add static lib support in CMake
* ggml-virtgpu-backend: validate the consistency of the received objects
This patch adds consistency checks in the
ggml-virtgpu-backend (running on the host side) to ensure that the
data received from the guest is consistent (valid pointers, valid
sizes and offsets).
* ggml-virtgpu-backend: add fallback/skips for optional ggml backend methods
```
1. bck->iface.synchronize(bck)
2. buft->iface.get_alloc_size(buft, op)
3. buft->iface.get_max_size(buft)
```
these three methods are optional in the GGML interface. `get_max_size`
was already properly defaulted, but `backend sychronize` and `butf
get_max_size` would have segfaulted the backend if not implemented.
* ggml-virtgpu-backend: fix log format missing argument
* ggml-virtgpu-backend: improve the abort message
* ggml-virtgpu-backend: more safety checks
* ggml-virtgpu-backend: new error code
* ggml-virtgpu-backend: initialize all the error codes
* ggml-virtgpu: add a missing comment generated by the code generator
* ggml-virtgpu: add the '[virtgpu]' prefix to the device/buffer names
* ggml-virtgpu: apir_device_buffer_from_ptr: improve the error message
* ggml-virtgpu: shared: make it match the latest api_remoting.h of Virglrenderer APIR
(still unmerged)
* ggml-virtgpu: update the code generator to have dispatch_command_name in a host/guest shared file
* ggml-virtgpu: REMOTE_CALL: fail if the backend returns an error
* docs/backend/VirtGPU.md: indicate that the RAM+VRAM size is limed to 64 GB with libkrun
* ggml-virtgpu: turn off clang-format header ordering for some of the files
Compilation breaks when ordered alphabetically.
* ggml-virtgpu: clang-format
* ggml-virtgpu/backend/shared/api_remoting: better comments for the APIR return codes
* vulkan: allow using fp16 in scalar flash attention shader
* split rows inside of subgroups for faster synchronization
* use row_split when Br >= 4, change reductions to use shared memory if row_split == 1
* use f32 scalar FA if f16 is not supported by device
* fix amd workgroup size issue
* optimize masksh use
* add medium rows FA shader Br size
* fixes
* add padding to mask shmem buffer
* cache q values into registers for KQ
* fuse lf accumulation, pf and v accumulation into a loop
* stage K loads through shmem
* stage V loads through shmem
* only stage through shmem on Nvidia
* default to Bc 32
* also stage V through shmem when this is done for K
* dynamic subgroups for intel
* use vectorized stores
* use float_type for dequantize4 functions
* use smaller scalar rows size for smaller rows count
* relax flash attention split_k condition to allow non-gqa use
* use minimal subgroup size on Intel
* fix shmem support function
* fix rebase issues
* fixes
* Bc 4 for scalar FA is not a valid configuration
* Use wave32 on AMD RDNA for scalar FA
* add Intel shader core count lookup-table
* fix regressions
* device tuning
* tmpsh size fix
* fix editorconfig
* refactor fa tuning logic into a single place
* fix gqa opt logic
* fix block_rows with small n_rows
* amd tuning
* fix hsk=72/80 issue
* tuning
* allow condition skipping for column check
* use float16 for Of if available
* address feedback
* fix bad RDNA performance on head size <= 128 by limiting occupancy
* allow printing pipeline stats
* cleanup and fixes
* limit occupancy for GCN for small batch FA with large HSK
* disable f16 FA for GCN AMD GPUs on the proprietary driver
* hexagon: refactor set/get/sum-rows ops to use local context
* hexagon: refactor ROPE and Softmax Ops to use local context
Improves performance a bit by precomputing things and saving in the context.
* hexagon: refactor activation ops to use local context struct
* hexagon: refactor unary ops to use local context struct and DMA/VTCM
* hexagon: use aligned hvx_scale function
* hexagon: remove unused fields from op_context
* hexagon: rewrite ROPE to use DMA and VTCM scratchpad
* hex-rope: keep N rows in scratchpad (instead of just two)
* hex-rope: introduce rowidx cache
* hex-rope: remove unused fields
* hex-rope: rewrite dma prefetch logic to allow for multi-row fetch/compute
also removes the need for fastdiv.
* hex-rope: minor formatting
* hex-rope: use indices and unroll the loops
* hex-rope: more updates to cleanup rope-block handling
* hexagon: cleanup supported type/dims checks
* hexagon: all reduce funcs replicated across lanes
There is no need to explicitly replicate the first value.
* snapdragon: update adb and windows scripts to use ubatch-size 256
Updated Ops support handles larger ubatches.
* Improve CUDA graph capture
Currently, CUDA graphs are eagerly enabled on the first call to ggml_backend_cuda_graph_compute. If the graph properties keep changing (4+ consecutive updates), the graph is permanently disabled. This is suboptimal because:
- The first call always incurs CUDA graph capture overhead even if the graph is unstable
- Once permanently disabled, CUDA graphs never re-enable even after the graph stabilizes (e.g., switching from prompt processing to decode)
The new approach delays CUDA graph activation until warmup completes: the same cgraph must be called at least twice with matching properties before CUDA graph capture begins. This avoids wasted capture overhead on volatile graphs and allows graphs to become eligible once they stabilize.
This also fixes issues such as https://github.com/ggml-org/llama.cpp/discussions/19708
* Update ggml/src/ggml-cuda/ggml-cuda.cu
Co-authored-by: Johannes Gäßler <johannesg@5d6.de>
* Remove EM dashes
* Update ggml/src/ggml-cuda/ggml-cuda.cu
Co-authored-by: Aman Gupta <amangupta052@gmail.com>
---------
Co-authored-by: Johannes Gäßler <johannesg@5d6.de>
Co-authored-by: Aman Gupta <amangupta052@gmail.com>
* Basic JIT compilation for mul_mat, get_rows, and scale (#17)
* scale jit working
* preliminary working jit for getrows and mulmat, needs refining
* simplified mul_mat preprocessing switch statement
* get_rows fixes, mul_mat refinement
* formatted + last edits
* removed some extraneous prints
* fixed get_rows, fixed workgroup dispatch in mul_mat. no gibberish
* small fix
* some changes, working
* get_rows and mul_mat jit fixed and working
* Update formatting
* formatting
* Add header
---------
Co-authored-by: Neha Abbas <nehaabbas@ReeseLevines-MacBook-Pro.local>
Co-authored-by: Reese Levine <reeselevine1@gmail.com>
* Start work on all-encompassing shader library
* refactor argmax, set_rows
* Refactor all but flashattention, mat mul
* flashattention and matrix multiplication moved to new format
* clean up preprocessing
* Formatting
* remove duplicate constants
* Split large shaders into multiple static strings
---------
Co-authored-by: neha-ha <137219201+neha-ha@users.noreply.github.com>
* vulkan: split mul_mat into multiple dispatches to avoid overflow
The batch dimensions can be greater than the max workgroup count limit,
in which case we need to split into multiple dispatches and pass the base
index through a push constant.
Fall back for the less common p021 and nc variants.
* address feedback
* opencl: optimize mean and sum_row kernels
* opencl: add comment for max subgroups
* opencl: format
---------
Co-authored-by: Li He <lih@qti.qualcomm.com>
When LTO enabled in build environments it forces all builds to have LTO
in place. But feature detection logic is fragile, and causing Illegal
instruction errors with lto. This disables LTO for the feature
detection code to prevent cross-module optimization from inlining
architecture-specific instructions into the score function. Without this,
LTO can cause SIGILL when loading backends on older CPUs (e.g., loading
power10 backend on power9 crashes before feature check runs).
Avoids issues with ROCm 6.4.4.
Closes: https://github.com/ggml-org/llama.cpp/issues/19580
Fixes: 6845f7f87 ("Add a workaround for compilation with ROCWMMA_FATTN and gfx9 (#19461)")
Signed-off-by: Mario Limonciello (AMD) <superm1@kernel.org>
* Updated repack.cpp
* Updated repack.cpp
* Updated repack.cpp
* Added if condition to support only vector length 256.
* Changed the format removed comments and duplicate variable
* If SVE 256 not present then was using generic function to compute, hence slowing the performance.
So added code if SVE 256 is not present then use NEON code.
* Code format change suggestion
---------
Co-authored-by: Vithule, Prashant <Prashant.Vithule@fujitsu.com>
* cuda: optimize iq2xxs/iq2xs/iq3xxs dequantization
- load all 8 int8 for a grid position in one load
- calculate signs via popcnt instead of fetching from ksigns table
- broadcast signs to drop individual shift/mask
* cuda: iq2xxs: simplify sum scaling
express `(sum * scale + sum / 2) / 4` as `(sum * (scale * 2 + 1)) / 8`
express `((aux32 >> 28) * 2 + 1)` as `(aux32 >> 27 | 1)`
saves 3 registers for mul_mat_vec_q (152 -> 149) according to nsight
AFAICT no overflow can occur here as iq2xxs values are far too small
* uint -> uint32_t
error: identifier "uint" is undefined
This commit addresses a build issue with the KleidiAI backend when
building multiple cpu backends. Commmit
3a00c98584e42a20675b6569d81beadb282b0952 ("cmake : fix KleidiAI install
target failure with EXCLUDE_FROM_ALL") introduced a change where
FetchContent_Populate is called instead of FetchContent_MakeAvailable,
where the latter does handle this case (it is idempotent but
FetchContent_Populate is not).
I missed this during my review and I should not have commited without
verifying the CI failure, sorry about that.
* ggml-cpu: FA add GEMM microkernel
* add guard for sizeless vector types
* fix case where DV % GGML_F32_EPR !=0
* move memset out of the loop
* move another memset out of the loop
* use RM=4 for arm
* simd_gemm: convert everything to int
* convert everything to size_t to avoid warnings
* fixup
* add pragma for ignoring aggressive loop optimizations
* cmake: fix KleidiAI install target failure with EXCLUDE_FROM_ALL
Fix for the bug #19501 by adding EXCLUDE_FROM_ALL to FetchContent_Declare. This properly excludes KleidiAI from both build and install targets, preventing install failures when GGML_CPU_KLEIDIAI=ON is used.
The KleidiAI source files are still compiled into libggml-cpu.so, preserving all functionality.
* addressed code review comments
last_graph is only available without OpenMP, but
ggml_graph_compute_thread() is called in both cases.
Signed-off-by: Adrien Gallouët <angt@huggingface.co>
