* tests : update the Metal fusion baseline
Re-record the MTL fusion baseline with the current build: 276 rows over 131
dummy models.
Assisted-by: pi:llama.cpp/ggml-org/Qwen3.8-Flash-Next-GGUF (MacBook, M5 Max, 128GB)
* tests : fail test-fusion when a baseline row stops matching
The report was built from the counters the run produced, and the baseline was
only ever used as a lookup table, so a fusion that stopped matching produced no
row and was never compared. qwen4exp and mellum lost their fusions this way
while the Fusion / metal job stayed green.
Report a baseline row that the run does not reproduce as a regression. Scope
the pass to the arches that were actually run so --model keeps working.
Assisted-by: pi:llama.cpp/ggml-org/Qwen3.8-Flash-Next-GGUF (MacBook, M5 Max, 128GB)
* pi : never write inline comments unless the user asks
Assisted-by: pi:llama.cpp/ggml-org/Qwen3.8-Flash-Next-GGUF (MacBook, M5 Max, 128GB)
* tests : keep the mellum dummy model softmax-gated
mellum reads expert_gating_func from the GGUF since #30578, while the fixture
writes sigmoid for every MoE arch, so the Mellum dummy model stopped building
the softmax MoE routing fusion. Write softmax for mellum, matching the arch
default and the GGUFs that conversion/mellum.py produces.
Re-record the MTL baseline: the routing row is back, 277 ok / 0 failed.
Assisted-by: pi:llama.cpp/ggml-org/Qwen3.8-Flash-Next-GGUF (MacBook, M5 Max, 128GB)
* pi : use emoji in the AI stats disclosure
Assisted-by: pi:llama.cpp/ggml-org/Qwen3.8-Flash-Next-GGUF (MacBook, M5 Max, 128GB)
* pi : list the disclosure model after the hardware
Assisted-by: pi:llama.cpp/ggml-org/Qwen3.8-Flash-Next-GGUF (MacBook, M5 Max, 128GB)
* pi : use monochrome symbols in the AI stats disclosure
Assisted-by: pi:llama.cpp/ggml-org/Qwen3.8-Flash-Next-GGUF (MacBook, M5 Max, 128GB)
* tests : add muse-spark to the Metal fusion baseline
Assisted-by: pi:llama.cpp/ggml-org/Qwen3.8-Flash-Next-GGUF (MacBook, M5 Max, 128GB)
* hex-repack: Remove need_repack hack and streamline the rules -- breaks GET_ROWS tests for K-Quants, will fix in the next round
Add support for GET_ROWS with tiled Q5_K
Add support for raw k-quant tensors
* hex-get-rows: vectorize raw k-quant ukernels
* hex-binary: refactor dma-loop thread functions (no need for macros, etc)
* hex-quant: move/cleanup flat and tiled dequant ukernels
* hex-repack: add support for repacking/tiling F16/F32/BF16 2D weights
* hex-get-rows: remove redundant cast
* hex-repack: proper filtering for BF16 that were leaking through during the tests
* hex-repack: streamling tiling rules (broken MM for now)
* hex-repack: fix mmid after tiling updates
* hex-repack: rename repacked to tiled in all flags/funcs/kernels/vars
* hex-mm: fix valid_rows calc in various kernels
* hex-mm: remove unused hvx_mm_nx_2d_flat
* hex-mm: cleanup and optimize hvx-tiled vec_dot_f16
* hex-mm: another pass at hvx-tiled f16 matmul
* hex-get-rows: optimize small src1
* hex-get-rows: one more pass at recovering get-rows perf regression
* hex-repack: export GGML_HEXAGON_RT_REPACK via run.py
* hex-mm-tiled: fix accuracy regression with hmx-tiled-f16 matmul
* hex-mm: fold tiled_f16 checks into the general flow
* hex-get-rows: remove dead checks for buffer_is_hexagon and tiling during supports_op
* hex-quant: proper tail handling in new dequant ukernels
* hex-get-rows: remove unused code and cleanup support checks
* hex-mm: cleanup ne1 padding/tiling checks
* hex-mm: cleanup fusion checks for tiled tensors
* hex-repack: add a little can_repack helper to reduce duplication
* hex-repack: never repack host buffers
* hex-repack: factor out repack functions into separate .h and .cpp
* hex-tensor: cleanup tensor ifaces for tiled/padded sizes
* hex-repack: update k-quant tiling comments and cleanup #defines
* hex-dcache: factor out l2/dcache dirty tracking and flushing
* hex-tensor: update docs and remove #defs duplication
* hex-tensor: remove unused/incorrect get_data interface
* hex-get-rows: cleanup vtcm layout build
* tests: disable huge MM tests
* hex-dcache: add missing header
The scheduler uploads each view of a host input on its own, so the per-layer reshape of new_pool_idxs (and of sel_mask in glm5-next) was copied once per layer.
* enable OPENSSL=ON as default in build script, update it in CI and dockerfile
* change to LLAMA_BUILD_BORINGSSL on windows
* refactor
* rm Download UI build
* Apply suggestion from @CISC
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@huggingface.co>
* Apply suggestion from @CISC
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@huggingface.co>
* Apply suggestion from @CISC
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@huggingface.co>
---------
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@huggingface.co>
* model: add Muse Spark Open (text, vision and audio)
Muse Spark Open is a latent space MoE: the residual stream stays at 8192
while the 256 experts live in 4096, with a projection down and up around
them, top-8 sigmoid routing with a correction bias. Attention interleaves
three sliding layers at a 2048 window with rope for one global layer
without rope, with a sigmoid output gate and a weightless QK-norm.
Residuals are norm preserving, their gate is folded at conversion time.
Vision reuses the Muse Glimmer tower. Audio is a causal convolutional
pyramid with no attention and no spectrogram, four stages folding 16, 6,
5 and 4 samples into the channel dim, one embedding per 80 ms of 24 kHz.
The quantizer keeps the latent projections and the attention gate at
Q8_0, a little over 1% of the weights, since the whole residual stream
passes through them and the low-bit types turned the output into a
repeating loop. The model saver writes the post norm epsilon, and the
arch is registered in test-llama-archs.
Co-authored-by: Xuan Son Nguyen <son@huggingface.co>
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>
* Muse Spark: drop the redundant forward expand
* Muse Spark: drop the redundant forward expand and tighten the comments
* Update src/llama-quant.cpp
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@huggingface.co>
* Update src/llama-graph.cpp
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@huggingface.co>
* Update conversion/muse_spark.py
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@huggingface.co>
* Update conversion/muse_spark.py
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@huggingface.co>
* Update conversion/muse_spark.py
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@huggingface.co>
* Update conversion/muse_spark.py
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@huggingface.co>
---------
Co-authored-by: Xuan Son Nguyen <son@huggingface.co>
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@huggingface.co>
* pi : expand AI disclosure with hardware and stats
Prefer PI_HARDWARE for the hardware field, spell out the disclosure block
template (model link, harness, hardware, stats) and source [STATS] from
~/.pi/stats.sh. Record the hardware in the Assisted-by commit tag.
Assisted-by: pi:llama.cpp/ggml-org/Qwen3.8-Flash-Next-GGUF (MacBook, M5 Max, 128GB)
* pi : drop square brackets from placeholders
Brackets inside a markdown link text break the link on GitHub. Describe the
disclosure bullets in words instead of embedding a bracketed template.
Assisted-by: pi:llama.cpp/ggml-org/Qwen3.8-Flash-Next-GGUF (MacBook, M5 Max, 128GB)
Small helper around the GitHub Actions API: lists the workflow runs that are not
finished yet (in_progress, queued, pending, waiting, requested), grouped by status.
Supports selecting runs by status, by branch (server-side, to reduce API traffic)
or by a regex on the branch name, and can cancel the selected runs via --kill.
Assisted-by: pi:llama.cpp/Qwen3.8-Flash-Next
The first evaluated token attends to the positions above
pos - n_swa, so a SWA state starting at pos - n_swa + 1 holds the
whole window, but the reuse threshold asked for two more cells.
A saved SWA state keeps only the cells inside its window, so a
state read back from a slot file or from the RAM prompt cache was
always rejected and the slot rolled back to a checkpoint.
The SWA threshold now matches the window, while a state without SWA
keeps the exact bound. The test harness gains override_kv, used to
give the SWA test model a window smaller than the test context.
[no release]
The slot file held the target context and the context checkpoints,
but never the draft context, so a restored slot drafted with an
empty or stale draft KV until the cache refilled.
The file now ends with the live draft state, the full draft context
and the speculative state, in the same appendix style as the
checkpoints. A draft state that does not fit the draft context is
dropped and the draft starts empty, while older files and older
servers keep working.
[no release]
Slot selection takes free slots by id or by last use only, so child
tasks often land on a slot holding a parent state while empty slots
are idle, and overwrite it without saving it to the prompt cache.
Free slots are now ordered empty first, then least recently used, for
both the LRU fallback and the child slots, and a child task saves the
state it overwrites. With 4 grouped decision requests in flight and
12 states in rotation, prompt cache reads go from 5 to 36.
[no release]
The registry hard-coded 10 for AGENTIC_MAX_TURNS while DEFAULT_AGENTIC_CONFIG
said 100. The registry seeds SETTING_CONFIG_DEFAULT, so the loop's
`|| DEFAULT_AGENTIC_CONFIG.maxTurns` fallback never fires and users got 10
turns. Reference the shared constant, as the adjacent MCP timeout does.
Assisted by: llama-ui (ggml-org/Qwen3.8-Flash-Next:IQ4_NL)
* Add Support to maion-coder arch
* iAdd Support to support Draft models
* Address comments
* Remove maion context shift function
---------
Co-authored-by: Karthik Vijayan <kavijay@microsoft.com>
tojson wrote nan, inf and -inf for these floats, as values and as dict
keys; json.dumps writes NaN, Infinity and -Infinity. The string and safe
filters of a float keep nan and inf, as Python's str() does, so they no
longer use the tojson function. float_to_json is renamed float_repr, as
it now gives str(); float_json gives the json.dumps text.
Also say in a comment why a last digit 9 is not bumped at a power of 2.
* vulkan : fix concurrent first device init
* tests : load models in parallel in test-thread-safety
Loading them serially never exercised concurrent first backend init, so the crash reported in #29764 was invisible to this test.
* vulkan : create fence before retaining device in buffer context
* replace oneAPI package by DLE+oneDNN package
* support to install oneDNN in windows, in release and docker
* for internal test
* remove test code
* Update intel.Dockerfile
* llama : split llama_prefetch() into merge and prefetch steps
Merge the ranges first, then call the system functions.
This fixes -Wunreachable-code-return on Windows.
Signed-off-by: Adrien Gallouët <angt@huggingface.co>
* Use const_cast
Signed-off-by: Adrien Gallouët <angt@huggingface.co>
---------
Signed-off-by: Adrien Gallouët <angt@huggingface.co>
* vulkan : copy strided f16 KV once so reads spread across memory channels
Assisted-by: Claude (Opus 4.8 / Opus 5)
* tests : cover the f16 KV copy to scratch
Assisted-by: Claude (Opus 4.8 / Opus 5)
* vulkan : gate the f16 KV scratch copy to AMD
Assisted-by: Claude (Opus 4.8 / Opus 5)
* vulkan : reuse the generic copy shader for the f16 KV scratch fill
The f16 clause needs only a strided copy, not a dequant, so drop the
dedicated shader and dispatch the existing copy pipeline instead. The
copy is described in source memory order so the strided access lands on
the writes, which stream, instead of the reads, which would concentrate
on a few memory channels.
Assisted-by: Claude (Opus 4.8 / Opus 5 / Fable 5)
* vulkan : extend the strided KV scratch copy to bf16
Assisted-by: Claude Opus 5
* vulkan : clean up comments
Assisted-by: Claude Opus 5
* consolidate copy shaders
* disable on RDNA4
---------
Co-authored-by: Ruben Ortlam <rortlam@redhat.com>
* cpu: add F16 SWIGLU_OAI reference implementation and F16 activation op test coverage
Adds ggml_compute_forward_swiglu_oai_f16() to the CPU backend, mirroring
the existing F32 path, and extends test-backend-ops with F16 test cases
for SWIGLU_OAI so downstream backends(e.g. Hexagon) can be CPU-diffed
for correctness.
* ggml-openvino: fix f16 SWIGLU_OAI type mismatch in alpha/one constants
translate_glu_swiglu_oai creates alpha_const and one as f32 constants
unconditionally, so f16 inputs hit a type mismatch in Swish/Add on GPU
("Swish inputs must have the same type but they are: f16 and f32").
translate_glu_swiglu_clamp already upcasts to f32 and converts back at
the end; apply the same pattern here.
---------
Co-authored-by: ravi9 <ravi.panchumarthy@intel.com>
* llama: honor PARTIAL_ONLY in the plain KV cache
A non-SWA llama_kv_cache rolls back with seq_rm, so it holds no
partial state, yet state_write ignored the flag and serialized every
cell. A draft context made of a plain KV cache (MTP head, dense -md
draft) therefore copied its whole KV into each server checkpoint,
growing it by the draft KV size per token.
The cache now writes and reads nothing for PARTIAL_ONLY unless it is
SWA, and the iSWA wrapper no longer gates its base cache since it
filters itself.
* Update src/llama-kv-cache.cpp
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* llama: delegate PARTIAL_ONLY to the caches of every memory wrapper
The hybrid, hybrid-idx and dsa-iswa wrappers skipped their attention
caches on PARTIAL_ONLY, which also dropped an SWA window that is
partial state. Each cache now applies the flag itself, so the
wrappers only delegate. The indexer is marked stale only when a
restore rewrites cells.
test-llama-context gains a case checking that the partial state is
not larger than the full state and, without SWA, does not grow with
the tokens.
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
An embedding-only graph has no logits, and build_pooling() returns
early when embeddings are off, so since the models no longer expand
t_embd the graph ends up empty and its inputs are never allocated.
Expand t_embd in that case.
* server: save only restorable slot states to the prompt cache
The idle-slot save loop stores every idle slot whenever a task starts,
while only completion tasks restore from the prompt cache. Rerank,
embedding, decision and infill states fill the cache up to its limit
without ever being read back, and slots already cached are offered
again at every task start.
Each slot now tracks with cache_pending whether its state is restorable
and missing from the cache: a completion release or an explicit slot
restore sets it, prompt_save consumes it, prompt_clear resets it. Both
save paths share this rule, and the LRU path restores only for
completion tasks.
* server: test that decision slots skip the prompt cache
Decision tasks never mark their slot as cacheable, so starting new
tasks must not save their idle slots to the RAM prompt cache, with or
without a unified KV cache.
* server: restore any reusing task from the prompt cache
Embedding and rerank tasks never reuse their state, so their slots are
cleared on release like child slots and the cache only holds states
that a later task can restore. Infill and decision tasks now restore
from the cache too, which replaces the cache_pending flag.
Decision models disable the prompt cache unless --cache-ram is given,
since their states seldom repeat.
* spec : add lilicorr support
* convert : use convention for hparam assignment
* add todo for hparam use in llama-batch.cpp
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* typo fix
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
FINAL-Bench
Trivalik
Aaron Teo
Neo Zhang
Titaniumtown
Georgi Gerganov
Ruben Ortlam
Karthik VijayanandKarthik Vijayan
Mikhail Podvitskii
Max Krasnyansky
Aman Gupta
Gaurav Garg
Frosty40
Xuan-Son Nguyen
Nigel JonesandGabe Goodhart
Matt Rutkowski
Said Taghadouini
Kartik Gulia
Aleksander Grygier
William Tan
Yuri Khrustalev
losewayy
lhezandHongqiang Wang
Adrien Gallouët
Simone IacominoandSigbjørn Skjæret
Nathan WilsonandRuben Ortlam
cqderekandravi9
Ethan Guo