* TP: fix ggml context size calculation, memory leak
* move split state cache back into the context
* revert to constant ggml context size for cgraphs
* increase headroom for statically allocated tensors
* remove obsolete include
* ggml: implement `gguf_init_from_buffer`
* test: `gguf_init_from_buffer`
* fix: memory breakdown for a model loaded with `no_alloc` from a file is consistent with being loaded from a buffer
* fix: use `GGML_UNUSED`
Co-authored-by: Copilot <copilot@github.com>
* fix: remove `total_size` from `gguf_reader`
* fix: file offset calculation, rename `offset` to `data_offset`
Co-authored-by: Copilot <copilot@github.com>
* refactor: extract model loader bug fixes to another PR
* feat: add `gguf_init_from_callback`
* fix: always require a max expected size
* fix: change `gguf_reader_callback_t`'s `output` type to `void *`, change `max_expected_size` and offsets to `uint64_t`
* fix: harden against offset overflow in buffer read
* fix: remove seek behavior from the callback
* feat: `max_chunk_read == 0` means `SIZE_MAX`
* fix: seeking in a gguf file with no tensors
---------
Co-authored-by: Copilot <copilot@github.com>
* fix(action): update SpacemiT toolchain URL and version
Change-Id: If4cc1c738a855274103f8c3ad52daa33528acd0c
* fix(action): add -L flag to curl command for URL redirection
Change-Id: I9b6c37390f0c7a733a36308c8fb53d22d234ab06
- Use OpenMP to parallelize iq2xs_init_impl and iq3xs_init_impl.
- Move the OpenMP detection from ggml-cpu to ggml-base.
- Update OpenMP dependencies in ggml-config.cmake.in.
* common : add common_chat_split_by_role
* cont : fix spans to reach end of message
* server: fix checkpoints creation
- extract message_spans from chat templates
- find the prompt token position before the latest user message
- split prompt batching at that position
- create a context checkpoint before the latest user input
- avoid periodic mid-prompt checkpoints when that position is known
- handle multimodal prompts when mapping text/template positions to server prompt tokens
- add --checkpoint-min-step to control minimum spacing between checkpoints
* cont : clean-up
* Support autoparser detection for message barriers
* server: fix message span delimiter and update docs
---------
Co-authored-by: Alde Rojas <hello@alde.dev>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
Co-authored-by: Piotr Wilkin <piotr.wilkin@syndatis.com>
* ci : remove tag from build-self-hosted.yml
* ci : slim -> self-hosted
* ci : prevent heavy CPU jobs from running on fast runners
* ci : prevent cmake pkg to run on dedicated fast runners
* ci : try to bump 3.11 -> 3.13
* ci : move lint back to 3.11
* ci : back to 3.11
* ci : add comment about UI jobs
* ci : move python requirements check to CPU runners
this job is a bit slow for a dedicated "fast" runner
* ci : add self-hosted ui workflow
* ci : fix UI naming
* tmp to check if arm64 fast is compatible with all jobs
* revert last commit
* requirements: relax torch~=2.6.0 to torch>=2.6.0 for convert_hf_to_gguf
The ~=2.6.0 operator resolves to >=2.6.0, <2.7.0, which fails on
PyPI for platform/CPython combinations where 2.6.x is not present.
The accompanying comment already says 'PyTorch 2.6.0 or later', so
the looser >=2.6.0 matches the documented intent and unblocks
pip install -r requirements/requirements-convert_hf_to_gguf.txt.
Fixes#23408
* requirements: bump torch floor to 2.11.0 per maintainer
* requirements: pin torch to ==2.11.0 per project policy
* requirements: pin mtmd torch and torchvision to 2.11.0/0.26.0 per project policy
* requirements: suppress check_requirements pin warning on mtmd
The check_requirements script flags '==' on lines in files matched by
*/**/requirements*.txt. Append the documented suppression comment to the
pinned torch and torchvision lines (and to the s390x platform marker lines)
so the check passes while keeping the pins required by project policy.
* ty: silence Tensor/Module union check on model[0].auto_model
With torch 2.11.0 stubs, nn.Sequential.__getitem__ now returns
Tensor | Module rather than Module, so model[0].auto_model fails ty
on the SentenceTransformer code path. The runtime behavior is
unchanged because SentenceTransformer always wraps a Module at
index 0. Adding a targeted unresolved-attribute ignore keeps the
type-check green without altering behavior. A follow-up issue
tracks typing the variable explicitly.
- change `k_copy_src1_to_contiguous` so that uses a precomputed contiguous mapping where all rows "owned" by an expert are in one slice with a know starts and ends
- switch the `O(n_as * n_routed_rows)` contraption to a counting sort-based procedure with `O(n_as + n_routed_rows)` complexity
* SYCL: add BF16 to DMMV kernel path for ~4x token generation speedup
BF16 models had no dedicated token generation kernel — they fell through
to the generic full-GEMM path, resulting in ~14% memory bandwidth
utilization on Intel Arc GPUs. This adds BF16 support to the DMMV
(dequantize mul-mat-vec) path, matching the existing F16 implementation.
Fixes#20478
* SYCL: fix BF16 DMMV out-of-bounds when ncols % 64 != 0
The qk=1 kernel (used for F16 and BF16) iterates with stride
2*GGML_SYCL_DMMV_X (= 64 on Intel targets where WARP_SIZE=16). When
ncols is a multiple of DMMV_X (32) but not of 2*DMMV_X (64), the last
warp iteration accesses elements at col >= ncols, producing NaN for the
final row and wrong values for interior rows.
Fix: tighten can_use_dequantize_mul_mat_vec to require ne[0] %
(2*DMMV_X) == 0 for F16/BF16 types, and update the ASSERT in the BF16
launcher to match. Quantized types use block-structured kernels with
different access patterns and keep the existing DMMV_X check.
Verified: test-backend-ops MUL_MAT passes 913/913 on Intel Arc Pro B70.
Previously failing: m=128/129 n=1 k=1056 cases (NaN and ERR > 0.0005).
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
---------
Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com>
* pi : update
* ci : fix ios build
* ci : fix andoroid
* ci : fix apple builds
* cmake : add install() for impl libraries
Add install(TARGETS <target> LIBRARY) for all -impl libraries that were
changed from STATIC to shared (controlled by BUILD_SHARED_LIBS) in
commit bb28c1fe2. Without this, cmake --install fails to copy the shared
libraries, causing runtime errors like:
llama-server: error while loading shared libraries: libllama-server-impl.so
Ref: https://github.com/ggml-org/llama.cpp/issues/23494#issuecomment-4512912515
Assisted-by: llama.cpp:local pi
* ci : fix xcframework build
* cmake : remove STATIC from impl libraries, allow BUILD_SHARED_LIBS control
Remove explicit STATIC from all -impl libraries (server, cli, completion, bench,
batched-bench, fit-params, quantize, perplexity) so BUILD_SHARED_LIBS controls
shared vs static linkage.
Add WINDOWS_EXPORT_ALL_SYMBOLS ON for proper DLL export on Windows.
Assisted-by: llama.cpp:local pi
* cmake : enable LLAMA_BUILD_APP by default
Assisted-by: llama.cpp:local pi
* ci : disable app in build-cmake-pkg.yml
* vulkan: fuse snake activation (mul, sin, sqr, mul, add)
Add snake.comp shader with F32 / F16 / BF16 pipelines and
ggml_vk_snake_dispatch_fused. The matcher recognizes the naive 5 op
decomposition emitted by audio decoders (BigVGAN, Vocos) for snake
activation y = x + sin(a*x)^2 * inv_b and rewrites it to a single
elementwise kernel.
test_snake_fuse from the CUDA PR now also compares CPU naive vs
Vulkan fused across F32 / F16 / BF16.
* vulkan: address jeffbolznv review for fused snake activation
Rename T / C to ne0 / ne1 in the shader and push constants to match
the standard naming convention used across the Vulkan backend.
Tighten ggml_vk_can_fuse_snake: require x and dst to be contiguous
(the shader uses idx = i0 + i1 * ne0) and require a / inv_b to be
tightly packed on the broadcast dim (the shader reads data_a[i1]).
* vulkan: tighten snake fusion type checks for all operands (address jeffbolznv review)
* vulkan: reject snake fusion when ne[2] or ne[3] > 1 (address jeffbolznv review)
* vulkan: address 0cc4m review for fused snake activation
snake.comp is renamed to follow the ggml DATA_A_* / A_TYPE convention.
A_TYPE now applies to the activation tensor data_a instead of the
broadcast multiplier, and the bindings become data_a (A_TYPE), data_b
(float), data_c (float) and data_d (D_TYPE). A header at the top of
the shader maps each buffer to its role in y = x + sin(b * x)^2 * c.
On the C++ side, ggml_vk_can_fuse_snake reuses the existing snake_pattern
constant instead of duplicating the op list, sin_node is extracted as a
named local alongside the other chain nodes, and the broadcast operands
a and inv_b are now required to be GGML_TYPE_F32 to match the hardcoded
float bindings on data_b and data_c (the previous a->type == x->type
would silently reject any future BF16 or F16 chain once the supports_op
gate for SIN / SQR is lifted). ggml_vk_snake_dispatch_fused gets an
explicit GGML_TYPE_F32 case and GGML_ABORT on default in place of the
silent f32 fallback, and a stale comment about data_a[i1] / data_inv_b[i1]
is refreshed to match the new binding names.
* tests : move save-load-state from examples to tests
- Move examples/save-load-state/ to tests/test-save-load-state.cpp
- Remove subdirectory reference from examples/CMakeLists.txt
- Add test to tests/CMakeLists.txt as a model test
- Remove CODEOWNERS entry for removed example directory
Assisted-by: llama.cpp:local pi
* cont : update ci
Add n_prompt_tokens, n_prompt_tokens_processed, and n_prompt_tokens_cache
to the /slots JSON response. These fields are already tracked internally
but were not exposed, making it impossible for clients to monitor prompt
evaluation progress during processing.
* metal : fix GGML_OP_SET kernel threads
* tests : extend test_cpy to support different src/dst shapes
Extend test_cpy to support different source and destination tensor shapes
for CPY operations (reshaping), where the total number of elements must match.
- Renamed ne -> ne_src, added ne_dst parameter (default: use src shape)
- Added 50 new reshaping test cases covering 1D<->2D<->3D<->4D conversions
- Tests exercise 1024 boundary, small shapes, and large dimensionality changes
- Fixed dangling reference bug (storing & to temporary std::array)
- Updated all existing test calls with permute/transpose args for compatibility
Assisted-by: llama.cpp:local pi
* metal : optimize concat kernel with row batching for small widths
When ne0 < 256, batch multiple rows into a single threadgroup to improve
occupancy. This avoids underutilizing the GPU when processing narrow tensors.
- Dispatch nth = min(256, ne0) threads per group
- Calculate nrptg (rows per threadgroup) to fill up to 256 threads
- Update kernel index calculation to handle the row batching
- Add boundary check for i1 >= ne1
Assisted-by: llama.cpp:local pi
* tests : clean-up
* tests : refactor CPY shape tests to use dimension permutations
Replace 75 hardcoded test cases with a loop over permutations of
{3, 5, 7, 32} (total elements: 3360). Each src permutation is tested
against canonical sorted and reverse dst, skipping identical shapes.
Covers F32, F16, and Q4_0 (when both src and dst ne0 == 32).
Assisted-by: llama.cpp:local pi
The destroy() function in server_context_impl only cleaned up the main
model and context (via llama_init.reset()) but did not free the speculative
decoder (spec), draft context (ctx_dft), or draft model (model_dft).
For MTP (Multi-Token Prediction) models, ctx_dft holds GPU-allocated
resources (KV cache, compute buffers) that are not freed when entering
the sleeping state. On each sleep/resume cycle, new resources are
allocated without the old ones being freed, leading to a VRAM leak
that eventually crashes the server with out-of-memory errors.
Fix by explicitly resetting spec, ctx_dft, and model_dft in destroy()
before resetting llama_init, ensuring proper cleanup order to avoid
use-after-free.
ref: https://github.com/ggml-org/llama.cpp/issues/23395
Assisted-by: llama.cpp:local pi
