Adds an opt-in LLAMA_BUILD_MTMD CMake option so build-xcframework.sh
can link libmtmd.a into the framework binary without pulling in the
rest of tools/ (which doesn't cross-build cleanly to iOS/tvOS/visionOS).
- CMakeLists.txt: new option, default OFF. When on with
LLAMA_BUILD_TOOLS=OFF, only the tools/mtmd subdir is added. Useful
for any binding that wants just libmtmd (Apple XCFramework, WASM).
- tools/mtmd/CMakeLists.txt: gate the CLI exe targets on
LLAMA_BUILD_TOOLS. Gating on LLAMA_BUILD_COMMON is not enough — it
defaults ON in standalone builds and visionOS xcodebuild then fails
with "install TARGETS given no BUNDLE DESTINATION for MACOSX_BUNDLE
executable target 'llama-mtmd-cli'".
- build-xcframework.sh: turn the option on, pass -DLLAMA_BUILD_MTMD,
add libmtmd.a to combine_static_libraries, and copy mtmd.h and
mtmd-helper.h into the framework Headers dir. The umbrella module
map then exposes them, so Swift / Obj-C consumers can import the
mtmd C API directly.
After this, nm on ios-arm64/llama.framework/llama shows 52 _mtmd_
symbols. Verified end-to-end: a Swift target links the produced
framework and calls mtmd_default_marker, mtmd_bitmap_init, etc.
without a shim on macos / iphoneos / iphonesimulator / xros slices.
Co-authored-by: Abraham Gonzalez <abraham@theabecaster.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>
* feat: add video support for Qwen3.5
* various clean up
* revise the design
* fix llava-uhd case
* nits
* nits 2
---------
Co-authored-by: andrewmd5 <1297077+andrewmd5@users.noreply.github.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>
* requirements: relax torch~=2.6.0 to torch>=2.6.0 for convert_hf_to_gguf
The ~=2.6.0 operator resolves to >=2.6.0, <2.7.0, which fails on
PyPI for platform/CPython combinations where 2.6.x is not present.
The accompanying comment already says 'PyTorch 2.6.0 or later', so
the looser >=2.6.0 matches the documented intent and unblocks
pip install -r requirements/requirements-convert_hf_to_gguf.txt.
Fixes#23408
* requirements: bump torch floor to 2.11.0 per maintainer
* requirements: pin torch to ==2.11.0 per project policy
* requirements: pin mtmd torch and torchvision to 2.11.0/0.26.0 per project policy
* requirements: suppress check_requirements pin warning on mtmd
The check_requirements script flags '==' on lines in files matched by
*/**/requirements*.txt. Append the documented suppression comment to the
pinned torch and torchvision lines (and to the s390x platform marker lines)
so the check passes while keeping the pins required by project policy.
* ty: silence Tensor/Module union check on model[0].auto_model
With torch 2.11.0 stubs, nn.Sequential.__getitem__ now returns
Tensor | Module rather than Module, so model[0].auto_model fails ty
on the SentenceTransformer code path. The runtime behavior is
unchanged because SentenceTransformer always wraps a Module at
index 0. Adding a targeted unresolved-attribute ignore keeps the
type-check green without altering behavior. A follow-up issue
tracks typing the variable explicitly.
- HunyuanOCR shares the same HF arch and vision layout as HunyuanVL butwas split into a separate path that skipped the +0.1 bilinear sampler used by the HF reference.
- Collapse OCR into the HUNYUANVL projector + HUNYUAN_VL text arch
* mtmd : deepseek-ocr fixes, improvements and refactoring
- image processing changes to achieve full parity with Pillow (reference impl)
- SAM mask casting only when flash-attn is on
- SAM refactor (build_sam() extracted so deepseek-ocr-2 can reuse it)
- llama-chat changes to fix server/WebUI issue (new media_markers_first())
- adapted test-chat-template and added test cases for deepseek-ocr
- changed regression test for deepseek-ocr to use CER+chrF scores for ground-truth comparison; removed embedding-model
- ty.toml ignore unresolved-import for tools/mtmd/tests/**
* image-text reordering fix removed
* refactor bool add_padding + pad_rounding enum into a single pad_style enum
* mtmd: fit_params now take into account mmproj
* rename alloc_compute_meta to reserve_compute_meta
* rm unused functions
* add ggml_backend_dev_t support
* add debug log
* common: refactor common/debug to move abort_on_nan into base_callback_data
Passing bool abort_on_nan as template parameter for common_debug_cb_eval is unnecessary and creates an issue with LTO.
It should just be a member of the base_callback_data instead.
* cont : cleanup
* common : use pimpl in debug.h to reduce header dependencies
Move common_debug_cb_user_data's data members (std::regex,
std::vector<uint8_t>) into a private impl struct in debug.cpp.
This removes the includes of common.h and <regex> from debug.h,
reducing transitive dependencies for any translation unit that
includes the header.
Assisted-by: llama.cpp:local pi
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* mtmd, llama : add HunyuanVL vision-language model support
- add LLM_ARCH_HUNYUAN_VL with M-RoPE (XD-RoPE) support
- add PROJECTOR_TYPE_HUNYUANVL with PatchMerger vision encoder
- add HunyuanVL-specific M-RoPE position encoding for image tokens
- add GGUF conversion for HunyuanVL vision and text models
- add smoke test in tools/mtmd/tests.sh
* fix: fix HunyuanVL XD-RoPE h/w section order
* fix: Remove redundant code
* convert : fix HunyuanOCR / HunyuanVL conversion
- Tested locally: both HunyuanOCR and HunyuanVL-4B convert to GGUF
- successfully and produce correct inference output on Metal (F16 / Q8_0).
* clip : fix -Werror=misleading-indentation in bilinear resize
* fix CI: convert_hf_to_gguf type check error
- convert_hf_to_gguf.py: give HunyuanVLTextModel.__init__ an explicit `dir_model: Path` parameter so ty can infer the type for load_hparams instead of reporting `Unknown | None`.
---------
Co-authored-by: wendadawen <wendadawen@tencent.com>
* feat: (vocab) fix stray text appended in llama_decode_text
Remove accidental concatenation of the full `text` string when
formatting UNK_BYTE hex escapes. Only the closing "]" should be appended.
* feat(mtmd): add Yasa2 vision encoder support
Add a Yasa2 (ConvNeXtV2-based) vision encoder for reka-edge:
- Register PROJECTOR_TYPE_YASA2 and tensor name definitions
- Add yasa2_block/yasa2_stage model structs
- Implement graph builder with ConvNeXt stages, GRN, adaptive pooling
- Wire into clip.cpp switch statements and mtmd.cpp init_vision
- Use mtmd_image_preprocessor_fixed_size for image preprocessing
* feat(chat): add reka-edge template handler (tools, thinking)
- Add chat-reka.cpp/h implementing PEG-based parser for reka-edge format
- Add Reka-Edge.jinja chat template
- Detect reka-edge template in try_specialized_template()
- Add LLAMA_EXAMPLE_MTMD to chat-template-file arg
* feat: add reka vlm to gguf conversion script
Converts Reka Yasa2 hf checkpoints to GGUF format:
- Text decoder: Llama-arch with tiktoken/BPE vocab
- Mmproj (--mmproj): ConvNeXt vision backbone + language_projection
- Generates 2D sincos positional embeddings for vision encoder
* test: add Reka Edge chat template and parser tests
- test-chat-template: oracle tests comparing Jinja engine output vs
common_chat_templates_apply for text, tools, thinking, images, video
- test-chat: PEG parser tests for Reka Edge format, round-trip tests
for image/video content parts, common path integration tests
* scripts: add Reka Edge mixed quantization helper
Q4_0 base quantization with Q8_0 override for the last 8 transformer
blocks (layers 24-31) via --tensor-type regex.
* fix: adapt chat-reka and tests to upstream API
- Use autoparser::generation_params (not templates_params)
- Add p.prefix(generation_prompt) to PEG parser
- Simplify reasoning parser to match LFM2 pattern
- Remove image/video oracle tests (unsupported by oaicompat parser;
no other multimodal models test this path)
* fix: avoid duplicate tensor loading in yasa2 vision encoder
TN_YASA_PATCH_W and TN_PATCH_EMBD both resolve to "v.patch_embd.weight",
causing the same tensor to be loaded twice into ctx_data and overflowing
the memory pool. Reuse the tensors already loaded by the common section.
* chore: update image pre-processing settings
The reka-edge model depends on the following settings in an older
fork of llama.cpp:
1. Fixed square resize
2. BICUBIC
3. add_padding=false
In current llama.cpp, this means setting:
- image_resize_algo = RESIZE_ALGO_BICUBIC
- image_resize_pad = false
* chore: remove reka gguf conversion script
* chore: remove reka quantization script
* chore: remove unnecessary changes from PR scope
This commit removes a couple of unnecessary changes for the PR scope:
1. BPE decoder bug fix - this affects reka edge because there's a bug
in our tokenization that doesn't represent <think> tokens as special
tokens. However this isn't meant to be a thinking model so when run
with --reasoning off the edge case does not affect us
2. --chat-template-file support from llama-mtmd-cli - the focus is on
llama-server and the reka edge gguf contains the necessary metadata
to detect the chat template
3. reka edge oracle test cases - no other model has similar test cases,
so I removed it for standardization
* chore: remove unnecessary ggml_cast
This commit removes unnecessary ggml_cast after updating the
reka vlm -> gguf conversion script on hugging face.
* chore: remove redundant code
* chore: remove unnecessary ggml_cont calls
This commit removes all ggml_cont calls except the four that
precede ggml_reshape_3d/ggml_reshape_4d. Those are necessary
because ggml_reshape recomputes strides assuming contiguous
layout and asserts ggml_is_contiguous.
Other operations (ggml_mean, ggml_add, ggml_mul etc.) use
stride-based indexing and handle non-contiguous inputs
correctly and so we are ok to remove ggml_cont for those.
* chore: remove unnecessary ggml_repeat calls
This commit removes unnecessary ggml_repeat calls because the underlying
ops already broadcast automatically.
Every ggml_repeat in yasa2.cpp was expanding a smaller tensor to match
a larger one's shape before passing both to an elementwise op (ggml_add,
ggml_sub, ggml_mul, or ggml_div). This is unnecessary because all four
of these ops already support broadcasting internally.
* chore: restore ggml_cont needed for cpu operations
* refactor: locate reka chat template handler in chat.cpp
* chore: remove unnecessary warmup tokens
* chore: add code comments on image_resize_pad
* chore: remove custom reka parsing code
* chore: revert common/chat.cpp
* Uncomment debug logging for PEG input parsing
---------
Co-authored-by: Piotr Wilkin (ilintar) <piotr.wilkin@syndatis.com>
Abraham Gonzalez