* model : add LiquidAI/d1-omni-600M decision model
Assisted-by: Claude Opus 5.5
* mtmd : keep conformer GLU sigmoid on CUDA
Assisted-by: Claude Opus 5.5
* server : take d1omni audio through images and input_audio, scope memory-less lfm2 to non-causal
Assisted-by: Claude Opus 5.5
* common : rename decision type d1omni to lfm2-d1-omni, server : make images an alias of files
Assisted-by: Claude Opus 5.5
* bugfix: infinite recursion caused by a tool named 'call'(#29967)
* chat : use index for schema and argument rules
* tests : remove tests
* tests : add expect_rules to peg test parser
---------
Co-authored-by: Alde Rojas <hello@alde.dev>
* model : add LiquidAI/d1-3b decision model
mtmd : read LFM2 image resize algo from GGUF
Assisted-by: Claude Opus 5.5
* common : rename decision type d1 to lfm2-d1
Assisted-by: Claude Opus 5.5
The CUDA FWHT covers widths 64 to 512. It runs one row per warp and keeps N/32
values per lane, so wider blocks need more registers per lane than that layout
allows.
fwht_cuda_block runs one row per thread block with 256 threads, so each thread
keeps N/256 values. Stages below the warp width still shuffle, those up to the
block width go through shared memory, and the rest stay in registers. Same
butterfly and sign convention as the warp kernel.
Widths 64 to 512 keep the warp kernel. 1024 through 8192 use the new one, for
both F32 and F16 sources. ggml_cuda_op_mul_mat_use_fwht (the shared
supports_op/dispatch predicate added in #29096) does not check width, so it
needed no change here: any width it admits that ggml_cuda_op_fwht can't serve
already falls through correctly to the cuBLAS path.
Rebased onto current master with #29096's F16 commit underneath it, since this
depends on the same F16 template infrastructure; that commit applied cleanly,
the only conflict was in test-backend-ops.cpp where an unrelated intervening
commit's own test additions landed near this block.
test-backend-ops on an A10 (lambdalabs): MUL_MAT 1297/1297, including all
FWHT/Hadamard cases (18 existing, 4 new F32 wide, 4 new F16 wide, 2 new
many-rows, 1 too-big boundary moved to 16384).
* llama: share the nextn tensor flags between models
Follow-up of the TODO in glm5-next: move the trunk-only and MTP-only
detection that each model copied into a nextn_flags helper of
llama_model_base. It probes the first trunk layer and the first NextN
layer, and adds TENSOR_SKIP when MTP is not loaded. qwen4exp probes
hc_attn_norm since it has no attn_norm.
deepseek4, nemotron-h, qwen35, qwen35moe, qwen3next and qwen4exp now
also accept a trunk-only file, like the other models.
* llama: avoid capturing structured bindings in the nextn flags
Lambdas that capture structured bindings need C++20, and GCC 15
rejects them under -Werror, so the models read the trunk and MTP
flags into plain variables.
The generic few-row MMA kernel works for any type with a 16-weight
dequantizer, so it now also takes BF16, Q1_0, Q2_0, MXFP4, Q2_K, Q3_K,
TQ2_0 and the IQ types. Each type starts at the row count where it beats
the current kernels on an M3 Ultra: 5 rows for TQ2_0, 4 for BF16, 3
for MXFP4, Q2_0, Q2_K and IQ4_NL, and 2 for the others.
test-backend-ops perf -o MUL_MAT, m=4096, k=14336, M3 Ultra, time of this
change over master (mean of two interleaved runs each): 0.23 to 0.98 from
the threshold to 8 rows, 0.24 to 0.33 at 9 to 16 rows, and 0.99 to 1.01 at
1 and 512 rows.
* metal : fix MUL_MAT+ADD fusion when the residual is itself a MUL_MAT
ggml_metal_op_mul_mat_mma picks the residual of a fused MUL_MAT+ADD as
"the ADD operand whose op is not MUL_MAT". When both operands of the ADD
are mat-mul outputs (x = W1 @ u + W2 @ v), that test is true for both, so
the residual resolves to the fused mat-mul's own, never-written output and
the kernel adds whatever that buffer holds.
The fusion check (ggml_metal_mul_mat_add_operand) already selects the
operand by identity; make the encoder do the same.
Clef decision models hit this in their head (proj_option_context @ ctx +
proj_option_lexical @ lex, 9 option rows): on Metal, /v1/systemone
probabilities collapse toward uniform (billing 0.28 where the CPU backend
gives 0.977, Cloudflare_clef-flash Q8_0), deterministic per memory layout,
correct with GGML_METAL_FUSION_DISABLE=1. Not a quantization issue: the
same file is right on CPU.
Add a MUL_MAT_ADD mode to test-backend-ops where the residual is a second
mat-mul; on Metal it fails 27 of 28 cases before this change (the one pass
is f16 n=2, under the MMA row threshold, so nothing fuses).
* Update tests/test-backend-ops.cpp
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* llama : add GLM5-Next NextN (MTP) graph
Build the GLM5-Next multi-token-prediction head as graph_mtp: the NextN block
embeds enorm(tok)+hnorm(h) through eh_proj, runs one plain DSA layer and the
shared lm_head, reusing the trunk's builders through the no_build tag ctor.
llama_memory_recurrent also tolerates a partial seq_rm when the context holds
no recurrent layers, which is what the MTP draft context needs.
Assisted-by: Claude
* llama : glm5-next: skip dead compute in headless NextN forwards
A NextN forward with no output rows (the MTP catch-up and the draft-context
prefill) persists only through its cache writes, so the headless graph keeps
the MLA latent, indexer key|gate and pooled-key writes and drops the query
path, the indexer selection, the attention body, the FFN and the LM head. The
4-token catch-up falls from 6.9 ms to 0.33 ms of kernels; the greedy output
hashes and the draft acceptance are unchanged.
Assisted-by: Claude
* llama : glm5-next: fix NextN extraction contracts and shared-tail rollback
Three fixes from the architectural review. The headless graph prune now also
requires that no unmasked nextn extraction is live, because that mode reads
n_tokens hidden rows regardless of the logits flags. Masked extraction
publishes the hidden rows gathered by the output ids, so a batch whose output
flags are not a prefix exports the right rows. A partial recurrent rollback
whose tail cell is shared with another sequence is now rejected instead of
silently moving that sequence's tail.
Assisted-by: Claude
* llama : glm5-next: tidy comments in the MTP changes
Assisted-by: Claude
* llama : glm5-next: crop the MTP graph to the output rows instead of pruning it
Replace the headless NextN prune with the crop pattern the other MTP
graphs use: gather the attention output and the block input at the
output ids before the position-wise FFN and the shared head. A NextN
forward with no output rows (the MTP catch-up and the draft-context
prefill) then runs the FFN and the head over zero rows. The 4-token
catch-up falls from 6.9 ms to 2.9 ms of kernels; greedy output hashes
are unchanged.
Assisted-by: Claude
* glm5-next: use the nextn crop helpers in the MTP graph
Replace the local crop condition and the masked select of t_h_nextn
with crop_before_nextn and crop_after_nextn, so the MTP graph narrows
its rows the same way as the main graph and the other models.
Describe the shared cell and empty filter branches of the recurrent
partial rollback.
* glm5-next: load MTP-only and trunk-only GGUF files
Make the trunk tensors optional when the file only holds the NextN
layer, and the NextN tensors optional when the file only holds the
trunk, so the split MTP GGUF loads as a draft model.
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
---------
Co-authored-by: Pascal <admin@serveurperso.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* sampling : use greedy selection for eligible temperature-zero chains
Assisted-by: OpenAI Codex
* Apply suggestion from @ggerganov
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* sampling: simplify zero-temperature greedy eligibility
Allow the same greedy selection on CPU and grammar/reasoning-budget paths.
Keep distribution sampling for dynamic temperature and requested probabilities.
Cover the common sampler selection and probability behavior in the existing sampler tests.
Assisted-by: OpenAI Codex
* sampling : use greedy selection after final top-k with k=1
Assisted-by: OpenAI Codex
* cont : clean-up
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* convert : Fix token configuration for PLaMo-3
The reasoning and tool calling tags in PLaMo-3 consist of three tokens
each. For example, for reasoning:
* reasoning start: `<|plamo:begin_`, `think`, `:plamo|>`
* reasoning end: `<|plamo:end_`, `think`, `:plamo|>`
Registering `<|plamo:begin_`, `<|plamo:end_`, and `:plamo|>` as
`USER_DEFINED` so that they are parsed correctly.
Also PLaMo-3 models use <|plamo:tag|> as EOT, while PLaMo-2 models
use <|plamo:op|>.
Take EOT token as a parameter and look it up so that PLaMo-2 and
PLaMo-3 can use their appropriate ones.
* use NORMAL instead of USER_DEFINED
* fix mixed different model GPUs issue
* Update ggml/src/ggml-sycl/ggml-sycl.cpp
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* sycl: accelerate GLM MLA prefill with MKL flash attention
GLM-4.7 Flash uses an MLA shape with 576-wide Q/K heads, a 512-wide
V head, GQA 20, and F16 KV. The SYCL dispatcher rejects this shape
because the normal MKL flash-attention gate requires matching K/V
widths and caps the head dimension at 512, so prompt processing falls
back to the substantially slower TILE kernel.
Admit only the validated 576/576/512, GQA-20 F16 shape to the existing
MKL pipeline. Keep all other mismatched K/V shapes on their current
fallback paths.
Handle GLM's V cache as a narrower strided view of K rows. Select the
strided F16 descriptor when row stride is padded, and alias K/V
dequantization buffers only when their logical widths match. Restrict
the stride exception to a real V view sharing K's row stride.
Add the exact 576/512, GQA-20 prompt-path backend test.
On an Intel Arc Pro B70 at master e613ef2, pp8192 improves from
432.80 to 1292.29 tok/s (2.99x, +198.6%). tg256 remains unchanged
within noise at 45.67 versus 45.65 tok/s. The exact MLA test passes
and debug output confirms MKL dispatch.
* sycl: store MKL flash attention scores in F16
Keep the QK GEMM output in F16 instead of F32. The online softmax still
converts each score to F32 for its max, exponent, and sum, so the
per-element math is unchanged apart from score rounding, and the F32
matrix was being written only to be consumed as F16 probabilities.
The F32 score matrix is the largest flash-attention intermediate on this
path; storing it as F16 halves its size and traffic. This builds on the
coalesced softmax loads from 1aa2954bd, which read each score row
cooperatively, so the smaller dtype pays off.
Measured on an Intel Arc Pro B70 with the dispatch from the previous
commit, -ngl 999 -b 4096 -ub 1024 -ctk f16 -ctv f16 -fa on:
pp8192 1583.9 -> 1657.1 tok/s (+4.6%)
pp64000 610.0 -> 684.5 tok/s (+12.2%)
pp131072 ~354 -> 402.1 tok/s (+13.5%)
tg256 at 8k context is unchanged (32.64), and the FLASH_ATTN_EXT suite
shows no new failures. The exact GLM MLA backend cases pass against CPU.
Adjust the ~354 baseline figure if you prefer citing only measured pairs (the 131k dispatch-only point came from the equivalent maintained build). Optionally add Assisted-by: <tool name> per the contribution guidelines since AI contributed to the change.
* Revert "sycl: store MKL flash attention scores in F16"
This reverts commit 265f974816.
* hex-cpy: replace more paths with dma and simplify l2flush
* hex-cpy: use DMA in all sametype paths
* hex-cpy: rewrite the rest of the copy paths (diff type) to use dma
* hex-concat: use dma for multi-dev path which also removes the need for l2-line alignment
* hex-concat: proper support for mdev splitting
* hex-concat: cleanup ctx and kern params usage
* hex-cpy: cleanup contex and remove left-over non-dma checks
* hex-cpy: clean dma_cpy naming
* hex-cpy: proper kernel params and kernel selection
* hex-build: resolve left-over rebase conflicts
* hex-cpy: update dev guide to clarify 128 byte alignment requirement
* hex-concat: make sure we go through mdev barrier
* hex-concat: make sure to flush dma-queue
* hex-cpy/concat: cleanup kparams and vtcm layout handling
* hex-cpy: remove dead check for contig (routed to diff kernel) and update comments
* hex-dev: update developer guide based on latest changes
* hex-cpy: safe skip of noop copies
* hex-dup: route DUP to CPY
The XIELU CUDA kernel template is already generic over the element
type; only the F32/F16 type assertion and the else-if dispatch were
missing. Add the nv_bfloat16 branch to the launcher, and drop the
temporary supports_op gate in ggml-cuda.cu that rejected BF16+XIELU.
test-backend-ops gains two BF16 cases ([10,5,4,3] and [512,16,1,1]).
docs/ops/CUDA.csv and docs/ops.md are regenerated; the F32 xIELU row
flips from no to yes as well, i.e. the previous record was stale.
Tested:
- Mac CPU: xIELU F32/F16/BF16, 6/6
- Mac Metal: existing F32/F16, 4/4; BF16 still unsupported
- RTX 4090 CUDA: xIELU F32/F16/BF16, 6/6
- RTX 4090 CUDA BF16-only: 2/2
- git diff --check passes
* vocab : implement PLaMo-3 tokenizer pre-segmentation
The PLaMo-3 tokenizer inserts hard boundaries before running the Unigram
DP, around <|plamo:...|>-looking text, and around runs of at least 4
identical characters or 2 spaces. Without them llama.cpp tokenizes code
indentation and repeated punctuation differently from the reference.
Reproduce the two re.sub() passes in llm_tokenizer_plamo2 by encoding
each segment independently.
* add vocab type "plamo3"
* Update src/llama-vocab.cpp
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@huggingface.co>
* misc change
---------
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@huggingface.co>
* model: K2 Horizon gguf conversion code
* model: loading hparams and tensors in k2-horizon.cpp
* model: K2 Horizon compute graph
* model: K2 Horizon compute graph adjustment and registering tokenizers
* model: K2 Horizon chat template and accomodate safetensors naming
* unicode : add the K2-Horizon pre-tokenizer splitter
The K2-Horizon regex had no arm in unicode_regex_split_custom and fell through to the
general std::regex fallback, which fails two ways.
On MSVC std::regex rejects \p{...}, so no K2-Horizon GGUF loads on Windows at all:
llama-quantize, llama-imatrix and llama-perplexity all abort with
regex_error(error_escape) before a token is produced.
Where the fallback does compile it is still wrong. unicode_regex_split collapses each
codepoint to a single byte naming its Unicode category before matching, and U+200C/U+200D
are category Control, which has no entry in k_ucat_cpt, so both become the 0xD0 fallback
byte. The literal and alternatives in K2's regex can then never match and
every ZWNJ or ZWJ ends a letter run.
The splitter is the existing llama3 one with a single rule widened, since K2's regex
differs from llama3's only in that a letter run also takes marks, ZWNJ and ZWJ.
tests/test-unicode.cpp gains a case for this: it fails before the change with
[Amy] [ZWNJ khaham] and passes after with the run intact.
* tests: expand K2 Horizon unicode splitter coverage
* unicode: handle K2 Horizon case folding and empty input
Assisted-by: Codex
* jinja : support sequence indices in selectattr and rejectattr
Assisted-by: Codex
* model : add K2 Horizon dense and MoVA support
Includes the K2 Horizon implementation from ifm-ai/llama.cpp with converter, tensor-parallel and model save/reload fixes.
Assisted-by: Codex
* chat : support K2 Horizon reasoning and tool calls
Assisted-by: Codex
* conversion: remove obsolete K2 Aurora alias
Assisted-by: Codex
* k2-horizon: enforce response schemas and load YaRN betas
Constrain final JSON after reasoning, accept flexible JSON tool envelopes,
enforce XML dialects, and handle repeated or alternate thinking markers.
Load YaRN beta metadata instead of retaining the default values.
Add schema, streaming, continuation, and model reload regressions. Validate
CUDA and CPU builds and 0.9B, 4B, and MoVA conversation/tool round trips.
Assisted-by: Codex
* renaming template fixture
* adressing cisc follows ups
* desloppify the parser / adress aldehir comments
* clean test-chat
* remove fallback : model trained mostly on high anyway
* fix k2 attn_v_exp tn splitting and metal fusion baseline
* k2-horizon : forward expand views before sums
* k2-horizon: copy embds before group norm to fix TP
* disable tesnor parallelism
---------
Co-authored-by: Ryandito Diandaru <ryandito.diandaru@mbzuai.ac.ae>
Co-authored-by: WestWaters <mario.papaleo2013@gmail.com>
Co-authored-by: Natani L. Mayday <71436458+TaskPuppyNatani@users.noreply.github.com>
Co-authored-by: West <100190545+WestWaters@users.noreply.github.com>
Co-authored-by: aaryamonvikram <aaryamonvikram@gmail.com>
Co-authored-by: aaryamonvikram <96529820+aaryamonvikram@users.noreply.github.com>
The gather path attended over the selected latents with a plain
matmul and softmax. It only ran with n_ubatch <= 16, and the flash
attention backends now skip the masked rows through n_kv_max, so
the scatter path covers every case.
Drop the gather flag, the gathered attention branch and
gather_mla_rows. set_input_kpool always maps padding to the n_kv
sentinel, and the slot mask becomes sel_mask since only the
scatter reads it.
* ggml: fix CLAMP on non-contiguous views (CPU, CUDA)
CUDA clamped ggml_nelements values flat and ignored the view strides.
CPU addressed row j as j*nb01 and ignored nb02/nb03. Both now follow the
strides of dims 1..3; CUDA supports_op requires contiguous rows, like
Metal. test_clamp gains a non-contiguous view case.
* cuda: clamp kernel uses fastdiv for the view strides
A 1.0 loader (e.g. Android 8.1) has no vkEnumerateInstanceVersion, so
backend init called a null pointer. Treat it like any loader under 1.2.
Fixes#29871.
Assisted-by: Claude Opus 5.5
* mimo2 : always emit h_nextn
the other nextn-capable models set it unconditionally
Assisted-by: pi:llama.cpp/MiMo-V2.6-Flash-MOPD
* models : consolidate nextn row cropping into shared helpers
- replace the duplicated crop conditions and the per-model flags (narrow_early,
crop_before_ffn, crop_last_layer, emit_h_nextn) with two helpers on llm_graph_context:
crop_before_nextn() / crop_after_nextn()
- models that only tested embeddings_nextn_masked now share the same condition, so they
crop the last layer before the nextn capture whenever extraction is off
- t_h_nextn is now set unconditionally in mimo2, qwen4exp and deepseek4 (as in the other
nextn-capable models); host-side reads stay gated by cparams.embeddings_nextn
Assisted-by: pi:llama.cpp/MiMo-V2.6-Flash-MOPD
The speculative MTP init enables NextN extraction on the target and draft
contexts after both were created and their schedulers reserved. With
unmasked extraction the trunk graph keeps every token through the last
layer instead of cropping to the output rows, so the first decode
reallocates to that batch's shape and the next, wider batch trips
GGML_SCHED_DEBUG_REALLOC. Invalidate the reserve when the flags change so
the next compute re-reserves with the new graph shape.
Assisted-by: Claude
kurquhar
Tarek Dakhran
Nicolas Budyn
Frost-54andAlde Rojas
bri-prism
Terrence Zhao
Aman Gupta
ShobhitandGeorgi Gerganov
Pascal
pratiknarola-t
Niko MaroulisandGeorgi Gerganov
SIDDARTHA REDDY
Hrishith ThadicherlaandGeorgi Gerganov
Sigbjørn Skjæret
Toki Nasin
R0CKSTAR
Alessandro de Oliveira Faria (A.K.A.CABELO)
yarikandyarik
Masashi Yoshimura
Evan Huus
Neo ZhangandGeorgi Gerganov
Anant Shrivastava
Pratyush Kumar
Ruben Ortlam
Clemens Wasser
Mendy Berger
Max Krasnyansky
qiao_px
Harkirat Gill
Xuan-Son Nguyen
lhez
thelittlefiremanandJohannes Gäßler
Foad Abo Dahood
Ian McKellar
Daniel Bevenius
Kartik GuliaandSigbjørn Skjæret