* Added sve tuned code for gemm_q8_0_4x8_q8_0() kernel
* Change arrays to static const in repack.cpp
---------
Co-authored-by: Vithulep <prashant.vithule@fujitsu.com>
* ggml-cuda: add flash-attn support for DKQ=320/DV=256 with ncols2=32 (GQA=32)
Adds MMA-f16 and tile kernel configs, dispatch logic, template instances,
and tile .cu file for Mistral Small 4 (head sizes 320/256), restricting to
ncols2=32 to support GQA ratio 32 only.
* Adding check to return BEST_FATTN_KERNEL_NONE in case GQA!=32
* Apply suggestions from code review
Address review comments
Co-authored-by: Johannes Gäßler <johannesg@5d6.de>
* Address review comments and making kernel config default to DQK=512, DV=512 instead of DQK=256,DV=256
* Fixed bug with sinks=1, with ncols=32, there are two warp-groups created but sinks index is same(0,...,15) for both the groups hence with sinks=1, output is not matching with CPU output. Added sink_base which will be base index for each warp_group (threadIdx.y / np)
* Apply suggestions from code review
Co-authored-by: Johannes Gäßler <johannesg@5d6.de>
* Update ggml/src/ggml-cuda/template-instances/generate_cu_files.py
Co-authored-by: Johannes Gäßler <johannesg@5d6.de>
---------
Co-authored-by: Johannes Gäßler <johannesg@5d6.de>
DONE state absorbs all tokens including a new start tag, causing any think blocks after the first to run unbudgeted. Observed on unsloth/Qwen3.6-27B-GGUF which interleaves multiple <think> blocks per response.
Fixed by advancing start_matcher in DONE branch and re-arming to COUNTING with a fresh budget on match. Adds regression test (test-reasoning-budget: test 6).
Some SPIR-V compilers (notably mesa) don't handle the current
vulkan Q4_K/Q5_K scale load pattern in mul_mat particularly well.
While reading three `u8`s from the 12-byte scale array should (at
least on some hardware) result in loading the full 12 bytes in a
single LOAD followed by whatever extraction is needed, at least
the ANV Intel driver really can't practically perform this
optimization.
`mesa`'s unsigned upper bound logic doesn't handle tracking bounds
through ternary, resulting in the `(is < 4) ? ... : is - 4` having
an infinite upper bound (as it cannot prove `is - 4` doesn't
underflow). While this could still be rectified if mesa looked at
the array bounds, it currently doesn't and `glslc` currently emits
SPIR-V that doesn't allow for this optimization anyway (though
maybe it will at some point, see
https://github.com/KhronosGroup/glslang/issues/4206).
In mul_mat_vecq we took a different approach to loading the same
fields. We read the first two bytes we needed from `scale` then
took a branch before deciding whether we needed to read a third
byte. In mesa this did, indeed, lead to a top-level branch with
conditional loads. As such these loads ended up not being
coalesced either (at least in the ANV driver) resulting in
additional instructions in our hot loop.
Instead, here, we go ahead and force loading the full 12 bytes and
extract the bits we need from the packed-u32s instead. In mul_mat
there's a few less ternaries and only one extra shift, so even on
drivers that did optimize the previous loads properly the only
material change should be pulling a few extra bytes into registers
(which on most hardware won't cost anything anyway, though
ironically on Intel it theoretically could). In mul_mat_vecq this
requires a bit of extra math and may read bytes from the u32 that
weren't needed, but it seems likely avoiding the branch is a win
on most platforms.
On Intel Xe2/mesa 26.0.4 with the optimizations from
https://gitlab.freedesktop.org/mesa/mesa/-/work_items/15162,
for shader matmul_id_subgroup_q4_k_f32_f16acc_aligned_l:
* Instruction Count: 2753 -> 2688
* SEND Count: 269 -> 261
* Cycle Count: 273976 -> 266138
* Max live registers: 248 -> 246
* Non SSA regs after NIR: 381 -> 382
for shader matmul_id_subgroup_q5_k_f32_f16acc_aligned_l:
* Instruction Count: 2767 -> 2702
* SEND Count: 271 -> 263
* Cycle Count: 274140 -> 268144
* Max live registers: 248 -> 246
* Non SSA regs after NIR: 381 -> 382
for shader mul_mat_vec_id_q4_k_q8_1_f32:
* Instruction Count: 1930 -> 1646
* SEND Count: 116 -> 71
* Cycle Count: 1348306 -> 843350
* Max live registers: 78 -> 84
* Non SSA regs after NIR: 300 -> 135
for shader mul_mat_vec_id_q5_k_q8_1_f32:
* Instruction Count: 2207 -> 1922
* SEND Count: 131 -> 86
* Cycle Count: 1392012 -> 1037836
* Max live registers: 90 -> 90
* Non SSA regs after NIR: 300 -> 135
for shader mul_mat_vec_q4_k_q8_1_f32:
* Instruction Count: 2029 -> 1749
* SEND Count: 111 -> 66
* Cycle Count: 1347278 -> 840118
* Max live registers: 74 -> 80
* Non SSA regs after NIR: 299 -> 134
for shader mul_mat_vec_q5_k_q8_1_f32:
* Instruction Count: 2307 -> 2022
* SEND Count: 126 -> 81
* Cycle Count: 1379820 -> 954042
* Max live registers: 86 -> 86
* Non SSA regs after NIR: 299 -> 134
On one Arc Pro B60, unsloth/Qwen3.5-35B-A3B-GGUF:UD-Q4_K_XL:
* pp512: 907.34 ± 9.28 -> 941.94 ± 10.53 (+4%)
* pp2048: 897.95 ± 1.82 -> 931.55 ± 1.79 (+4%)
* tg128: 49.49 ± 0.02 -> 49.86 ± 0.05 (+ <1%)
On one Arc Pro B60, unsloth/Qwen3.5-27B-GGUF:Q4_K_S:
* pp512: 324.13 ± 10.52 -> 354.33 ± 6.81 (+9%)
* pp2048: 329.80 ± 0.25 -> 357.10 ± 0.06 (+8%)
* tg128: 17.11 ± 0.01 -> 18.11 ± 0.01 (+6%)
On four Arc Pro B60s, unsloth/Qwen3.5-122B-A10B-GGUF:Q5_K_S with
-sm layer (note that -sm tensor improvements will naturally be
less):
* pp512: 264.55 ± 2.81 -> 280.45 ± 3.94 (+6%)
* pp2048: 319.32 ± 2.72 -> 335.70 ± 3.48 (+5%)
* tg128: 26.39 ± 0.01 -> 26.67 ± 0.01 (+1%)
* ggml : revert to -lm linking instead of find_library
`find_library(MATH_LIBRARY m)` was introduced recently, but it breaks
CUDA compilation with GGML_STATIC. I could not find any valid use case
where we would prefer `find_library` over the standard `-lm` approach.
This commit is also meant to start a discussion if there is a valid
reason to keep `find_library(MATH_LIBRARY m)`, we should clarify what
problem it was solving and find an alternative fix that does not break
CUDA with GGML_STATIC.
Signed-off-by: Adrien Gallouët <angt@huggingface.co>
* ggml : use MATH_LIBRARY only if defined
Signed-off-by: Adrien Gallouët <angt@huggingface.co>
* ggml : fix initial broken condition
Signed-off-by: Adrien Gallouët <angt@huggingface.co>
* ggml : always respect MATH_LIBRARY when defined
Signed-off-by: Adrien Gallouët <angt@huggingface.co>
---------
Signed-off-by: Adrien Gallouët <angt@huggingface.co>
New operators:
- GGML_OP_SET: implement via aclnnInplaceCopy on target region
- GGML_OP_CUMSUM: implement via aclnnCumsum
- GGML_OP_FILL: implement via aclnnInplaceFillScalar
- GGML_OP_DIAG: implement via aclnnInplaceCopy on diagonal strides
- GGML_OP_TRI (lower/lower_diag/upper_diag/upper): implement via
aclnnTril(-1/0) and aclnnTriu(0/1) with appropriate diagonal offsets
- GGML_OP_SOLVE_TRI: implement via aclnnTriangularSolve
- GGML_UNARY_OP_SOFTPLUS: implement via aclnnSoftplus
Optimizations:
- GLU (SwiGLU/GeGLU/GeGLU_ERF/GeGLU_QUICK): fuse with aclnnSwiGlu /
aclnnGeGluV3 when applicable; fallback conditions now checked inside
each function rather than at the call site
- CROSS_ENTROPY_LOSS: replace 5-kernel sequence (LogSoftmax→Mul→
ReduceSum×2→Muls) with single aclnnSoftmaxCrossEntropyWithLogits call
- L2_NORM: fix in-place ClampMin on norm result (was clamping wrong
tensor); add eps clamping before division to avoid divide-by-zero
- PAD_REFLECT_1D: eliminate per-ne[3] loop; assert contiguity and call
ReflectionPad1d once on the full 4-D view; remove redundant nb copies
- GET_ROWS: replace IndexSelect with GatherV2 per batch slice; refactor
helper into gather_batched lambda with batch loop inlined
- SET_ROWS: replace IndexCopy with InplaceIndexCopy per batch slice;
refactor helper into scatter_batched lambda with batch loop inlined
- OUT_PROD: replace O(ne[3]*ne[2]*ne[1]) Ger+InplaceAdd loop with
per-slice Matmul loop (src0 @ src1^T); handles strided-broadcast
batch dims where ne02/ne03 may differ from ne2/ne3
- backend memset_tensor: implement via aclrtMemset (was NULL)
Bug fixes:
- COUNT_EQUAL: use non-inplace EqTensor into a same-type temporary
buffer instead of InplaceEqTensor, avoiding corruption of src0
- ACL graph cache (USE_ACL_GRAPH): restore node_type and src_type[]
fields in ggml_graph_node_properties; has_matching_properties() was
missing type checks, causing F16 and BF16 tensors (same nb[0]=2) to
incorrectly share cached graphs and produce wrong results (ERR≈679)
- graph cache op_params matching: compare full GGML_MAX_OP_PARAMS
bytes so that ops differing only in parameters are not incorrectly
replayed from cache
* This commit enables the router to forward form-data to model server.
Fixes#22044 (enabling to use the /v1/audio/transcriptions in router mode)
* * Applied the suggestion from Copilots first comment: using the non-throwing json::parse overload.
* Addressed Copilots third comment by extending the files representation to also include filename and content-type
* Addressed Copilots fourth comment by making the RNG thread_local
* Changed variable body from std::string to std::ostringstream in build_multipart_body
as suggested by ngxson in https://github.com/ggml-org/llama.cpp/pull/22118#discussion_r3127099053
* Added sanitize_field lambda in build_multipart_body for key, filename and content_type
as suggested by ngxson in https://github.com/ggml-org/llama.cpp/pull/22118#discussion_r3127104647
* explicitly checking if value/item is string before calling value/item.get<std::string>()
as requested by ngxson in https://github.com/ggml-org/llama.cpp/pull/22118#discussion_r3127111279
* Added double quote to the sanitize lambda and throw on json parse failure
---------
Co-authored-by: Ralph Paßgang <ralph@trust-it.de>
* 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>
The previous code worked only for full tensor reads and writes and was hitting `GGML_ASSERT(size == ggml_nbytes(tensor)); ` assert when tested with llama-server.
* Optimize Metal Tensor API usage for matmul2d
Separates the Metal Tensor API (matmul2d) path in kernel_mul_mm into its own standalone kernel, gated by GGML_METAL_HAS_TENSOR.
The legacy simdgroup_matrix kernel is preserved under #else.
Previously both paths were interleaved via #ifdef blocks within a single kernel, forcing the tensor path to share the legacy kernel's data layout and threadgroup memory scheme. Splitting the kernel enabled memory and dispatch optimizations that weren't possible when the two paths shared code structure.
* cont : cleanup
* cont : cleanup
* cont : cleanup
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
Change the default `ftype` in `llama_model_quantize_params` from
`LLAMA_FTYPE_MOSTLY_Q5_1` to `LLAMA_FTYPE_MOSTLY_Q8_0`.
In case some external program naively uses the default quantization
params, we should probably default to a known-good type like Q8_0 rather
than Q5_1, which is rather old.
* opt arc770 for Q4_0
* add for Q4_0
* update the script
* add help script for windows
* update guide
* fix format issue
* convert from dos to unix for format issue
* fix missed -sm parameter
* switch ubuntu-latest to ubuntu-slim
* Fix the path for upload so CI doesn't fail
* Update .github/workflows/build-and-test-snapdragon.yml
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* Use -slim image for key check and consistent naming for artifact dir
Signed-off-by: Max Krasnyansky <maxk@qti.qualcomm.com>
* Remove check-secret extra job
* move QDC key check for Run QDC jobs step specifically
* add a step before to check the secret for qdc jobs
---------
Signed-off-by: Max Krasnyansky <maxk@qti.qualcomm.com>
Co-authored-by: Max Krasnyansky <maxk@qti.qualcomm.com>
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* ggml-webgpu: add tile flash attention fallback
* ggml-webgpu: add new fields and discard usage of mnk for tile version
* ggml-webgpu: modify the vec path to discard the mnk parameter
* ggml-webgpu: enable flash attention vec and tile version for broswer
* ggml-webgpu: stagging KV for flash attention tile version
* formatting
* turn on subgroup uniformity check
* remove Q_TILE as it is always 1 for vec path
* make row_max and exp_sum to local register
* make different bindings with same underlying buffer to have the same usage flags
* move path selection into the shader library and have the host consume a single flash-attn decision object.
* turn off skip_validation and address buffer overlapping when nwg==1
* formatting
* merge binding when kv overlap
This change implements the third requested change in issue 20429.
Because defaults.sampling contains the reasoning budget token count and
the reasoning budget message, it's not necessary to assign them to
struct variables.
* hexagon: restore HTP_OPMASK_QUEUE
* hexagon: honor OPMASK_SKIP_COMPUTE in hmx-matmul
* hex-prof: restore op profiling
* hex-prof: enable PMU
* hexagon: simplify and improve op-queuing with full profiling support
Add separate profile descriptors.
* hexagon: remove opsync and rename opmask into opstage
opsync is no longer needed since the profiler is fully async now.
opmask name was confusing and opstage is more accurate.
* hexagon: refactor opbatch queue handling
* hexagon: add iface hooks for enabling profiler from the host
Also move all the PMU setup stuff out of the hex-utils since it's not inteded for normal use.
* hexagon: make profiler mode configurable
On older devices getting PMU counters is expensive so it's now optional.
* hexagon: add support for setting profiler pmu events from env
* hexagon: simplify profiler output (no need to print buffs, etc)
* hexagon: simplify pmu counter formating
* hexagon: add a simple profile post-proc tool
* hex-prof: add support for reading logs from stdin
* hexagon: document GGML_HEXAGON_PROFILE
* hex-prof: update default width for dims field
* hex-prof: fix linter warnings and errors
* Update ggml/src/ggml-hexagon/htp/htp-ops.h
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* Update scripts/snapdragon/ggml-hexagon-profile.py
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
---------
Co-authored-by: Trivikram Reddy <tamarnat@qti.qualcomm.com>
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* Add the tests that we want to run on external CI
* remove extra files
* Fixes python issues, reove the deadlock on CI
* remove unecessary changes
* use override to ty.toml
* fix pre-commit and try tests with secret in external repo not upstream
* skip if key is unavailable
* Fix feedback
* switch hexagon to snapdragon
* cleanup
* fix secrets
* remove the copyrights at the top of the files
When testing claude code against llama.cpp, I noticed that only
n_past 18577 was used even when context was 60k or more. The log
in llama-server says:
```
slot update_slots: id 3 | task 10342 | old: ... ; cch= | defa0;You are
slot update_slots: id 3 | task 10342 | new: ... ; cch= | 1c8b4;
```
I observed that the cch value changed every time. Reading about that,
the x-anthropic-billing-header system message seems to be specially
handled inside of the anthropic api. I could remove it, but there
is a meaningful string sometimes included at the end. So instead,
I just replace the changing cch checksum with fffff.
I'm treating this as an anthropic message body API detail - I think this
is the right way to do this, but by all means please correct me!
It's always 5 hexadecimal characters, but I've written the replacement
defensively in case they change the protocol.
* model-conversion : fix mmproj output file name [no ci]
This commit updates the convert-model.sh script to properly handle
mmproj output files.
The motivation for this that currently the same name as the original
model is used as the mmproj file, which causes the original model to
be overwritten and no mmproj-<model_name>.gguf to be created.
* model-conversion : use MODEL_NAME [no ci]
* gitignore: add AGENTS.local
Assisted-by: llama.cpp:local pi
Signed-off-by: Georgi Gerganov <ggerganov@gmail.com>
* gitignore: rename AGENTS.local to AGENTS.local.md
Assisted-by: llama.cpp:local pi
Signed-off-by: Georgi Gerganov <ggerganov@gmail.com>
---------
Signed-off-by: Georgi Gerganov <ggerganov@gmail.com>
Fixes#22237 — the find_library(MATH_LIBRARY m) result was being
discarded and the target linked against the literal 'm' string.
This prevents users from overriding the math library (e.g. for AMD AOCL)
via CMake variables. Now the discovered MATH_LIBRARY is used directly.
* upgrade oneAPI to 2025.3.3
* update
* seperate SYCL CI and support release binary package for ubuntu 24
* add dependence
* remove wrong copy lines
* add missed line
* remove other task to test the release for SYCL
* rm more for test release
* fix file name
* correct the error in running
* support build for fp32/fp16
* rm ubuntu-24-sycl-fp16 for duplicated
* refactor build setting
* update guide for ubuntu 24 release package, restore the release.yml for other backend
* user docker replace to install oneAPI
* use download installation package to replace docker
* use wget to download and install oneapi, replace the apt cmd
* enable ccache for oneAPI installation
* fix format error
* enable cache for oneAPI installation
* update guide
* Update .github/workflows/release.yml
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* Update .github/workflows/release.yml
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* Update .github/workflows/build-sycl.yml
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* Update .github/workflows/release.yml
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
---------
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* sycl : fused MoE mul_mat_vec_q for TG
Create an MMVQ kernel so ggml_sycl_mul_mat_id can consolidate
n_experts_used matmuls in a single kernel launch. The kernel
also reads expert IDs directly, removing a per-call host sync.
This is similar to the CUDA backend's ggml_cuda_mul_mat_vec_q*
paths.
All types supported in the current MMVQ are supported here as well:
Q2_K, Q3_K, Q4_K, Q5_K, Q6_K, Q4_0, Q4_1, Q5_0, Q5_1, Q8_0
It will fall back to the existing per-expert path when src0 has been rewritten
by opt_for_reorder(), and for any shape the fused path doesn't handle.
test-backend-ops passes for supported type/shape combos.
Benchmark: Qwen3-Next-35B-A3B Q4_K_M on Intel Arc B70 (SYCL0),
baseline 707c0b7a6, 16k context, -fa 0.
build/bin/llama-bench -hf unsloth/Qwen3.5-35B-A3B-GGUF:Q4_K_M \
-p 1024 -n 128 -d 16384 -ngl 99 -fa 0 -ub 2048 -r 2 -dev SYCL0
Before (3 runs on 707c0b7a6):
| test | run 1 | run 2 | run 3 |
| --------------- | ----------------:| ----------------:| ----------------:|
| pp1024 @ d16384 | 533.26 ± 4.87 | 535.20 ± 2.78 | 524.27 ± 3.10 |
| tg128 @ d16384 | 33.47 ± 0.02 | 33.31 ± 0.02 | 33.17 ± 0.05 |
After (3 runs on 707c0b7a6 + this patch):
| test | run 1 | run 2 | run 3 |
| --------------- | ----------------:| ----------------:| ----------------:|
| pp1024 @ d16384 | 534.06 ± 0.97 | 531.95 ± 0.02 | 520.94 ± 20.10 |
| tg128 @ d16384 | 45.85 ± 0.21 | 45.95 ± 0.45 | 46.22 ± 0.12 |
disclosure: Claude wrote it, but I reviewed and understand the implementation
(albeit my C is a little rusty).
* sycl: also support nvfp4 and mxfp4 expert types
* sycl: terser comments/nested dispatch in response to review
* sycl: more comment cleanup in mmvq.cpp/hpp
---------
Co-authored-by: Debian <aaron@openllmi.net.bots.is>
* shader(im2col): implement the im2col shader
* shader(im2col): clean the formatting issues
* shader(im2col): clean the editorconfig checker warning
* fix(shader): address the workgroup issues of im2col and conv2d
In #11362 hip graph was disabled by default as, at the time, its performance impact was negative. Due to improvements in rocm and our usage and construction of graphs this is no longer true, so lets change the default.
* Only run webgpu CI on my fork
* Implement set_tensor_async
* Implement synchronize api
* Implement event creation and deletion API
* Cleanup
* Cleanup
* Comment out jobs for local CI run
* Add webgpu only workflow
* Delete .github/workflows/build-webgpu.yml
* Cleanup
* Cleanup
* Update API with function handlers
* Run clang-format
* Replace one-shot buffer with a direct queue.WriteBuffer using the buffer context
* fused rms_norm_mul + mul
* Add GGML_WEBGPU_DISABLE_FUSION for being able to disable kernel fusion.
* Decouple num_fused_ops from webgpu_context; misc cleanup
* Fix eps handling and remove disable_fusion.
* Fix not to use c++20 initializers.
* chat: fix parallel_tool_calls default setting based on model capabilities, add tests for parallel tool calls and structured outputs
* Fix ty errors.
* Fix flake8 err
* sycl: size mul_mat_id staging buffers by routed rows
Previously src1_contiguous/dst_contiguous in ggml_sycl_mul_mat_id were
sized to ggml_nelements(src1/dst), which over-allocates when ne12 > 1
and can fail with UR_RESULT_ERROR_OUT_OF_HOST_MEMORY on Level Zero for
MoE models (notably with --cpu-moe). Size them by the actual number of
routed rows (ids->ne[1] * n_ids) instead.
* sycl: add bf16 mul_mat fast path via DNNL
When src0 is BF16 (commonly the case for lm_head / output.weight), the
existing f16 path is skipped because bf16 isn't covered, and the f32
fallback dequantizes the entire src0 slab to f32 in a single pool alloc
(row_diff*ne00 floats). For large-vocab models this can reach several
GB and fail with UR_RESULT_ERROR_OUT_OF_HOST_MEMORY on Level Zero.
Add a bf16xbf16 -> f32 DNNL matmul fast path that uses the bf16 storage
in place and only materializes a small src1 bf16 conversion buffer. bf16
matmul accumulates in f32, so it's correct even when the op requests
GGML_PREC_F32 (as lm_head does).
- gemm.hpp: map bfloat16 to dnnl::memory::data_type::bf16.
- convert.{hpp,cpp}: expose ggml_get_to_bf16_sycl for f32/f16/bf16 -> bf16.
- ggml-sycl.cpp: take the bf16 path early in ggml_sycl_op_mul_mat_sycl
when DNNL and GGML_SYCL_HAS_BF16 are both available.
* 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>
This change refactors the reasoning_budget_message parameter from the
common params into the sampling parameters specifically. It also removes
the reasoning_budget common parameter and standardizes on the existing
reasoning_budget_tokens parameter in the sampling configuration.
Issue: https://github.com/ggml-org/llama.cpp/issues/20429
Original PR: https://github.com/ggml-org/llama.cpp/pull/20297
* ggml(webgpu): fix the busy-polls in Emscripten in the waitAny after #20618, and remove the busy webgpu log
* Merge with upstream
* Fix GET_ROWS packed integer NaN when using f16 as memory buffer in shader quants
* Update Unary wgsl EXP and EXPM1 for f16 stability
* Fix GET_ROWS IQ4_XS strcut for NaN f16 canonicalization
* Fix numerical percision for unary sqrt when working with f16
* Fix NaN canonicalization for packed integers using f16
* Update err threshold for binary div ops when using f16
* backend: Keep one Dawn/WebGPU instance alive for the lifetime of the static backend
* clean: uncomment existing code logs
* clean: clean the unncessary debug info
* Refactor and generalize dequant helpers
* Remove deprecated quant structs
* Refactor shader defines to reduce repetition
* Remove error override for F16 type
* fix: fix the accidential removal of the proper initialization of ctx
* clean: clean legacy and format code
* fix: did not modify tests ops
* shader(conv2d): add conv2d shader kernels and pass f32 and f16 tests
* shader(conv2d): fix the out of bounds memory access in the weight indexing
* shader(conv2d): clean unused variables and optimize the computation
* merge: use the new entries function
* clean: address the formatting issues
* clean: address the warning issues
* clear: clean the shader editorconfig-checker issues
* clear: clean the shader editorconfig-checker with utf-8
---------
Co-authored-by: Jeremy J. Hartmann <jeremy@mtion.tv>
* 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>
* Thread safety per request only
* Fix ROPE yarn case
* Fix sticky stateful config
* Use i4/i8 directly for symmetric quant
* Use weightless caching
* Add WeightlessCacheAttribute to reduce NPU memory usage
* Gelu tanh support (#125)
* Imrope support (#126)
* fix(openvino): explicit ov::Tensor frees in ggml_backend_openvino_free
* add GPU,NPU support in OV Dockerfile
* add build-openvino.yml ci
* Fix sticky stateful config
* add concurrency to ov-gpu ci runs. Move OV CI to build-openvino.yml
* fix thread-safety of shared runtime context
* rope type abstraction for frontend translations
* fix editorconfig
---------
Co-authored-by: Mustafa Cavus <mustafa.cavus@intel.com>
Co-authored-by: Dan Hoffman <dhoff749@gmail.com>
Co-authored-by: Ravi Panchumarthy <ravi.panchumarthy@intel.com>
