This commit removes the `-c, --ctx-size N` from the llama-server
command in the model card template for causal models.
The motivation for this is that -c 0 is the default and specifying it
is redundant.
* fix: Remove unnecessary `h` loops where `h` was only ever 0
Branch: CleanUpT5InputBuilders
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix: Remove unnecessary padding loop that is never hit anymore
The upper bound used to use GGML_PAD(n_tokens, GGML_KQ_MASK_PAD), but was
removed in https://github.com/ggml-org/llama.cpp/pull/17910 leaving the
loop dead.
Branch: CleanUpT5InputBuilders
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
---------
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* server : add arg for disabling prompt caching
Disabling prompt caching is useful for clients who are restricted to
sending only OpenAI-compat requests and want deterministic
responses.
* address review comments
* address review comments
This commit adds the --kv-unified flag to the batched example. This flag
is currently specified in the README.md as required, but is currently
not available as a command line option for the batched example.
The motivation for this is that specifying this flag as the README
instructs, will lead to an error about the flag not being recognized,
and without this option the example fail with the following error:
```console
split_equal: sequential split is not supported when there are coupled
sequences in the input batch (you may need to use the -kvu flag)
decode: failed to find a memory slot for batch of size 4
main: llama_decode() failed
```
This fixes incoherent output in Llama-4-Maverick-17B-128E-PAB-Q8_0, which
has a mul_mat_id with an A matrix that's Q8_0 8192 x 5120 x 128.
This should work when the number of blocks in the A matrix is less than 2^32
(for mul_mat_vec or mul_mm_cm2), or for mul_mm I think the limit is like
2^32*LOAD_VEC_A elements.
- Divide batch_stride by QUANT_K earlier, so the block index calculation works in 32b.
- Each vk_pipeline_struct has a linked list of pipelines that will allow it to handle
variants. So far this change just adds a single use case for this, compiling with the
e64BitIndexingEXT flag.
- Use the 64b indexing variant when the A matrix is larger than maxStorageBufferRange.
64-bit indexing has some cost - around 3-5% in MoE models, so it's worth the effort
to avoid enabling it unconditionally.
* vulkan: Enable and optimize large matmul parameter combination for AMD
* limit tuning to AMD GPUs with coopmat support
* use tx_m values instead of _l
* debug : include LLAMA_POOLING_TYPE_UNSPECIFIED in pooling check
This commit updates the pooling check in the debug example to
also include LLAMA_POOLING_TYPE_UNSPECIFIED and not just
LLAMA_POOLING_TYPE_NONE.
* debug : normalize both pooled and token embeddings
This commit updates debug.cpp to normalize embeddings for both pooled
and non-pooled outputs. For pooled embeddings, normalization is applied
to the single vector, and for non-pooled embeddings, normalization is
applied to each token embedding vector individually.
The motivation for this is to enable non-pooled embeddings to be
normalized which was not possible previously.
* qwen3next: simplify qkvz projection
* use ggml_swiglu_split
* revert swiglu_split, but remove redundant repeat()
* fix missing reshape
* rm 2 redundant transposes
* move mul_mat(k,q) to outside of chunking
* rm redundant cont
* improve g_cs_chunk
* add comments about no cont
* use std::pair instead of ggml_concat
* vectorize key_gdiff calculation
* rm unused tensor
* avoid ggml_concat inside loop
* bring back ggml_concat as it may not work on other backend
* nits
This commit introduces a mechanism to embed all licenses directly
into the compiled binaries.
This eliminates the need to distribute separate LICENSE files alongside
the executable, making the binaries self-contained and simplifying
deployment.
* Add Gemma3nVisionModel - MobileNetV5 vision encoder convertor to convert_hf_to_gguf.py. Add gemma3n to vision projectors in gguf-py/gguf/constants.py.
* Add mobilenetv5 impl
* Fix comments, remove unused vars
* Fix permute and remove transpose of projection weights
* Fix comments, remove debugging prints from hf_to_gguf
* 1. Hard-code image_mean = 0 and image_std = 1
2. Use available tensor mapping logic
3. Remove redundant chat template replacement of soft tokens placeholder with media placeholder
* 1. Move mobilenetv5 helpers declarations to `clip_graph_mobilenetv5` struct and definitions to mobilenetv5.cpp
2.Remove unused `clip_is_gemma3n` func declarations and definitions
3. Remove redundant `rescale_image_u8_to_f32` func and use `normalize_image_u8_to_f32` with zero mean and unit std
4. Calculate n_patches using image_size / patch_size
* Remove obsolete comments
* - convert_hf_to_gguf.py & constants.py & tensor_mapping.py: Use explicit mapping: Custom map for double indexed blocks and tensor_mapping.py for rest
- convert_hf_to_gguf.py: Unsqueeze Stem Bias and Layer scale tensors to correct shape while converting to gguf
- mobilenetv5.cpp: Remove explicit reshaping of Stem Bias and Layer scale which are now handled while converting to gguf, replace fprintf with LOG_*
- clip.cpp: Remove unused embedding and hard_emb_norm tensor loading
* - Rename tensors to v.conv..., v.blk..., v.msfa... to better align with already existing terminology
* Fix stem conv bias name
* Remove explicit handling of bias term for stem conv
* - Change order of addition in "project_per_layer_inputs" to support broadcasting of vision inp_per_layer
- Simplify the vision embeddings path of "get_per_layer_inputs" to output [n_embd_altup, n_layer, 1], broadcastable
* clean up conversion script
* fix code style
* also preserve audio tensors
* trailing space
* split arch A and V
* rm unused gemma3 func
* fix alignment
---------
Co-authored-by: Xuan Son Nguyen <son@huggingface.co>
* arg: support remote preset
* proof reading
* allow one HF repo to point to multiple HF repos
* docs: mention about multiple GGUF use case
* correct clean_file_name
* download: also return HTTP status code
* fix case with cache file used
* fix --offline option
* FlashAttention (#13)
* Add inplace softmax
* Move rms_norm to split row approach
* Update debug for supports_op
* clean up debug statements
* neg f16xf32xip builds and runs, havent actually ran a model that uses neg kernel yet though
* neg passes backend test
* unary operators pass ggml tests
* rms_norm double declaration bug atoned
* abides by editor-config
* removed vestigial files
* fixed autoconfig
* All operators (inlcluding xielu) working
* removed unnecesarry checking if node->src[1] exists for unary operators
* responded and dealt with PR comments
* implemented REPL_Template support and removed bug in unary operators kernel
* formatted embed wgsl and ggml-webgpu.cpp
* Faster tensors (#8)
Add fast matrix and matrix/vector multiplication.
* Use map for shader replacements instead of pair of strings
* Wasm (#9)
* webgpu : fix build on emscripten
* more debugging stuff
* test-backend-ops: force single thread on wasm
* fix single-thread case for init_tensor_uniform
* use jspi
* add pthread
* test: remember to set n_thread for cpu backend
* Add buffer label and enable dawn-specific toggles to turn off some checks
* Intermediate state
* Fast working f16/f32 vec4
* Working float fast mul mat
* Clean up naming of mul_mat to match logical model, start work on q mul_mat
* Setup for subgroup matrix mat mul
* Basic working subgroup matrix
* Working subgroup matrix tiling
* Handle weirder sg matrix sizes (but still % sg matrix size)
* Working start to gemv
* working f16 accumulation with shared memory staging
* Print out available subgroup matrix configurations
* Vectorize dst stores for sg matrix shader
* Gemv working scalar
* Minor set_rows optimization (#4)
* updated optimization, fixed errors
* non vectorized version now dispatches one thread per element
* Simplify
* Change logic for set_rows pipelines
---------
Co-authored-by: Neha Abbas <nehaabbas@macbookpro.lan>
Co-authored-by: Neha Abbas <nehaabbas@ReeseLevines-MacBook-Pro.local>
Co-authored-by: Reese Levine <reeselevine1@gmail.com>
* Comment on dawn toggles
* Working subgroup matrix code for (semi)generic sizes
* Remove some comments
* Cleanup code
* Update dawn version and move to portable subgroup size
* Try to fix new dawn release
* Update subgroup size comment
* Only check for subgroup matrix configs if they are supported
* Add toggles for subgroup matrix/f16 support on nvidia+vulkan
* Make row/col naming consistent
* Refactor shared memory loading
* Move sg matrix stores to correct file
* Working q4_0
* Formatting
* Work with emscripten builds
* Fix test-backend-ops emscripten for f16/quantized types
* Use emscripten memory64 to support get_memory
* Add build flags and try ci
---------
Co-authored-by: Xuan Son Nguyen <son@huggingface.co>
* Remove extra whitespace
* Move wasm single-thread logic out of test-backend-ops for cpu backend
* Disable multiple threads for emscripten single-thread builds in ggml_graph_plan
* Refactored pipelines and workgroup calculations (#10)
* refactored pipelines
* refactored workgroup calculation
* removed commented out block of prior maps
* Clean up ceiling division pattern
---------
Co-authored-by: Neha Abbas <nehaabbas@eduroam-169-233-141-223.ucsc.edu>
Co-authored-by: Reese Levine <reeselevine1@gmail.com>
* Start work on flash attention
* Shader structure set up (many bugs still)
* debugging
* Working first test
* Working with head grouping, head sizes to 128, logit softcap, mask/sinks enabled, f32
* Generalize softmax to work with multiple subgroups, f16 accumulation, mask shared memory tiling
* Start work on integrating pre-wgsl
* Separate structs/initial shader compilation library into separate files
* Work on compilation choices for flashattention
* Work on subgroup matrix/tile size portability
* subgroup size agnostic online softmax
* Cleanups, quantization types
* more cleanup
* fix wasm build
* Refactor flashattention to increase parallelism, use direct loads for KV in somce cases
* Checkpoint
* formatting
* Update to account for default kv cache padding
* formatting shader
* Add workflow for ggml-ci webgpu
* Try passing absolute path to dawn in ggml-ci
* Avoid error on device destruction, add todos for proper cleanup
* Fix unused warning
* Forgot one parameter unused
* Move some flashattn computation to f32 for correctness
