* model : add SenseNova U1 understanding support
Add support for the dense SenseNova U1 understanding branch, including
the U1.5-8B-MoT model.
- Add HF-to-GGUF conversion for NEOChatModel, excluding MoT generation
tensors and concatenating temporal and spatial Q/K normalization
weights.
- Register the sensenova_u1 GGUF architecture and custom spatial RoPE
metadata.
- Implement temporal/spatial Q/K normalization with 4-axis RoPE.
- Keep U1-specific text position handling in the model implementation.
- Add synthetic architecture coverage for sensenova_u1.
Only the dense understanding branch is supported; generation
branches are intentionally rejected during conversion.
* conversion : treat one-expert SenseNova U1 configs as dense
* mtmd : add SenseNova U1 vision support
Add conversion support for the SenseNova U1 vision encoder and implement
its CPU mtmd projector graph.
The graph applies patch embedding, GELU, adjacent-pair 2D RoPE, spatial
downsampling, and dense projection for dynamic-resolution image inputs.
* mtmd : fix SenseNova U1 vision token layout
* model : clean up SenseNova U1 RoPE metadata
Replace the hard-coded spatial RoPE key with shared GGUF and LLM_KV
definitions used by conversion, model loading, and architecture tests.
Clarify why SenseNova U1 retains its custom text-position input: U1 uses
[t, 0, 0, 0], while the generic M-RoPE input uses [t, t, t, 0].
* mtmd : simplify SenseNova U1 projector integration
Map the SenseNova U1 dense projection to the standard mm.0 weight and
bias tensors and reuse the shared projector fields in the loader and graph.
Read vision parameters from the checkpoint configuration and remove the
U1-specific tensor names and model fields while preserving its strided
projection, bias, and row-major token layout.
* mtmd : fix SenseNova U1 image positions and attention
* fix spacing
* mtmd : restore generic test input chunks
* mtmd : restore SenseNova U1 integration after upstream merge
* Update conversion/sensenova_u1.py
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@huggingface.co>
* Update src/models/sensenova-u1.cpp
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@huggingface.co>
* model : remove SenseNova U1 BF16 activation casts
Use standard backend precision handling for QKV, FFN, and output
projections instead of casting inputs based on the output tensor type.
Assisted-by: Codex
* Update src/models/sensenova-u1.cpp
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@huggingface.co>
---------
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@huggingface.co>
* llama: create new fused op for QSA scoring
* enroll glm5.3-next too
* tests : initialize the remaining tensors in test_kpool_mask
The harness sentinels were left uninitialized, so leftover memory could read as NaN and fail the NaN check.
* ggml: generalize the k-pool mask op into ggml_top_k_mask
The k-pool mask op only covered the pooled QSA selection of qwen4exp
and glm5-next, while the DSA models and the DeepSeek V4 CSA build the
same top-k mask by hand with fill, set_rows and add.
ggml_top_k_mask(mask, top_k, score, blocks, extra) keeps the mask
cells of the live picks and the extra cells, and sets the rest to
-inf. Every row tensor is 4D in the layout of top_k, and the mask is
broadcast to those rows, so a selection can also be made per head.
score, blocks and extra are optional: without score every pick is
live, without blocks a pick is a cell, and extra holds the cells a
row keeps without a pick.
llm_graph_context::build_fused_top_k_mask views the row tensors in
the layout of kq_mask and registers the fused node. The k-pool
models, the DSA attention of build_attn and the DeepSeek V4 CSA mask
use it when the probe enables the op, and keep their current path on
backends without it.
---------
Co-authored-by: Pascal <admin@serveurperso.com>
* llama : move LFM2 shortconv and FFN blocks into a shared LFM base
* Update src/models/lfm-base.cpp
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* model : drop the redundant precision overrides in the Kolibri-1 router
Follow-up to the review in #30673: the router matmul works with the
default precision, so both ggml_prec_set_* calls can go away.
Assisted-by: pi:llama.cpp/ggml-org/Qwen3.8-Flash-Next-GGUF (MacBook, M5 Max, 128GB)
* cont : fix comment
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@huggingface.co>
---------
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@huggingface.co>
* model : add Kolibri-1 (Aleph-Alpha) support
Kolibri-1 is a bilingual German/English MoE reasoning model with 78B
parameters and 3.46B active per token: 50 layers, 384 routed experts plus
one shared expert.
Attention interleaves four sliding layers at a 513 window with one full
layer. The sliding layers rotate with NeoX RoPE, the full layers are NoPE.
GQA runs 48 query heads against 4 KV heads at head_dim 128 with a
QK-RMSNorm, and every block carries four RMSNorms, so the attention and the
MLP output are normalized before they rejoin the residual stream. Routing
picks the experts from logits + expert_bias and weights them with the
sigmoid of the unbiased logits, without renorm.
The converter reuses AfmoeModel for the per-expert checkpoint layout and
the expert selection bias, and writes the shared expert and the per layer
sliding window pattern. The released FP8 checkpoint needs no special code,
it goes through the generic weight_scale_inv path. Three tensor names are
added to the mapping (post_attn_norm, post_ffn_norm,
moe.router.expert_bias) and the 128k vocabulary registers the qwen2
pre-tokenizer by hash. The arch is registered in test-llama-archs, with GQA
and the sliding window pattern.
Assisted-by: DeepSeek Harness (DeepSeek V4.1 Flash)
* Update src/models/kolibri1.cpp
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@huggingface.co>
* Add fusion records for kolibri1
* Update src/models/kolibri1.cpp
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@huggingface.co>
---------
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@huggingface.co>
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.
* 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>
* 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>
* 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>
These graphs only produce embeddings, and build_pooling() already
expands its output for every pooling type, so the expand at the end of
the model graph is redundant.
* llama : build the k-pool selection in the causal mask layout
- the glm5-next indexer folded the causal mask in through a [n_kv, n_tokens]
view: one reshape of the mask ahead of the add, and one of the result ahead
of every layer reading the selection, including the shared-indexer layers
- build the selection directly in the mask layout, as qwen4exp already does,
and drop the now-degenerate reshape in its attention as well
- assert the selection width matches the input it is taken from
Assisted-by: pi:llama.cpp/Qwen3.8-Flash-Next
* cont : remove comment
* graph: skip the LM head for pooled embeddings
A context that extracts pooled embeddings outputs one vector per
sequence, but every model still expanded its LM head, so the graph
computed n_vocab logits per token and the context kept a host buffer
for them.
The models now only declare t_logits, and set_outputs() expands it
unless the context extracts embeddings with a pooling type other than
NONE. Since the toggle changes the graph shape for a pooled context,
set_embeddings() schedules a re-reserve in that case.
* graph: drop the redundant t_embd expand
Address review from @CISC: with embeddings on, build_pooling() already
expands its output for every pooling type, and a decoder graph without
embeddings does not read t_embd, so set_outputs() only expands
t_logits.
* graph: add build_logits() for the LM head output
Address review from @ggerganov: the LM head is expanded by a dedicated
build_logits(), called from llama_model::build_graph() like
build_pooling(), and set_outputs() only marks the outputs again.
* graph: call build_logits() before build_sampling()
Address review from @ggerganov: build_sampling() reads t_logits, so the
LM head is built first. A pooled context then skips the sampling layers
too, since it has no logits to sample from.
* nit
The no-cache path builds a [n_tokens, n_tokens] mask and passes K and V
unpadded, so FLASH_ATTN_EXT sees K->ne[1] == n_tokens. The CUDA kernels
for head size 512 require a multiple of FATTN_KQ_STRIDE, so the op falls
back to the CPU for most lengths, and the -fa auto probe (1 token)
disables flash attention for such models.
Pad the mask like llama_kv_cache::get_n_kv and pad K and V with zeros up
to the KV dimension of the mask. clef builds its own causal mask and
pads it the same way.
* model : use exact GELU for ModernBERT encoders
Assisted-by: Codex
* model : keep tanh GELU aliases on ggml_geglu
Assisted-by: Claude Opus 5.5
* model : map gelu_python to ggml_geglu_erf
Assisted-by: Claude Opus 5.5
* model : support classifier_activation for rerankers
Assisted-by: Claude Opus 5.5
* model : map classifier gelu to gelu_erf and accept tanh
Assisted-by: Claude Opus 5.5
* model : default act_cls to tanh, ModernBERT falls back to gelu_erf
Assisted-by: Claude Opus 5.5
* 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
* 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.
* 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>
* 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.
* 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
* llama: re-pool each shared k-pool rep once
With shared cells every pool is re-pooled, and since the pooled keys
are always scattered, the pools a seq_cp shares between sequences
wrote the same rep row from several scatter entries, a data race on
the CPU backend. Mark each rep once: the sharing sequences read the
same row through pool_cells.
* llama: assert whole-sequence seq_cp in the hybrid idx memory
The recurrent state is always copied whole whatever the range, and a
k-pool cell shared by a partial copy could carry two pool groupings
with a single pooled row. Every caller copies whole sequences, so
reject partial ranges instead of supporting them.
* llama: drop the k-pool cache_safe mode
With whole-sequence seq_cp, sequences sharing cells share their pools
too, so the pooled row of a shared rep is valid for all of them. Mark
each rep once in every ubatch instead of re-pooling everything while
cells are shared, which removes the sharing scan and the stale-all
workarounds in seq_rm, state_read and state_drop. seq_cp now only
stales the destination.
* server: support vision input for Clef
* move input_attn_causal to private
* extend old server_batch::embd
* server_batch::token::pos to multi dim
* nits
* fix abort
* fix img tokens cap
* fix yield_to_queue mutate data
* llama : fix unexpected graph reallocation in the k-pool models
Both k-pool models built a graph shape that depends on state the
full-context reserve cannot know:
- qwen4exp branched on inp->cache_safe, which turns false as soon as
llama_memory_seq_cp shares cells (e.g. batched-bench -pps): the QSA
layers swapped scatter+gather for fill+concat and dropped the
new_pool_rep leaf, so the decode graph had 12 fewer nodes than the
reserved one
- glm5-next branched on gather = n_tokens <= 16 && n_kv > n_sel, so the
TG decode built the gather shape (7564 nodes) while the last reserve,
the PP one, had the dense shape (7762 nodes)
Either mismatch forces a decode-time re-reserve that drops the
worst-case sizing and bakes in the current state, so the next state
growth (n_pool, n_kv, n_new) needs more room at an unchanged graph size
and aborts under GGML_SCHED_DEBUG_REALLOC=1. Reproduce with, e.g.:
GGML_SCHED_DEBUG_REALLOC=1 ./bin/llama-batched-bench \
-hf ggml-org/GLM-5.3-Flash-GGUF:Q2_K -npp 2500 -ntg 32 -npl 1,2 \
-c 32768 -pps -kvu
Always scatter+gather the pooled keys, and pick gather from context
constants only: n_ubatch bounds every ubatch, top_k + kpool - 1 bounds
n_sel. Every graph of a context then shares one shape, which the
reserve covers, and the dense path measured faster than the gather path
at 2.5k and 16k context.
Assisted-by: pi:llama.cpp/MiMo-V2.6-Flash-MOPD
* llama : drop the unused k-pool cache_safe graph API
The k-pool graphs no longer branch on cache_safe, so nothing reads
get_kpool_cache_safe() or the conditional new_pool_rep any more: both
models always pass the scatter target, which set_input_kpool now
requires instead of merely preferring.
Also drop the cache_safe copy in kpool_build_sizes(), a sizes-only
helper. The layout and state flag itself stays, it still decides which
pools a layout with shared cells must re-pool.
Assisted-by: pi:llama.cpp/MiMo-V2.6-Flash-MOPD
* tests : add a shared-seq graph reserve regression test
Decode a prompt into seq 0, share its cells with seq 1 via
llama_memory_seq_cp (what llama-batched-bench does for -pps), then keep
decoding both sequences. For the k-pool models sharing clears
cache_safe, which changes the graph topology while the pools keep
growing, so a scheduler that re-reserves with the current state
instead of the worst-case one aborts under GGML_SCHED_DEBUG_REALLOC=1.
The test registration sets that flag, and the test aborts on both
k-pool models before 2220411ec1.
kimi-linear and minimax-01 are skipped: they reserve the final pp graph
with n_seqs = 1 (see [TAG_RESERVE_DIAG_DECAY] in llama-context.cpp), so
every multi-seq graph has a different layout and re-reserves by design.
Assisted-by: pi:llama.cpp/MiMo-V2.6-Flash-MOPD
* cont : add TODOs
* cont : fix comment
* cuda: match the moe weighted reduction on empty ubatches
ggml_cuda_match_moe_weighted_reduction rejected tensors with zero
rows. A ubatch without outputs shrinks the last layer to zero rows
through inp_out_ids, so graph_optimize dropped its alloc dep there and
the scheduler graph lost one node compared to the reserved one. The
scheduler then re-reserved at the size of that ubatch, and the next
ubatch with the same node count but larger tensors aborted under
GGML_SCHED_DEBUG_REALLOC=1.
The compute loop already skips empty nodes before trying any fusion,
so the guard only made the alloc deps depend on the row count.
* tests: build the rollback test only where internal symbols link
The shared-seq case calls llm_arch_from_string, which libllama does
not export through LLAMA_API, so linking test-recurrent-state-rollback
fails on Windows with shared libraries. Its build now sits in the
NOT WIN32 OR NOT BUILD_SHARED_LIBS block, next to test-llama-archs and
the test registration it already lives under.
* tests: skip archs by name in the shared-seq reserve test
The skip of kimi-linear and minimax-01 went through llm_arch_from_string,
which libllama does not export through LLAMA_API, so the test could not
link on Windows with shared libraries. It now compares the
general.architecture string directly, and the test builds on every
platform again.
---------
Co-authored-by: Pascal <admin@serveurperso.com>
* llama: support both embd + raw tokens in batch
* add to test-llama-archs
* also check case llm_arch_supports_mixed_batch = false
* constant graph topology
* have dedicated input for mixed case
* rm set_tensor_backend
* is_embd --> type
* consolidate m-rope pos handling into one place
* nits
* qwen4exp : halve the indexer score memory
The indexer scored all heads in one product and rectified a copy of it,
so two [n_pool, n_idx_h, n_tokens] f32 tensors were live at once, the
largest buffers of the graph at long context. Each head now gets its
own product, rectified and summed in place into one [n_pool, n_tokens]
score.
* qwen4exp: let the allocator reuse the indexer score buffers
Address review from CISC: use plain ggml_add and ggml_relu in the
indexer head loop. The graph allocator already runs them in place when
their source has no other consumer, so the _inplace variants are not
needed. The compute buffer and the speed are unchanged.
* cuda: support 4 heads in the lightning indexer
Dispatch 4 heads to the vector kernel, too few for a wmma tile, and
accept them in supports_op. test-backend-ops covers 4 heads.
* metal: take the lightning indexer head count as a function constant
The kernel reads the head count from a function constant and zero fills
the last head tile, so any head count runs and 64 heads is unchanged.
* qwen4exp: compute the indexer score with the lightning indexer
Address review from am17an: the unweighted sum of the rectified head
scores scaled by 1/sqrt(head_dim) is the lightning indexer with every
head weight set to that scale, so the indexer calls
ggml_lightning_indexer on the pooled keys with an f16 pool mask. The
keys are read once for all heads and no per head score is
materialized.
* vulkan: tile the lightning indexer over keys and tokens
A workgroup scores 64 keys against 8 tokens: the keys are staged once
in shared memory, the queries one head at a time, and each invocation
owns one key for two tokens, so no dot product needs a cross invocation
reduction. The subgroup variant and the flat dispatch are gone, the grid
is keys x tokens x streams.
* vectorize vulkan loads and use fp16 dot product
---------
Co-authored-by: Ruben Ortlam <rortlam@redhat.com>
* init conversion
* convert: ok
* model loaded
* add server code
* improve conversion script
* support shared prompt prefix
* add docs, imorove UX a bit
* add vision support
* add openjev tiny model for testing
* add dev docs
* support lev & kev
* clean up
* fix lev noul
* fix py lint
* nits docs
* clarify about not supporting date_facts
Maphist0andSigbjørn Skjæret
Aman GuptaandPascal
Tarek DakhranandGeorgi Gerganov
fzowlandSigbjørn Skjæret
Xue ZhenliangandSigbjørn Skjæret
Daniel Han
Mikhail Podvitskii
Xuan-Son Nguyen
Kartik Gulia
Karthik VijayanandKarthik Vijayan
Adam CaldwellandAdam Caldwell
Oliver Simons
Muhammad SaadandSigbjørn Skjæret
bri-prismandGeorgi Gerganov
bosh
Hrishith Thadicherla
