* hexagon : fix DMA transfers with strides > 24 bits
On v75+ the 2D DMA descriptor stores row_size and strides in 24 bits.
dma_queue_push sent oversized strides to fallback_2d, which only splits
nrows, so strides of 16 MB or more were truncated - wrong addresses.
Fix will fall back to one transfer per row in that case.
Also added two CONT tests with source strides over 16 MB.
* hex-dma: refactor the fallback paths to remove duplication
---------
Co-authored-by: Max Krasnyansky <maxk@qti.qualcomm.com>
* chat : accept the think block in the qwen3-coder response format with reasoning_format none
Assisted-by: Claude Opus 5.5
* chat : define both qwen3-coder think block variants up front
* memory: refuse rollbacks past the snapshots of the last ubatch
The recurrent memory and the DSV4 cache accepted any rollback up to
n_rs_seq, whether or not the last ubatch wrote that snapshot. Each now
records the deepest rollback its last ubatch guarantees and seq_rm
refuses anything past it:
- recurrent: n - 1 for a ubatch of n <= n_rs_seq + 1 tokens, 0 for a
longer one, which only guarantees the final state
- DSV4: min(n, n_rs_seq), the snapshots are rebuilt from the state
before the ubatch
The dead rollback block of the rollback test becomes the assertion it
asked for, and rollback_bound checks the exact bound of each memory
after ubatches of 1, 2, n_rs_seq + 1 and n_rs_seq + 2 tokens.
* tests: address review from ggerganov
Detect architectures with llama_arch_from_string in comp_padding and
arch_reserves_single_seq, as in rollback_bound.
* WIP OCP FP8 E4M3 support
* Fix python type-checker
* Materialize input_scales also for FP8 when converting
* Fix conversion of https://huggingface.co/RedHatAI/Qwen3.8-27B-NVFP4
1. Per row FP8 wrongly fell to NVFP4 conversion, as NVFP4 checked on
dims alone. Also check on dtype for FP8
2. Need to reshape FP8 QKV projections scales in the same way that
weights are reshaped
* Allow remaining FP8 tensors to be dequantized during conversion
* Allow tensor-strategy for FP8-only compressed-tensor ckpts
* Fix and test gguf-py behavior for FP8_E4M3
* Disallow QKV-fusion for FP8 temporarily
* Fix conversion of fused qkvz & FP8 for Qwen3-Next
* Avoid fusing FP8 for --fuse-gate-up-exps as we may have different ws
* Emit warnings when not fusing FP8 during conversion
* More FP8 conversion fixes
* Add supported for strided copies to/from FP8 in CPU
* remove unneeded scratch buffer from FP8 cpu cpy/dup path
* add explanatory comments and TODOs
* Add dedicated encoder and decoder tests for FP8
* Whitelist ggml_op_get_rows in metal instead of blacklisting
required to fix failing CI, and white-listing should be more
future-proof than adding FP8 to the black-list
https://github.com/ggml-org/llama.cpp/actions/runs/38433916498/job/115422880633?pr=27512
* Fix more conversion bugs
* Defer weight materialization during conversion
* Blacklist fp8 in metal backend instead of white-list
* Adjust indents
* Fix lint + execute tests
* Add fast CPU kernel for F8_E4M3
* Conversion fixes
* Fix remaining gaps
* Enable tied weights with shared scales
* Make mandatory-F32 rules apply to FP8 during conversion
* Fix python type checks
https://github.com/ggml-org/llama.cpp/actions/runs/39765670052/job/119780379595?pr=27512
* Fix kaleidai unit-test failures
https://github.com/ggml-org/llama.cpp/actions/runs/39765670125/job/119780383150
* Relax numeric precision constraints for pre-check
* Leave fast-FP8 CPU on RISC-V for follow-up
We already exclude arm from the fast-path
* Allow output scale tensors to be located in any gguf shard
* Move test_conversion_fp8.py test execution to behavior-check.yaml
* Apply batched suggestions from code review
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@huggingface.co>
* Move mixed compressed-tensor logic into dequant_fp8 + add unit-tests for it
* Install all requirements also on Windows for behavior-check
Fixes https://github.com/ggml-org/llama.cpp/actions/runs/40535852590/job/122300758223?pr=27512
* Unmap lazy tensors before clean-up in Windows
Should fix https://github.com/ggml-org/llama.cpp/actions/runs/40539797824/job/122314782459?pr=27512
* LF line endings
https://github.com/ggml-org/llama.cpp/actions/runs/40539798168/job/122314791118?pr=27512
* Extract FP8 tests from llama-arch to its own test-fixture
See https://github.com/ggml-org/llama.cpp/pull/27512#discussion_r4400628221
* Update gguf-py/gguf/scripts/gguf_convert_endian.py
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@huggingface.co>
---------
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@huggingface.co>
* gguf-py : add Limite arch
Assisted-by: Claude Opus 5.5
Signed-off-by: Giuseppe Scrivano <giuseppe@scrivano.org>
* convert : add Limite support
Assisted-by: Claude Opus 5.5
Signed-off-by: Giuseppe Scrivano <giuseppe@scrivano.org>
* llama : add Limite support
Assisted-by: Claude Opus 5.5
Signed-off-by: Giuseppe Scrivano <giuseppe@scrivano.org>
---------
Signed-off-by: Giuseppe Scrivano <giuseppe@scrivano.org>
Requested in the #31125 review. The cases duplicate coverage that is
already in the file and add CI time. Test-only, nothing changes in the
kernel, the docs or the CMake option.
Assisted-by: Claude
* hex-upscale: basic upscale ops (not fully vectorized)
* hex-upscale: optimize nearest 2x, 4x and bilinear 4x
* hex-upscale: add support for non-contig rows and dims collapsing
* hex-upscale: use vgather instead of scalar staging
* hex-upscale: move all kernel select logic to the host, review comments and cleanups
* tests: add permuted UPSCALE test to cover qwen3-vl image->text scenarios
* sycl : add DPAS flash attention kernel for quantized KV cache
Decode and small batches (1 to 31 query tokens) now run flash attention
straight on q4_0 and q8_0 KV blocks, instead of scalar dequant or
converting the whole cache to f16 per call. Q.K uses s8 x s4 and s8 x s8
DPAS on XMX, V.P stays f16. dpas.s4.s8 is inline vISA since joint_matrix
has no s4.
Covers head sizes 64 to 512, GQA up to 32 and attention sinks. Prompts
stay on the existing kernels. GGML_SYCL_FA_DPAS=0 turns it off.
B580, Llama 3.1 8B Q4_K_M, q4_0 KV, tg128: 31.3 to 54.2 t/s at 16K
context. FLASH_ATTN_EXT 4582/4584, same 2 failures as master.
Assisted-by: Claude
* sycl : 192 head dropped, Wildcat Lake added to docs
Assisted-by: Claude
* sycl : stricter checks from review
Assisted-by: Claude
* [SYCL] : rename XMX macro, add GGML_SYCL_SUPPORT_XMX option, document DPAS
Assisted-by: Claude
* hexagon: IM2COL over multiple threads with output-col blocks
Assisted-by: Claude Opus 5.5
* hex-im2col: fix padding handling
* hex-im2col: loop unroll common cases
---------
Co-authored-by: Max Krasnyansky <maxk@qti.qualcomm.com>
* llama : fix DSV4 compressor padding for simple batches
Count actual tokens per sequence when padding compressor writes. Add a graph reuse regression test for odd-sized DeepSeek V4 and V4.1 batches.
Assisted-by: Codex
* tests : cover multiple sequences in DSV4 padding regression
Exercise one and two sequences at different positions while keeping a fixed ubatch shape.
Assisted-by: Codex
* llama : remove simple batch splitting for DSV4
Use the existing sequence splitting to keep n_seq_tokens consistent with
the compressor padding calculation. Extend the graph reuse regression to
6-token batches.
Assisted-by: Codex
* chat : strip trailing whitespace from parsed reasoning and content
Assisted-by: Claude Fable 5.1
* chat : stop qwen3-coder reasoning and content before template separators
Assisted-by: Claude
* tests : remove the Qwen parser case from the chat session tests
Assisted-by: Claude
* common : handle partial prefix until match at end of generation
---------
Co-authored-by: Zeshawn Zahid <zzshawnn@gmail.com>
Co-authored-by: Alde Rojas <hello@alde.dev>
* server: expose the reasoning effort levels of the chat template
The capability probe renders a user turn for each candidate level
(minimal, low, medium, high, xhigh, max) and for an unknown level. A
level is honored when it renders without raising and differently from
the unknown level, which shows the template fallback. Levels sharing a
render are aliases and the highest one is kept.
The caps serialize to JSON, and chat_template_caps in /props reports
the list as reasoning_effort_levels, from lowest to highest.
* jinja: keep the caps free of the json dependency
The caps keep reporting their flags as a map, and the effort levels
stay a plain member. common_chat_templates_get_caps() builds the JSON
the server reports from both, so jinja depends on json only for the
probe inputs.
In --models mode the thread count per model is computed and printed,
but never set in the params, so every context runs on the common
default, the number of physical cores. With several models in
parallel this oversubscribes the CPU, and the busy-wait barriers of
the CPU backend make it collapse.
Set the count in cpuparams and cpuparams_batch.
* common : support deferred tools and tool_reference content parts
Assisted-by: Claude Opus 5.5
* common : credit the deferred tools co-author
Co-authored-by: Aldehir Rojas <765227+aldehir@users.noreply.github.com>
Assisted-by: Claude Opus 5.5
* tests : fix the type check of the deferred tools test
Assisted-by: Claude Opus 5.5
* model : add LiquidAI/LFM3-1.7B and LiquidAI/LFM3-VL-2B
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@huggingface.co>
Co-authored-by: Xuan-Son Nguyen <son@huggingface.co>
Assisted-by: Claude Opus 5.5
* tests : set the SWA rope base of the LFM3 test model
Assisted-by: Claude Opus 5.5
* llama : enable recurrent state rollback for LFM3
Assisted-by: Claude Opus 5.5
* Apply batched suggestions from code review
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@huggingface.co>
Co-authored-by: Tarek Dakhran <github@dakhran.com>
* gguf-py : align the lfm3vl mapping comments
Assisted-by: Claude Opus 5.5
* llama : fix CRLF line endings from the review suggestions
Assisted-by: Claude Opus 5.5
* tests : add the LFM3 Metal fusion baseline
Assisted-by: Claude Opus 5.5
* llama : keep the LFM3-VL depthwise conv kernels in F32 when quantizing
Assisted-by: Claude Opus 5.5
---------
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@huggingface.co>
Co-authored-by: Xuan-Son Nguyen <son@huggingface.co>
* tests : speed up tensor data generation and model saving
Refactor save_models to create only the model, not a llama_context,
which is not needed for saving. Replace the slow std::normal_distribution
draw with a splitmix64 + box-muller normal generator.
Assisted-by: dsh:llama.cpp/ggml-org/DeepSeek-V4-Flash-Vision-Exp-GGUF (Mac Studio, M2 Ultra, 192GB)
* tests : use a shared pool of normal draws for tensor data
Generate a pool of normal draws once with std::mt19937 and
std::normal_distribution, then have every tensor copy a cyclic slice of
it instead of running a per-tensor box-muller. The name-keyed slice
selection keeps the draw deterministic.
Assisted-by: dsh:llama.cpp/ggml-org/DeepSeek-V4-Flash-Vision-Exp-GGUF (Mac Studio, M2 Ultra, 192GB)
* tests : log the size of saved models
Report the produced file size in bytes and MB after each save.
Assisted-by: dsh:llama.cpp/ggml-org/DeepSeek-V4-Flash-Vision-Exp-GGUF (Mac Studio, M2 Ultra, 192GB)
* tests : simplify the model save log
Put the model name, target and size on one line per saved model.
Assisted-by: dsh:llama.cpp/ggml-org/DeepSeek-V4-Flash-Vision-Exp-GGUF (Mac Studio, M2 Ultra, 192GB)
* vulkan: allow dim 3 broadcast in MUL_MAT
supports_op rejected every MUL_MAT whose src0 and src1 differ in ne3,
although mul_mm, mul_mmq and mul_mat_vec already broadcast src0 over
dim 3 through broadcast3. Such a matmul then ran on the CPU with a
copy of the weight, and the split layout changed with the number of
streams in the ubatch.
Keep the strict check for MUL_MAT_ID only, and route the broadcast
cases away from mul_mat_vec_nc, the one path that requires equal ne3.
* tests : harden MUL_MAT with permuted dim 3 broadcast cases
Harden the MUL_MAT coverage with a weight broadcast over dim 3 against
a permuted per-stream activation, the shape of an MLA output
projection with a non unified KV cache, for a single token, several
heads and several tokens per stream.
* model : add MiniCPM-OCR-Jev support
The model is a Qwen3.5-VL trunk with a value head read at the last token: it
scores an OCR text against an image rendered from it, it does not generate.
Add the openbmb-minicpm-ocr-jev decision type, served by POST /v1/systemone.
The class scores are the pooled output of the sequence, read with last pooling,
so the conversion turns score.weight into cls.output and sets
embedding_length_out. The checkpoint has no LM head, so text generation is
rejected instead of using the output weights duplicated from the token
embeddings.
The task is fixed, so its prompt is entirely in the systemone template, the
same way h2o-lightning keeps its default there. The processor of the checkpoint
allows up to 16384 merged tokens per image, more than the Qwen-VL default of
mtmd, so the mmproj keeps the image budget of the preprocessor config. The
checkpoint declares MiniCPMOCRForSequenceClassification in its config.json, as
other models do.
Signed-off-by: tc-mb <tianchi_cai@icloud.com>
* fix code
Signed-off-by: tc-mb <tianchi_cai@icloud.com>
* use gguf meta classifier_output_labels
* remove redudant code changes
---------
Signed-off-by: tc-mb <tianchi_cai@icloud.com>
Co-authored-by: Xuan Son Nguyen <son@huggingface.co>
* ggml : relax scale to non-contiguous views
SCALE requires src0 to be 1D-contiguous. Relax this to dim-0
contiguity with arbitrary row strides (ggml_is_contiguous_rows),
same as the unary ops in the Metal backend.
- ggml: relax the assert in ggml_scale_impl
- cpu: address src0 rows through their byte strides (f32, bf16)
- cuda: stride-aware kernel, fastdiv row decomposition
- metal: support bf16 scale (guarded unary instantiations)
- sycl: keep the gate at contiguous, the kernel is flat f32
Add view variants to test_scale. Verified on CPU, Metal and CUDA.
Assisted-by: dsh:llama.cpp/ggml-org/Qwen3.8-Flash-Next-GGUF (RTX 5090 32GB, DDR5 128GB)
* cuda : skip the softcap fusion on non-contiguous views
The fused softcap kernel reads src and writes dst as dense arrays,
so a view reaching SCALE -> TANH -> SCALE now that SCALE accepts
non-contiguous rows would be read wrongly. Keep such graphs on the
strided kernels until the fusion becomes stride aware.
---------
Co-authored-by: Pascal <admin@serveurperso.com>
* tests : fix multi-seq split replay against trained models
The rollback snapshot is captured by the graph that decoded the rolled-back
tail, while the reference context decodes the same prefix with different
batch shapes. The two states differ in the last bits and trained models
amplify the difference with every replayed token. Compare only the first
replayed token of each seq, then restore the rollback state into the
reference context before the seq-1-only tail.
Assisted-by: dsh:llama.cpp/ggml-org/Qwen3.8-Flash-Next-GGUF (MacBook, M5 Max, 128GB)
* tests : require bit-exact seq-1 isolation in split replay
The seq state save is not always bit-lossless - SWA-masked cells are not
saved - so reload the restored state back into the rollback context. That
way both contexts enter the seq-1-only tail with byte-identical memory,
including the cell layout, and the tail logits must match bit-exact. Also
assert that the restored state round-trips bit-identical.
Assisted-by: dsh:llama.cpp/ggml-org/Qwen3.8-Flash-Next-GGUF (MacBook, M5 Max, 128GB)
* tests : drop the n_swa partial-state check from ps
The check keys off llama_model_n_swa(), which returns 0 for dsv4 even
though its raw KV cache has a 128-cell sliding window. The partial state
grows with the tokens there by design, so the flatness check is wrong.
Keep the partial <= full invariant.
Assisted-by: dsh:llama.cpp/ggml-org/Qwen3.8-Flash-Next-GGUF (MacBook, M5 Max, 128GB)
Every dst row comes from src0 or src1, or for dim 0 from the row of
src0 followed by the row of src1, so a single kernel covers every
type: a dense source row is one memcpy, quantized types included,
and a strided source is copied element by element with the element
size known at compile time.
Rows are split across threads instead of i2 alone, which is often 1,
and are copied in tiles of 32: for a transposed source the elements
are walked in the outer loop, so the rows of a tile read neighboring
addresses instead of jumping a full row stride per element.
Add transposed b cases to test_concat, the layout of the delta-net
conv input, and a perf case for it.
* tests: pin model resolution on real repo listings
Extend test-model-resolution with 63 repo listings captured verbatim
from the HF API, chosen to cover every file naming pattern found across
1237 GGUF repos of the major publishers and pruned to whole quant groups.
Each repo pins its resolved plans and its cache listing.
The expectations follow what each file really is per its GGUF header,
not what the current resolver picks, so a mismatch is a resolution bug.
Mismatches are collected and reported together before the test fails.
* tests: leave the listing unchecked on paths the filesystem refuses
Some real split names (DevQuasar's Model.Q2_K.gguf-00001-of-00012.gguf
under a quant folder) exceed MAX_PATH once materialized in the temporary
cache on Windows, so the listing of such a repo is skipped with a note
while its plans stay checked.
* tests: pin real listings to master on GGUF convention names only
Replace the real repo table with 171 HF listings whose file names follow
the GGUF naming convention (lowercase sidecar prefix, - delimited
components, quant last), one per layout pattern found across 1174 GGUF
repos and pruned to whole quant groups. Repos outside the convention are
dropped. Each repo pins the plans and the cache listing master gives
today, so the test is a pure harness: any change in resolution shows up
here, and mismatches are reported together.
test-llama-archs included src/llama-arch.h and src/llama-model-saver.h,
which prevented building on Windows with shared libraries since the
used symbols are not exported from the DLL.
- declare llama_arch_* wrappers around the internal llm_ arch query
functions in the llama-ext.h staging header, together with
llama_arch_kv_name() and llama_arch_supports_save()
- slim llama_model_saver down to the private implementation of
llama_model_save_to_file(): the fixture-writer half used by the test
moves into tests/test-llama-archs.cpp as a local kv_writer
- replace the roundtrip saver usage with the public
llama_model_save_to_file() writing to a temp path (also removes the
tmpfile() fd leak and its Windows workaround)
- move test-llama-archs, test-generate-models, test-llama-context and
test-fusion out of the NOT WIN32 OR NOT BUILD_SHARED_LIBS guard
Assisted-by: pi:llama.cpp/ggml-org/GLM-5.3-Flash-GGUF (2x DGX Spark (RDMA, TP=2))
* Metal : stop overflow from happening in large matrix output offsets
Assisted-by: Codex
* metal : widen batch offsets and MUL_MAT_ID token indices
Assisted-by: Codex
---------
Co-authored-by: feni6 <270037094+feni6@users.noreply.github.com>
* kv-cache : use ggml_backend_tensor_copy to refill the DSV4 K mirrors
* llama : move the Engram pad id to llama_hparams and the token map to llama_model
The saver no longer needs the deepseek41 model class.
* deepseek41 : drop the F32 src1 request on the HC mixes matmul
* ggml : add softplus TODOs in the backends that still use log(1 + exp(x))
* convert : make the DSV4/DSV4.1 modify_tensors generators, drop the redundant vision filter
* deepseek41 : clean up comments
* deepseek41 : drop the F32 src1 requests on the router and indexer matmuls
* convert : use format_tensor_name directly in DeepseekV4Model
* s/comp_kv_src/comp_il_src/g
* cont : fix TODOs for log1p
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* common, grammar: add recursive depth limit, make it a requirement
* catch more recursive functions
* common : tidy up peg parser changes
* grammar : reject candidates in a loop, without a depth limit
The depth limit in llama_grammar_reject_candidates_for_stack counted
the code points of a vocab token, so any token of 256 chars or more
was always rejected under a grammar, such as the 512-space token of
GLM-4.7-Flash or the long space tokens of Laguna-S-2.1.
The rejection now walks the code points with its own stack of levels
instead of recursing: there is no limit and no stack risk, whatever
the length of a token from the GGUF, and the masks match master.
* grammar : track rejected candidates as indices into the input
At a given code point a candidate only differs from its input by the
position of its code point pointer, so the levels now carry indices
into the input candidates instead of copies. The first level needs no
special case and the rejects map back to the input directly.
Grammar sampling gets faster than master on GLM-4.7, Laguna, Qwen3.6
and gemma-4 with the json, list and spaces grammars, same masks.
* nit : shorten the comment of llama_grammar_reject_step
* common : add depth_guard for schema walks
* grammar : move GRAMMAR_RECURSIVE_LIMIT into the .cpp
* common : reduce recursion limit for peg parser
* improve
---------
Co-authored-by: Alde Rojas <hello@alde.dev>
Co-authored-by: Pascal <admin@serveurperso.com>
* llama-quant : fix quantizing 4D tensors
nrows_total was ne[1]*ne[2], so for a 4D tensor only the first ne[2]
slices were quantized and the rest of the rows were dropped. Use
ggml_nrows(tensor) to count all rows, including ne[3].
Fixes#29916
* llama-quant : detect expert tensors by name and fix imatrix size for 4D tensors
The MXFP4_MOE path used ne[2] > 1 to detect MoE expert tensors. This is
wrong for non-expert tensors with 3 or more dims, e.g. MLA projections
(BailingMoE3) and 4D conv kernels. Use the tensor name instead: all
tensors used by GGML_OP_MUL_MAT_ID are named *_exps or *_chexps. This
also removes the BailingMoE3 special case.
The imatrix holds one vector of ne[0] values per 2D slice, so the
expected size is ne[0]*ne[2]*ne[3], not ne[0]*ne[2]. This matches how
the imatrix is indexed per row for 4D tensors in llama_tensor_quantize_impl.
Assisted-by: Claude Opus 5.5
* cuda: fuse FFN gate/up and GLU in MMQ with interleaved gate/up rows
For prompt processing, run the gate and up matmuls of a dense FFN as one
mul_mat_q launch and apply the GLU in the write-back.
- The loaders take the gate weights as a second pointer and fill the weight
tile with up and gate rows interleaved in chunks of 8, so each thread holds
an up value and the matching gate value (rows r and r + 8 of an MMA tile).
- The MMA loop is unchanged: same tile, warp layout, vec_dot and one sum[]
per thread. The fused write-back computes the GLU from registers and writes
half the rows.
- The fused path uses the regular tiling (no stream-k) with the column tiles
innermost so that neighbouring blocks share weight tiles.
- Enabled for Q4_K, Q5_K, Q6_K, Q8_0 and IQ4_XS on NVIDIA Turing and newer.
* cuda: assert the fused MMQ gate argument and trim redundant comments
* cuda: move the fused MMQ hint check to should_fuse and add TODOs
* cuda: pass prec_src1 to the fused MMQ write-back
* cuda: fuse only with efficient tiling and no fallback, merge the GLU write-back
* cuda: require has_fusion explicitly, drop the fusion-only src1 precision check
* cuda: launch the fused MMQ kernel through the stream-k kernel with one block per tile
* cuda: inline the MMQ tiling-efficiency check
* tests: one fused gate/up MMQ case per type and GLU op
* cuda: always launch the fused MMQ kernel with one block per tile
* tests: shorten the fused gate/up MMQ test comment
* cuda: select the fused MMQ loader and write-back in ggml_cuda_mmq_get_util_funcs
* cuda: run the fused MMQ kernel on the plain tiling path
* cuda: put the column tiles innermost in the MMQ plain tiling path
* vulkan: Add a larger tile size for conv3d (coopmat2 only)
This adds a larger tile size, in practice it is only for coopmat2 where
we don't need shared memory to store the output (would use more than 64KB).
Add some additional representative backend tests. At least, this is what codex
came up with from looking at sd.cpp Wan VAE.
* only apply large tile size for newer driver versions
* chat : reject JSON nested more than 128 levels before walking it
The schema builder, the jinja engine and the decision code walk request
JSON with recursion. A request nested deep enough overflows the stack of
the HTTP thread and llama-server dies (SIGBUS on macOS, SIGSEGV on Linux):
for a tool schema from a few hundred levels on macOS and about 10000 on
Linux.
Check the depth first and answer 400 past 128 levels: the chat inputs in
common_chat_templates_apply (tools, tool-call arguments,
chat_template_kwargs, json_schema), the json_schema of /completion, and the
whole /v1/systemone body. A gemma4 tool result that is too deep stays text.
* fix the root, not the call side
---------
Co-authored-by: Xuan Son Nguyen <son@huggingface.co>
* model : add DeepSeek-V4.1-Flash (deepseek41)
* deepseek41 : remove MXFP8, convert the Engram table to BF16
* deepseek41 : compute the compressed-KV read layers once in the cache
* deepseek41 : use get_cells() for the shared-history check, drop llama_kv_cache::seq_pos_tok_le
* deepseek41 : gather the Engram rows in-graph like qwen4exp, drop the host gather
* deepseek41 : keep the Engram hash constants in hparams.ple_*, one module per Engram layer
* deepseek41 : drop the LLAMA_DSV41_KV_MIRROR toggle
* deepseek41 : keep il last in build_hc_mixes
* deepseek41 : sort the top-k rows before the sparse gather
* tests : add deepseek41 to the Metal fusion baseline
---------
Co-authored-by: Daniel Han <danielhanchen@gmail.com>
* hexagon: KV-head team split for HMX flash attention in multicore
* hexagon: merge FA_HEAD_SPLIT and FA_SPLIT into single GGML_HEXAGON_FA_SPLIT flag
0 = off (token-split only); 1 = head-split, plus team-split when
n_kv_heads < N_cores so stranded cores still participate (e.g. gemma-4
with 2 KV heads on 4 cores). Where heads divide evenly across cores,
plain head-split is used.
The src1 power-of-two rescale in kernel_mul_mm_id is superseded by the
per expert-token amax scaling done in the compute graph
(ref: https://github.com/ggml-org/llama.cpp/pull/30861), which keeps the
MoE down-projection activations within f16 range before the op runs.
Drop the two-stage reduction kernels, their scratch buffer and the
GGML_PREC_F32 dispatch added in https://github.com/ggml-org/llama.cpp/pull/26223,
as well as the test cases that drive MUL_MAT_ID src1 out of f16 range.
Assisted-by: pi:llama.cpp/ggml-org/Qwen3.8-Flash-Next-GGUF (MacBook, M5 Max, 128GB)
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