* hexagon: use non-host bufs by default and make the backend fully async
* hex-hb: remove optional hostbuf support and fix async copy
* hex-unary: relax supported unary check
* hex-bufs: use same get_alignment for host bufs
* snapdragon: bump android_platform to 34
* hex-rows: super hacky get/set rows for q8_0
* hex-get-rows: fix q8_0
* hex-get-rows: supprot for f16 and cleanup for q8_0
* hex-get-rows: generic macros and specialized thread funcs
* hex-get-rows: add DMA pipeline, vtcm_layout and kernel params
* hex-set-rows: fix q8_0 support, add dma and tracing
* hex-tests: override nmse threshold for HTP of Q8_0 quants
* hex-fa: add support for Q8_0 with inplace dequantizers
* hex-get-rows: simplify type dispatch
* hex-rows: simplify GET/SET_ROWS DMA pipeline
* hex-async: add events, set/get-tensor-async and rest of the async api support
* hex-repack: use slice instead of expert in repack functions
* hex-cpy: update event/async-cpy logging
* hex-set-rows: optimize smaller tensors
* hex-geglu: fix perf regression with larger tensors
* hex-get-rows: add missing header
* hex-set-rows: add missing header
* hex-bufs: ressurect GGML_HEXAGON_HOSTBUF but disable it by default
* hexagon: do not reject ops with non-heaxon buffers
* hex-get-rows: apply >=32 restriction only for q8_0
* hex-res: bump vtcm acquire timeout to 10 seconds
* hex-bufs: add support for cloning buffers between sessions to speed up tensor copies
* hex-async: rework event recording and batch flushing and integrate with meta backend
* hex-bufs: improved handling of repacked tensors
* hex-repack: handle get_tensor_2d offsets
* hex-dev: add support for devices with multiple NPUs
* hex-sync: add support for sync tokens to synchronize npu devices for async splits
* hex-mmap: cleanup mmap calls and add a retry for robustness
* hex-sync: add failsafe if sync wait gets stuck
* hex-sync: use sync_seq to check for completed events
* hex-sync: rotate tokens for extra robustness
* hex-devs: add supprot for legacy device names for now
* hex-bufs: add support for auto-cloning buffers from diff sessions
* hex-fusion: simplify and optimize htp-opnode fusion handling
* hex-sync: override opnode name so that it shows up in the profiles
* hex-trace: update scripts to handle multiple devices
* hex-sync: bump the size of the opbatch queue and number of sync tokens
* hex-cpy-sync: do not explicitly flush opbatches in cpy_tensor_async and add support for cpy-dma
* hex-sync: add graph-flush threshold to avoid single op batches
* hex-sync: add sync_peer so that we can flush peers we depend on during cross-device ops
* hex-bufs: introduce tensor->extra and shadow_bufs for repacking
* hex-l2: flush tiny tensors inline
* hex-sync: use explicit l2flush for sync tokens
* hex-extra: track weight flags via tensor extra
* hex-fence: rename sync to fence
* hex-repack: proper handling of set-tensor-2d in the shadow_buf
* hex-trace: remove obsolete opstage mask that we used for profiling
* hex-env: remove obsolete use_hmx variable
* hexagon: new unified run.py and build.py and updated docs
* snapdragon: update run script to auto-escapt test-backend-op -p argument
* hex-scripts: fix trailing spaces
* hex-scripts: fix flake8 warnings
* snapdragon: cleanup dst lib/bin dirs before copying new build
* hex-ops: add support for allreduce
* hex-ar: improved allreduce with dma pipeline
* hex-ar: align macros
* hex-ar: consistent use of fence_seq
* hex-ar: add AR_SELECT env var to select ALLREDUCE kernel or fallback
* hex-ar: add proper synchronize handling for ALLREDUCE
* hex-opbatch: looks like we now just rely on backend.synchronise to flush the batches, no need to flush them by threshold
* hex-ar: bump block size to improve dma efficiency
* hex-ar: fused ALLREDUCE+ADD
* hex-ar: cleaner fence buffer management
* hex-ar: futher allreduce tweaking to remove race conditions
* hex-ar: add simple solver and remove non-dma kernels
* hex-ar: add row-broadcast to fuse with bias ADD
* hex-fence: pass seq numbers via op_params
* hex-ar: allow for both entry/exit seq for completing entry wait
* hex-ar: align macros
* hex-ar: do not refetch broadcast row
* hex-fusion: move all fusion into opbatch::add_op for consistency with ALLREDUCE and things
* hex-fusion: fix incorrect MUL_MAT reordering
* hex-mm: make fused 2x and 3x matmuls more generic
* hex-fusion: move tensor fusion tagging to graph_compute
* hexagon: make sure to copy tensor->extra by value
* hex-get-rows: fix offset calc with row-chunking
* hex-repack: get_tensor_2d fixes for non-zero offsets
* snapdragon: make profile/trace scripts more robust and donot mix stdout/stderr by default
* hex-devices: use legacy device nameing by default to ease the transition
* hex-devices: hardcode CDSP domain IDs for current devices for now
* hex-optrace: improve multi-NPU timestamp alignment and overall handling of cycle values
* hex-optrace: more robust handling of the fence events
* ui : open MCP servers in a dialog from the chat form
Replace the MCP servers submenu with a single "MCP Servers" item that opens
a new DialogMcpServers dialog instead of navigating to the /mcp-servers route.
Assisted-by: pi
* ui : browse MCP resources from the server card
Make the Resources capability badge clickable so it opens the MCP resources
browser dialog, and drop the page-only chrome from SettingsMcpServers.
Assisted-by: pi
* ui : remove mcp-servers route and sidebar entry
MCP servers are now managed in a dialog, so drop the dedicated route and the
sidebar icon that navigated to it.
Assisted-by: pi
* ui : remove unused MCP servers submenu component
The submenu was replaced by the MCP servers dialog, so delete the component
and its export.
Assisted-by: pi
* feat(ui): add DialogSettingsChat dialog
* refactor(ui): switch SettingsChat to in-app section navigation
* feat(ui): open settings as dialog from sidebar
* refactor(ui): remove settings route and URL-based settings navigation
* fix(ui): adjust MCP dialogs for new base sizing
* chore: Formatting & linting
* feat(ui): make base dialog responsive and support sticky headers
* ui: move dialog close button to the sticky header
Assisted-by: pi
* chore: Formatting & linting
* ci : inline UI version resolution into ui-build.yml
* ci : build UI once and reuse the artifact in release jobs
Server jobs now extract the ui-build artifact into tools/ui/dist instead of npm-building the UI. Also removes the get-version job and the no-op -DHF_UI_VERSION flags.
Assisted-by: pi:Kimi-K3
* ui : disable the npm UI build by default (LLAMA_BUILD_UI=OFF)
The flag now only controls building the UI from source via npm. The UI
is still embedded by default from local tools/ui/dist or the prebuilt
download (LLAMA_USE_PREBUILT_UI=ON). CI jobs no longer npm-build the
UI; server-sanitize does not need node anymore.
Assisted-by: pi:Kimi-K3
* ci : rename the ui-build artifact to llama-ui.zip
Consistent with the other artifact names in the Actions summary.
Assisted-by: pi:Kimi-K3
* ci : clarify the windows artifact merge in release.yml
The windows-cuda/vulkan/sycl jobs build only the backend library;
llama-server (with the embedded UI) is injected into their zips from
the windows-cpu package during the release. State this in the job
comments and use accurate wording in the merge step.
Assisted-by: pi:Kimi-K3
* cuda: unblock mmq for MoE on sm_60
* cuda: duplicate mmq-config-pascal for dp4a and older
* cuda: reduce occupancy on non-dp4a pascal for Q2_K, Q4_K, Q5_K, Q6_K
This commit contains a fix for the conversion of NVIDIA Nemotron 3.5
Lightning which currently incorrectly converts when using a transformers
version later than 5.5.1.
When converting using [convert](https://github.com/ggml-org/convert) the
transformers version is 5.13.1 and this produces the following:
```console
WARNING:gguf.gguf_writer:Duplicated key name 'nemotron_h_moe.attention.head_count_kv', overwriting it with new value [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] of type ARRAY
```
This does not happen with transformers 5.5.1. The reason seems to be
that the configuration is different in later versions, for example when
using 5.13.1 the configuration block looks like this:
```console
transformers 5.13.1
raw has layers_block_type: True
autoconfig has layers_block_type: True
autoconfig layers_block_type: [
'linear_attention',
'moe',
'linear_attention',
'moe',
'linear_attention',
'full_attention',
'moe',
...
]
```
And with 5.5.1 we get:
```console
transformers 5.5.1
raw has layers_block_type: True
autoconfig has layers_block_type: True
autoconfig layers_block_type: [
'mamba',
'moe',
'mamba',
'moe',
'mamba',
'attention',
'moe'
...
]
```
In our conversion script we only match for attention, not full attention
which is causing this issue.
With the changes in this commit the output with transformers 5.13.1 will
be:
```console
(venv) $ gguf-dump models/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16.gguf | grep head_count_kv
INFO:gguf-dump:* Loading: models/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16.gguf
29: [INT32] | 52 | nemotron_h_moe.attention.head_count_kv = [0, 0, 0, 0, 0, 2, ...]
```
Resolves: https://github.com/ggml-org/llama.cpp/issues/27718
Refs: https://github.com/ggml-org/convert/actions/runs/32949047680/job/98116096069#step:5:2391
* metal: WIP chunked SSD SSM_SCAN kernels for multi-token prefill
* metal: drop scalar SSD path; MMA + sequential tail
* drop WIP ssm scan test noise
* remove state_from_dst and rename CS and NSG constants
* remove unrelated added whitespace padding
* added clarity to mma_tokens calculation
* added clarity to use_mma bool checks
* added comments to metal ssd op constants for clarity
* reserve K tokens for sequential kernel rollback snapshots
* reset concurrency between mma and seq tail
* remove print args no longer used
* fixed comment to no longer point to specific line
* add FC_SSM_SCAN so seq path skips token offlset unless it's mma tail
* added changes to new ssm.metal for rebase after ggml-metal.metal refactor
* specialize ssm_scan tail with a template instead of a function constant
---------
Co-authored-by: dpantaleoni <dominikpantaleoni@gmail.com>
Co-authored-by: forforever73 <690105611@qq.com>
* rpc: support apple RDMA as an RPC transport
* remove set_tensor micro optimization, rpc socket pinning per CR
* remove transparent reconnect
* trigger apple builds on RPC changes
---------
Co-authored-by: Ryan Churaman <rschu@meta.com>
* devops: use GGML_NATIVE=OFF for OpenVINO
Same as in other Dockerfiles.
Should fix#23100
* enable backend dl and cpu all variants
---------
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@huggingface.co>
* server: fix tool calls getting silently stripped with --prefill-assistant
Last assistant carries tool_calls + --prefill-assistant is on → request
flips into continuation mode, add_generation_prompt forced off, tail
rebuilt from reasoning_content + content only. Tool calls just vanish.
- Auto-continuation now skips trailing assistant msgs that have tool calls
- continue_final_message on those throws a clear error instead of
silently corrupting the prompt
- Regression tests included, red before / green after
Fixes#27588
Developed with AI assistance, disclosed per the contribution policy.
* server : address review: fail on prefill-assistant + trailing tool_calls
Move validation into oaicompat_chat_params_parse (next to the existing
two-or-more-assistant check) and remove it from common_chat_templates_apply,
which has no precedent for validation. Drop the regression tests.
Per review: --prefill-assistant with a trailing assistant message
containing tool calls is not supported and should fail loudly.
* add ccache-buckets action
* use ccache-buckets
* only save on master
* install python3-venv for hip
* add jq and python3 for cuda
* only delete caches older than 5 minutes
* metal : null-check ggml_metal_buffer_init result to avoid OOM crash
ggml_backend_metal_buffer_type_alloc_buffer used the result of
ggml_metal_buffer_init without checking for NULL. ggml_metal_buffer_init
returns NULL when the underlying Metal allocation fails (e.g. an
out-of-memory condition), and the following ggml_metal_buffer_is_shared(res)
call dereferences it, turning a recoverable allocation failure into a hard
crash (EXC_BAD_ACCESS). This is easy to hit on memory-constrained devices
such as iOS when a model/context exceeds the available Metal budget.
Log the failure using the existing GGML_LOG_ERROR convention and return
NULL so the allocator surfaces a diagnosable error up the stack instead of
crashing.
* cont : fix log
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* grammar : accept "\-" escape in character classes
gbnf_escape_char_class() escapes '-' as "\-" but parse_char() rejected
that escape, so generated tool-call grammars failed to parse.
Assisted-by: Claude Code <claude@anthropic.com>
* tests : add parser test for "\-" in char classes
Assisted-by: Claude Code <claude@anthropic.com>
* tests : add integration test for "\-" in char classes
Assisted-by: Claude Code <claude@anthropic.com>
* tests : drop integration and parser tests
* metal : per-device tuned (Q, NE) for flash-attn vec (#25750)
* rebase Q-generic FA vec body from 01dc93607 (#23114)
* add 53 f16 (Q,NE) flash-attn vec instantiations (vec 80 -> 133)
* add FA vec (Q,NE) tuning table + dispatch wiring + SMEM cap fallback
* add FA vec (Q,NE) perf sweep
* fill tuning result
* fold family table into a per-family representative SKU
* refactor tuning result format
* extend FA vec tuning to quantized KV caches
* sync fa vec tuner bucketing with runtime, use pointwise tuning regret
* update tuned table
* format and cleanup
* prefix fa_vec tuning procs with ggml_backend_metal_tuning_, drop unused fa_vec_override_active
* add device id -> token lookup for the offline tuning tool
* add ggml-metal-tuning skeleton
* add op-agnostic perf cell + median timing for the tuner
* add FA-vec graph build + tensor init to the tuner
* tools : add FA-vec (Q,NE) sweep, compression and table emit
* cool down and re-measure the dirty window on thermal drift
* test-backend-ops : replace the FA vec tune mode with a bounded (Q,NE) slice
* tools : document the Metal tuner, point the table comment at it
* abort on unknown KV type, single-source fa_vec_legal_ne
* cleanup
* honor -o in the FA vec (Q,NE) slice
* retune FA-vec (Q, NE) under a pointwise no-harm gate
* cont : add fa-vec tunings for M1 Pro, M2 Ultra, M5 Max
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* metal : per-op source split + parallel compile (#24021)
* preliminary extract common header
* op source split
* split metallib into 8 libs && load in parallel
* derive kernel->library routing from functionNames
* x-macro lib list + underscore filenames, dedup QK_NL, MRC fixes
* op source split 8 to 20
* improve robustness of source fallback
* clean up
* change bool -> atomic_bool
* only prepend headers that source actually includes
* no semaphore, use GCD global queue
* dedup library compile path, fix NSError lifetime, rename gla
* relocate upstream concat/rope_back/repeat kernel changes into split files
* move ggml-common.h from common.h into dequantize.h to shrink binary size
---------
Co-authored-by: lvyichen <lvyichen@stepfun.com>
* metal: add col2im_1d op (f32/f16/bf16) (#25176)
* metal : add set_rows with src0 f16 (#25434)
* metal : add CONV_2D_DW (depthwise convolution) support (#21565)
* metal : add Q2_0 support (#25419)
* metal: fuse snake activation (mul, sin, sqr, mul, add) (#25459)
* ggml-metal: FWHT kernel for metal backend (#25924)
* metal : port new kernels into the split sources
Move the kernels added on master after the split (lightning indexer,
DSv4 hyper-connections, silu_back, f16 bin ops, TQ2_0, the flash-attn KV
dequantization pass, rope offset/inplace, ssm_scan rollback, packed q8_0
dequantization and the tensor-API mat-mat K clamp) into the corresponding
kernels/*.metal sources. Copied verbatim, no functional change.
---------
Co-authored-by: lvyichen <lvyichen@stepfun.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
`repetition_penalty` is standard HF key for repetion penalty.
Currently, only `penalty_repeat` is mapped, read `repetition_penalty`
and map it to `metadata.sampling_penalty_repeat`.
* ci : apply ccache-clear with older/min/dry-run to all ccache jobs
Assisted-by: llama.cpp:DeepSeek-v4-Flash-0731
* ci : install gh in ccache-clear if missing (container jobs)
The ccache-clear action relies on the gh CLI, which is not present in
container-based jobs. Install it on demand so those jobs can clear caches.
Assisted-by: llama.cpp:DeepSeek-v4-Flash-0731
* ci : install gh via apt repo in ccache-clear
The install.sh script used previously is no longer served (404). Switch to
the official GitHub CLI apt repository, which is still available.
Assisted-by: llama.cpp:DeepSeek-v4-Flash-0731
* ci : pass --repo to gh cache commands in ccache-clear
In container jobs gh cannot auto-detect the repository from git, so
gh cache list/delete fail with 'failed to run git: not a git repository'.
Pass the repository explicitly via --repo using GITHUB_REPOSITORY.
Assisted-by: llama.cpp:DeepSeek-v4-Flash-0731
* ci : drop -new suffix from vulkan ccache key
The -new suffix was only needed to force a fresh cache. With
ccache-clear now evicting stale caches, the original key can be used
again. The old ccache-vulkan-ubuntu-24.04-arm-new entries still match
the ccache-clear key prefix and are cleaned up automatically.
Assisted-by: llama.cpp:DeepSeek-v4-Flash-0731
* ci : fix ccache-clear date parsing on macOS (BSD date)
macOS ships BSD date, which has no -d option. The older cutoff check
was silently disabled there: 'date: illegal option -- d' errors in the
log and the loop was only stopped by the min limit, risking deletion
of caches not older than the cutoff (e.g. saved by a concurrent job).
Parse the ISO-8601 timestamps with GNU date when available and fall
back to BSD date otherwise (TZ=UTC, fractional seconds dropped).
Assisted-by: llama.cpp:DeepSeek-v4-Flash-0731
* ci : extract ccache-clear logic into scripts/ccache-clear.sh
The composite action now consists of a dedicated step that installs the
GitHub CLI when missing (e.g. in container jobs) and a thin step that
calls the new script. The script follows the make-release-checks.sh
conventions (usage/env header, set -euo pipefail, CLI flags) and only
checks that gh is available. The action inputs are unchanged, so the
workflow steps are untouched.
Assisted-by: llama.cpp:DeepSeek-v4-Flash-0731
* ci : remove unused apple ccaches
* DSV4: sm tensor
* set coarser granularity for head splits
* fix dspark
* add model saving for dsv4 + allow dflash to return on specific device
* add comment about dsv4 seq_rm
* simplify
* add shared expert delayed allreduce
* remove special test for dsv4
* readme : update links
* readme : update maintainer PRs list
Add the new members of the `ggml-org` `maintainers` team to the
author filter of the maintainer PRs link (nikwen, marty1885,
Titaniumtown), keeping the canonical team ordering. The list now
matches the team exactly (35 members).
Assisted-by: pi:llama.cpp/Qwen3.8-27B
Run test-llama-archs with 1 to 4 GGML_METAL_DEVICES, mirroring the
existing CUDA runs, and dispatch the job unconditionally since the
per-backend guards now decide what to run.
Assisted-by: llama.cpp:DeepSeek-v4-Flash-0731
The device_info loop iterates over the discovered devices and gets
the available and total memory counts. With the CUDA backend (and
possibly others too) this requires creating a GPU context, which,
in case of CUDA, results in a 550 MB VRAM allocation.
For this information to be used in any way, the log verbosity must
be set to LOG_LEVEL_TRACE. If it's not, including in the default
configuration, the contexts get created, memory sizes get queried,
then the log function quietly discards the data.
In certain cases the user may not want to use any GPU resources.
The device_loop iteration is the only place touching the GPU that
cannot be skipped.
Fix by checking the verbosity level and skipping the loop if there
would be no output.
