the dsv4_hc_pre kernels hardcoded hc = 4 via a constexpr used with
simd_shuffle, so the op was rejected by supports_op for any other hc
and fell back to CPU. Kimi-K3 uses dsv4_hc_pre with hc equal to the
number of banked checkpoints in the cross-layer residual stack, which
grows with the layer index.
pass n_hc as a function constant (FC_DSV4_HC) with per-n_hc pipeline
variants, and loop over it in both pre kernels with direct loads
add test-backend-ops cases for hc = 1, 2, 3, 5, 8 and 65, gated and
not gated
Assisted-by: pi:llama.cpp/Qwen3.8-27B
* ui : let the chat column shrink below its content width
The chat column is a flex item, so its automatic minimum size kept it as wide as the widest row inside it. Message rows cap at max-w-3xl plus padding, so a narrower window pushed a page-level horizontal scrollbar.
Set min-w-0 on the column so the inner scroll containers take over.
Assisted-by: pi:deepseek-ai/DeepSeek-V4.1-Flash
* ui : wrap markdown tables in a scroll container
Markdown tables render as a bare <table>, which keeps its content-driven minimum width and can stretch the chat column past the window. The table-wrapper CSS already existed, but nothing produced the wrapper.
Add a rehype plugin that wraps each table in div.table-wrapper, following the existing enhance-* plugins.
Assisted-by: pi:deepseek-ai/DeepSeek-V4.1-Flash
* ui : scroll long inline content inside markdown blocks
Long unbreakable content (inline code, paths, hashes) widened the message row and spilled over the neighbour elements. Give each markdown block a horizontal scroll container, and the content root one as well, since the trailing block renders with display: contents and has no box of its own.
Assisted-by: pi:deepseek-ai/DeepSeek-V4.1-Flash
* ui : use exact transition properties for markdown images
transition: all repainted every property and 300ms felt sluggish. Name transform and box-shadow at 200ms ease-out, and gate the hover scale behind (hover: hover) and (pointer: fine) so touch taps do not trigger it.
Assisted-by: pi:deepseek-ai/DeepSeek-V4.1-Flash
* ui : fit wide image attachments to the message width
Attachment thumbnails used a fixed height with w-auto, so a wide image kept its aspect-driven width and, being flex-shrink-0 in a right-aligned bubble, overflowed to the left of the message row.
Cap the thumbnail with max-height and max-width instead of a fixed height so it scales down proportionally, and let it shrink outside the single-row carousel.
Assisted-by: pi:deepseek-ai/DeepSeek-V4.1-Flash
* ui : keep long tool call titles inside the message row
A tool title could not shrink below its content, so a long path escaped the message row. Let the title span shrink and scroll, and for the file tools put the value on its own line only when it does not fit, with the value as the only scroll container.
Assisted-by: pi:deepseek-ai/DeepSeek-V4.1-Flash
* ui : render get info as a collapsible block with a table
get_info rendered its own always-open row with the values trailing the label. Use the shared ToolCallBlock chrome so it collapses like the other tools, and list os and cwd as table rows with the key as a row header.
The error and pending states now show inside the body, including the plain-string errors the server tools path produces.
Assisted-by: pi:deepseek-ai/DeepSeek-V4.1-Flash
* test : pin the server mode in the add menu a11y story
The story asserts the add menu's first enabled item is the reasoning submenu, which is mounted only outside router mode. The vitest dev server proxies /props to whichever server is running, so the assertion depended on the machine's server mode and failed whenever a router was up.
Pin the mode in the story, including props.role so a re-detection cannot flip it back.
Assisted-by: pi:deepseek-ai/DeepSeek-V4.1-Flash
* ui: wrap long markdown tokens instead of scrolling every block
Making each markdown block and the content root a horizontal scroll
container turns any hover transform into a scrollbar: the blockquote
translate and the image zoom overflow their block and flash a scrollbar
under it. Each block also becomes a block formatting context, so the
paragraph margins stop collapsing across blocks and the spacing doubles.
Drop both overflow-x rules and let long unbreakable tokens wrap with
overflow-wrap: break-word on the content root. break-word leaves the
min-content width untouched, so wide tables and code blocks keep
scrolling inside their own containers.
---------
Co-authored-by: Pascal <admin@serveurperso.com>
* metal: add F16 input to the FWHT
The Metal FWHT kernel accepts F32 input only. This change makes the source
type a template parameter, so the kernel reads an F16 source directly instead
of requiring a converted copy. The F32 instantiations are unchanged.
The pipeline name now carries the source type, and supports_op accepts an F16
src1 for the Hadamard hint at the four sizes the kernels cover. Every other
F16 src1 path still goes through ggml_metal_supports_mul_mat_op.
These are the test cases mentioned in #27779.
test-backend-ops on M5 Pro: MUL_MAT_HADAMARD 16/16, MUL_MAT 1265/1265.
* metal: ask the same FWHT question in supports_op and the dispatch
supports_op admitted an F16 src1 on the type, the hint and the width alone, but the
dispatch also requires src1 and dst to be contiguous and the same shape. A Hadamard
hinted MUL_MAT that passed the first and failed the second reached the generic path,
which has no F32 src0 by F16 src1 kernel, and aborted on a nil pipeline:
kernel not found in any metal library: base = 'kernel_mul_mv_f32_f16_4'
ggml_metal_encoder_set_pipeline: nil Metal pipeline
ggml_metal_use_fwht now holds the whole condition and both callers use it, so they
cannot drift apart again. The added test case has src1 and dst of different shapes,
which aborted before this change and is declined by the Metal backend after it.
* metal: branchless butterfly select in the FWHT simdgroup kernel
Review suggestion. Replaces the ternary in the shuffle stages with
val2 - val + 2*((lane & i) == 0)*val, which is the same value without the
select.
Measured on M5 Pro, interleaved A/B, five rounds, first discarded, on a
Hadamard matmul with block 512 and 65536 rows so the kernel rather than the
launch dominates: 1324.6 us before, 1285.0 us after, a 3.0% gain, and faster
in every round. At the shapes already in the perf suite the op runs 1.6 to
3.9 us against a 1.6 us launch floor, so the difference is not visible there.
FOR_UNROLL on the same loops was also measured and made no difference, the
delta changing sign between rounds, so it is not included.
* metal: move the FWHT dispatch predicates to ggml-metal-common
Review feedback. ggml_metal_use_fwht and ggml_metal_fwht_supported_size were
static inline in ggml-metal-device.h. They now follow the
ggml_metal_op_mul_mat_use_mm pattern: declared in ggml-metal-common.h and
implemented in ggml-metal-common.cpp, which is already the home for helpers
shared between supports_op and the op dispatch. The predicate is named
ggml_metal_op_mul_mat_use_fwht to sit alongside the _use_mm pair it parallels.
This also fixes the macos-latest-arm64 build. The header needed ggml-impl.h
for ggml_get_op_params_i32, but ggml-metal-device.h is reached from
tools/tuning through ggml-metal-tuning.h, and that target does not have
ggml/src on its include path. ggml-metal-common.cpp already includes
ggml-impl.h, so the accessor is used normally there and the header goes back
to needing nothing extra.
* metal: keep the FWHT size check internal and group the dispatch helpers
Applies the patch from the review. ggml_metal_fwht_supported_size becomes
static in ggml-metal-common.cpp since nothing outside it needs the size list,
which also drops stdint.h from the header again, and
ggml_metal_op_mul_mat_use_fwht joins the existing _use_mm declarations under
their shared comment instead of carrying its own block.
* tests: drop the mismatched-shape Hadamard case
I added a case with m != k to cover an abort, but the hint is a promise that
src0 is a Hadamard matrix, so src0 is square and dst has the same shape as
src1. Every other case in the suite holds to that. The case was not a valid
op, and on CPU it compared the FWHT against a real matmul of a non-square
src0, which cannot agree.
The supports_op and dispatch conditions still come from one predicate, which
is what keeps them from disagreeing on contiguity.
* chat: add dedicated Ling 3.0 (Bailing V3) parser
Ling 3.0 Flash templates pre-open the think block in the generation
prompt, so the model never emits an opening <think>, and a tool call can
arrive before any </think>. The generated autoparser terminated reasoning
only at the close tag, which classified such tool calls entirely as
reasoning_content: clients received content="" with no tool_calls and
agent loops died as reasoning-only turns.
Adds a specialized parser that terminates reasoning at the think close
tag or at a <tool_call> start, mirroring the hand-written Qwen3-Coder and
Kimi K3 parsers and the reference vLLM/SGLang Ling3 parser (which treats
<tool_call> as an implicit reasoning terminator). Detection is gated on
the <role>...</role> section markers, unique to this family among the
tagged-argument templates.
Adds the Ling 3.0 Flash chat template and tests covering the
unclosed-think tool call (full parse and streaming), healthy closed-think
paths, trailing prose, parallel calls, marker-like strings in argument
values, string-union and non-string argument types, and
reasoning_format=none.
Assisted-by: Kimi Code
* tests : move Ling 3.0 test
---------
Co-authored-by: aetherbird <aetherbird@users.noreply.github.com>
Co-authored-by: Alde Rojas <hello@alde.dev>
* server-models : show source per model in log
- Show [source] tag (preset/models_dir/cache) per model instead of cryptic * marker
- Show HF hub cache path in the 'Loaded cached model presets' log
- Add hf_cache::get_cache_dir() public accessor
Assisted-by: pi:llama.cpp/Qwen3.8-27B
* cont : pad log
* metal : add top-k MoE fusion
Adds a Metal fusion for SOFT_MAX + ARGSORT + GET_ROWS with optional
routing-weight normalization and scale, matching the top-k MoE fusion
available in the CUDA and Vulkan backends. The fused kernel writes the
selected expert ids and routing weights directly, eliding the separate
softmax, argsort, get-rows, sum-rows, clamp, div and scale kernels.
Assisted-by: pi:llama.cpp/DeepSeek-V4-Flash-Vision-Exp
* metal : add MoE weighted reduction fusion
Fuses MUL(experts, weights) plus the expert VIEW/ADD chain into one kernel
that computes the weighted sum directly. The graph_optimize hook keeps the
expert and weight buffers alive until the fused output so the allocator cannot
reuse them while the kernel is still reading them.
Assisted-by: pi:llama.cpp/DeepSeek-V4-Flash-Vision-Exp
* tests : expose MoE weighted reduction in fusion baseline
Use 2 experts per token in the generated MoE test models so the Metal
MoE weighted reduction fusion (MUL + ADD) is exercised by test-fusion.
Assisted-by: pi:llama.cpp/DeepSeek-V4-Flash-Vision-Exp
* metal : fuse RMS_NORM + SCALE
Adds NORM/RMS_NORM + SCALE fusion to the Metal backend by reusing the
norm+mul kernel with a scalar scale flag. Adds test coverage for both
NORM+SCALE and RMS_NORM+SCALE and regenerates the fusion baseline.
Assisted-by: pi:llama.cpp/DeepSeek-V4-Flash-Vision-Exp
* metal : use function constant for RMS_NORM + SCALE
Replaces the runtime use_scale karg with a Metal function constant. The
norm+mul kernel is compiled with FC_norm_use_scale=false for MUL fusion and
FC_norm_use_scale=true for SCALE fusion, so the fused kernel has no runtime
branch.
Assisted-by: pi:llama.cpp/DeepSeek-V4-Flash-Vision-Exp
* metal : use function constant for top-k MoE with_norm
Replaces the runtime with_norm karg with a Metal function constant. The
top-k MoE kernel is compiled separately for the normalized and non-normalized
routing variants, removing the runtime branch.
Assisted-by: pi:llama.cpp/DeepSeek-V4-Flash-Vision-Exp
* metal : rename moe_weighted_reduction suffix to moe_reduce
Shortens the MoE weighted-reduction fusion identifiers, kernel, pipeline,
matcher, args struct, and test op name from moe_weighted_reduction to
moe_reduce.
Assisted-by: pi:llama.cpp/DeepSeek-V4-Flash-Vision-Exp
* metal : add MUL_MAT + UNARY and MUL_MAT + ADD + UNARY fusion
Adds dense mat-vec activation fusion for sigmoid/silu and bias+softplus.
The mat-vec kernels apply the activation/bias epilogue via function
constants, avoiding the separate unary/add passes.
Assisted-by: pi:llama.cpp/DeepSeek-V4-Flash-Vision-Exp
* metal : revert MUL_MAT + UNARY and MUL_MAT + ADD + UNARY fusion
The mat-vec activation fusion regressed decode throughput on Qwen3.6-35B-A3B
by ~8% (tg32 81.5 vs 88.5 t/s). The regression is caused by loss of
concurrency: the standalone unary kernels previously overlapped with other
mat-vec work, while fusing the activation into the mat-vec kernel serializes
it on the critical path.
Assisted-by: pi:llama.cpp/DeepSeek-V4-Flash-Vision-Exp
* metal : add SSM_CONV + UNARY (silu) fusion
The SSM_CONV kernels apply silu directly via a function constant, eliding
the separate unary pass. Regenerates the fusion baseline.
Assisted-by: pi:llama.cpp/DeepSeek-V4-Flash-Vision-Exp
* metal : address fusion review comments
- Fix declaration/table alignment
- Rename top-k MoE kargs fields to val_clamp / val_scale
- Move moe-reduce alloc-deps handling into a general fusion helper
- Remove the public moe-reduce matcher API
Assisted-by: pi:llama.cpp/DeepSeek-V4-Flash-Vision-Exp
* metal : fix unused parameter in top-k MoE fusion check
Assisted-by: pi:llama.cpp/DeepSeek-V4-Flash-Vision-Exp
* metal : guard SSM_CONV fusion lookup behind use_fusion
Assisted-by: pi:llama.cpp/DeepSeek-V4-Flash-Vision-Exp
* metal : track all fused outputs in graph reorder
Assisted-by: pi:llama.cpp/DeepSeek-V4-Flash-Vision-Exp
* metal : keep top-k MoE logits alive until fused output
Assisted-by: pi:llama.cpp/DeepSeek-V4-Flash-Vision-Exp
* metal : refactor alloc deps to pattern-driven approach
Assisted-by: pi:llama.cpp/DeepSeek-V4-Flash-Vision-Exp
* metal : check fused kernel destination in concurrency tracking
Assisted-by: pi:llama.cpp/DeepSeek-V4-Flash-Vision-Exp
* meta : forward graph_optimize to underlying backends
Assisted-by: pi:llama.cpp/DeepSeek-V4-Flash-Vision-Exp
* metal : use vector for fusion table
Assisted-by: pi:llama.cpp/DeepSeek-V4-Flash-Vision-Exp
* meta : keep graph_optimize unimplemented
Assisted-by: pi:llama.cpp/DeepSeek-V4-Flash-Vision-Exp
* parallel : fix non-deterministic prompt selection
Assisted-by: pi:llama.cpp/DeepSeek-V4-Flash-Vision-Exp
* parallel : support dummy models and add global logits run hash
Assisted-by: pi:llama.cpp/DeepSeek-V4-Flash-Vision-Exp
* metal : sync cross-device copies with destination completion event
Assisted-by: pi:llama.cpp/DeepSeek-V4-Flash-Vision-Exp
* metal : avoid const_cast in fusion alloc deps
Assisted-by: pi:llama.cpp/DeepSeek-V4-Flash-Vision-Exp
* metal : skip fusions with aliased sources
Assisted-by: pi:llama.cpp/DeepSeek-V4-Flash-Vision-Exp
* metal : hide fusion pattern definition
Assisted-by: pi:llama.cpp/DeepSeek-V4-Flash-Vision-Exp
* metal : use vector fusion op sequences
Assisted-by: pi:llama.cpp/DeepSeek-V4-Flash-Vision-Exp
* metal : drop redundant struct keywords
Assisted-by: pi:llama.cpp/DeepSeek-V4-Flash-Vision-Exp
* metal : add alloc deps comment separator
Assisted-by: pi:llama.cpp/DeepSeek-V4-Flash-Vision-Exp
* metal : generalize fusion output memory ranges
Assisted-by: pi:llama.cpp/DeepSeek-V4-Flash-Vision-Exp
* metal : rename fusion out_offsets to outs
Assisted-by: pi:llama.cpp/DeepSeek-V4-Flash-Vision-Exp
* metal : avoid dst vector in memory range check
Assisted-by: pi:llama.cpp/DeepSeek-V4-Flash-Vision-Exp
* metal : optimize fusion matching and multi-output handling
- use pointer arithmetic for fusion info count lookup
- avoid heap allocations in top-k MoE and MoE reduce pattern matchers
- use fusion outs for multi-output subgraph checks
Assisted-by: pi:llama.cpp/DeepSeek-V4-Flash-Vision-Exp
* Revert "parallel : support dummy models and add global logits run hash"
This reverts commit 57c7caf941c1b43c270fd5009c9f175063522e96.
* fusion : update MTL.csv
* metal : unroll constant loops in top-k MoE kernel
Assisted-by: pi:llama.cpp/DeepSeek-V4-Flash-Vision-Exp
* metal : use function constants for top-k MoE n_expert and top_k
Assisted-by: pi:llama.cpp/DeepSeek-V4-Flash-Vision-Exp
* metal : rename fusion kargs to scale and clamp
Assisted-by: pi:llama.cpp/DeepSeek-V4-Flash-Vision-Exp
* metal : use function constants for moe_reduce and ssm_conv
Assisted-by: pi:llama.cpp/DeepSeek-V4-Flash-Vision-Exp
* fusion : update MTL.csv
Add support for the new DSV4 HC op variants used by qwen4exp:
- hc_pre with per-element sigmoid gate (gated variant)
- hc_post with identity mixing (comb == nullptr)
Assisted-by: pi:llama.cpp/Qwen3.8-27B
argsort_f32_i32_cuda_cub called the one-shot DeviceRadixSort::SortPairs
API with d_keys_in == d_keys_out (temp_keys, temp_keys). CUB's internal
double-buffer ping-pong requires distinct key buffers: with aliased
buffers the sort partially overwrites its own input mid-pass and emits a
corrupted permutation, surfacing as intermittent garbage indices (e.g.
backend top_k over a 248k-column vocab on Maxwell/CUDA 12.5/CCCL 2.x,
which then triggered out-of-bounds gathers in downstream get_rows).
Use a distinct keys-out buffer for all six call sites (plain and
segmented, ascending and descending, size-query and execute).
---------
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
Co-authored-by: Oliver Simons <osimons@nvidia.com>
Allow HMX flash-attention to run with head_dim not a multiple of 64
(e.g. SigLIP head_dim=72), by operating on DK/DV rounded up to 64 with
zero-filled tail lanes.
* ggml-cpu: add F16 input to the FWHT
The CPU FWHT accepts F32 input only. This change makes the source type a
template parameter. The CPU path now accepts F16 input and F32 input.
The CPU MUL_MAT reference now converts an F16 src1 to F32. It does this when
the caller sets the Hadamard hint.
No backend has an F16 FWHT kernel yet. The test cases come with the backend
changes that add one.
* ggml-cpu: assert the F16 FWHT input path, and use the bulk converter
Address review feedback.
The F16 branch writes plain floats into wdata, which is only correct when
vec_dot_type is F32. That invariant held because supports_op only accepts an
F16 src1 for the Hadamard hint with F32 src0 and dst, but nothing enforced it.
Assert it next to the existing src1 type check so widening supports_op cannot
silently break the write.
Replace the hand-rolled conversion loop with ggml_cpu_fp16_to_fp32.
* llama: read the SWA pattern as a period or a per-layer array
Add llama_model_base::load_swa_pattern(), which reads
sliding_window_pattern either as one flag per layer or as a period
expanded by set_swa_pattern(), and use it in every loader that reads
the key as a period.
These loaders silently ignored an array and applied their default
period, although the converters of olmo2, gemma3n and exaone4 write
arrays. The published GGUFs match the defaults, so their outputs do
not change. The loaders that already accepted both forms lose their
duplicated scalar-then-array block, and use their declared default
period when the key is absent.
* model-saver: write the SWA pattern and the MLA SWA geometry
Write sliding_window_pattern as one flag per layer, nextn layers
included, for every model using SWA. The array is never collapsed to
a scalar, since the loaders read a scalar as a period.
Also write the MLA key/value lengths and KV LoRA rank of the SWA
layers, required by dots3note.
This enables the saver for plamo3, gemma3, cohere2, cohere2moe,
olmo2, exaone-moe, afmoe, mimo2, spark2_5, muse-glimmer, mellum,
laguna, granite_swa, dots3note and maple, all passing the bit-exact
roundtrip of test-llama-archs.
* vulkan: add IQ3_S MMQ matmul kernels
* Make block_a_to_shmem do 2-byte loads (110 bytes is divisible by 2)
* Align the check, IQ3_S is also using K tile size
* vulkan: raise the hoisted row-id limit for mul_mat_id to 512 experts
The expert-count shader (count_experts.comp) sizes its shared arrays
with BLOCK_SIZE, which is 256. Because of that, row-id hoisting is
switched off for any model with more than 256 experts, and every
mul_mat_id workgroup has to rescan the whole ids tensor on its own.
Qwen3.8-Flash-Next has 512 experts and was quietly running on that
slow path.
This change sizes the arrays with a separate MAX_EXPERTS constant (512),
clears them in a loop instead of one entry per thread, and raises the
matching limit on the host side.
On Strix Halo at batch 2048 the expert matmuls drop from 12.5 to 9.5 ms
(iq3_s) and from 14.0 to 7.5 ms (iq4_nl) per op, and prompt processing
gets about 19 % faster at 8k tokens. test-backend-ops MUL_MAT_ID passes
(891/891) with new 512-expert test cases.
Assisted-by: Claude Fable 5.1
* vulkan: raise the hoisted row-id limit for mul_mat_id to 1024 experts
Follow-up to review feedback: 1024 matches LLAMA_MAX_EXPERTS instead of
stopping at 512. The three shared arrays in count_experts.comp grow to
3 * 1024 * 4 = 12 KiB, which fits the 16 KiB that Vulkan guarantees for
maxComputeSharedMemorySize.
Adds mul_mat_id test cases at 1024 experts alongside the existing 512
ones. test-backend-ops MUL_MAT_ID passes on Vulkan (RADV, Strix Halo,
Radeon 8060S): 889/889.
* 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>
* 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>
* 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.
* 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>
Georgi Gerganov