- Implement GGML_OP_DSV4_HC_COMB, GGML_OP_DSV4_HC_PRE, and
GGML_OP_DSV4_HC_POST with SIMDgroup register and shuffle optimized kernels.
- Add Metal dispatch and support plumbing and test the production Sinkhorn
iteration count and embedding width.
Assisted-by: Codex
Co-authored-by: Thiago Padilha <thiago@padilha.cc>
Adds trace logging in server-context.cpp for slot similarity checking
during prompt cache slot selection, including skip reasons and similarity
calculation details.
Assisted-by: llama.cpp:Qwen3.6-27B
* common: auto-download dflash- and eagle3- HF sidecars
Mirror the existing mtp- sidecar logic to support auto-discovery and
download of DFlash (dflash-) and Eagle3 (eagle3-) speculative decoding
sidecars from Hugging Face repos.
Changes:
- Add --dflash and --eagle3 CLI flags to trigger sidecar download
- Add find_best_dflash() and find_best_eagle3() using find_best_sibling
- Exclude dflash- and eagle3- filenames from primary model selection
- Filter dflash- and eagle3- from cached model listings
- Wire download tasks that set speculative.draft.mparams as fallback
Assisted-by: pi:llama.cpp/Qwen3.6-27B
* docs : regen
* ci : add HF_TOKEN to self-hosted workflows
Pass the HF_TOKEN_CI repo secret as HF_TOKEN env var in the self-hosted
build and server workflows.
Fix the stale build.yml path reference.
Assisted-by: pi:llama.cpp/Qwen3.6-27B
* cont : add comment
---------
Co-authored-by: ggerganov <ggerganov@users.noreply.github.com>
* server : clear checkpoints upon prompt clear
* server : move the prompt state data to the server_prompt_cache
Assisted-by: pi:llama.cpp/Qwen3.6-27B
* server : handle batched slot being cleared
dict.get("key", default) returns None (not default) when the key
exists but its value is explicitly None. This caused an AttributeError
in _escape_html() when a task errored before grading and answer was
set to None.
Assisted-by: pi:llama.cpp/Qwen3.6-27B
Absorb get_slot_by_id logic into get_available_slot so slot selection
is handled by a single function call. When a specific slot id is
requested, the LCP similarity check still runs to enable proper
prompt cache updates.
Assisted-by: pi:llama.cpp/Qwen3.6-27B
Reuse existing rope kernels with a function constant to toggle forward/backward
rotation, avoiding duplicate kernel code.
Assisted-by: pi:llama.cpp/Qwen3.6-27B
* metal : add f16 and bf16 support for concat operator
Extend the Metal backend concat operator to support f16 and bf16 tensor
types in addition to the existing f32 and i32 support.
- Template kernel_concat on type T with specializations for float, half,
bfloat, and int
- Add type-specific pipeline getter ggml_metal_library_get_pipeline_concat()
- Update device support check to allow f16 unconditionally and bf16 when
device supports bfloat16
- Update dispatch to select the correct kernel specialization by type
Assisted-by: pi:llama.cpp/Qwen3.6-27B
* metal : extend concat operator to support f16, bf16, i8, i16 and i64
Assisted-by: pi:llama.cpp/Qwen3.6-27B
When reasoning-budget is set in model.ini, the per-request
thinking_budget_tokens from the WebUI was ignored because the
model.ini value took unconditional precedence.
Swap the precedence so the WebUI per-request value is checked
first, with the model.ini value serving as a fallback default.
Assisted-by: pi:llama.cpp/Qwen3.6-27B
* tests : refactor test-save-load-state to accept token input
- Default prompt is now empty; when not provided, generate n_batch
random tokens (useful for models without a tokenizer)
- Tokenization happens once upfront; pass token vector to test functions
- generate_tokens prints token IDs instead of decoded pieces
- Use llama_model_get_vocab / llama_vocab_n_tokens API
- Upgrade log level from LOG_TRC to LOG_INF for visibility
Assisted-by: llama.cpp:local pi
* cont : use llama_tokens alias
Reduce the number of parallel jobs in server-self-hosted.yml by stacking
test configurations as sequential steps within a single job, following the
pattern from #23927.
- server-metal: 4 matrix jobs -> 1 job with 4 sequential test steps
- server-cuda: 2 matrix jobs -> 1 job with 2 sequential test steps
- server-kleidiai: removed unnecessary single-entry matrix
- removed unused Setup Node.js step from server-metal
Total: 7 parallel jobs -> 3 parallel jobs
Assisted-by: llama.cpp:local pi
* speculative : add common_speculative_n_max helper function
Extract the speculative max-draft-size logic from server_n_outputs_max
into a reusable common_speculative_n_max() function in common/speculative.
Assisted-by: llama.cpp:local pi
* cont : draft context always has n_parallel outputs
* llama : log n_outputs_max
* speculative : remove draft-simple auto-enable
* ci : enable server tests on PRs
Fixes: https://github.com/ggml-org/llama.cpp/pull/23927#discussion_r3332213086
The cpu-x64-high-perf job was missing the Linux label in its runs-on
specification, causing the runner to not be discovered. All other
self-hosted Linux jobs include this label.
Assisted-by: llama.cpp:local pi
* ci : disable libcommon build from xcframework
* ocd : fix name
* ci : ios-xcode change to macos-26
* cont : pin xcode
* cont : pin xcode to minor version
* ci : ios use macos-15 again
* ci : add and test ccache-clear
* cont : fix
* cont : set permission
* cont : another permission
* cont : token
* cont : print key
* cont : bring back perms
* cont : test windows
* cont : add token
* cont : cleanup
* ci : make release jobs clean-up their ccache
* ci : separate CUDA windows workflow + fix names
* ci : rename workflow
* ci : prefix cache names with workflow name
* ci : rename build.yml -> build-cpu.yml
* ci : cache keys
* ci : fix windows cuda/hip concurrency of release workflow
* ci : fix apple cache names
* ci : add TODOs
* cont : keep just the last cache
* ci : update release concurrency to queue
* ci : move the release trigger to ubuntu-slim
* ci : hip add TODO
* cont : improve words
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* ci : server windows set build type explicitly
* cont : try windows-2025
* ci : use llvm
* cont : use ninja
* cont : fix shell
* ci : set number of jobs correctly
* ci : fix windows with vulkan ccache by using llvm
* ci : server ccache only on master
* ocd : fix job names
[no release]
* ci : fix undefined sanitizer build to use Debug build type only
* ci : ccache the server builds
* cont : remove ui dependency + reuse ccache for both ubuntu jobs
* tmp : force ccache save
* Revert "tmp : force ccache save"
This reverts commit a857b03a10b1304d456129a017e0e46b185618ee.
* cont : no need for node.js
* ci : move [no release] check to dedicated check_release job
Move the workflow-level \`if\` condition that skips builds when the commit
message contains \`[no release]\` into a lightweight \`check_release\` job.
All build jobs now depend on it via \`needs\` and check its output.
This ensures the skip logic is evaluated at the job level rather than at
the workflow level, which is the recommended approach for conditional jobs.
Assisted-by: llama.cpp:local pi
* cont : use `fast` runner
* ci : skip release workflow on master when commit message contains [no release]
Assisted-by: llama.cpp:local pi
* ci : restrict sanitizer builds to x86_64 + fix build type
the spark is apparently too slow for some reason
* tests : fix undefined warning
[no ci]
* ci : disable SYCL f16 builds
* ci : extract android and hip into separate workflows
* ci : move webgpu to separate workflow
* ci : move the rpc to a separate workflow
* ci : extract s309x and ppcl jobs
* ci : extract opencl job into a separate workflow
* 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
* 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
* 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
* 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
ggml_backend_dev_by_name always appends a nullptr sentinel to the devices
vector. Skipping nullptr entries prevents assertion failure in
ggml_backend_dev_name.
Assisted-by: llama.cpp:local pi
Georgi Gerganov