Pascal [Mon, 8 Jun 2026 17:20:28 +0000 (19:20 +0200)]
graph: guard iswa kq_mask on its own buffer (#24294)
A SWA-only draft head (e.g. StepFun MTP) leaves the base sub-cache
empty, so its kq_mask buffer stays null and asserts at load. Guard
each mask on its own buffer in set_input and can_reuse, base and swa.
Jeff Bolz [Mon, 8 Jun 2026 08:40:37 +0000 (03:40 -0500)]
vulkan: Use cm2 decode_vector for mul_mat_id B matrix loads (#23991)
This allows vec4 loads of the B elements. Also increase BK to 64 when this is
enabled. Neither of these alone is consistently faster, but together these give
a nice speedup.
In ggml-vulkan.cpp, we need to make sure the B matrix alignment and stride are
multiples of 4.
ddh0 [Sun, 7 Jun 2026 20:48:11 +0000 (15:48 -0500)]
common : relax sampler name matching (#23744)
* common : relax sampler name matching
Currently, in some cases, the alternative names for samplers (like
`top-k` and `min-p` instead of the canonical `top_k` and `min_p`) are
not always recognized by the `common_sampler_types_from_names` function
in `common/sampling.cpp`.
This PR changes the signature of this function to remove the `bool
allow_alt_names` flag, and removes all occurences of the flag from call
sites. Therefore, the function will now always match all known names.
I also changed the logic of the function to unconditionally check the
provided sampler names against both the canonical and alternative names,
and to be case-insensitive.
This fixes an issue I was seeing wherein samplers specified in the
`llama-server` UI were not recognized as valid when the alternative
names were used.
* add more alt names
* cont. fix
* cast to unsigned char for correctness
* common : unify sampler name mapping
* annotate canonical vs. alt sampler name mappings per @CISC
* Update common/sampling.cpp
Co-authored-by: Sigbjørn Skjæret <redacted>
* common : auto-generate sampler name aliases per @ngxson
* use merged map for matching
* use `.merge` instead of iterating
* nit: simplify comment
* nit: use insert everywhere, not index assignment
David Friehs [Sun, 7 Jun 2026 19:41:39 +0000 (21:41 +0200)]
convert : fix conversion for Mistral-Medium-3.5-128B (#24268)
Mistral explicitly sets `moe` and `llama_4_scaling` to `null` in
params.json, breaking `key in dict` checks during conversion. Replace
with `dict.get(key) is not None` where this matters.
Pascal [Sun, 7 Jun 2026 15:33:00 +0000 (17:33 +0200)]
kv-cache: follow the source cache size when sharing cells (#24267)
A fitted target context can end up smaller than the draft default, the
oversized assistant views then overflow the shared K/V tensors and trip
the ggml_view_4d size assert during graph reserve.
Gabe Goodhart [Fri, 5 Jun 2026 15:44:59 +0000 (09:44 -0600)]
model, mtmd: Granite4 Vision (#23545)
* feat(convert): Get language model conversion working for 4.1 vision
Branch: Granite4Vision
AI-usage: none Signed-off-by: Gabe Goodhart <redacted>
* feat(convert): Skip multimodal tensors for GraniteMoeHybrid (vision 4.0)
Branch: Granite4Vision
AI-usage: none Signed-off-by: Gabe Goodhart <redacted>
* fix: Disable vocab padding for non-hybrid models that use GraniteMoeHybrid
Branch: Granite4Vision
AI-usage: none Signed-off-by: Gabe Goodhart <redacted>
* feat: Plumb python-side vision projector names and mappings
There are several awkward things here:
1. Most of these are essentially identical to the audio qformer tensors. On
the c++ side, that's mapped using the prefix, so the rest of the GGUF
name needs to align, but on the python side there's no prefix notion, so
they all get duplicated.
2. There are a couple of net-new tensors for vision, in particular
PROJ_NORM. In both speech and vision, the QF_PROJ_NORM is qualified as
belonging to the qformer portion, but the GGUF name is simply proj_norm
which conflicts with the ideal name for this new PROJ_NORM that is not
qualified as part of the qformer. To get around this, I used
"proj_layernorm" as the GGUF name.
Branch: Granite4Vision
AI-usage: none Signed-off-by: Gabe Goodhart <redacted>
* feat: Add python side architecture name
Branch: Granite4Vision
AI-usage: none Signed-off-by: Gabe Goodhart <redacted>
* feat: Add python-side plumbing for setting FEATURE_LAYERS hparam
Branch: Granite4Vision
AI-usage: none Signed-off-by: Gabe Goodhart <redacted>
* feat: Add c++ side tensor naming defines
NOTE: Usage of these hasn't been updated to include prefix yet
Branch: Granite4Vision
AI-usage: none Signed-off-by: Gabe Goodhart <redacted>
* feat(mtmd): Convert vision_feature_layer to an ordered vector
We need to preserve the ordering of these feature index values so that they
can be mapped to the sub-tensors within the stacked projectors.
Branch: Granite4Vision
AI-usage: full (OpenCode + qwen3.5:122b) Signed-off-by: Gabe Goodhart <redacted>
* feat(wip): Add partial conversion for mmproj
This handles stacking the projector tensors and setting the new harams
Branch: Granite4Vision
AI-usage: none Signed-off-by: Gabe Goodhart <redacted>
* feat: Add gguf_writer and constant support for new hparams and deepstack layer arr
Branch: Granite4Vision
AI-usage: draft (OpenCode + qwen3.5:122b) Signed-off-by: Gabe Goodhart <redacted>
* feat: Full conversion for mmproj w/ tensor mappings
Branch: Granite4Vision
AI-usage: full (OpenCode + qwen3.5:122b) Signed-off-by: Gabe Goodhart <redacted>
* fix: Add lm_head skip for mmproj for 4.0
Branch: Granite4Vision
AI-usage: none Signed-off-by: Gabe Goodhart <redacted>
* fix: De-alias text_config architecture in convert_lora_to_gguf.py
Branch: Granite4Vision
AI-usage: none Signed-off-by: Gabe Goodhart <redacted>
* feat: Add --trust-remote-code arg to convert_lora_to_gguf.py
This defaults to False, but allows a user to enable it programmaticly
instead of using the interactive prompt.
Branch: Granite4Vision
AI-usage: none Signed-off-by: Gabe Goodhart <redacted>
* fix: De-alias model.language_model. -> model. for lora adapters
Branch: Granite4Vision
AI-usage: full (OpenCode + qwen3.5:122b) Signed-off-by: Gabe Goodhart <redacted>
* fix: Extend language model tensor dealiasing in adapters
Branch: Granite4Vision
AI-usage: none Signed-off-by: Gabe Goodhart <redacted>
* fix: Remove unnecessary registration for GraniteSpeech in language model
Branch: Granite4Vision
AI-usage: none Signed-off-by: Gabe Goodhart <redacted>
* feat: Plumb through mm prefix formatting for qformer tensors
Branch: Granite4Vision
AI-usage: none Signed-off-by: Gabe Goodhart <redacted>
* refactor: Refactor vision projector tensors to use predictor ID as the block
This is cleaner than stacking them. The modeling file hard-codes
single-layer qformers, so we can punt on the multiipule multi-layer
projectors problem.
The old logic hard coded a correspondence between the first N layers of the
LLM and the 1->N entries in the input embeddings. Now, that relationship is
maintained at loading time if the GGUF value is single-valued. If it is
multi-valued, it loads directly allowing for deepstack layers to be spaced
out throughout the model.
Branch: Granite4Vision
AI-usage: none Signed-off-by: Gabe Goodhart <redacted>
* fix: Use try/catch for single/multi valued deepstack info
The alternative would be to use get_key_or_arr, but then the single value
would be populated through the entire array and we'd need to detect that
and update it with the right correspondence.
Branch: Granite4Vision
AI-usage: none Signed-off-by: Gabe Goodhart <redacted>
* feat: Add deepstack injection point for granite LLM
The use of ggml_add here assumes that the elements of inp_embd will be pre-
arranged to be the full embedding length with only the vision-mask'ed
portions non-zero from the projector. This matches how Qwen3VL does it.
Branch: Granite4Vision
AI-usage: full (OpenCode + Qwen 3.6-35B) Signed-off-by: Gabe Goodhart <redacted>
* refactor: Hoist qformer tensors into qf_block and hold a vector for multi-proj
Branch: Granite4Vision
AI-usage: none Signed-off-by: Gabe Goodhart <redacted>
* fix: Fix missing prefix template for TN_QF_PROJ_LINEAR
It's not strictly necessary since vision uses the blockwise version, but it
makes the loading consistent.
Branch: Granite4Vision
AI-usage: none Signed-off-by: Gabe Goodhart <redacted>
* fix: Add embedding scale and image grid pinpoints hparams in conversion
Also remove dead parsing for self._deepstack_layer_arr
Branch: Granite4Vision
AI-usage: none Signed-off-by: Gabe Goodhart <redacted>
* feat: Add mtmd KEY_ section for hparams shared with the LLM
In this case, we need the EMBEDDING_SCALE so we can unscale the image
embeddings to compensate for applying embedding scale to the input
embeddings
Branch: Granite4Vision
AI-usage: none Signed-off-by: Gabe Goodhart <redacted>
* feat: Implement c++ hparam parsing
Branch: Granite4Vision
AI-usage: draft (Claude Code) Co-authored-by: Eli Schwartz <redacted> Signed-off-by: Gabe Goodhart <redacted>
* fix: Flatten pinpoints in conversion
Branch: Granite4Vision
AI-usage: none Signed-off-by: Gabe Goodhart <redacted>
* fix(convert): Fix confusion between proj.norm and proj.qformer.layernorm
Branch: Granite4Vision
AI-usage: none Signed-off-by: Gabe Goodhart <redacted>
* fix: Use the right portion of speech for tensor loading!
Also plumb through the layernorm -> post_norm naming change
Branch: Granite4Vision
AI-usage: none Signed-off-by: Gabe Goodhart <redacted>
* feat: Add logging of deepstack_layers_arr if set
I also changed the print_f output type to int32_t to avoid printing
overflow values for -1. This could cause overflows on the other side, but
I can't imagine a value for any of the current array hparams that would
trigger that.
Branch: Granite4Vision
AI-usage: none Signed-off-by: Gabe Goodhart <redacted>
* fix: Make sure input embeddings are cont before f_embedding_scale
Branch: Granite4Vision
AI-usage: none Signed-off-by: Gabe Goodhart <redacted>
* feat: Add init and mmproj_embd cases for g4v
The n_mmproj_embd is 1+ to make space for the text embedding and all 8
projectors
Branch: Granite4Vision
AI-usage: none Signed-off-by: Gabe Goodhart <redacted>
* fix: Reorder projectors based on llm index and skip the first injection
The multi-projector stack has a strange asymmetry based on how it's
currently implemented for qwen3vl: on the mmproj side, it's all N
projectors, but the output of the "first" (by inp_embd index) projector is
automatically consumed as if it were a standard single-projector mmproj,
so the deepstack portion needs to only contain the 1-N entries.
Branch: Granite4Vision
AI-usage: none Signed-off-by: Gabe Goodhart <redacted> Co-authored-by: Eli Schwartz <redacted>
* fix: Fix mmproj hparams in conversion
Branch: Granite4Vision
AI-usage: none Signed-off-by: Gabe Goodhart <redacted> Co-authored-by: Eli Schwartz <redacted>
* fix: Fix ordering/logic for deepstack injection in granite
Branch: Granite4Vision
AI-usage: none Signed-off-by: Gabe Goodhart <redacted> Co-authored-by: Eli Schwartz <redacted>
* fix: Fix preprocessing config to match what the model needs
Branch: Granite4Vision
AI-usage: none Signed-off-by: Gabe Goodhart <redacted> Co-authored-by: Eli Schwartz <redacted>
* wip: Partial port of Eli's implementation
This is still pretty broken, but it's getting closer. It now happily
generates tokens, but the values are quite incorrect still. I suspect it's
caused by the mapping of projectors from safetensors to their respective
orders here.
Also, this implementation breaks encapsulation pretty badly in mtmd_encode.
This will need a big refactor to put the G4V-specific encoding logic
somewhere more appropriate.
Branch: Granite4Vision
AI-usage: draft (Claude Code, Bob) Signed-off-by: Gabe Goodhart <redacted> Co-authored-by: Eli Schwartz <redacted>
* fix: Fix the pre-scaling on the input embeddings to correctly invert the scale
We've got tokens! They still don't line up quite right, so something's a
little off, but we're getting much closer now.
Branch: Granite4Vision
AI-usage: none Signed-off-by: Gabe Goodhart <redacted>
* feat: invert embedding multiplier -> base_scale at load
Branch: Granite4Vision
AI-usage: none Signed-off-by: Gabe Goodhart <redacted>
* fix: Fix setting image_resize_pad after new enum introduced
Branch: Granite4Vision
AI-usage: none Signed-off-by: Gabe Goodhart <redacted>
* fix: Add G4V to mmproj mapping in conversion
Branch: Granite4Vision
AI-usage: none Signed-off-by: Gabe Goodhart <redacted>
* fix: Re-add padding disable for non-hybrid hybrid models
This is slightly more efficient and flexible for when we implement the
unpad cropping. IMO, it's also clearer that it is adding the number of
image_newline tokens (embeddings) to the grid, rather than recomputing the
entire count.
Branch: Granite4Vision
AI-usage: none Signed-off-by: Gabe Goodhart <redacted>
* feat: Add new clip APIs for post-tile-encoding assembly
Granite 4 Vision uses llava-next style pack-and-unpad which requires
injecting the learned newline after each row of the tile grid. A row here
is a single row of the grid which is composed of (grid_x * cols_per_tile) *
(grid_y * rows_per_tile), so the result is newlines injected in between
individual tile rows, thus not something that can be handled with the
standard llava-uhd block-wise endcoding.
Branch: Granite4Vision
AI-usage: draft (Claude Code + Opus 4.7) Signed-off-by: Gabe Goodhart <redacted>
* feat: Add model interfaces for granite 4 vision assembler
I'm on the fence about the best organization of this. These free functions
allow the per-architecture logic in clip.cpp to access the model-specific
graph building, but they still require a fair bit of model-specific logic
in clip.cpp which is not ideal.
I think a better approach may be to replicate what is done with the
graph builders themselves (and possibly even make the assembler part of the
model's existing graph builder).
Branch: Granite4Vision
AI-usage: full (Claude Code + Opus 4.7) Signed-off-by: Gabe Goodhart <redacted>
* refactor: Remove all g4v-specific branching from mtmd.cpp in favor of clip assembler
Branch: Granite4Vision
AI-usage: full (Claude Code + Opus 4.7) Signed-off-by: Gabe Goodhart <redacted>
* refactor(mtmd): Consolidate assembler logic into clip_assembler class family
Just like `clip_graph` is the base class for building the model-specific
encoder graphs, `clip_assembler` will be the base class for building the
model-specific assembler graphs. This allows the assembly pattern to follow
how the encoder pattern is implemented where the model-specific logic lives
in a subclass co-located with the encoder graph builder that gets
constructed by a simple factory method.
Branch: Granite4Vision
AI-usage: full (Claude Code + Opus 4.7) Signed-off-by: Gabe Goodhart <redacted>
* style: Comment improvement
Branch: Granite4Vision
AI-usage: none Signed-off-by: Gabe Goodhart <redacted>
* fix: Remove dead codepath for Qwen3VL add_vision_is_deepstack
These pieces were never used on the c++ side (removed there in an earlier
commit), so this is just cleanup that I missed before.
Branch: Granite4Vision
AI-usage: none Signed-off-by: Gabe Goodhart <redacted>
* fix: Oops! I did not mean to commit one of my prompt files
But now it's too far back in history to effectively rebase out, even with
interactive and --rebase-merges :(
Branch: Granite4Vision
AI-usage: none Signed-off-by: Gabe Goodhart <redacted>
* fix: Add missing <algorithm> include for std::find
It seems that this was already pulled in on some platforms, but not on
others
Branch: Granite4Vision
AI-usage: none Signed-off-by: Gabe Goodhart <redacted>
* fix: Fix Flake8 warnings in granite conversion module
Branch: Granite4Vision
AI-usage: none Signed-off-by: Gabe Goodhart <redacted>
* refactor: Remove clip_assembler in favor of clip_image_f32.append_token
Per conversation in the PR, the clip_assembler pattern was too invasive.
This is a compromise that limits model-specific blocks to add_media where
each preprocessed tile is annotated with an injection type, after which all
the token counting logic is generic and the newline injection itself is
handled in the graph based on the value for the given tile image.
Branch: Granite4Vision
AI-usage: full (Bob, OpenCode + Qwen3.6-35b) Signed-off-by: Gabe Goodhart <redacted>
* refactor(src): Handle n_deepstack_layers and deepstack_layers GGUF keys
Branch: Granite4Vision
AI-usage: draft (Bob, OpenCode + Qwen3.6-35b) Signed-off-by: Gabe Goodhart <redacted>
* fix: Fix GGUF key for deepstack_layers_arr
Branch: Granite4Vision
AI-usage: none Signed-off-by: Gabe Goodhart <redacted>
* refactor: Remove pre-scaling embeddings and skip scaling for raw embd inputs
This follows how gemma3 and gemma4 handle embedding scaling by skipping the
multiplier for raw input embeddings.
Branch: Granite4Vision
AI-usage: none Signed-off-by: Gabe Goodhart <redacted>
* refactor: Fully revert changes to n_deepstack_layers and qwen3vl*
Since we're going to keep the GGUF KVs separate, it makes sense to just
keep the hparams separate too to limit the scope of this branch. The down
side is that n_deepstack_layers and deepstack_mapping_arr are potentially
conflicting.
Branch: Granite4Vision
AI-usage: none Signed-off-by: Gabe Goodhart <redacted>
* fix: Revert removal of "is_deepstack_layers" GGUF KV
This KV is not used at all on the c++ side, so it's fully dead, but there's
also no need to conflate this cleanup with the addition of G4V.
Branch: Granite4Vision
AI-usage: none Signed-off-by: Gabe Goodhart <redacted>
* fix: Remove unnecessary ggml_cont and build_forward_expand in cbx
Branch: Granite4Vision
AI-usage: none Signed-off-by: Gabe Goodhart <redacted>
* style: Clean up comments
Branch: Granite4Vision
AI-usage: none Signed-off-by: Gabe Goodhart <redacted>
* fix: Tighter and more flexible code for g4v_build_block
This could be refactored to look a lot more like granite-speech, but the
overall block constructs before/after the qformer are pretty different, so
for now I'm going to leave it as is and just tighten a bit.
Branch: Granite4Vision
AI-usage: none Signed-off-by: Gabe Goodhart <redacted>
* fix: Remove unnecessary `unordered_set` include
Branch: Granite4Vision
AI-usage: none Signed-off-by: Gabe Goodhart <redacted>
* fix: Add architecture guard on deepstack_mapping_arr printout
Branch: Granite4Vision
AI-usage: none Signed-off-by: Gabe Goodhart <redacted>
* fix: Always initialize deepstack_mapping_arr with -1 values
This was causing `test-llama-archs` to fail, likely due to trying to save
the uninitialized values, then re-loading them. It's safer to always
initialize so that other models don't forget and end up with undefined
behavior.
Branch: Granite4Vision
AI-usage: none Signed-off-by: Gabe Goodhart <redacted>
* style: Remove TODO about block/vs non-block tensor mapping
Branch: Granite4Vision
AI-usage: none Signed-off-by: Gabe Goodhart <redacted>
* refactor: Move is_vision_feature_layer logic into clip_hparams
Branch: Granite4Vision
AI-usage: none Signed-off-by: Gabe Goodhart <redacted>
* refactor: Use a bool for append_token
Branch: Granite4Vision
AI-usage: none Signed-off-by: Gabe Goodhart <redacted>
* fix: Remove unused get_model api
yikes!
Branch: Granite4Vision
AI-usage: none Signed-off-by: Gabe Goodhart <redacted>
* refactor: Rearrange helpers for g4v to be private members and use build_attn
Branch: Granite4Vision
AI-usage: full (Bob, OpenCode + Qwen3.6-35b) Signed-off-by: Gabe Goodhart <redacted>
* fix: Fix off-by-one in vision layer index
This was inherited from the Claude Code implementation that pushed the
negative index inversion down into the model file.
Branch: Granite4Vision
AI-usage: none Signed-off-by: Gabe Goodhart <redacted>
* fix: Fix norm/post_norm mixup in conversion
face. palm. :(
Branch: Granite4Vision
AI-usage: none Signed-off-by: Gabe Goodhart <redacted>
* style: More descriptive tensor names
Branch: Granite4Vision
AI-usage: none Signed-off-by: Gabe Goodhart <redacted>
* fix: Apply PR cleanup for new conversion changes
NOTE: format_string is not available in granite.cpp (and including
clip-impl.h to get it doesn't compile, so I think it violates the intended
encapsulation), so std::stringstream is the simplest answer.
Branch: Granite4Vision
AI-usage: none Signed-off-by: Gabe Goodhart <redacted>
---------
Daniel Bevenius [Fri, 5 Jun 2026 05:57:36 +0000 (07:57 +0200)]
ci : build-msys job slimming [no ci] (#24157)
This PR attempts to slim down the dependencies for build-msys jobs
making the same changes that we applied in whisper.cpp to reduce the
size of the github actions cache, and should also improve the run time
due to fewer dependencies that need to be installed.
I realize this is a scheduled job but I think it would still make sense
to apply these changes.
Mason Milburn [Fri, 5 Jun 2026 05:10:31 +0000 (01:10 -0400)]
sycl : port multi-column MMVQ from CUDA backend (#21845)
mmvq:
Port the ncols_dst optimization from ggml-cuda/mmvq.cu to SYCL.
Read weights once per dispatch instead of once per column.
Covers all standard quant types + reorder paths for Q4_0, Q8_0,
Q3_K, Q4_K, Q5_K, Q6_K. IQ types (except IQ4_XS) excluded due to
incompatible vec_dot signatures.
ggml-sycl:
The weight reorder was only bootstrapped on single-token mat-vec
(ne[1] == 1). Speculative / MTP verify issues only multi-column mat-vec,
so it never triggered the reorder and ran on the slower non-reorder
kernel. Bootstrap it on small multi-column batches (ne[1] <= 8) too.
Aleksander Grygier [Thu, 4 Jun 2026 14:23:08 +0000 (16:23 +0200)]
ui: Fixed packages (#24119)
* chore(ui): pin package versions to currently installed
- Update all dependencies and devDependencies to match exactly what's in package-lock.json
- This ensures reproducible builds by locking to specific versions rather than semver ranges
* chore: Update packages
* chore: Move remaining dependencies to devDependencies
Kartik Sirohi [Thu, 4 Jun 2026 13:12:38 +0000 (18:42 +0530)]
ggml: vectorize ggml_vec_dot_q4_1_q8_1 with WASM SIMD128 (#22209)
* ggml: vectorize ggml_vec_dot_q4_1_q8_1 with WASM SIMD128
Optimize the inner loop of ggml_vec_dot_q4_1_q8_1_generic using
WASM SIMD128 intrinsics, gated behind #ifdef __wasm_simd128__ so
non-wasm builds are completely unaffected.
Approach:
- single wasm_v128_load covers all 32 packed 4-bit weights
- nibbles unpacked via AND/SHR into two u8x16 registers
- widened to i16 before multiply (WASM SIMD has no i8*i8 instruction)
- 4x wasm_i32x4_dot_i16x8 calls accumulate all 32 element pairs
- horizontal reduce via 4x wasm_i32x4_extract_lane
Correctness verified against scalar reference across 10 random seeds
with exact output match.
* ggml: move q4_1_q8_1 WASM SIMD implementation to wasm backend
Relocate the SIMD128 implementation of ggml_vec_dot_q4_1_q8_1 to ggml/src/ggml-cpu/arch/wasm/quants.c to follow architecture-specific layout. Restore the generic implementation in ggml/src/ggml-cpu/quants.c.
Move for loop in the else block.
* ggml: use generic q4_1_q8_1 fallback in wasm backend
Yongyue Sun [Thu, 4 Jun 2026 13:09:01 +0000 (21:09 +0800)]
server: avoid unnecessary checkpoint restore when new tokens are present (#24110)
* server: avoid unnecessary checkpoint restore when new tokens are present
The pos_min_thold calculation unconditionally subtracts 1 to ensure at
least one token is evaluated for logits when no new tokens exist.
However, when the request contains new tokens beyond the cached prefix,
this -1 is overly conservative and may trigger an unnecessary checkpoint
restore.
Conditionally apply the -1 only when n_past >= task.n_tokens() (no new
tokens), avoiding redundant KV state restoration when there is actual
work to do.
Key the disabled set, counts and toggles by a stable per-tool key
instead of bare function name, deduped from one canonical list. Per-tool
checkboxes become presentational (single row handler, no nested button),
category checkboxes drop the tristate (n/total carries partial). One
getEnabledToolsForLLM keeps normalized MCP schemas and dedupes by name.
* ui: use SvelteSet and SvelteMap for local tool collections to satisfy svelte/prefer-svelte-reactivity
Gerard Martinez [Thu, 4 Jun 2026 10:58:25 +0000 (03:58 -0700)]
build : use umbrella Headers directory for XCFramework module map (#23974)
The XCFramework generated by build-xcframework.sh creates a module map
that manually lists public headers.
That list can fall out of sync with the framework's Headers directory.
The module map is currently missing ggml-opt.h, which is present in the
framework headers. This can cause downstream Apple builds to fail with:
Include of non-modular header inside framework module 'llama'
Use the framework's Headers directory itself as the module map umbrella
instead of maintaining a manual header list. This makes all public headers
under the generated framework's Headers directory part of the llama module.
Georgi Gerganov [Thu, 4 Jun 2026 05:06:36 +0000 (08:06 +0300)]
tests : refactor test-save-load-state to accept token input (#24073)
* tests : refactor test-save-load-state to accept token input
- Default prompt is now empty; when not provided, generate n_batch
random tokens (useful for models without a tokenizer)
- Tokenization happens once upfront; pass token vector to test functions
- generate_tokens prints token IDs instead of decoded pieces
- Use llama_model_get_vocab / llama_vocab_n_tokens API
- Upgrade log level from LOG_TRC to LOG_INF for visibility
Assisted-by: llama.cpp:local pi
* cont : use llama_tokens alias
Andreas Kieslinger [Wed, 3 Jun 2026 11:56:42 +0000 (13:56 +0200)]
Avoid PDL race conditions by disabling __restrict__ when PDL is used (#24030)
* Removes __restrict__ from PDL kernel headers due to incompatibility with
PDL. Adds preprocessor directives based on arch in kernel body to add
__restrict__ to retain performance on older architectures.
* Simplifies new __restrict__ usage via macro
* Add hopper to PDL __restrict__ fix.
Co-authored-by: Oliver Simons <redacted>
---------
Hans Florian [Tue, 2 Jun 2026 15:55:11 +0000 (11:55 -0400)]
model : support granite multilingual embeddings R2 (ibm-granite/granite-embedding-{97,311}m-multilingual-r2) (#22716)
* Add support for the ibm-granite/granite-embedding-{97m,311m}-multilingual-r2 embedding models:
* Added a version of the gpt4o tokenizer that has a fixed regex (better handling of marks), and different token merging setting for the 97m model
* Reused gemma4 tokenizer for the 311m model
* granite-embedding-*-multilingual-r2 : add support SwiGLU FFN for Granite Embedding Multilingual R2
* added new GGUF key <arch>.hidden_activation (LLM_KV_HIDDEN_ACT) + writer
* added a forward declaration of llm_ffn_op_type to llama-hparams.h
* added llm_ffn_op in hparams
* added LLM_FFN_NONE = 0 sentinel to llm_ffn_op_type (value-initialization), modern-bert: explicitly assigns LLM_FFN_GEGLU before reading GGUF (unchanged).
* centralized hidden_act mapping in llama-model.cpp, added llm_ffn_op_type_from_string() helper, mirroring rope_scaling_type/llama_rope_scaling_type_from_string()
* modern-bert reads the GGUF key (when present) and uses the resulting op in its FFN graph
* Added granite-embedding-{97m,311m}-multilingual-r2 to the converter code
* Added the hashes for the granite embedding multilingual R2 models
* Set the hidden_activation in the GGUF if the field is present in config.json (such as for the granite embedding models)
Daniel Bevenius [Tue, 2 Jun 2026 13:44:15 +0000 (15:44 +0200)]
common : fix state save in common_prompt_batch_decode (#23468)
* common : fix state save in common_prompt_batch_decode
This commit addresses a bug in common_prompt_batch_decode that affects
the session state store/restore in completion.cpp and
save-load-state.cpp.
The motivation for this is that currently the code is saving n-1 tokens
in both the session_tokens and in the KV cache. Then when loading the
session tokens, and if the prompt matches, it would replay the last
saved token (n-1) into the next position, effectively replaying the
same token in the wrong position.
The fix is to store all n tokens in session_tokens, while the memory
state only reflects n-1 processed tokens as the saving happens before
the last token is decoded in common_prompt_batch_decode.
I ran both completion.cpp and save-load-state.cpp with a transformer, a
recurrent, and a hybrid model.
Georgi Gerganov [Tue, 2 Jun 2026 10:17:59 +0000 (13:17 +0300)]
ci : reduce self-hosted server workflow jobs (#24012)
Reduce the number of parallel jobs in server-self-hosted.yml by stacking
test configurations as sequential steps within a single job, following the
pattern from #23927.
- server-metal: 4 matrix jobs -> 1 job with 4 sequential test steps
- server-cuda: 2 matrix jobs -> 1 job with 2 sequential test steps
- server-kleidiai: removed unnecessary single-entry matrix
- removed unused Setup Node.js step from server-metal
Marcos Del Sol Vives [Tue, 2 Jun 2026 08:45:25 +0000 (10:45 +0200)]
ui: simplify network error handling (#23431)
Previously error to string conversion was split in two different files,
with one converting errors into strings, and another function analyzing
those strings to generate yet another string.
Now the the error handling for network fetches has been centralised and
uses directly HTTP error codes whereas possible to generate the
human-readable error strings.
It also fixes an issue where all JSON errors reported from the backend,
such as "Invalid API key", would get turned incorrectly in to
"Failed to connect to server" due to poor matching logic in the
now-gone getErrorMessage function.
Max Krasnyansky [Tue, 2 Jun 2026 06:40:08 +0000 (23:40 -0700)]
hexagon: MUL_MAT, MUL_MAT_ID, FLASH_ATTN and GDN cleanup and optimizations for latest models (#23989)
* hex-mm: initial support for F32 * F32 -> F32 matmuls
* hex-rms-norm: fix src1 stride use in fused rms_norm_mul
* hex-ops: clear spad pointers in the ops that clober it
This fixes an odd case where fused rms-norm-mul was failing but only in qwen3.5-2B and only at searth op-bath sizes.
* hmx-mm: add support for F32 * F32 -> F32 matmul_2d on HMX
Decided to use Q4_0 * F32 -> F32 matmul for this.
Q4_0 gets dequantized and tiled into F16, and here we quantize and tile F32 into F16.
Super simple and pretty efficient.
* hmx-mm: route f16 2D matmuls through the same kernel used for all other types
* hmx-mm: re-introduce pipelined vs non-pipelined mode that we used to have but is much more generic way
This update futher improves matmul performance and at the same time removes most of the redudant logic
we had in different paths.
* hmx-fa: slighlty improved pipeline simimar to matmul updates
* hmx-mm: initial version of MAT_MUL_ID support for HMX
* hmx-mm: fixed mxfp4 handling for MUL_MAT_ID
* hex-gdn: optimize GATED_DELTA_NET
DMA prefetch/double-buff, vectorize everything with HVX, in other words -- the usual :)
* hmx-mm: missed one more case where we can use fastmod
* hexagon: update DCVS settings for a slight perf bump
* hmx-fa: use fastdiv in hmx-flash-attn
* hmx-fa: precompute slope values to avoid disrupting the inner loop
* hvx-utils/fa: new HVX helpers for powf and logf and using those to speed up FA alibi
* hex-ops: fixed a bug in fusion logic that was messing up the order of the src tensors when some srcs are empty
* hex-fa: correctly fallback to HVX if we have sinks or the dims are not quite right
Pascal [Tue, 2 Jun 2026 05:26:20 +0000 (07:26 +0200)]
server: real-time reasoning interruption via control endpoint (#23971)
* server: real-time reasoning interruption via control endpoint
Builds on the manual reasoning budget trigger from #23949. Adds a
CONTROL task that mirrors the CANCEL path on the live slot and calls
common_sampler_reasoning_budget_force to end thinking mid-generation.
POST /v1/chat/completions/control with { id_slot, action }, opt-in
reasoning_control arms the budget sampler on demand. Router and single
model. Minimal WebUI button as a skeleton for further UI work.
* ui: track reasoning phase via explicit streaming state
Add isReasoning to the chat store, mirroring the isLoading pattern:
per conversation map, private setter, public accessor and reactive
export. Set from the stream callbacks, true on reasoning chunks, false
on the first content chunk, reset on stream end and resynced on
conversation switch. The skip button now keys off isReasoning so it
shows only during the thinking phase, not the whole generation.
* ui: extract control endpoint and action into constants
Move the chat completion routes, the slots route and the reasoning
control action out of chat.service into api-endpoints and a dedicated
control-actions module. No behavior change, drops the magic strings so
the control protocol has a single source of truth.
* server: target reasoning control by completion id
Address @ngxson review on the control endpoint.
Switch from id_slot to the chat completion id to avoid a TOCTOU: the
slot can be reassigned between the lookup and the control request, so
matching the live completion (oaicompat_cmpl_id) is safe and a finished
one simply matches nothing. Rename the action to reasoning_end, guard
it on the reasoning_control flag of the target slot, and reduce the
response to {success} with an optional message.
* ui: target reasoning control by completion id
Keep the streamed completion id on the message and post it back to the
control endpoint instead of probing /slots. Drops the slot discovery
and the TOCTOU that came with it. Action renamed to reasoning_end,
response read as {success}.
* server: address review from @ngxson
Move the control fields into task_params and drop the redundant
comments on the control path.
* server: document the reasoning control endpoint
* Update tools/ui/src/lib/types/database.d.ts
Co-authored-by: Aleksander Grygier <redacted>
* ui: rename cmplId to completionId
Per @allozaur review, clearer name for the streamed completion id.
* ui: wire completion id capture through the agentic flow
The webui streams through the agentic flow, which relayed onModel but
not onCompletionId, so the completion id never reached the message and
the control request was never sent. Relay it through the flow and its
callbacks type, declare id on the chunk type, and log an explicit error
when the button fires without a usable id.
* ui: target reasoning control model from the message
The model is a property of the completion, so read it from the streaming
message like the id, not from the model dropdown which is unrelated UI
state. Makes the request self-consistent by construction instead of just
unlikely to drift.
overview / pkg / ggml / sources / llama.cpp / log