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Skip the nb[3] check when ne[3] == 1, the shader never reads it for a single stream. Cache views carry the full-buffer stride there, so the old check reduced to n_kv == kv_size and the path only engaged with the cache full.
* CUDA: Allow CUDA optimization per split for multi-GPU. Previous guard caused multi-GPU to skip the graph optimization. The graph is already split per device and the optimization doesnt run over the whole model but once per split, and thus should be allowed. However, the CUDA event ggml_cuda_concurrent_event belongs to whichever GPU was "current" when created. If the pass ran while GPU 0 was current, it would stick and during event creation for the second GPU it would land on GPU 0. The fix: set the device explicitly ggml_cuda_set_device(cuda_ctx->device); Default behaviour remains unchanged, only active for GGML_CUDA_GRAPH_OPT=1. Explicit device setting pattern re-used from ggml_backend_cuda_graph_compute. * Update ggml/src/ggml-cuda/ggml-cuda.cu Co-authored-by: Aman Gupta <amangupta052@gmail.com> --------- Co-authored-by: tannerbruhn <tannerbruhn@users.noreply.github.com> Co-authored-by: Aman Gupta <amangupta052@gmail.com>
* mtmd : mark context as const in more methods Mark `mtmd_context` as `const` in: - mtmd_bitmap_init_lazy - mtmd_tokenize - mtmd_tokenize_from_parts - mtmd_helper_support_video - mtmd_helper_bitmap_init_from_file - mtmd_helper_bitmap_init_from_buf - mtmd_helper_video_init - mtmd_helper_video_init_from_buf - mtmd_helper_model_can_chat The tokenization functions in particular are useful to have marked `const`, as that allows more easily telling the compiler that we can safely tokenize from multiple threads (`mtmd_tokenize` is already documented as thread-safe, this just reifies that in the signature). * mtmd : mark tokenization input pointer as const Mark the `bitmaps` and `parts` pointers in `mtmd_tokenize` and `mtmd_tokenize_from_parts` as `const`. This allows more easily calling these with immutable arrays / vectors. * mtmd : mark llama_context as const in mtmd_helper_model_can_chat
* metal : fix glu dispatch with ne00 = 1 * tests : disable ill-defined tests
* metal : support n_kv_max sparse mask hint in flash attention vec kernel
- add kernel_flash_attn_ext_vec_idx: compacts finite mask entries into
a per-row index list (Hillis-Steele scan, one threadgroup per row)
- extend vec FA kernel with optional sparse index gathering (FC slot 5)
- add host-side gate: sparse path when n_kv_max > 0, mask present,
supported head sizes / KV types, n_kv_max <= 4096
- new buffer region extra_idx for the index list
- pipeline getter extended with has_sparse param
- add test cases: head sizes, quant types, nb>1, nr23 variants,
sinks, ALiBi, softcap, permute, v_view_of_k, no-mask fallback
Note: multi-row (nb*nr23[1] > 1) cases still failing - rid mapping
in the store phase needs revisiting for the sparse path.
Assisted-by: pi:llama.cpp/Qwen3.8-27B
* metal : fix sparse flash attention row addressing
- kernel_flash_attn_ext_vec_idx: mask param is half* but nb31 is a byte
stride, so the per-row mask offset was scaled by 2x; cast to char*
before applying the byte strides
- kernel_flash_attn_ext_vec: sparse pidx param is char* so the per-row
element offset was under-scaled by sizeof(int); scale it by sizeof(int)
to get the correct byte offset
- fixes the multi-row (nb*nr23[1] > 1) sparse flash attention failures
Assisted-by: pi:llama.cpp/DeepSeek-v4-0731
* cont : use sparse vec FA for prefill
* metal : single-pass flash attention sparse index compaction
The idx kernel previously read the mask row twice: once to count the finite
entries (for the prefix scan) and again to recover their positions. Since the
kernel is memory-bound, this doubled the mask traffic.
Keep the finite positions in a per-thread register array during the count
pass and write them out directly, avoiding the second mask read. A dense
mask with more than NLOCAL finite entries in a slice falls back to re-reading
the mask to write the remaining positions.
Assisted-by: pi:llama.cpp/DeepSeek-v4-0731
* tests : add perf cases for sparse flash attention prefill
Measure the sparse vec FA kernel across KV sizes, n_kv_max hints and batch
sizes. Run with:
./build/bin/test-backend-ops -b MTL0 -o FLASH_ATTN_EXT -p "n_kv_max=[1-9]" perf
Assisted-by: pi:llama.cpp/DeepSeek-v4-0731
* qwen4 : enable sparse attention
* cont : adjust nsg
* cont : sync test-backend-ops
* cont : disable Qwen4 for now
* cont : clean-up + tests
* server : use pytest-xdist for server tests This commit adds pytest-xdist to the server tests. This is pytest plugin that distributes test execution across multiple CPU cores. Assisted-by: pi:llama.cpp/qwen3.8-27B Refs: ggml-org#26734 (comment) * remove server_base_port and BASE_PORT * use worksteal and pytest builting tmp_path
* snapdragon: update CI script to use new snapdragon/run.py * snapdragon: update build.py to not set +x on /lib
…ns (speculative decoding/MTP) (ggml-org#26477) * opencl: quant lm_head / decode GEMV and medium-batch GEMM optimizations * opencl: guard q4_K/q6_K tiled_ns convert-kernel registration for non-Adreno build * opencl: gate q4_K MUL_MAT+GLU fusion dispatch to Adreno * opencl: require the noshuffle weight layout in the q4_K GLU fusion gate * opencl: do not take the vectorized f16 mrow GEMV path on an unaligned row stride * opencl: pass the new get_scale_min_k4 stride argument at the row-major call sites * opencl: enable the q4_K split-K decode GEMV only where it is measured to win * opencl: record the X1-85 split-K datapoint (neutral, exclusion confirmed) * opencl: restrict the tiled lm_head/embed GEMV default to X2E/A8X * opencl: fix q4_K variant kernels to read the transposed scales layout * opencl: keep the flat-GEMV large-m escape opt-in * opencl: guard the o4 GEMV store against the rounded-up dispatch tail * opencl: restore the tiled q4_K/q6_K layout on tensor read-back * opencl: split-K for the q8_0 decode GEMV at small M * opencl: keep the q6_K noshuffle correctness escape ahead of the opt-in gate
Fuse RMS_NORM+MUL+ADD and ADD+ADD under GGML_SYCL_ENABLE_FUSION. ADD+ADD uses the same binbcast indexing and type matrix as standalone add() (f32, f16, f16/f32, i32, i16, bf16, including broadcast and non-contiguous). Unsupported combinations fall back to two add() launches.
* src : add n_expert_used_max function With Commit c61b98b ("model: add NVIDIA Nemotron-3-Puzzle-75B-A9B (NemotronHPuzzle) support (ggml-org#25444)") it is now possible for each layer to have a specific number of experts but there are a few checks that need to be updated to handle this upon model loading. For example: ```console llama_model_load: error loading model: model has expert layers but no expert layers are used ``` And later: ```console /llama.cpp/src/llama-model-loader.cpp:955: GGML_ASSERT(n_ids_used > 0) failed ``` This commit adds the n_expert_used_max function so that these checks can use it. Refs: ggml-org#25444 (comment) * src : use hparams.n_expert_used_max in llama_model_base::load_hparams * src : use 0 as initial value for n_expert_used_max
Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>
Let llama_print_build_info write to a caller-provided FILE* instead of hardcoding stderr. The parameter defaults to stderr so existing callers keep their current behavior. The version command in llama-app now passes stdout, so plain version output goes to stdout where users expect it. Signed-off-by: Adrien Gallouët <angt@huggingface.co>
Signed-off-by: Adrien Gallouët <angt@huggingface.co>
…8271) Use std::error_code overloads of fs::current_path() and fs::directory_iterator in ggml_backend_load_best() so an inaccessible search path (WebDAV mount, removed CWD) is skipped instead of terminating the process with an uncaught filesystem_error. Signed-off-by: Adrien Gallouët <angt@huggingface.co>
This commit adds a cmake version configuration file to replace the current compile definition solution for the version. The motivation for this change is that I made a mistake and did not take into consideration that the compile definition means that this will become a compiler flag for all sources in the target. This means that when a version update happens that will recompile all sources in the target even if they have not changed. Refs: ggml-org#28278
* model, mtmd: fix gemma4 vision handling * nits
* ggml : rename and make private ggml_op_alloc_size_may_expand() (ggml/0) cont ggml-org#27960 * ggml : bump version to 0.23.0 (ggml/1618) * sync : ggml
…ed (ggml-org#24292) The server caches the most recent compute graph per device so that GRAPH_RECOMPUTE can re-execute it without resending tensor data. The cached graph nodes hold direct pointers to backend buffers that were live at graph_compute() time. If any of those buffers is later released via FREE_BUFFER, the next GRAPH_RECOMPUTE re-executes the cached graph through the dangling pointers (use-after-free). The bug is reachable by an unauthenticated remote client. The dangling pointers point into chunks an attacker can reshape via subsequent ALLOC_BUFFER/SET_TENSOR commands, and the resulting read/write through the cached graph is sufficient to leak libc addresses and hijack the buffer iface vtable used by BUFFER_CLEAR, yielding remote code execution. Discard all cached graphs in free_buffer(). The existing null-check in graph_recompute() then rejects the request and the client falls back to GRAPH_COMPUTE on the next call. No protocol or API change.
The `sizeof(int16_t)` branch in `permute_transpose_impl` calls `rvv_transposed_s32_mn_to_nm` instead of `rvv_transposed_s16_mn_to_nm`. This is a copy-paste bug from the `sizeof(int32_t)` branch above it. The s32 function uses 32-bit segment load/stores (`vssseg8e32.v`) on 16-bit data, reading 2x bytes per element and producing completely wrong transposition results -- 14 out of 16 positions are corrupted for a 4x4 int16 matrix. The correct function `rvv_transposed_s16_mn_to_nm` already exists (line 390) and is used elsewhere in flash attention (line 1488).
…7625) * model : add support for HrmTextForCausalLM (DFM Mimir 1B) HRM-Text runs two transformer stacks (low, high) in an alternating cycle over the same token stream. The low-cycle state z_l starts from a learned [n_embd] tensor and is broadcast over positions. - conversion: new writer for the fused gqkv projection (order gate,q,k,v) remapped to llama.cpp q/k/v plus a separate sigmoid gate tensor - loader: block_count = lps * h_cycles * (l_cycles + 1) cache slots aliasing 2*lps physical blocks via struct copies - graph: looped build with sigmoid-gated attention, SwiGLU FFN and parameterless RMS norms; learned embedding_scale applied in build_inp_embd - saver: pointer-deduplicated layer loop (looped archs alias tensors) - tests: hrm_text fixture (lps 1, h 2, l 3) in test-llama-archs Limitations: causal attention only - the upstream prefix-LM mode is not implemented (the prefix_lm GGUF key round-trips unused). The KV cache holds one entry per pass: 128 layers for Mimir 1B, i.e. 4x a same-width 32-layer model - about 3072 MiB at ctx 4096 in F16 (halves with q8_0 KV + FA). Every token runs all 128 block passes, so decode cost is roughly 4x a dense model of equal width (2.65 t/s BF16, 8-thread desktop CPU). Verified against the HF reference: identical argmax at 334/334 positions across 20 prompts (BF16 GGUF vs FP32 golden). q8_0 requant: 95.8% top-1, all remaining misses inside the HF top-5 (accumulated error over 128 sequential blocks). AI usage disclosure: YES Used GLM-5.3 for the majority of code AI-generated under my direction, all gates verified locally. All in all I could say that I have written less than 20% of the code and most of the heavy lifting has been done by the model. As such, this should be considered experimental. * Update conversion/hrm_text.py Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@huggingface.co> * Update src/llama-arch.cpp Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@huggingface.co> * convert : add gguf_writer methods for hrm_text metadata replace raw add_uint32/add_bool calls with dedicated GGUFWriter methods, following the add_embedding_scale pattern Assisted-by: GLM-5.3 * convert : map regular hrm_text tensors via tensor_mapping delegate unfused checkpoints to the base tensor mapping; training-style attn. names are renamed to self_attn. so the patterns match Assisted-by: GLM-5.3 * model : format hrm-text build_* calls as in other models one argument group per line, matching sibling model files Assisted-by: GLM-5.3 * llama : move hrm z_l_init table entries out of the nemotron group place the name and tensor-info entries with the other global input tensors Assisted-by: GLM-5.3 * convert : slim down hrm_text comments Assisted-by: GLM-5.3 * convert : build hrm_text block tensor names from the {bid} template The tensor map holds concrete per-block names, so format the template with the computed layer index before handing it to super(). * llama : name hrm metadata keys in their own hrm. namespace The four keys are arch-independent, unlike the arch-substituted Keys.LLM entries, so group them under Keys.HRM (like Keys.Split) and rename the llm_kv entries to LLM_KV_HRM_*. Only our own GGUFs carry the old hrm_text.* keys; they are regenerated. * Update src/llama-model-saver.cpp Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@huggingface.co> * llama : keep hrm metadata keys arch-substituted Per review: the GGUF keys stay "{arch}.h_cycles" style, so the Python members drop the LLM_KV_HRM_ prefix and keep arch templates; C++ keeps the LLM_KV_HRM_* enums. GGUF output is unchanged - existing files and HF uploads stay valid. * Update gguf-py/gguf/constants.py Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@huggingface.co> * Update src/llama-arch.cpp Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@huggingface.co> * Update src/llama-arch.cpp Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@huggingface.co> * convert : rename hrm writer methods to add_hrm_* Generic names like add_h_cycles/add_prefix_lm are too broad on the shared GGUFWriter; prefix them with hrm_ like the metadata keys. * model : fix meta-split lookup for archs with aliased cache slots Cache tensors of archs that alias physical blocks across looped slots (hrm_text, nanbeige with num_loops > 1) can reference block indices without weight tensor names. Take the output projection from the layer array instead of asserting; all other lookups are unchanged. * model : replicate hrm_text tensors on meta devices instead of splitting The aliased cache slots rotate split states differently from their physical weights, so the meta-split execution invariants (set_rows requires the cache state to match the token indices) cannot hold for any device count. Replicate all hrm_text tensors on every meta device instead; single-device and non-meta paths are unchanged. Assisted-by: Claude Sonnet --------- Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@huggingface.co>
…gml-org#28989) * allows nemotron models to get by with just defining layer_norm_epsilon * made changes to load_arch_hparams instead
llama probes weight placement with a rope where all params are 0, so rejecting n_dims == 0 or freq_base == 0 puts rope_freqs on the CPU. That splits the decode graph at every full-attention layer (gemma-4-E2B: 5 splits instead of 2). Assisted-by: Claude Opus 5
implement q6k/q4k kernels Squashed from: feat: implement q6k kernel hex-q6k: improve unpack accuracy hex-q4_k: add support for Q4_K kernels Co-authored-by: Max Krasnyansky <maxk@qti.qualcomm.com>
* Improve CUDA graph usage for MTP * Rename field * Address review feedback
* switch jobs to ubuntu-slim * ubuntu slim almost takes 15 minutes for check requirements so use something faster
…l-org#28965) * model: calculate split states for attn_qkv from n_head * n_embd_head_k required for gemma4 with --fuse-qkv, where n_embd is 5376 but Q is 8192. * model: handle fused full attention layers for qwen35/qwen35moe * model: add TODO: [TAG_SPLIT_QGATE_QWEN]
* vulkan: support qwen4exp hc ops * fix stale comment [no-ci]
…8996) Both im2col.comp and im2col_3d.comp declare D_ptr without an explicit buffer_reference_align, so glslang emits writes through it as Aligned 16. The shaders advance the pointer by D_SIZE, a per-variant define set to 4 for float and 2 for float16_t, so most write addresses are not 16-byte aligned. This triggers VUID-RuntimeSpirv-PhysicalStorageBuffer64-06315 under GPU-AV. Declaring buffer_reference_align = D_SIZE matches the alignment to the actual write stride and takes validation hits from 20 to 0 for both IM2COL and IM2COL_3D. Fixes ggml-org#28960
* opencl: fix warnings * opencl: fix warnings for non adreno
ggml-org#28993) * gguf : align the data section relative to the GGUF start, not the file gguf_init_from_file_ptr reads a GGUF from the current file position, but padded the data section from file offset 0, so a GGUF embedded at an offset that is not a multiple of the alignment loaded without error and returned wrong tensor data. Also adds llama_adapter_lora_init_from_file_ptr, and disables mmap with a warning when an embedded data section is not aligned, instead of asserting in ggml. Assisted-by: Claude Opus 5 * llama : load lora from path through the FILE* variant The test now checks that mmap is disabled only for an unaligned offset. Assisted-by: Claude Fable 5.1 * Update ggml/src/gguf.cpp Co-authored-by: Johannes Gäßler <johannesg@5d6.de> * Update include/llama.h Co-authored-by: Johannes Gäßler <johannesg@5d6.de> * llama : error on unaligned mmap of an embedded GGUF, drop test-load-file-ptr --------- Co-authored-by: Johannes Gäßler <johannesg@5d6.de>
…g#29008) * chat : add message delimiters to the DeepSeek V3.2/V4 parser Assisted-by: Claude Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@huggingface.co>
* ci : add API/ABI check to make-release workflow [no ci] This commit adds an API/ABI compatibility check to the make-release workflow. The motivation for this to allow us to detect any potential breaking changes in API/ABI compatibility between releases and fail the the release if there are any. The workflow can be triggered manually as before and this check can be skipped if needed as it does take some time which might be useful when doing a dry-run and not specifically interested in the API/ABI check. By default this will check the current release against the latest release, but this can also be configured in the workflow, or in the script run on the command line, to check a different tag. * add check for minor version bumps [no ci] This commit also changes the build type to be RelWithDebInfo so that the reported information is more useful.
…org#27985) * ui: fix accidentally removed reasoning menu in single model mode on desktop * ui: formatting task run to fix storybook test * ui: mount the add menu reasoning submenu outside router mode only The models selector already owns the reasoning submenu in router mode, so the add menu only mounts it in single model mode. The first enabled item of the add menu is now the reasoning submenu, the accessibility story expects it. --------- Co-authored-by: Ben Babik <work@benjaminbabik.com> Co-authored-by: Pascal <admin@serveurperso.com>
…org#29009) * Update to openvino-2026.4 * Update OV docs * ggml-openvino : fix clangd and MSVC warnings * fix int to ptr cast, more internal linkage enforcement, and avoiding duplicate switch case --------- Co-authored-by: Mostafa Faheem <mostafaaafaheem@gmail.com>
* first fix * removed unnecessary declarations
required for qwen35moe if MTP tensors are fused but not loaded
Co-Authored-By: Claude <noreply@anthropic.com>
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Sync AMD-Ecosystem master with ggml-org/llama.cpp upstream master through 972d231.
Preserves the fork deprecation README and the GGML_HIP_UNSAFE_MATH option.
Validation: representative CPU test targets and the HIP gfx1151 test-backend-ops target build successfully.