* llama : make all KQ masks (except the lightning indexer one) f16 if FA is used and remove zero attention bias in DeepSeek V4
* llama : remove dead code that repeats unified raw_k cache for each stream in DeepSeek V4 - no longer needed as raw_k is always non-unified.
---------
Co-authored-by: Stanisław Szymczyk <sszymczy@gmail.com>
llm_graph_input_attn_kv::set_input and llm_graph_input_attn_kv_iswa::set_input
call set_input_k_rot / set_input_v_rot whenever the rotation tensor pointer is
non-null, but the tensor's buffer can be unallocated (NULL) when a graph only
stores K/V without attending -- e.g. DFlash speculative decoding's KV-injection
pass. set_input_k_rot then calls ggml_backend_buffer_is_host() on a NULL buffer
and aborts with GGML_ASSERT(buffer).
Guard the four k_rot/v_rot inputs with the same "&& ->buffer" check that the
adjacent kq_mask inputs already use in these two functions. When the buffer is
unallocated there is no data to upload, so skipping is correct.
Fixes#25191
Signed-off-by: liminfei-amd <91481003+liminfei-amd@users.noreply.github.com>
* convert: add dsv4 conversion
* add basic setup
* add llm_graph_input_dsv4
* add save-load state
* add sinkhorn eps - correction by @fairydreaming
* add rope fix
* cleanup dead code
* fix bugs
* support pro model: added by @fairydreaming
* remove redundant V cache
* Chat template
* remove debugging leftovers
* Add mechanism for inlining templates based on architecture
* s/deepseek-v4-flash/deepseek4/g
* s/deepseek-v4-flash/deepseek4/g continued
* enable graph reuse
* enable FA
* fix test llama archs
* rename
* compatibility with antirez ds4 GGUFs
* simplified set_gguf_parameters() by calling super class method, replaced moe.score_func with expert_gating_func.
* reserve worst-case kv-cache
* revert max split inputs
* address review comments
* add padding to enable FA
* pad only the final value of plan.n_kv to 256
* remove built-in cpp chat template
* cont: remove cpp built-in template
* rm outdated test
* replace ggml_view_3d() with ggml_reshape_3d()
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* only support n_seq=1 for now
* remove unused var
* cont: remove unused var
* use scale bias
* use correct ptr for can_reuse
* remove gen-chat-inline-templates.py
* simplify graph reuse
* cont: cleanup
* remove unused inputs
* enable partial checkpointing
* add correct shape for kq_mask + set llama_model_n_swa to 0 for dsv4
* precompute source_idx + add comment about dummy write
* support multi-seq
* remove restored_trim_pos
* use split_equal when possible
* fix indent
* address review comments
* use LLM_KV
* fix ci
---------
Co-authored-by: Piotr Wilkin <piotr.wilkin@syndatis.com>
Co-authored-by: Stanisław Szymczyk <sszymczy@gmail.com>
Co-authored-by: Xuan Son Nguyen <son@huggingface.co>
Co-authored-by: fairydreaming <166155368+fairydreaming@users.noreply.github.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* llama-graph : apply embedding scale when deepstack is not used
* nits: remove non-existant hunyuan-vl from the tests
* apply suggestion from @gabe-l-hart
---------
Co-authored-by: Xuan Son Nguyen <son@huggingface.co>
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.
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* feat(convert): Get language model conversion working for 4.1 vision
Branch: Granite4Vision
AI-usage: none
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* feat(convert): Skip multimodal tensors for GraniteMoeHybrid (vision 4.0)
Branch: Granite4Vision
AI-usage: none
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix: Disable vocab padding for non-hybrid models that use GraniteMoeHybrid
Branch: Granite4Vision
AI-usage: none
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* 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 <ghart@us.ibm.com>
* feat: Add python side architecture name
Branch: Granite4Vision
AI-usage: none
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* feat: Add python-side plumbing for setting FEATURE_LAYERS hparam
Branch: Granite4Vision
AI-usage: none
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* 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 <ghart@us.ibm.com>
* 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: none
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* feat(mtmd): Add architecture label plumbing
Branch: Granite4Vision
AI-usage: full (OpenCode + qwen3.5:122b)
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* 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 <ghart@us.ibm.com>
* 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 <ghart@us.ibm.com>
* feat: Full conversion for mmproj w/ tensor mappings
Branch: Granite4Vision
AI-usage: full (OpenCode + qwen3.5:122b)
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix: Add lm_head skip for mmproj for 4.0
Branch: Granite4Vision
AI-usage: none
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix: De-alias text_config architecture in convert_lora_to_gguf.py
Branch: Granite4Vision
AI-usage: none
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* 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 <ghart@us.ibm.com>
* fix: De-alias model.language_model. -> model. for lora adapters
Branch: Granite4Vision
AI-usage: full (OpenCode + qwen3.5:122b)
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix: Extend language model tensor dealiasing in adapters
Branch: Granite4Vision
AI-usage: none
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix: Remove unnecessary registration for GraniteSpeech in language model
Branch: Granite4Vision
AI-usage: none
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* feat: Plumb through mm prefix formatting for qformer tensors
Branch: Granite4Vision
AI-usage: none
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* 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.
Branch: Granite4Vision
AI-usage: none
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* feat: Add spatial offests array hparam conversion
Branch: Granite4Vision
AI-usage: none
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* feat: Add stub plumbing for granite vision in mtmd
Branch: Granite4Vision
AI-usage: draft (OpenCode + qwen3.5:122b)
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* feat: Add new hparam and tensor naming in clip-impl.h
New hparams:
- KEY_PROJ_SAMPLE_QUERY_SIDE
- KEY_PROJ_SAMPLE_WINDOW_SIDE
- KEY_PROJ_SPATIAL_OFFSETS
New tensors:
- TN_MULTI_PROJ_IMG_POS
- TN_MULTI_PROJ_QUERY
- TN_MULTI_PROJ_LAYERNORM
- TN_MULTI_PROJ_LINEAR
- TN_MULTI_PROJ_NORM
Branch: Granite4Vision
AI-usage: none
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix: Move deepstack_layer_arr to llm hparam instead of mmproj
Branch: Granite4Vision
AI-usage: none
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix: Remove IS_DEEPSTACK_LAYERS
This appears to have been added during Qwen3 VL
(https://github.com/ggml-org/llama.cpp/pull/16780), but it was never
actually used.
Branch: Granite4Vision
AI-usage: none
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* refactor: n_deepstack_layers -> deepstack_layer_arr
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 <ghart@us.ibm.com>
* 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 <ghart@us.ibm.com>
* 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: none
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix: add missing vision attn layernorm eps
Branch: Granite4Vision
AI-usage: full (OpenCode + Qwen 3.6-35B)
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* refactor: Hoist qformer tensors into qf_block and hold a vector for multi-proj
Branch: Granite4Vision
AI-usage: none
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* 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 <ghart@us.ibm.com>
* 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 <ghart@us.ibm.com>
* 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 <ghart@us.ibm.com>
* feat: Implement c++ hparam parsing
Branch: Granite4Vision
AI-usage: draft (Claude Code)
Co-authored-by: Eli Schwartz <eliyahu.schwartz@ibm.com>
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix: Flatten pinpoints in conversion
Branch: Granite4Vision
AI-usage: none
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix: Add missing break
Branch: Granite4Vision
AI-usage: none
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix: No reason to have modality prefix for img_pos
Branch: Granite4Vision
AI-usage: none
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* feat: Add tensor loading
Branch: Granite4Vision
AI-usage: none
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix(convert): Fix confusion between proj.norm and proj.qformer.layernorm
Branch: Granite4Vision
AI-usage: none
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* 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 <ghart@us.ibm.com>
* 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 <ghart@us.ibm.com>
* fix: Make sure input embeddings are cont before f_embedding_scale
Branch: Granite4Vision
AI-usage: none
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* 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: draft (Bob)
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix: Invert (h, w) -> (w, h) pinpoints
Branch: Granite4Vision
AI-usage: none
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* 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 <ghart@us.ibm.com>
Co-authored-by: Eli Schwartz <eliyahu.schwartz@ibm.com>
* fix: Fix mmproj hparams in conversion
Branch: Granite4Vision
AI-usage: none
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
Co-authored-by: Eli Schwartz <eliyahu.schwartz@ibm.com>
* fix: Fix ordering/logic for deepstack injection in granite
Branch: Granite4Vision
AI-usage: none
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
Co-authored-by: Eli Schwartz <eliyahu.schwartz@ibm.com>
* fix: Fix preprocessing config to match what the model needs
Branch: Granite4Vision
AI-usage: none
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
Co-authored-by: Eli Schwartz <eliyahu.schwartz@ibm.com>
* 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 <ghart@us.ibm.com>
Co-authored-by: Eli Schwartz <eliyahu.schwartz@ibm.com>
* 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 <ghart@us.ibm.com>
* feat: invert embedding multiplier -> base_scale at load
Branch: Granite4Vision
AI-usage: none
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix: Fix setting image_resize_pad after new enum introduced
Branch: Granite4Vision
AI-usage: none
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix: Add G4V to mmproj mapping in conversion
Branch: Granite4Vision
AI-usage: none
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix: Re-add padding disable for non-hybrid hybrid models
Branch: Granite4Vision
AI-usage: none
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* refactor: Simplify G4V n_tokens computation
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 <ghart@us.ibm.com>
* 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 <ghart@us.ibm.com>
* 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 <ghart@us.ibm.com>
* 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 <ghart@us.ibm.com>
* 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 <ghart@us.ibm.com>
* style: Comment improvement
Branch: Granite4Vision
AI-usage: none
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* refactor: granite_vision -> granite4_vision
Branch: Granite4Vision
AI-usage: none
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* 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 <ghart@us.ibm.com>
* 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 <ghart@us.ibm.com>
* 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 <ghart@us.ibm.com>
* fix: Fix Flake8 warnings in granite conversion module
Branch: Granite4Vision
AI-usage: none
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* 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: draft (Bob, OpenCode + Qwen 3.6 35b)
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* refactor(convert): Split n_deepstack_layers and deepstack_layers (array)
Branch: Granite4Vision
AI-usage: full (Bob, OpenCode + Qwen3.6-35b)
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* 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 <ghart@us.ibm.com>
* fix: Fix GGUF key for deepstack_layers_arr
Branch: Granite4Vision
AI-usage: none
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* 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 <ghart@us.ibm.com>
* refactor: deepstack_layers(_arr) -> deepstack_mapping(_arr)
Branch: Granite4Vision
AI-usage: none
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* 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 <ghart@us.ibm.com>
* 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 <ghart@us.ibm.com>
* fix: Remove unnecessary ggml_cont and build_forward_expand in cbx
Branch: Granite4Vision
AI-usage: none
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* style: Clean up comments
Branch: Granite4Vision
AI-usage: none
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* 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 <ghart@us.ibm.com>
* fix: Remove unnecessary `unordered_set` include
Branch: Granite4Vision
AI-usage: none
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix: Add architecture guard on deepstack_mapping_arr printout
Branch: Granite4Vision
AI-usage: none
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix: Remove unnecessary AI-gen comment
Branch: Granite4Vision
AI-usage: none
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* 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 <ghart@us.ibm.com>
* style: Remove TODO about block/vs non-block tensor mapping
Branch: Granite4Vision
AI-usage: none
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* refactor: Move is_vision_feature_layer logic into clip_hparams
Branch: Granite4Vision
AI-usage: none
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* refactor: Use a bool for append_token
Branch: Granite4Vision
AI-usage: none
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* style: Remove unnecessary comment
Branch: Granite4Vision
AI-usage: none
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix: Remove unused get_model api
yikes!
Branch: Granite4Vision
AI-usage: none
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* 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 <ghart@us.ibm.com>
* 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 <ghart@us.ibm.com>
* fix: Fix norm/post_norm mixup in conversion
face. palm. :(
Branch: Granite4Vision
AI-usage: none
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* style: More descriptive tensor names
Branch: Granite4Vision
AI-usage: none
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix: Apply PR cleanup for new conversion changes
AI-usage: none
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* fix(convert): Remove duplicate V_ENC_EMBD_IMGNL
Branch: Granite4Vision
AI-usage: none
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* refactor: append_token -> add_newline
Branch: Granite4Vision
AI-usage: none
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* style: Comment cleanup
Branch: Granite4Vision
AI-usage: none
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* feat: Cleaner error handling/checking
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 <ghart@us.ibm.com>
---------
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
When a model has zero non-SWA attention layers (e.g. a SWA-only slice of Gemma 4),
the base KV cache has no layer tensors. The input tensors (self_k_idxs, self_v_idxs,
self_kq_mask) are created as graph input nodes but never consumed by any compute node,
so the backend scheduler never allocates a buffer for them. Calling
mctx->get_base()->set_input_k_idxs() on an unallocated tensor then hits
GGML_ASSERT(buffer) at ggml-backend.cpp:194.
The same scenario applies symmetrically: if a model had zero SWA layers, the SWA
tensors would be unallocated.
Fix: guard both the base and SWA set_input calls with null/buffer checks, matching
the pattern already used by llm_graph_input_mem_hybrid_iswa::set_input (line ~674)
which has the comment: 'base tensors may not be allocated if there are no non-SWA
attention layers'.
Also fix can_reuse() in the same class to skip the ne[0] and kq_mask checks for
unallocated tensors, preventing a null-dereference on the reuse path.
* spec: support MTP
* fix batch size
* rename files
* cont : simplify (#7)
* MTP: clean-up (#9)
* MTP: clean-up
* review: use llama_context_type instead of llama_graph_type
* review: remove llama_model_has_mtp
* review: fix convert issues
* convert: fix pycheck
* review: formatting
* use `mtp-` for identifying mtp models
* convert: fix mtp conversion
* mtp -> draft-mtp
* remove unused llama_arch
* add need_embd in speculative
* llama: allow partial seq_rm for GDN models for speculative decoding
Currently speculative checkpoint needs to restart from a checkpoint
after some draft tokens are not accepted, this leads to some wastage in
running the target again. This PR adds the ability to rollback upto
`draft_max` by storing the GDN intermediates.
* fix pending state
* vulkan: add GDN partial rollback
* meta: extend check to axis 1
* metal: add GDN partial rollback
Extend the gated delta net kernel to store intermediate states for
partial rollback support on the Metal backend.
- Add K (snapshot slot count) as a function constant
- Read input state from slot 0 of the 3D state tensor
- Write intermediate states to different slots during token loop
- For K=1, maintain backward-compatible single-slot behavior
Ref: 8c05923630
Assisted-by: llama.cpp:local pi
* delta_net_base: use ggml_pad instead of new_tensor
* review: add need_rs_seq
* review: rename part_bounded to n_rs
* review: deslop comments
* review: rename, add asserts
* server : adjust checkpoint logic (#11)
* server : adjust checkpoint logic
* cont : rm asserts
* server-context: fix early exit
* spec : fix compatibility with n-gram and add TODOs (#13)
* metal : cleanup
* llama : fix faulty bitwise check in recurrent memory
* server : disable RS-based MTP in combination with other spec types
* spec : add TODOs
* cont : fix comment
* cont : update comment
* common : fix logic for ngram + mtp compat
* llama-memory: enable checkpointing with partial rollback
* cont: add test-case for loading into a dirty ctx
* llama-memory-recurrent: clear rs_idx in clear
* download: fix mtp path
* llama-arch: fix enorm op
* docs: update docs
* conversion: fix type annotations
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* model : refactor QKV into common build_qkv and create_tensor_qkv helpers
* model : extend build_qkv to bert/mpt/dbrx/olmo/lfm2/nemotron-h/granite-hybrid/gemma3n-iswa/t5-dec and fix wqkv_s
* ggml: backend-agnostic tensor parallelism
* support for GPT-OSS, Qwen 3 MoE
* partial Vulkan fix
* add support for 4/8 GPUs
* unconditional peer access
* re-use buffers + ggml contexts
* fix output pattern
* NCCL support
* GGML: HIP: add RCCL support
* Remove shfl and AllReduce from backend interface
* move allocation workaround out of ggml-alloc.c
* 2d tensor set/get support
* Fix the seg fault without NCCL
* Apply suggestion from JohannesGaessler
* support for tensor dims % n_devs != 0
* fix view_offs scaling
* arbitrary num. of GPUs/tensor split
* fix compilation
* better granularity estimate
* Support device-specific host buffer types if all underlying backends expose the same type. This allows using pinned memory instead of pageable memory for CUDA.
Fix compilation errors.
* partial Qwen 3 Next support
* Fix qwen3 30b (#8)
* Fix crash with Qwen-30B-A3B Q4_0
Qwen-30B-A3B Q4_0 has an intermediate dimension of 768. Using a granularity of 256 forces an uneven split between GPUs, which is not supported by the current implementation.
* Decide block size based on tensor quantization type
* Fix crashes due to KV cache serialization (#9)
KV cache serialization requires non-zero offsets on the tensor. Add support in the meta backend to set/get a tensor with a non-zero offset.
* metal : fix build (#7)
* static memory allocations, fix usage count
* fix tensor granularity
* more even memory distribution
* use BF16 for allreduce
* rebase fixup
* better error message for unsupported architectures
* Fix device mismatch during scatter of allReduce. (#11)
There is a mismatch between the dst buffer device and the backend device, causing the use of sync copies
* Enable the previous allreduce implementation. It is better in both perf and stability (#12)
* delay AllReduce for Moe for less I/O
* build : clean-up compile warnings
* backend : move most of the meta backend API to ggml-backend-impl.h
* cont : hide unused public API in the implementation
* llama : use llama_device + remove ggml_backend_dev_is_meta()
* ggml-backend : remove unused alloc include
* minor : remove regex include
* ggml : introduce ggml-ext.h for staging new APIs
* rebase fixup
* fix tests
* llama : more robust logic for determining Meta devices (#16)
* llama : more robust logic for determining Meta devices
* cont : fix devs size check
Co-authored-by: Johannes Gäßler <johannesg@5d6.de>
* cont : fix log type
Co-authored-by: Johannes Gäßler <johannesg@5d6.de>
---------
Co-authored-by: Johannes Gäßler <johannesg@5d6.de>
* disable roundtrip for meta backend
* fix arch selection
* Qwen 3.5 support
* fix Gemma 4 MoE
* fix OpenVino, SYCL
* fix test-llama-archs for CPU-only builds
* Fix Qwen 3.5 MoE
* disable meta backend tests for WebGPU
* tests : filter CPU-based devices from the Meta backend tests (#17)
* meta : formatting, naming, indentation (#18)
* formatting : llama-model.cpp
* formatting : ggml-ext.h
* formatting : ggml-backend-meta.cpp
* meta : add TODO
* add documentation
* better error messages
* fix GPT-OSS
---------
Co-authored-by: Carl Philipp Klemm <carl@uvos.xyz>
Co-authored-by: Gaurav Garg <gaugarg@nvidia.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* mtmd: llama.cpp DeepSeekOCR support
init commit
* loading sam tensors
* mtmd: fix vision model processing
* deepseek-ocr clip-vit model impl
* mtmd: add DeepSeek-OCR LM support with standard attention
* mtmd: successfully runs DeepSeek-OCR LM in llama-cli
* mtmd: Fix RoPE type for DeepSeek-OCR LM.
* loading LM
testing Vision model loading
* sam warmup working
* sam erroneous return corrected
* clip-vit: corrected cls_embd concat
* clip-vit: model convert qkv_proj split
* corrected combining of image encoders' results
* fix: update callback for ffn_moe_weighted and add callback for attn_out in deepseek2 model
* concat image_newline and image_seperator tokens
* visual_model warmup (technically) works
* window partitioning using standard ggml ops
* sam implementation without using CPU only ops
* clip: fixed warnings
* Merge branch 'sf/deepseek-ocr' of github.com:sfallah/llama.cpp into sf/deepseek-ocr
* mtmd: fix get_rel_pos
* mtmd: fixed the wrong scaler for get_rel_pos
* image encoding technically works but the output can't be checked singe image decoding fails
* mtmd: minor changed
* mtmd: add native resolution support
* - image encoding debugged
- issues fixed mainly related wrong config like n_patches etc.
- configs need to be corrected in the converter
* mtmd: correct token order
* - dynamic resizing
- changes are concerning PR https://github.com/sfallah/llama.cpp/pull/4
* mtmd: quick fix token order
* mtmd: fix danling pointer
* mtmd: SAM numerically works
* mtmd: debug CLIP-L (vit_pre_ln)
* mtmd: debug CLIP-L & first working DeepSeek-OCR model
* mtmd : add --dsocr-mode CLI argument for DeepSeek-OCR resolution control & all native resolution modes work
* mtmd: simplify SAM patch embedding
* mtmd: adapt Pillow image resizing function
* mtmd: simplify DeepSeek-OCR dynamic resolution preprocessing
* mtmd: remove --dsocr-mode argument
* mtmd: refactor code & remove unused helper functions
* mtmd: fix tensor names for image newlines and view separator
* clean up
* reverting automatically removed spaces
* reverting automatically removed spaces
* mtmd: fixed bad ocr check in Deepseek2 (LM)
* mtmd: support combined QKV projection in buid_vit
* using common build_attn in sam
* corrected code-branch when flash-attn disabled
enabling usage of --flash-attn option
* mtmd: minor fix
* minor formatting and style
* fixed flake8 lint issues
* minor editorconfig-check fixes
* minor editorconfig-check fixes
* mtmd: simplify get_rel_pos
* mtmd: make sam hparams configurable
* mtmd: add detailed comments for resize_bicubic_pillow
* mtmd: fixed wrong input setting
* mtmd: convert model in FP16
* mtmd: minor fix
* mtmd: remove tweak to llama-mtmd-cli & deepseek-ocr template
* fix: test-1.jpg ORC issue with small (640) resolution
setting min-resolution base (1024) max large (1280) for dynamic-resolution
* minor: editconfig-check fix
* merge with changes from https://github.com/ggml-org/llama.cpp/pull/17909
added new opt to tests.sh to disable flash-attn
* minor: editconfig-check fix
* testing deepseek-ocr
quick and dirty test script comparing results of Qwen2.5-VL vs DeepSeek-OCR
* quick and (potential) dirty merge with https://github.com/ggml-org/llama.cpp/pull/17909
* refactoring, one single builder function and static helpers
* added deepseek-ocr test to tests.sh
* minor formatting fixes
* check with fixed expected resutls
* minor formatting
* editorconfig-check fix
* merge with changes from https://github.com/ggml-org/llama.cpp/pull/18042
* minor
- added GLM-4.6V to big tests
- added missing deps for python test
* convert: minor fix
* mtmd: format code
* convert: quick fix
* convert: quick fix
* minor python formatting
* fixed merge build issue
* merge resolved
- fixed issues in convert
- tested several deepseek models
* minor fix
* minor
* Update convert_hf_to_gguf.py
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* - removed clip_is_deepseekocr
- removed redundant RESIZE_ALGO_BICUBIC_PILLOW resize-algo
- simplified image-preprocessing
- removed/simplified debug functions
* - cleaning commented out code
* fixing instabilities issues reintroducing resize_bicubic_pillow
* - use f16 model for deepseek-ocr test
- ignore llama-arch test for deepseek-ocr
* rename fc_w --> mm_fc_w
* add links to OCR discussion
* cleaner loading code
* add missing .weight to some tensors
* add default jinja template (to be used by server)
* move test model to ggml-org
* rolling back upscale change
* Update convert_hf_to_gguf.py
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
---------
Co-authored-by: bluebread <hotbread70127@gmail.com>
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
Co-authored-by: Xuan Son Nguyen <son@huggingface.co>
Co-authored-by: Xuan-Son Nguyen <thichthat@gmail.com>
* WIP: add NVFP4 quantization support
* tests
* improve NVFP4 dot product implementation performance and fix bad super call
* typo
* Use nvfp4 kvalues
* vulkan : fix NVFP4 shader compilation by including kvalues_mxfp4 lookup table
* vulcal and perf fixes
* wip
* Fix metal
* fix vulcan
* Rename threshold & fix wrong scale
* Fix MOE
* Shelf backend implementations (CUDA, Metal, Vulkan, arch-specific SIMD)
Remove NVFP4 support from GPU backends and architecture-specific
optimized dot products. These should be added in separate PRs so
backend specialists can review them independently.
Reverted files:
- ggml-cuda: common.cuh, convert.cu, mmq.cu/cuh, mmvq.cu, vecdotq.cuh,
quantize.cu/cuh, mma.cuh, ggml-cuda.cu, fattn-tile.cuh
- ggml-metal: ggml-metal.metal, ggml-metal-device.cpp, ggml-metal-impl.h,
ggml-metal-ops.cpp
- ggml-vulkan: ggml-vulkan.cpp, all vulkan-shaders/*
- ggml-cpu arch: arm/quants.c, x86/quants.c, powerpc/quants.c, s390/quants.c
Core NVFP4 support (type definition, CPU fallback dot product,
quantization, dequantization, conversion) is retained.
* Fix arch-fallback.h: add NVFP4 generic fallback for all platforms
After shelving backend-specific SIMD implementations, the generic
CPU dot product needs to be aliased on ARM, x86, PowerPC, and s390
platforms that previously relied on arch-specific versions.
* quantize: add NVFP4 as a quantization type option
* Fix ggml_fp32_to_ue4m3: handle subnormal values
Previously, values with ue4m3_exp <= 0 were clamped to 0, causing
all small scales to underflow. This made NVFP4 quantization via
llama-quantize produce garbage (PPL = 5.8M) since typical transformer
weights have amax/6.0 in the range 0.001-0.01, which falls in the
UE4M3 subnormal range.
Now subnormals are properly encoded as man * 2^-9 (exp=0, man=1..7),
matching the decode path in ggml_ue4m3_to_fp32.
Result: NVFP4 requantization now produces PPL = 15.25 (vs F16 = 14.33),
comparable to Q4_1 (PPL = 15.81) at slightly lower BPW (4.70 vs 5.15).
* Restore ARM NEON NVFP4 dot product implementation
Restores the optimized ggml_vec_dot_nvfp4_q8_0 for ARM NEON using
vqtbl1q_s8 lookup and ggml_vdotq_s32 dot products.
tg128 performance: 4.37 t/s (generic) -> 13.66 t/s (NEON) = 3.1x speedup
* Optimize ARM NEON NVFP4 dot product: LUT + vpaddq + vfmaq
- Add ue4m3_scale_lut[128] to ggml-common.h replacing branch-heavy
ggml_ue4m3_to_fp32() in the hot loop
- Use vpaddq_s32 for pairwise int32 reduction instead of vaddvq_s32
- Accumulate with vfmaq_f32 into float32x4_t vector accumulators
tg128: 8.1 -> 31.0 t/s (3.8x speedup, 77% of Q4_1 speed)
* ARM NEON NVFP4: rearrange q8 to match nibble layout
Alternative approach: rearrange q8 data to match the NVFP4 lo/hi
nibble layout instead of rearranging the looked-up NVFP4 values.
Eliminates vcombine_s8(vget_low, vget_low) shuffles.
Performance is equivalent (~18.5 t/s) - the bottleneck is the 2x
block overhead from QK=16 vs QK=32, not the shuffle instructions.
* CPU only backend 64 super-block layout
* cleanup
* Remove unused LUT
* int
* exclude NVFP4 from unsupported ops in metal build
* remove quantization for now
* store scales as native UE4M3, preserve original model bits when possible
* Update convert_hf_to_gguf.py
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* correct comment
* format
* reduce duplication and cleanup
* Address comments
* move detection to prepare_tensors
* Use math instead of const
* Move
* fix comment
* Shelf quantize tests
* Rebase and move check
* cleanup
* lint
* Update gguf-py/gguf/scripts/gguf_convert_endian.py
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* Use fallback quant config
* Simplify
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* organize
* Refactor
* Update convert_hf_to_gguf.py
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* Update convert_hf_to_gguf.py
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* Update convert_hf_to_gguf.py
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* add quantize_nvfp4 (required for test_quants.py)
* add quantize_nvfp4 (required for test_quants.py)
* add quantize_nvfp4 (required for test_quants.py)
* fix return type
---------
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* tests: add end-to-end tests per model architecture
* fixup for rebase
* fix use-after-free in llama-model-loader.cpp
* fix CI
* fix WebGPU
* fix CI
* disable CI for macOS-latest-cmake-arm64
* use expert_weights_scale only if != 0.0f
* comments
* model: add JAIS-2 architecture support
Add support for the JAIS-2 family of Arabic-English bilingual models
from Inception AI (https://huggingface.co/inceptionai/Jais-2-8B-Chat).
Architecture characteristics:
- LayerNorm (not RMSNorm) with biases
- ReLU² (ReLU squared) activation function
- Separate Q/K/V projections with biases
- Simple MLP without gate projection (up -> act -> down)
- RoPE positional embeddings
- GPT-2 BPE tokenizer
Supported model sizes:
- Jais-2-8B (32 layers, 26 heads, 3328 hidden)
- Jais-2-70B (68 layers, 56 heads, 7168 hidden)
Tested with quantizations: BF16, Q8_0, Q6_K, Q5_K_M, Q5_0, Q4_K_M, Q4_0, Q3_K_M, Q2_K
Note: JAIS-2 requires F32 precision accumulators for numerical stability
and uses standard attention (not flash attention) on CUDA backends.
* fix: run convert_hf_to_gguf_update.py for jais-2 tokenizer hash
* fix: use NEOX RoPE type for JAIS2
* fix: remove Q/K permutation (NEOX RoPE doesn't need it)
* fix: enable flash attention for JAIS2 (fixed by #19115)
* fix: add dedicated JAIS2 pre-tokenizer type and control vector support
- Add LLAMA_VOCAB_PRE_TYPE_JAIS2 with cascading whitespace regex
- Include original regex from tokenizer.json as comment
- Add build_cvec call for control vector support
* no longer necessary to override set_vocab
---------
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* model : Add tokenizer from LFM2.5-Audio-1.5B
[LFM2.5-Audio-1.5B](https://huggingface.co/LiquidAI/LFM2.5-Audio-1.5B) introduced lightweight audio tokenizer.
Tokenizer based on LFM2 architecture and acts as "embedding" model with
different input `n_embd` and output `n_embd_out`.
To be used in https://github.com/ggml-org/llama.cpp/pull/18641.
To convert use
```shell
python3 convert_hf_to_gguf.py /path/to/LFM2.5-Audio-1.5B/audio_detokenizer
```
* Update convert_hf_to_gguf.py
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* Formatting
* Rework check for attention layers
* Add LFM2 SWA model support
* Address PR feedback
* Set vocab to none
* Move helper function definitions to cpp file
---------
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* full modern bert support
* added gelu op in rank pooling for modern bert
* still working on stuff, added mean calculation before classifier head
* Update convert_hf_to_gguf.py
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* first layer is dense, as per modern bert research paper
* Update src/llama-graph.cpp
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* fixed set input for mean pooling to check if pooling type is ranking since modern bert does mean & rank
* Update src/llama-graph.cpp
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* Update convert_hf_to_gguf.py
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
---------
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* kimi linear model implementation
* kimi linear convert_hf_to_gguf
* kimi linear constants.py tensor_mapping.py
* Kimi Linear ggml.h
* kimi linear ggml-cpu
* Kimi Linear ggml-cuda
* Kimi Linear ggml.c
* kimi linear src/llama
* remove "const int64_t n_seq_tokens = q->ne[2];" to get rid of unused variable warning
* remove type mismatch warning
* read MoE params
* removed some hard coded code
* removed all hard code
* use DeepseekV2 tokenizer
* removed unnecessary internal methods called by the old set_vocab of KimiLinear
* rewrite get_vocab for KimiLinear. Removed all kda_scan code
* removed all traces of kda_scan
* reduce OP count by 1 due to removal of kda_scan
* Move KIMI_LINEAR to llm_arch_is_hybrid to enable KV cache
* set n_embd_head_k/v to ensure kv cache works
* don't quantize conv1d of Kimi Linear
* Kimi Linear backend agnostic
* removed LOG_INFO
* naive chunking form implemented
* fixed some comments
* add Kimi-K2 specific tokens to be recognized as EOG
* build_kda_autoregressive is implemented to replace build_kda_recurrent for faster inference. sync'd to b7682
* replaced Akk and Aqk with mul_mat and clamp
* no clamp version
* Moved Aqk computation out of the loop
* fixed typo and split wkv_b into wk_b and wv_b
* MLA KV cache support
* fix trailing spaces
* moved const llama_model & model; around to follow qwen3next format and see if it cna pass the -Wunused-private-field error
* fix trailing whitespace
* removed traling whitespaces in empty line + make sure indentation is multiple of 4
* try to make lint happy
* remove blank lines to make lint happy
* removed at least blank line containing white space
* fixed flake8 complaints locally
* return ggml_tensor * pair in kda_autoregressive and kda_chunking as in ngxson's Qwen3Next improvement
* removed Kimi-Linear specific change that causes failure at server-windows
* removed private: from kimi_linear to make build checks happy
* removed unnecessary ggml_cont before ggml_reshape
* created static function causal_conv1d to abtract similar code for q/k/v
* merged dt_bias to SSM_DT. Do -exp(log_A) in convert_hf_to_gguf.py.
* reverted to original
* fixed find_hparam calls. Fixed e_score_correction_bias to use bias instead of weight. Removed all ssm_conv bias terms.
* remove DT_B from constants.py. remove one comment line in llama-model.cpp
* new class llm_graph_input_mem_hybrid_k to get around the new MLA change. switch the concat order of ggml_concat calls in kimi-linear.cpp to accommodate MLA changes. Removed support for exp_probs_b.weight
* remove ssm_o_norm_b
* remove ssm_o_norm_b
* changed hparams.kda_head_dim to hparams.n_embd_head_kda. added TODO comment for class llama_graph_mem_hybrid_k
* removed all ggml_cont b4 ggml_reshape_4d
* Whitespace
* replaced all hparams.get with find_hparams
* added new names for n_experts, n_experts_used and score_func in TextModel and removed their code in KimiLinear in convert_hf_to_gguf.py. Removed unnecessary ggml_cont and GGML_ASSERT in kimi-linear.cpp
* use is_mla to switch between different mem_hybrid types
* fixed logical errors in convert_hf_to_gguf.py pointed out by CISC
* removed if else for required parameters kv_lora_rank and qk_rope_head_dim
* add back ggml_cont for Vcur
* minor changes
* removed extra line in llama-vocab.cpp. Added back the comment in llama-graph.cpp
* f16 gguf cannot run without context length
* made a mistake of adding back n_ctx parsing
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Co-authored-by: Piotr Wilkin (ilintar) <piotr.wilkin@syndatis.com>
