* wip: port MTP architecture
Ports the Multi-Token Prediction (MTP) architecture to the older `llama.cpp` codebase used by `ikllama`.
Changes include:
- Updating `llama_batch` to support `mtp_params`.
- Modifying `llama_decode_internal` (and `encode`) to handle MTP operations (Warmup, Update, Draft).
- Adding public APIs for MTP state management (`llama_set_draft_input_hidden_state`).
- Adapting the embedding extraction logic to skip MTP update passes.
* Refactors `server_slot` to support generic speculative decoding (MTP or Draft Model).
* core: enable hybrid outputs (logits + embeddings) for MTP support
* fix(mtp): correct KV-cache slot finding for updates
* fix(mtp): persist hidden states to prevent context corruption during drafting
* refactor(mtp): clean unused code
* fix(mtp): update server to new functions name
* fix(mtp): fix graph and save hidden state
* mtp: refactor integration, context params and kv cache search
* mtp: fix hidden state extraction and speculative acceptance flow
* server: fix MTP warmup for long prompts and reset token buffer
* llama: refactor MTP operation state to context parameters
* server: fix n_past calculation in MTP acceptance
* llama: fix mtp enable flags
* speculative: refactor MTP to use common_speculative interface
* context: remove unused signatures
* clip: fix deprecated enum-enum conversion warning
* common: fix format string crash in help message
* context: fix mtp activation logic
* llamat: always use the extracted embedding
* llama: get all embeddings to kv cache
* llama: revert logit to not run mtp for not supported arch
* llama: allocate all the n_outputs for MTP
* wip
* server-context: get only the last embedding for hidden state
* ggml-backend: fix array of bounds in debug build
* server-context: run mt kv update to each prompt batch
* revert segmentation fault fixes
* glm-mtp(feat): optimize graph embedding and recursive drafting
* glm5-mtp(feat): add glm 5 mtp logic
* glm-mtp: standardize the MTP graph
* glm 5 mtp: apply post-layer cvec
* glm 5 mtp: mark head as mandatory
* get normed embeddings for glm 5
* Fix GLM5 MTP
* GLM5 MTP: just reuse the layer attention implementation
* Make MTP work with split mode graph
---------
Co-authored-by: samuel <samueloliveira32df@gmail.com>
* Disable K Hadamard transform if K-head size is not a power of 2
* Allow Hadamard transform for head sizes that are not power of 2
* Give more details why Hadamard is not possible
* Arghh
llm_load_tensors stores `default_layer_device[i]` as a local index into
`model.devices` (consistent with `device_mem[]`, `model.splits[]`, and
all graph-building consumers), but the four
`llama_default_buffer_type_offload(model, default_layer_device[i])`
callsites passed it through as if it were a raw post-CVD device id.
Under `-dev`/`-devd` subsets where `model.devices != {0..N-1}`, this
selected the wrong buffer type. Wrap with `model.devices[...]` to match
the existing `model.devices[main_gpu]` pattern on the adjacent lines.
llama_init_from_model has the same bug for `main_gpu`: every consumer
(auto-fit override at line 3428, MTP clamp, the `model.devices[main_gpu]`
translations at lines 3678/3682, and graph-building `splits[main_gpu]`)
treats it as a local index, but the five single-GPU backend init paths
(CUDA, Vulkan, SYCL, Kompute, CANN) pass `model->main_gpu` straight to
the backend init, which expects a raw device id. e.g. `-dev CUDA1` with
default `--main-gpu 0` and `split_mode=NONE` called
`ggml_backend_cuda_init(0)` instead of `cuda_init(1)`. Compute
`main_gpu_id` once and use it for all five paths.
* ggml: ggml_dequant_hadamard fused op for MLA -khad path
Adds a new ggml op that fuses (ggml_cast -> F32) + (ggml_hadamard) into a
single kernel. Reads a quantized (or F16/F32) source and produces a per-
Hadamard-block F32 chunk with the inverse transform applied, without
materializing a full-size F32 intermediate buffer.
Motivation: the MLA pp_opt path in build_deepseek2.cpp un-encodes the
H-applied cache_nope view at every PP call. Today that runs as a cast
(quant -> F32) followed by a separate ggml_hadamard kernel, costing two
full-size F32 passes per layer per rank per call. Fusing them halves
the bandwidth on the un-encode and removes one kernel launch.
CUDA kernels in dequant_hadamard.cu lift the Walsh-Hadamard butterfly
from hadamard.cu and dequant helpers from dequantize.cuh:
* qr=1 layout (q8_0): consecutive dequant pair, stage 1 fused with load
* qr=2 layout (q4_0 / q4_1 / q5_0 / q5_1 / q6_0 / iq4_nl): dequant pair
at stride qk/2, explicit stage 1 after sync
* F16 has a dedicated kernel
* F32 source falls back to the standalone Hadamard op
CPU impl in iqk_cpu_ops.cpp composes the existing type_traits.to_float
dequant with fast_ht for graph completeness. nh in {64, 128, 256, 512}.
* MLA-TP: Hadamard pretransform of wv_b/wk_b_pp for -khad
Fold the 64-block orthonormal Hadamard into wv_b and wk_b_pp once at
context init so the pp_opt mul_mats consume the K cache in its on-disk
encoded basis. The per-PP-call cache_nope un-Hadamard is then skipped
(rope half still un-applied — it goes to FA via concat, no wk_b multiply).
Math is identity by H^T H = I: mul_mat(H@wv_b, H@cache) = wv_b^T @ cache.
For mla=2/3 absorb, composes correctly with the existing post-FA
ggml_hadamard(kqv_compressed, 64).
All-or-nothing across layers under a castable type-allowlist (excludes
1-3 bpw IQ types whose requant blows up beyond PPL noise). Models with
ineligible weights fall back to the runtime un-Hadamard path unchanged.
Composes with the fused ggml_dequant_hadamard op (prior commit): with the
fold active only the rope half still runs the runtime transform, via the
fused kernel.
* MLA-TP: fix TG with -khad after wv_b/wk_b_pp fold
The absorb branch of build_deepseek2_tp_attention applies
ggml_hadamard to kqv_compressed after FA, then multiplies by
wv_b. Pre-fold this was needed because wv_b was un-encoded; with
the wv_b fold (prior commit) the mul_mat already expects
H-encoded kqv_compressed:
mul_mat(H @ wv_b, kqv_encoded) = wv_b^T @ H @ H @ kqv_unencoded
= wv_b^T @ kqv_unencoded (H @ H = I)
Skip the post-FA hadamard when model.khad_pretransformed is set
so the two H applications cancel instead of double-applying.
Affects the absorb branch: TG (n_tokens=1), short-context PP
(n_kv < 1024), and models without wk_b_pp. Long-context PP goes
through the pp_opt branch and is unrelated/unchanged.
Reported by @ikawrakow on PR 1852. Verified across mla={1,2,3} x
khad={on,off} x -ctk={q8_0,q4_0} on GLM-4.7-Flash IQ5_K and the
unsloth IQ4_XS variant ik used to reproduce.
* ggml_hadamard: accept F16 and quant sources; drop GGML_OP_DEQUANT_HADAMARD
Per @ikawrakow review on PR 1852: subsume the per-source-type dispatch
into the existing GGML_OP_HADAMARD instead of carrying a separate enum
entry, op constructor, and standalone files.
ggml_hadamard's API is unchanged from the call-site perspective. The
constructor's F32-only assertion is dropped; ggml_cuda_op_hadamard and
iqk_hadamard now dispatch internally:
- F32 source: existing F32 butterfly (unchanged)
- F16 source: dedicated kernel
- q8_0 / q4_0 / q4_1 / q5_0 / q5_1 / q6_0 / iq4_nl: fused dequant +
butterfly kernel (lifted from the deleted dequant_hadamard.cu)
- CPU side composes traits.to_float with fast_ht
Net diff: -80 lines. Removes dequant_hadamard.{cu,cuh}, the enum entry,
op table rows, ggml_dequant_hadamard constructor, dispatch cases, and
the DEQUANT_HADAMARD supports_op block.
Verified clean build + TG smoke (mla=3 +khad q8 on GLM-4.7-Flash-IQ4_XS,
same coherent output as prior commit on feat/dequant-hadamard).
* MLA TP prompt processing optimisation
Adds a per-rank prompt-processing path to build_deepseek2_tp_attention
that materialises K/V from the compressed latent cache and runs a
standard flash_attn instead of the FlashMLA-3 absorb kernel the TP
attention currently uses for all batch sizes. Affects MLA archs under
-sm graph (DEEPSEEK2, GLM_DSA, MISTRAL4).
Gated on n_tokens >= 128 (set by caller) AND n_kv >= 1024. Below
either threshold the absorb path runs unchanged. Token generation
takes the absorb path; only prompt processing at non-trivial context
materialises.
A second piece pre-computes wk_b in a pp_opt-favouring orientation
(wk_b_pp: [kv_lora_rank, qk_nope, n_head]) at llm_prepare_mla time,
so the per-PP-call materialise can mul_mat against the latent cache
directly without an F16 cast + permute + ggml_cont on wk_b each call.
Path A (wkv_b in GGUF) and Path B (only wk_b/wv_b in GGUF) both
populate wk_b_pp through the standard per-rank replica setup.
Measured on 8x RTX 3090, -sm graph -mla 2 -fa on:
DSV2.5 IQ2_XS c=8k ub=2048 PP +51% to +60%
GLM-4.7-Flash IQ4_XS c=32k ub=2048 PP -6% (PP@0) to +77% (PP@30720)
GLM-5.1 IQ1_S q4_0 c=16k ub=2048 PP +5% to +9%
PPL parity within +/-0.2 noise (DSV2.5 bit-identical 5.3917, GLM-4.7
8.83 vs 8.96, GLM-5.1 6.96 vs 7.00). Token-generation throughput
unchanged within noise.
Compute buffer at init:
DSV2.5 -54 MiB total (allocator noise)
GLM-4.7-Flash +1042 MiB total (~+173 MiB per non-output device)
GLM-5.1 0 (MoE intermediates dominate)
* MLA TP: respect mla=1 vs mla=3 distinction, rename attn_k_b_pp -> attn_kv_b
ikawrakow/ik_llama.cpp#1841 review feedback: the pp_opt path lost the
intended trade-off where mla=1 forgoes pp_opt to save VRAM and mla=3 pays
the wk_b_pp tensor cost for faster long-context PP.
- llm_prepare_mla second pass: gate wk_b_pp synthesis on mla > 1.
Models that ship wk_b in their GGUF (mainline format) no longer
allocate the pp_opt-favoring K weight under mla=1.
- llm_prepare_mla first pass (wk_b synthesis from wkv_b): keep
unconditional under -sm graph. The wk_b_pp materialization here
shares the wk_b_f32 intermediate with the wk_b synthesis above, and
isolating just the wk_b_pp branch leaves the synthesized wk_b in a
state that makes the absorb path produce inf on some quant combos
(DSV2.5 IQ2_XS). Trade: the synthesized-wkv_b path still pays the
wk_b_pp allocation under mla=1, but the bigger compute-buffer
saving (no pp_opt branch at runtime) still applies.
- build_deepseek2 outer pp_opt: include cparams.mla_attn > 1 in the
pp_opt definition itself, so mla=1 is bypassed throughout (TP and
non-TP attention paths).
- build_deepseek2 tp pp_opt: require wk_b_pp present. Drop the dead
runtime wk_b transpose fallback (unreachable now that wk_b_pp is
guaranteed when tp_pp_opt fires).
- llama_kv_cache_init: have_wkv_b probe now treats wk_b_pp (attn_kv_b)
as equivalent to wkv_b for the purposes of allowing mla>1 to stay
put. Without this, -sm graph models that have wk_b/wv_b separately
in the GGUF (no combined wkv_b) would silently downgrade to mla=1.
- Rename the synthesized tensor "attn_k_b_pp.weight" -> "attn_kv_b.weight"
to match the mainline naming ik uses.
GLM-5.1 in particular benefits: its mla=3 PP improvement over mla=1 is
negligible on this arch (~0.4% in our sweeps), so users save the
runtime cost by sticking to mla=1.
* grammar: Fix grammar root symbol check (#19761)
* grammar: fix bad check for root symbol, correct error logging
* add tests to demonstrate root symbol check failure
# Conflicts:
# tests/test-grammar-integration.cpp
* common/grammar: fix grammar parsing issues to prevent stack overflow and hangs (#18604)
* grammar: add test case for nullable symbol loop
Reproduce stack overflow (or OOM) with ( [x]* )* found while adding
GBNF support to ripgrep-edit.
llama-server reproducer:
curl \
-X POST \
-d '{
"messages": [{ "role": "user", "content": "write yes" }],
"grammar": "root ::= ( [x]* )*"
}' \
-H "Content-Type: application/json" \
http://localhost:8811/v1/chat/completions
* grammar: prevent stack overflow with nullable symbol loop
Fix a potential stack overflow in llama_grammar_advance_stack that
could occur when processing grammars with nullable symbols that lead
to infinite derivations of empty strings. The fix introduces cycle
detection by tracking visited stacks to prevent infinite recursion.
rg-edit regexp: llama_grammar_advance_stack
rg-edit extra-args: -A20
rg-edit directive: """Rewrite: fix the following segfault:
[..]
⚫ Testing segfault. Grammar:
root ::= ( [x]* )*
root ::= ( [x]* )*
Segmentation fault build/bin/test-grammar-integration"""
gptel-context:
(("~/llama.cpp/src/llama-grammar.cpp")
("~/llama.cpp/tests/test-grammar-integration.cpp")
("~/llama.cpp/grammars/./list.gbnf")
("~/llama.cpp/grammars/./json_arr.gbnf")
("~/llama.cpp/grammars/./json.gbnf")
("~/llama.cpp/grammars/./japanese.gbnf")
("~/llama.cpp/grammars/./english.gbnf")
("~/llama.cpp/grammars/./chess.gbnf")
("~/llama.cpp/grammars/./c.gbnf")
("~/llama.cpp/grammars/./arithmetic.gbnf")
("~/llama.cpp/grammars/./README.md"))
* grammar: convert recursive llama_grammar_advance_stack to iterative
This change converts the function to an iterative approach using
explicit stacks, which prevents deep recursion and eliminates the risk
of stack overflow.
rg-edit regexp: llama_grammar_advance_stack
rg-edit extra-args: -A30
rg-edit directive: """Rewrite: fix the following segfault:
[..]
⚫ Testing segfault. Grammar:
root ::= ( [x]* )*
root ::= ( [x]* )*
Segmentation fault build/bin/test-grammar-integration
convert from recursive to interactive"""
gptel-context:
(("~/llama.cpp/src/llama-grammar.cpp")
("~/llama.cpp/tests/test-grammar-integration.cpp")
("~/llama.cpp/grammars/./list.gbnf")
("~/llama.cpp/grammars/./json_arr.gbnf")
("~/llama.cpp/grammars/./json.gbnf")
("~/llama.cpp/grammars/./japanese.gbnf")
("~/llama.cpp/grammars/./english.gbnf")
("~/llama.cpp/grammars/./chess.gbnf")
("~/llama.cpp/grammars/./c.gbnf")
("~/llama.cpp/grammars/./arithmetic.gbnf")
("~/llama.cpp/grammars/./README.md"))
v2: Added a `std::set` to perform tree-based lookups with O(N log N)
complexity. Testing with a parallel run of `test-grammar-integration`
shows a double-digit percentage increase in runtime. An
`unordered_set` with O(1) hashing was also evaluated, but the overhead
of constructing hash keys from pointers made it significantly slower
than the rbtree implementation that only requires an ordering
operator. The performance regression in the test suite appears
justified by the overall reduction in algorithmic complexity.
Co-developed-by: Piotr Wilkin (ilintar) <piotr.wilkin@syndatis.com>
* grammar: add test case for hang in repetition grammar processing
This commit adds a new test case to the grammar integration tests that
specifically targets a hang scenario in the repetition grammar parser
found while adding GBNF support to ripgrep-edit.
llama-server reproducer:
curl \
-X POST \
-d '{
"messages": [{ "role": "user", "content": "write yes" }],
"grammar": "root ::= (([^x]*){0,99}){0,99}"
}' \
-H "Content-Type: application/json" \
http://localhost:8811/v1/chat/completions
* grammar: add repetition threshold check
The change introduces a maximum repetition threshold to avoid
excessive rule expansion during grammar parsing. When parsing
repetition patterns like {m,n}, the parser now calculates the
potential number of rules that would be generated and throws an error
if the product of previous rules and new rules exceeds the threshold.
A test case was added to verify the threshold is properly enforced for
deeply nested repetition patterns that would otherwise cause hangs.
---------
Co-authored-by: Asbjørn Olling <asbjornolling@gmail.com>
Co-authored-by: Andrea Arcangeli <aarcange@redhat.com>
* MLA tensor parallelism under -sm graph (DEEPSEEK2/GLM_DSA/MISTRAL4)
Extends -sm graph (split-mode graph) to MLA-style attention across the
DEEPSEEK2, GLM_DSA, and MISTRAL4 architectures. Previously these archs
fell back to -sm layer regardless of the user's flag.
Implementation:
- Per-rank attention build in build_deepseek2_tp_attention with
view-sliced FlashAttention, split-buffer output projection, and
ggml_reduce across devices
- wk_b / wv_b absorbed weights replicated per device via materialize()
in llm_prepare_mla (these can't live in a split buffer)
- KV cache replication path (replicated_k_l) for graph-mode TP
- distribute_mla_tensors_for_split_mode_graph routes attention/norm
tensors into ctx_split; expert tensors stay per-layer
- Implements ggml_backend_cuda_split_buffer_get_tensor for the
replicated / row-split / col-split inverse paths
- Early-reject guard in src/llama.cpp that auto-downgrades -sm graph
to -sm layer (with a warning) when incompatible loader flags are set:
-ncmoe, -cmoe, -ot, -rtr, -muge
New CLI flag:
- -gap | --graph-attn-precision <f16|f32> (default f16)
See the PR description for the full validation matrix (3 archs x 2/4/8
GPU counts), perf numbers, VRAM accounting, and known limitations.
* Some tweaks
* materialize lambda: per-head split for graph-mode tp_replicate
7dd19e19 changed wk_b/wv_b distribution from mirror to per-head split
(split_dim=2) via prepare_split_tensors. That path only fires when
wk_b/wv_b are loaded from GGUF.
Models that store only wkv_b in GGUF derive wk_b/wv_b at load via
llm_prepare_mla, going through the materialize lambda, which was
untouched and still produced mirror replicas (split_dim=-1, full n_head
per device).
build_deepseek2_tp_attention now does mul_mat(wk_b_local, q_nope_perm)
without the prior view_3d slice, so a mirror replica passes an n_head
tensor where the kernel expects n_head_local. Result: silent SIGSEGV
right after model load.
Mirror logic in materialize is replaced with the same per-head split as
prepare_split_tensors: head_offsets derived from wo split, each rank
gets a tensor with ne[2]=n_head_local, data copied from the appropriate
source byte slice. Singular `computed` tensor keeps full metadata for
tensors_by_name lookups.
Tested: 8x3090, -sm graph -mla 3 -fa on now boots cleanly and
sweep-benches without crash. Log confirms new path: "Computed
blk.X.attn_k_b.weight ... split across N devices on dim=2".
* cleanup: indent fix + remove dead view_3d slicing and debug printf
- build_deepseek2.cpp: re-indent the self_attention block in
build_deepseek2_layer_attention (lines 253-670). Block was at column 0
inside a function body; now at the expected 4/8-space indent.
- build_deepseek2.cpp: drop the commented-out view_3d slicing and debug
printfs left over after 7dd19e19's switch to direct mul_mat on
per-rank wk_b_local / wv_b_local. Update the stale 'wk_b is
replicated (split_dim=-1)' comment to match the new split_dim=2
reality.
- ggml-cuda.cu: remove the leftover debug printf in
ggml_backend_cuda_split_buffer_get_tensor.
No behavior change. Verified with a clean rebuild and DSV2.5 +
GLM-4.7-Flash sweep-bench runs.
* llm_load_tensors: gate incompatible-flag warning to MLA archs
The -ncmoe / -rtr / -muge / -ot warning under -sm graph currently fires
for all archs that support graph mode. That's an over-reach: the
incompatibility is specific to the MLA TP paths (DEEPSEEK2, GLM_DSA,
MISTRAL4) — Gemma4 graph mode existed pre-PR and works with those flags.
Gate the warning to MLA archs only.
Also refreshes two stale comments left over from the wk_b/wv_b
mirror -> per-head-split rewrite:
- src/llama.cpp llm_prepare_mla: "Replicate wk_b/wv_b ..." now reads
"Per-head split wk_b/wv_b ..." to match what the materialize lambda
actually does post-823a39e2.
- src/llama-load-tensors.cpp distribute_mla_tensors_for_split_mode_graph:
drop the wkv_b row-split mention (wkv_b is no longer created under
graph mode after 7dd19e19) and correct the wk_b/wv_b distribution
description (per-head split, not per-device replicated).
---------
Co-authored-by: Kawrakow <iwankawrakow@gmail.com>
* Avoid copying the per-step SSM state (CUDA)
* Avoid copying the per-step SSM state (CPU)
* Allocate only what is necessary for per-step SSM state
* Cleanup
ikawrakow asked the seq_rm zeroing be hoisted out of the per-cell loop and
preferably done as part of the compute graph. This rewrites the fix:
- llama_kv_cache gets a pending_recurrent_reset bitmap, sized to
qnext_state_slots and indexed by slot index.
- llama_kv_cache_seq_rm only marks the bitmap; no CPU-side tensor
writes, no work inside the per-cell loop.
- delta_net::build_layer_attn_linear ORs the bitmap into the existing
reset_state flag, so the in-graph ggml_scale(state, 0.0f) path that
already handled batch.pos[0] == 0 now also handles slot reset.
- build_qwen3next and build_qwen35 clear the bitmap after
ggml_build_forward_expand, once per graph build.
- llama_kv_cache_clear also drops the bitmap, since the full-buffer
clear it already does makes any pending in-graph reset redundant.
Previous behavior is preserved (every recurrent layer column for the
released slot is zeroed before the next request reuses it), but the work
moves from the CPU into the compute graph, runs once per graph instead
of once per cell, and reuses the existing reset path referenced in the
review.
* server: spec checkpoints for recurrent models
* fix: save/restore sampler state during speculative checkpoint
When speculative decoding rejects draft tokens and restores the
recurrent state checkpoint, the sampler (RNG, grammar, prev tokens)
must also be restored to maintain consistency. Without this, the
sampler state reflects the rejected draft tokens, leading to
potential divergence.
Uses common_sampler_clone() to snapshot the sampler before the
speculative batch decode, and restores it on rejection.
* server: snapshot recurrent state in tensor
* reset ngram mod state for rejected tokens
* server: refactor checkpoint state logic
* speculative: fix sampler for checkpoints
* recurrent model: implement recurrent kernel checkpoint
* recurrent model: refactor api
* spec: free rbudget before overwriting
Two related issues that manifest as 'llama_decode ret=-3' on hybrid
architectures (e.g. Qwen3.5/3.6 MoE, Qwen3-Next), matching the symptom
reported in #1576.
1) server_context::apply_checkpoint() was written around transformer KV
semantics (pos_min / pos_max per-token window). For hybrid and pure
recurrent models the per-token pos_min threshold does not apply: the
recurrent state is a single snapshot, and the server-side checkpoint
is a whole-prefix record. The old selector 'cur.pos_min < pos_min_thold'
can succeed on a checkpoint whose pos_max is past the current n_past,
and — more commonly — fall through to do_reset = true, which zeros
slot.n_past / slot.n_past_prompt. Zeroing in-place while the recurrent
state in the context is still populated makes the next decode batch
disagree with the live state, returning ret=-3.
This change gates the checkpoint path on
llama_model_has_recurrent(llama_get_model(slot.ctx)):
- selector uses pos_max <= slot.n_past && pos_max < pos_next
(whole-prefix match, leaves at least one token to decode);
- on miss, slot state is preserved rather than zeroed, letting
update_slots() continue from the already-valid n_past_prompt;
- the erase loop drops any checkpoint whose pos_max > pos_next,
matching the rewind semantics for recurrent state.
Transformer behavior is unchanged.
2) stop_internal_decode is a file-static global in src/llama.cpp, set by
llama_decode_stop() (called on client disconnect) and polled inside
the decode loop to bail out with ret=-3. The flag is only cleared on
one conditional path in server_slot::release(), so a stop signal that
arrives after the interrupted llama_decode() has already returned
bleeds into the NEXT decode call and causes an immediate ret=-3 with
no work performed. Clear it at the top of the public llama_decode()
entry so the signal is scoped to the in-flight decode it was meant
for.
Build-verified: llama-server with GGML_CUDA=ON, -DCMAKE_CUDA_ARCHITECTURES=86
(sm_86), IQK flash-attn + matmul enabled. No new APIs introduced —
llama_model_has_recurrent is already public and already used elsewhere in
server-context.cpp.
Closes#1576
* Autoparser - complete refactoring of parser architecture
Autoparser: add optional argument reshuffle capability
Autoparser: True streaming (#20177)
* Relax atomicity constraint for nicer, more pleasent, True Streaming parsing
* Whitespace
* Remove redundant atomics
Revert to OAI-compatible args (#20213)
* Revert to OAI-compatible args
* Apply workaround::func_args_not_string
Fix structured outputs (#20223)
* Fix structured outputs
* Update common/chat-auto-parser-generator.cpp
Co-authored-by: Aldehir Rojas <hello@alde.dev>
---------
Co-authored-by: Aldehir Rojas <hello@alde.dev>
Fix compile bug (#20203)
* Fix compile bug
* Update common/chat-auto-parser-helpers.cpp
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
---------
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
# Conflicts:
# common/chat-auto-parser-helpers.cpp
common : gracefully handle incomplete output (#20191)
* common : handle incomplete UTF-8 at end of input in PEG parser
* cont : if reached end prematurely, emit needs_more_input to propagate partial output
* cont: refactor peg parse context to add lenient flag
* cont : remove partial flag, keep lenient flag
PEG parser for LFM2 (#20251)
* PEG parser for LFM2
* Simplify using python_value()
common: map developer role to system (#20215)
* Map developer role to system
* Simplify
common: consolidate PEG string parsers (#20263)
* common : consolidate PEG string parsers
* cont : fix json_string_content()
examples : fix empty items in json_schema_to_grammar.py [no ci] (#19968)
* Fix logic for retrieving schema items in `json_schema_to_grammar.py`
If `schema['items']` is `{}` and `prefixItems not in schema', as `{}` is Falsy, the original code here will raise an error.
I think if `schema['items']` is `{}`, them items should just be `{}`
* Apply suggestion from @CISC
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* Add tests for arrays with empty items
Add two unit tests to `tests/test-json-schema-to-grammar.cpp` that validate handling of arrays when 'items' is an empty schema and when 'prefixItems' is present alongside an empty 'items'. Both tests expect the same generated grammar, ensuring the JSON Schema->grammar conversion treats an empty 'items' schema (and the presence of 'prefixItems') correctly and covering this edge case.
---------
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
Reduce level of content parser warning message to avoid log spam on non-debug verbosity (#20347)
do not return if template parse failed
add arg to enable parallel tool call
common : fix incorrect uses of stoul (#20313)
# Conflicts:
# common/arg.cpp
# src/llama-grammar.cpp
examples : fix empty items in json_schema_to_grammar.py [no ci] (#19968)
* Fix logic for retrieving schema items in `json_schema_to_grammar.py`
If `schema['items']` is `{}` and `prefixItems not in schema', as `{}` is Falsy, the original code here will raise an error.
I think if `schema['items']` is `{}`, them items should just be `{}`
* Apply suggestion from @CISC
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* Add tests for arrays with empty items
Add two unit tests to `tests/test-json-schema-to-grammar.cpp` that validate handling of arrays when 'items' is an empty schema and when 'prefixItems' is present alongside an empty 'items'. Both tests expect the same generated grammar, ensuring the JSON Schema->grammar conversion treats an empty 'items' schema (and the presence of 'prefixItems') correctly and covering this edge case.
---------
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
Add support for MiroThinker with new jinja template
common/parser: handle reasoning budget (#20297)
* v1
* Finished!
* Handlie cli
* Reasoning sampler
* Apply suggestions from code review
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* Less explosive terminology :)
* Add utf-8 case and tests
* common : migrate reasoning budget sampler to common
* cont : clean up
* cont : expose state and allow passing as initial state
* cont : remove unused imports
* cont : update state machine doc string
---------
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
Co-authored-by: Alde Rojas <hello@alde.dev>
common/parser: use nlohmann::ordered_json to preserve parameter order (#20385)
common/parser: add GigaChatV3/3.1 models support (#19931)
Co-authored-by: Mishusha <pmv26021975@gmail.com>
common/parser: gracefully handle undetected tool parser, print error message. (#20286)
fix: prevent nullptr dereference (#20552)
common : fix iterator::end() dereference (#20445)
# Conflicts:
# common/regex-partial.cpp
jinja : add capability check for object args (#20612)
common/parser: add `--skip-chat-parsing` to force a pure content parser. (#20289)
* Add `--force-pure-content` to force a pure content parser.
* Update common/arg.cpp
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
common : rework gpt-oss parser (#20393)
* common : rework gpt-oss parser
* cont : fix gpt-oss tests
* cont : add structured output test
* cont : rename final to final_msg
common : fix gpt-oss content removal (#20745)
common/parser: add proper reasoning tag prefill reading (#20424)
* Implement proper prefill extraction
* Refactor cli parameters, update docs, move reasoning budget sampler part to common/reasoning-budget.cpp
* Update tools/server/server-task.cpp
* refactor: move grammars to variant, remove grammar_external, handle exception internally
* Make code less C++y
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
chat : handle tool calls with no required args in TAG_WITH_TAGGED format (#20764)
* chat : handle tool calls with no required args in TAG_WITH_TAGGED format
* Update tests/test-chat.cpp [no ci]
Co-authored-by: Aldehir Rojas <hello@alde.dev>
---------
Co-authored-by: Piotr Wilkin (ilintar) <piotr.wilkin@syndatis.com>
Co-authored-by: Aldehir Rojas <hello@alde.dev>
common/parser : fix out_of_range crash in throw path (#20424 regression) (#20777)
* chat : fix out_of_range crash in throw path (#20424 regression)
#20424 introduced effective_input = generation_prompt + input, but the
throw path uses input.substr(result.end) where result.end is a position
within effective_input. Every thinking model with a non-empty
generation_prompt crashes with std::out_of_range instead of the intended
error message.
Test crashes on unpatched master, passes with fix:
cmake -B build -DLLAMA_BUILD_TESTS=ON -DLLAMA_BUILD_TOOLS=OFF
cmake --build build --target test-chat
./build/bin/test-chat
* Update test-chat.cpp
* Update test-chat.cpp
* Update test-chat.cpp
---------
Co-authored-by: Piotr Wilkin (ilintar) <piotr.wilkin@syndatis.com>
jinja : fix heap OOB read in value equality comparison (#20782)
Address GHSA-q9j6-4hhc-rq9p and GHSA-2q4c-9gq5-5vfp.
The three-iterator overload of std::equal in value_array_t::equivalent()
and value_object_t::equivalent() reads past the end of the shorter
container when comparing arrays or objects of different lengths.
Use the four-iterator overload (C++14) which checks both range lengths.
Found-by: Pwno
common : fix typo in debug log ('extracft' -> 'extract') (#20807)
common/parser: fix nasty bug causing subtle corruption of generation prompt (#20825)
jinja : refactor token advancement (#20864)
* refactor token advancement
* exercise sub-expressions
common/autoparser : detect reasoning markers when enable_thinking changes system prompt (#20859)
common : replace wrap_for_generation with a prefix convenience function and fix gpt-oss (#20912)
jinja: fix macro with kwargs (#20960)
* jinja: fix macro with kwargs
* Apply suggestions from code review
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* fix newline problem
---------
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
common : inhibit lazy grammar sampler while reasoning is active (#20970)
* common : inhibit grammar while reasoning budget is active
* cont : update force_pos in accept
* cont : fix tests
* cont : tweak should apply logic
* cont : return early not using grammar sampler
* Add tests
* cont : prevent backend sampling when reasoning budget enabled
* cont : fix typo
---------
Co-authored-by: Piotr Wilkin <piotr.wilkin@syndatis.com>
# Conflicts:
# common/reasoning-budget.h
# common/sampling.cpp
# tools/cli/cli.cpp
# tools/server/server-common.cpp
# tools/server/server-task.cpp
common/parser: fix reasoning whitespace bugs + extra parser tests (#21085)
* fix whitespace reasoning issues + add reconstruction tests
* Proper fix
* fix Nemotron autoparser test expectations to include newline in marker
common : add reasoning_format = none support to gpt-oss (#21094)
common/json-schema: fix: handle non-capturing groups (?:...) in JSON schema pattern converter (#21124)
The regex-to-grammar converter in _visit_pattern() crashes with SIGSEGV
when a JSON schema "pattern" field contains a non-capturing group (?:...).
Root cause: when the parser sees '(' followed by '?', it pushes a warning
but does not advance past '?:'. The recursive transform() call then
interprets '?' as a quantifier and calls seq.back() on an empty vector,
causing undefined behavior.
This commonly occurs when serving OpenAI-compatible tool calls from
clients that include complex regex patterns in their JSON schemas (e.g.,
date validation patterns like ^(?:(?:\d\d[2468][048]|...)-02-29|...)$).
The fix:
- Skip '?:' after '(' to treat non-capturing groups as regular groups
- For unsupported syntax (?=, ?!, etc.), skip to matching ')' safely,
handling escaped characters to avoid miscounting parenthesis depth
- Adjust the ')' unbalanced-parentheses check using direct char
comparisons instead of substr
- Add test cases for non-capturing groups (C++ only, as the JS/Python
implementations do not yet support this syntax)
common/parser: fix handling of tool definition with missing properties key (#21128)
jinja : handle empty expressions correctly (#20913)
* Reject empty computed member expressions before returning slices[0] from parse_member_expression_arguments().
* Treat empty computed member expressions with Jinja2 undefined semantics
Treat empty computed member expressions like `a[]` as undefined instead of
raising a parser error, to match Jinja2 behavior.
- return a noop expression for empty computed member arguments
- return undefined when a computed member key evaluates to undefined
- add Jinja tests covering `a[]|default('fallback')` and `a[] is undefined`
* Handle undefined computed member properties
Move undefined-property handling to the common member access path, and add a test covering `a[undefined] is undefined`.
* Use default undefined value in member access
Initialize val and then return it when property is undefined.
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* empty statement parses to blank_expression instead of noop_statement
---------
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
common : gpt-oss handle builtin and unsolicited tool calls (#21213)
fix: tool call parsing for LFM2 and LFM2.5 models (#21242)
* fix: tool call parsing for LFM2 and LFM2.5 models'
* refactor: add test / break out lfm2 and lfm2.5 parsing logic
# Conflicts:
# common/chat.cpp
Relax prefill parser to allow space. (#21240)
* Relax prefill parser to allow space.
* Move changes from prefix() to parser generation
* Only allow spaces if we're not having a pure content parser next
common : add commentary rules for gpt-oss-20b (#21286)
add reasoning budget
model, mtmd: fix gguf conversion for audio/vision mmproj (#21309)
* fix gguf conversion for audio/vision mmproj
* fix test
# Conflicts:
# convert_hf_to_gguf.py
# examples/eval-callback/eval-callback.cpp
# examples/mtmd/CMakeLists.txt
# examples/mtmd/clip-impl.h
# examples/mtmd/mtmd.cpp
# gguf-py/gguf/constants.py
# gguf-py/gguf/gguf_writer.py
# gguf-py/gguf/tensor_mapping.py
# src/CMakeLists.txt
# src/llama-arch.cpp
# src/llama-arch.h
# src/llama-model.cpp
# src/llama-model.h
# src/llama-vocab.cpp
# src/models/models.h
# tests/test-llama-archs.cpp
# tools/mtmd/clip-graph.h
# tools/mtmd/clip-model.h
# tools/mtmd/clip.cpp
# tools/mtmd/models/models.h
fix: gemma 4 template (#21326)
chat : avoid including json in chat.h (#21306)
jinja: coerce input for string-specific filters (#21370)
common : fix tool call type detection for nullable and enum schemas (#21327)
* common : fix tool call type detection for nullable and enum schemas
* common, tests : fix grammar delegation for nullable/enum schemas and add tests
Fix enum type inference to scan all enum values (not just index 0) so
schemas like {"enum": [0, "celsius"]} correctly detect string type.
Fix schema_delegates in peg-parser to handle nullable type arrays
(["string", "null"]) and typeless enum schemas in raw mode, allowing
the tagged parser to use raw text instead of JSON-formatted strings.
Add test cases for Qwen3-Coder (TAG_WITH_TAGGED format):
- nullable string ["string", "null"]
- nullable string with null first ["null", "string"]
- nullable integer ["integer", "null"]
- enum without explicit type key
common/parser: fix call ID detection (Mistral parser mostly) + atomicity for tag-json parsers (#21230)
* Fix call ID detection (Mistral parser mostly) + atomicity for tag-json parsers
* Rename
* Update common/chat-auto-parser-generator.cpp
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
---------
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
common : add gemma 4 specialized parser (#21418)
* common : add gemma4 dedicated parser
* cont : add '<|tool_response>' as eog
* cont : emit JSON from Gemma4 tool call AST
* cont : more fixes
* cont : refactor convert function
* cont : refine rules and mapping
* cont : add more tests
* cont : clean up
* cont : remove autoparser gemma4 implementation
* cont : more cleanup
* cont : rename gemma4.jinja to match the others
* cont : add custom template to support interleaved thinking
* cont : preserve reasoning in model turns
* cont : fix initializer error
* cont : fix unused vars
* cont : fix accidental static
* cont : fix specialized_template signature
* fix extra semicolon
* remove debug line and extra space [no ci]
fix reasoning budget
parser: fix MiniMax handling (#21573)
jinja : support ensure_ascii=true, string repetition and int/float self-filtering (#21623)
* feat: jinja engine improvements for reka-edge
Port three Jinja engine improvements needed for the reka-edge model:
1. Python-style string repetition ("ab" * 3 → "ababab")
2. ensure_ascii=true support for tojson filter (escapes non-ASCII to \uXXXX)
3. int() builtin on value_int_t (identity, needed for Reka Edge template)
* fix: escape invalid utf8 bytes when ensure_ascii=true
The json_ensure_ascii_preserving_format function does not correctly
handle an edge case where if UTF-8 parsing fails, it adds the non-ascii
character back to the output as a raw byte.
This commit fixes that by adding the unicode standard replacement
character \\ufffd to the output instead. This is the standard behavior
for various programming languages like Python, Rust, Go, etc.
* chore: address PR comments
1. Add todo comment for supporting string repetition for array/tuples
2. Add support for float identity operation
3. Move invalid ascii test case to test_fuzzing
* chore: accept suggestion for common/jinja/value.cpp
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
---------
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
common : simplify autoparser tagged parser rules (#21216)
* common : simplify autoparser tagged parser rules
* cont : remove upper limit on optional args
* cont : revert changes to parsing at the end
* cont : undo arbitrary ordering of optional args
* cont : fix uninitialized required parameters
* revert to simplify merge
* re-apply patches
* restore flexible optional arg ordering tests
common : fix ambiguous grammar rule in gemma4 (#21661)
* common : fix ambiguous grammar rule in gemma4
* cont : fix missing comma...
common : enable reasoning budget sampler for gemma4 (#21697)
* fix: enable reasoning budget sampler for gemma4
Add thinking_start_tag and thinking_end_tag to
common_chat_params_init_gemma4(). Without these, the reasoning
budget sampler never activates for gemma4.
Make the newline after "thought" optional in the PEG parser to
handle budget=0 (sampler forces end tag before the newline).
Add test case for empty thinking block.
Fixes#21487
* use p.space() instead of p.optional(p.literal("\n")) in gemma4 thought parser
common : better align to the updated official gemma4 template (#21704)
fix: Fix broken structured output when using $refs in json_schema (#21699)
chat: dedicated DeepSeek v3.2 parser + "official" template (#21785)
Hide render_message_to_json warning
common/gemma4 : handle parsing edge cases (#21760)
common: skip reasoning budget sampler when no budget is requested (#21870)
* common: skip reasoning budget sampler when no budget is requested
After I added thinking_start_tag / thinking_end_tag for gemma4 in #21697, the reasoning budget sampler gets unconditionally created even when no budget is configured (the default -1). The same applies to kimi_k2, lfm2, lfm2_5, and ministral_3 which also set these tags. The budget gets converted to INT_MAX, so the sampler never actually forces any tokens but still runs per-token checks (start tag matching in IDLE state, token-to-piece conversion + UTF-8 checks in COUNTING state).
More importantly, the mere existence of the sampler (non-null rbudget) disables backend sampling. Backend sampling lets the GPU select tokens directly, avoiding a full logits transfer from GPU to CPU every token. This could explain the 30% speed regression reported in #21784 (98 t/s to 70 t/s on Vulkan).
So I added a reasoning_budget_tokens >= 0 check to the sampler creation condition. When the budget is unlimited, the sampler is not created, backend sampling stays enabled, and no per-token overhead is added. When a budget is explicitly set (0, 128, 1024, etc.), the sampler is created and works as before.
* common: preserve rbudget when grammar is lazy
Following up on the review feedback on #21870: keep the reasoning budget sampler when grammar_lazy is true, so the thinking-block grammar suppression from #20970 still works when tools are in use. This way, we only skip the sampler when both no budget is set AND grammar is not lazy.
autoparser: support case of JSON_NATIVE with per-call markers (test case: Reka-Edge) (#21892)
* fix grammar
* fix add sampled token
---------
Co-authored-by: Piotr Wilkin (ilintar) <piotr.wilkin@syndatis.com>
Co-authored-by: firecoperana <firecoperana>
Upstream: llama.cpp#21428. GGUF stores bools as 1-byte int8_t but the
loader was casting through (const bool*) which has implementation-defined
sizeof. Use explicit int8_t cast with != 0 normalization.
Co-authored-by: Mark Alonzo <mark.alonzo@outlook.com>
* WIP: Gemma4 vision
Crashes on the GPU because of rms_norm requiring ne0 to be multiple
of warp_size.
Runs on the CPU, but produces garbage.
* Remove unnecessary assert in CUDA rms_norm
* GLU was not advertised as supported on CUDA
* Still not working
* This seems to work
In llama_kv_cache_init() call, params.type_v_first was incorrectly passed
twice instead of params.type_v_last.
This caused V cache in the last N layers to use type_v_first instead of type_v_last.
Fix: Replace second params.type_v_first with params.type_v_last.
* wip: separate llama_context for MTP with graph reuse
* wip: fix KV cache desync with separate MTP context
* refactor: remove dead mtp logic code, encapsulate KV mirroring
* mtp-context: derive args directly from the main model's context
* mtp: fix kv cache positions
* clean small comments
* minor refactor for context shift
* Use build_std_attention for Gemma4 when possible
It is possible for the 26b MoE and 31b dense models.
It is not possible for the E4B/E2B vaiants because they
don't have KV cache in each layer.
* Standardize Gemma4 dense ffn
* WIP: Gemma4 split mode graph
Runs but produces NaNs
* WIP: Gemma4 split mode graph
Runs but very high PPL. At least it is no longer NaN.
* WIP
* This works!
* Put attn_norm, attn_post_norm, ffn_norm, ffn_post_norm on all GPUs
* Fix crash when saving/loading KV cache
* WIP: split mode graph for Gemma4-MoE - crashes
* Split mode graph for Gemma4-MoE - this works
* Disable SWA optimization
Something goes wrong there
* Consolidate MoE and dense graph parallel
* Use build_std_attention for Gemma4 when possible
It is possible for the 26b MoE and 31b dense models.
It is not possible for the E4B/E2B vaiants because they
don't have KV cache in each layer.
* Standardize Gemma4 dense ffn
* WIP: Gemma4 split mode graph
Runs but produces NaNs
* WIP: Gemma4 split mode graph
Runs but very high PPL. At least it is no longer NaN.
* WIP
* This works!
* Put attn_norm, attn_post_norm, ffn_norm, ffn_post_norm on all GPUs
* Fix crash when saving/loading KV cache
* Little maintenance
* llama-quantize : Add the missing items in the help
* Add GGML_MAX_CONTEXTS define in the general cmakelist.txt
* Make the KV cache (CPU) based warnings clearer
* Correct placement of GGML_MAX_CONTEXTS definition
* Revert wrong indents
This reverts commit d0728cbb6c6d4d6d8dc13f062e542d232647a38d.
* Moving the GGML_MAX_CONTEXTS definition to src/CMakeLists.txt
* Update warning message for unsupported KV cache types
* forgotten antislash
* Gemma4: WIP
* Gemma4: WIP - runs with totally wrong results
* Gemma4: WIP - add CPU 512, 512 FA
* Gemma4: WIP
It gives a meaningful response in llama-cli, but PPL is still much too
high. Is this due to tokenizer issues?
* Gemma4: this works
I had forgotten the softcap on the final output.
* Remove log
* Gemma4: WIP E4B/E2B
* Gemma4: Q4B/E2B appear to work now
* gemma4: tokenizer fixes
* wip: port MTP architecture
Ports the Multi-Token Prediction (MTP) architecture to the older `llama.cpp` codebase used by `ikllama`.
Changes include:
- Updating `llama_batch` to support `mtp_params`.
- Modifying `llama_decode_internal` (and `encode`) to handle MTP operations (Warmup, Update, Draft).
- Adding public APIs for MTP state management (`llama_set_draft_input_hidden_state`).
- Adapting the embedding extraction logic to skip MTP update passes.
* Refactors `server_slot` to support generic speculative decoding (MTP or Draft Model).
* core: enable hybrid outputs (logits + embeddings) for MTP support
* fix(mtp): correct KV-cache slot finding for updates
* fix(mtp): persist hidden states to prevent context corruption during drafting
* refactor(mtp): clean unused code
* fix(mtp): update server to new functions name
* fix(mtp): fix graph and save hidden state
* mtp: refactor integration, context params and kv cache search
* mtp: fix hidden state extraction and speculative acceptance flow
* server: fix MTP warmup for long prompts and reset token buffer
* llama: refactor MTP operation state to context parameters
* server: fix n_past calculation in MTP acceptance
* llama: fix mtp enable flags
* speculative: refactor MTP to use common_speculative interface
* context: remove unused signatures
* clip: fix deprecated enum-enum conversion warning
* common: fix format string crash in help message
* context: fix mtp activation logic
* llamat: always use the extracted embedding
* llama: get all embeddings to kv cache
* llama: revert logit to not run mtp for not supported arch
* llama: allocate all the n_outputs for MTP
* wip
* server-context: get only the last embedding for hidden state
* ggml-backend: fix array of bounds in debug build
* server-context: run mt kv update to each prompt batch
* revert segmentation fault fixes
* glm-mtp(feat): optimize graph embedding and recursive drafting
* Allow using -rtr and -muge together
* Various Qwen-3.5 tweaks
* No need to make v, g, beta contiguous
* Adjust NEON delta-net to non-contiguous v, g, b
* Cleanup
---------
Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
* simpler n_rewind logic, delete old comments
* use more consistent names, add updt_w_cur to json schema
* align comments
* refactor review logic, update struct/variable names
* revert cosmetic changes
* check enable/disable in llama_prep_adaptive_p_impl()
* delete extra whitespaces after statement
* show target in debug prints
* more concise debug print
* delete old comments
* update with loop instead of move()
* comment out all adaptive p debug prints
* more debug prints
* move review() variables: common_sampler struct -> common_sampler_review() args
* match n_unsent type
* fix merge bugs, delete adaptive p references in buffer_and_check_string_ban()
* restore accidental erasure
* Revert "adaptive p: collect probability before logit bias"
This reverts commit 1434878461c49d1a2a9047fc15d5e7b78421fd2a.
* server : support multi-modal context checkpoints and prompt caching
do not create checkpoint right after image processing
improve mtmd check for slot ops
fix context shift
do not abort if template parse failed
* change to debug message when detecting ban token
---------
Co-authored-by: firecoperana <firecoperana>
* WIP: support pre-merged up/gate experts
Haha, mainline has elected to arrange the merged tensors
the other way around compared to what I had done in the on-the-fly merge.
* Change the order of on-the-fly packed up/gate
* OpenAI
* CUDA TG
* CPU
* Split mode graph for models with pre-merged ffn_up/ffn_gate experts
* WIP: support pre-merged up/gate experts
Haha, mainline has elected to arrange the merged tensors
the other way around compared to what I had done in the on-the-fly merge.
* Change the order of on-the-fly packed up/gate
* OpenAI
* CUDA TG
* CPU
* WIP
* WIP
* WIP
* WIP
* WIP
* WIP
* WIP
Loads and starts running, crashes with illegal memory access in
quantize_mmq_q8_1. This almost always indicates NaNs in the input
to the MoE FFN part.
* WIP
* WIP
Loads and runs, wrong results (very high PPL)
Performance looks promising, around 25% better than previous sm graph.
Needs f32 or bf16 graph reduce type.
* WIP - still wrong
* Fix after rebase
* WIP
* WIP
* This seems to be working for dense Qwen3.5!!!
* WIP: Qwen3-Next is not quite working
* Some cleanup
* Disable Qwen3-Next for now
* Disable graph parallel when mmproj was specified
* Read/write split recurrent state
* That should not crash
* Re-enable vision - it works now
* Recurrent layers should now be counted for split cache
---------
Co-authored-by: Piotr Wilkin <piotr.wilkin@syndatis.com>
common : add nemotron 3 parsing (#18077)
common : add parser for ministral/mistral large 3/devstral 2 (#17713)
common : default content to an empty string (#18485)
chat: make tool description and parameters optional per OpenAI spec (#18478)
Per the OpenAI API specification, both 'description' and 'parameters'
fields in tool function definitions are optional. Previously, the parser
would throw an exception if these fields were missing.
Attempts to fix#17667
common : implement new jinja template engine (#18462)
---------
Co-authored-by: Alde Rojas <hello@alde.dev>
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
jinja: correct member access rule (#18905)
jinja : fix lexing of float literals with sign (#18901)
jinja : add missing tojson filter for bool (#18900)
jinja : attribute support for join, map and sort (#18883)
jinja : fix object item order (and properly implement dictsort) (#18904)
tests : add test-jinja -py option for cross-checking (#18906)
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
---------
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
ci : run test-jinja -py on high perf [no ci] (#18916)
jinja : fix undefined keys and attributes and int/float as bool (#18924)
jinja: support none|string (#18995)
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
---------
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
jinja : implement mixed type object keys (#18955)
---------
Co-authored-by: Xuan Son Nguyen <son@huggingface.co>
jinja : undefined should be treated as sequence/iterable (return string/array) by filters/tests (#19147)
`tojson` is not a supported `undefined` filter
keep it DRY and fix some types
jinja : do not pass empty tools and add some none filters (#19176)
jinja : add unordered_map include to value.h [no ci] (#19205)
jinja : add missing 'in' test to template engine (#19004) (#19239)
The jinja template parser was missing the 'in' test from
global_builtins(), causing templates using reject("in", ...),
select("in", ...), or 'x is in(y)' to fail with
"selectattr: unknown test 'in'".
This broke tool-calling for Qwen3-Coder and any other model
whose chat template uses the 'in' test.
Added test_is_in supporting array, string, and object containment
checks, mirroring the existing 'in' operator logic in runtime.cpp.
Includes test cases for all three containment types plus
reject/select filter usage.
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
---------
Co-authored-by: Sid Mohan <sidmohan0@users.noreply.github.com>
Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com>
Co-authored-by: Xuan Son Nguyen <son@huggingface.co>
Add Jinja support for "indent" string filter (#19529)
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
---------
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
add vendor
refactor chat
server : support preserving reasoning_content in assistant message (#18994)
chat : fix translategemma crash on common_chat_format_example (#19019)
chat: fix language input for translategemma (#19052)
Co-authored-by: Aldehir Rojas <hello@alde.dev>
---------
Co-authored-by: Aldehir Rojas <hello@alde.dev>
chat: fix case where template accepts type content only (#19419)
mtmd : chat : Fix extra \n between text and media marker (#19595)
Thanks to @tugot17 for detecting and reporting the issue.
For vision models (e.g. LFM2.5-VL-1.6B and Qwen/Qwen3-VL-4B-Instruct) `llama-mtmd-cli` produces identical output to HF implementation.
However `llama-server` doesn't. I traced it down to extra newline
inserted after `<__media__>`.
This happens in `to_json_oaicompat`, that treats media markers as text
and joins all parts with `\n` separator.
PR introduces new type `media_marker` and uses it for media markers.
Extra logic is added to prevent insertion of newlines before and after
media markers.
With this change number of input tokens is identical to HF
implementation and as a result the output is also identical.
I explored other ways to address the issue
* remove completely `\n` between text parts in `to_json_oaicompat`
* merge text messages in server-common.cpp before sending them to `to_json_oaicompat`
Please propose alternative ways of fixing this issue.
Co-authored-by: Piotr Wilkin (ilintar) <piotr.wilkin@syndatis.com>
---------
Co-authored-by: Piotr Wilkin (ilintar) <piotr.wilkin@syndatis.com>
common : merge qwen3-coder and nemotron nano 3 parsers (#19765)
common : fix improper trimming in XML parser on complete message (#19805)
Co-authored-by: Jules LEIDELINGER <11395311+julio75012@users.noreply.github.com>
jinja: correct stats for tojson and string filters (#19785)
jinja : correct default size for string slices (#19913)
common : handle unicode during partial json parsing (#16526)
common : fix json schema with '\' in literals (#17307)
add back qwen_coder_xml and mirothinker
Co-authored-by: Aldehir Rojas <hello@alde.dev>
* fix adaptive p sampler rewinding too far back
* update comments
* correct default value for total_weight, more comments
* new variables/names
* update comment for n_rewind
* move null pointer check back to common_sampler_review()
* refactor weighted_sum and total_weight to vector<pair>, better boundary check in llama_review_adaptive_p_impl()
* Revive fused delta-net
* Add command line argument for fused delta net
* Simplify/improve CUDA delta-net
* Add -fdn to llama-bench
* More CUDA fused delta net optimizations
* CPU optimizations
* Much faster fused delta-net on the CPU
It seems it is faster than the chunked implementation!
* Change meaning of fdn from bool flag to threshold value
* Use eps = 1e-6
* Give some nodes a name
* Don't re-apply L2 norm - it has already been done
* This seems quite a bit better
* More tweaks
* Restore per context buffer size log
Not everybody uses models split in 2000 parts, and those who do,
actually want to see the biffer sizes.
* server: enable checkpoint for recurrent models
create checkpoint after cancel
fix ban string and rm context during rewind
add checkpoint interval
only save recurrent cache
* save checkpoint during pp
---------
Co-authored-by: firecoperana <firecoperana>
* Revive fused delta-net
* Add command line argument for fused delta net
* Simplify/improve CUDA delta-net
* Add -fdn to llama-bench
* More CUDA fused delta net optimizations
* CPU optimizations
* Much faster fused delta-net on the CPU
It seems it is faster than the chunked implementation!
* Change meaning of fdn from bool flag to threshold value
* Use eps = 1e-6
* Give some nodes a name
* Display the size of the tensors overriden during the tensor loading
Ex:
`Tensor blk.60.ffn_gate_exps.weight buffer type overriden to CPU
Tensor blk.60.ffn_up_exps.weight buffer type overriden to CPU`
become
`Tensor blk.60.ffn_up_exps.weight (size = 668467200 bytes) buffer type overriden to CPU
Tensor blk.60.ffn_gate_exps.weight (size = 668467200 bytes) buffer type overriden to CPU`
And pass in debug the later displayed size of the unnamed buffer overrides.
Ex : `llm_load_tensors: CPU buffer size = XXX.XX MiB`
That double display is cluttering the screen without being very informative.
* change bytes display to MiB.
Co-authored-by: Kawrakow <iwankawrakow@gmail.com>
---------
Co-authored-by: Kawrakow <iwankawrakow@gmail.com>
* Partial Requant feature for llama-quantize
- Inspired by the recently portcopied --dry-run feature.
- Allows to partially requantize a split quantized .gguf by requantizing only the missing splits in the destination directory.
- Works both for GGUF which are split tensors by tensors, or by group of several tensors (though this one is not very much tested beyond 2 tensors by split).
- Vibe coded.
* Create output directory if it doesn't exist in llama-quantize
* Create output directory if it doesn't exist in gguf-split
* Add exit when directory fails to be created on Windows
* Use std::filesystem
* cleanup
* wip: port MTP architecture
Ports the Multi-Token Prediction (MTP) architecture to the older `llama.cpp` codebase used by `ikllama`.
Changes include:
- Updating `llama_batch` to support `mtp_params`.
- Modifying `llama_decode_internal` (and `encode`) to handle MTP operations (Warmup, Update, Draft).
- Adding public APIs for MTP state management (`llama_set_draft_input_hidden_state`).
- Adapting the embedding extraction logic to skip MTP update passes.
* Refactors `server_slot` to support generic speculative decoding (MTP or Draft Model).
* core: enable hybrid outputs (logits + embeddings) for MTP support
* fix(mtp): correct KV-cache slot finding for updates
* fix(mtp): persist hidden states to prevent context corruption during drafting
* refactor(mtp): clean unused code
* fix(mtp): update server to new functions name
* fix(mtp): fix graph and save hidden state
* mtp: refactor integration, context params and kv cache search
* mtp: fix hidden state extraction and speculative acceptance flow
* server: fix MTP warmup for long prompts and reset token buffer
* llama: refactor MTP operation state to context parameters
* server: fix n_past calculation in MTP acceptance
* llama: fix mtp enable flags
* speculative: refactor MTP to use common_speculative interface
* context: remove unused signatures
* clip: fix deprecated enum-enum conversion warning
* common: fix format string crash in help message
* context: fix mtp activation logic
* adaptive p: upadte internal state only if not rewinding
* adaptive p: conditional update for speculative decoding
* adaptive p: refactor to rewind instead of update
* adaptive p fix: better comments
* fix rewind check
* add record to handle multi-token rewind
* better comment
* Optimizing q3next TG
* Fused add -> softplus -> mul on CUDA
* Remove forgotten debug log
* Increase ggml context size
Required for Qwen3-Next with batch/u-batch size of 4096
* WIP
* Avoid some contiguous ops
* Avoid some repeats
* Avoid some more repeats
* qwen3next: add architecture support and recurrent-state fixes
* qwen3next: optimize broadcast sub and single-seq ssm conv
* cuda: build MoE row mapping on device in mul_mat_id
* cuda: add guarded multi-seq fast path for ssm_conv
* docs: update qwen3next perf report for cuda MoE/SSM tuning
* cuda: reduce qwen3next moe/ssm sync overhead and refresh eval
* qwen3next: split cpu/cuda eval builds and tune PP scheduling
* qwen3next: harden seq-state flow and support optional dense FFN layers
* qwen3next: trim delta-net graph overhead in chunking path
* qwen3next: remove redundant v_conv cont in delta path
* qwen3next: avoid extra cont on linear attention output
* qwen3next: drop redundant cont before recurrent state flatten
* qwen3next: keep recurrent state in 4d layout through delta path
* qwen3next: add fused delta-net op and wire model path
* tests: add backend-op coverage for ggml_delta_net
* qwen3next: add runtime switch for fused delta-net path
* docs: refresh qwen3next perf review and benchmark matrix
* qwen3next: default fused delta-net off and document quality checks
* qwen3next: add decode-only fused delta mode
* qwen3next: make fused delta safe by default and fix fused tensor layout
* qwen3next: warn when forcing fused decode mode
* qwen3next: add fused-delta regression runner script
* qwen3next: integrate fused regression into eval harness
* qwen3next: clean up chunked delta-net shape handling
* qwen3next: add absolute sanity guards to fused regression
* qwen3next: add unified regression runner script
* qwen3next: disable flash-attn for cpu-only contexts
* docs: reconcile qwen3next status and remaining upstream gaps
* common: add qwen3next fused-delta runtime flag
* cuda: add qwen3next delta-net kernel dispatch override
* docs: update qwen3next quality and serving baseline findings
* qwen3next: keep fused delta on safe path and remove PR artifacts
* qwen3next: align autoregressive delta-net decode layout
* Revert "qwen3next: align autoregressive delta-net decode layout"
This reverts commit 9241164a5ea9e032a2456fbf2dd0bf798b264fd7.
* cuda: port solve-tri fast-paths for qwen3next delta-net
* qwen3next: add fused-delta runtime flag and drop env toggle
* qwen3next: make fused delta single-flag and default on
* Account for GPU arch differences
* Revert "cuda: build MoE row mapping on device in mul_mat_id"
This reverts commit 89e9ecfa840b04e88699ab3803eb732cd78727f9.
* qwen3next: drop non-essential MoE scheduling and split heuristics
* qwen3next: avoid generic ggml_sub broadcast changes
* llama: restore only_active_experts log message
* Remove unnecessary hacks, disable fusion for now.
* qwen3next: port hybrid recurrent state memory semantics
* qwen3next: clean up recurrent state slot plumbing
* qwen3next: fix hybrid V-cache layout plumbing
* qwen3next: guard recurrent state slots against kv capacity
* qwen3next: persist recurrent state in session data
- serialize/restore qwen3next cache.s_l in state/session paths\n- bump session and sequence-state file versions for format change\n- fallback to single-token chunking for mixed repeated seq_id batches
* qwen3next: drop unused fused-delta builder path
- remove dead build_delta_net_fused lambda\n- remove unused llm_build_context::fused_delta member
* qwen3next: remove unused fused-delta CLI/context plumbing
- drop -fd/-no-fd options and related YAML dump field\n- remove fused_delta fields from public/internal context params\n- remove fused_delta assignment and logging in context init
* ggml: remove unused DELTA_NET operator stack
* Missing include
* Reorder ops/unary ops
So we don't change again the enum values of the mul mat ops
* Minor
* Discard unnecessary changes in llama-build-context.cpp
* Minor
* Revert "Discard unnecessary changes in llama-build-context.cpp"
This reverts commit edadb80ed68c4c0831e9c22609a9a3af19be9735.
* Increase GGML_SCHED_MAX_SPLITS - required for larger u-batches
* Fix CPU concat in the TG case: 7.25 -> 10.5 t/s for Qwen3Next
* Fix CPU sum_rows: 10.5 -> 13.6 t/s for Qwen3Next
It was single-threaded and was taking ~25% of the computation time
during TG. It is now down to 2%.
Strangely enough, I measure 13.6 t/s with llama-bench, but if I
let the model give me an actual response with llama-cli, I get close
to 17 t/s.
* Fix CPU scale: 13.6 -> 16.7 t/s for Qwen3Next
For Qwen3Next there is a scale op on a largish tensor (548k elements)
that has a single row for TG, so was done in a single thread.
We now simply use blocks of 1024 elements.
* Optimize CPU mul: 16.7 -> 17.6 t/s for Qwen3Next
* CPU: fuse transpose -> cont -> sum_rows -> transpos: 17.6 -> 23.1 t/s for Qwen3Next
* Optimize CPU repeat: 176 -> 200 t/s for Qwen3Next PP-512
* Multithreading for OP_SUB
* Don't commit with timing trace on
* Multithread neg and sigmoid
* Be able to turn on/off fusion more easily (CPU)
* Name the mul_mat ops so we know where the time goes
* WIP
* Much better PP on CUDA
* CUDA: fuse transpose -> cont -> sum_rows -> transpose
Needs non-coontiguous variant of sum_rows.
On the CPU this gave 30+% improvement in TG performance,
on CUDA ist is disapointing 6-7%. I guess, this is because
Georgi's cont CPU implementation was so bad that skipping
it made such a big difference.
* CUDA: faster mul for special case relevant for Qwen3Next
Worth 1% in TG
* Fix CPU OP_CONT
---------
Co-authored-by: yurko <yurko@local>
Co-authored-by: Yurko <yurko@example.com>
Co-authored-by: yurko <yurko@pop-os.tail5a1a6b.ts.net>
Co-authored-by: Yurko Hoshko <YurkoHoshko@users.noreply.github.com>
* spec : add self speculative decoding and ngram-mod and refactor
common : use common_ prefix for common library function
llama : use LLAMA_TOKEN_NULL
spec : add self speculative decoding (no draft model required) + refactor
spec : add ngram-mod
spec : various improvements ton ngram-map + docs
spec : fix the check-rate logic of ngram-simple
common : add common_speculative_is_compat()
spec : simplify time measurement using common_time_meas
refactor common_sampler_init
refactor common_token_to_piece
refactor and fix cur_p bug
clean up
* spec : remove check rate
* spec: show warnings instead of abort
---------
Co-authored-by: firecoperana <firecoperana>
Co-authored-by: Sascha Rogmann <59577610+srogmann@users.noreply.github.com>
* WIP
* This works but is slow
* Turn off the up / gate clamps for now
* OK we need the clamping
* Fuse the clamp (CUDA)
* Fuse the clamp (CPU)
* WIP
* Be able to use merged q, k, v
* Be able to use merged up/gate experts
* Fuse the clamp (CUDA mmvq)
* WIP: graph parallel for Step-3.5
* WIP
* This should be it
* Cleanup
* Fix merge
* Not working attempt to extend fused_mul_unary to the Step-3.5 case
* It works now, but performance gain is very minor
* Fix graph parallel when ngl < n_layers
* Fix using ffn_norm
When using graph parallel with ngl < n_layers, the ffn_norm tensor
may have ended up being split, while the ffn tensors are on the CPU.
In that case we will get a crash because we attempt to use the not-split
buffer of ffn_norm, which is invalid. Thi commit fixes that.
* Cleanup
* WIP
* This works but is slow
* Turn off the up / gate clamps for now
* OK we need the clamping
* Fuse the clamp (CUDA)
* Fuse the clamp (CPU)
* WIP
* Be able to use merged q, k, v
* Be able to use merged up/gate experts
* Fuse the clamp (CUDA mmvq)
* WIP: graph parallel for Step-3.5
* WIP
* This should be it
* Cleanup
* Fix merge
* WIP
* This works but is slow
* Turn off the up / gate clamps for now
* OK we need the clamping
* Fuse the clamp (CUDA)
* Fuse the clamp (CPU)
* WIP
* Be able to use merged q, k, v
* Be able to use merged up/gate experts
* Fuse the clamp (CUDA mmvq)
* adaptive_p: fix history update + use current probability for high temp
* adaptive_p: fix history update bug, update with current probability if temp is high
* replace temp-as-signal with server argument
* adaptive_p: rename ema_w_cur_p to updt_w_cur
* delete test code
SamuelOliveirads
Kawrakow
Justin Martin
Samuel Oliveira Alves
thad0ctor
David Young
firecoperana
Lingfeng Ren
joelfarthing
dmaivel
markaalonzo
markaalonzo
Nexes the Elder
dungquixote42
gapeleon
usrlocalben
saood06