* Rebase GLM-Next support onto master, and migrate to llama-memory-hybrid-idx
* Add initial MTP support
* Merge branch optimizations. Reduce allocated compute buffer size, speed up long context decode, fla, and slight MTP improvements.
* Review driven changes, remove env vars, protect tensors
* Strip MTP for initial PR
* Clean up after mtp strip
* Clean up after mtp strip
* Update speculative.cpp
* Update llama-context.h
* Clean up after mtp strip
* Fix tokenizer ignore merges
* Improve quantization protection selection
* Refactor mhc helpers, graph base
* Lint Fixes
* Apply suggestions from code review
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@huggingface.co>
* Skip glm5-next in model saver, fix CRLF
* Skip glm5-next in sweep
* Remove T4 fallback
* Review cleanup
* Review suggestions
* Defer separate MTP gguf handling to MTP PR, drop filter
* Repad n_head_kv
* kpool init apply
* Order by descending score
* Drop guard
* read kpool from hparams, clarify kpool cache flags, remove kpool_build_state(nullptr)
* Add glm5-next support to model saver and add arch test fixture
* Review cleanup
* Kpool pooled caching clarify
* Add multi stream support
* Finish Rebase
* Sparse FA fir DSA prefill
* Const
* Update llama-model.cpp to fix rebase error
* gguf-py : merge tensor map entries for HC tensors
* model : use build_gdn_l2_norm in GLM5_NEXT implementation
* chore : remove trailing whitespace
* model : use new OP precision setting API in GLM5_NEXT implementation
* mtmd : use ggml_swiglu_clamp in GLM5V and apply the image token limit
The two clamps around swiglu_split are what ggml_swiglu_clamp already does,
so the clamp bounds collapse back to one value. GLM5V also never called
set_limit_image_tokens(), so --image-max-tokens had no effect.
Assisted-by: Claude Opus 5
(cherry picked from commit 46d18e12d422be4cc04a70e4a9a9e0168bb3d5b7)
* llama : keep the GLM5-Next k-pool layout across ubatches
The layout was rebuilt from a full cell scan on every ubatch. Pools are fixed
by the positions relative to the sequence's first one, so the layout now lives
on the memory and a ubatch only appends to it.
A sequence edit no longer stales every pooled key either, only the ones at or
after the edited position, which makes a tail seq_rm free. The pooling subgraph
is built unconditionally so the graph shape no longer changes every kpool
tokens, and the pool axis is folded into rows before soft_max, which otherwise
exceeds the CUDA gridDim.y limit past n_kv 262144.
Assisted-by: Claude Opus 5
(cherry picked from commit 5d1c40b93e17fddbf73b785efe43e0d02ccb3977)
* model : write the GLM5-Next recurrent rollback checkpoints
The conv state and the delta net state were only written to the live row, so a
rollback restored whatever the checkpoint rows happened to hold. Take the same
route as kimi-k3: build_recurrent_attn for the state, and write all K_rs conv
groups. That also drops a state view that assumed contiguous rows.
Enroll the arch in test-recurrent-state-rollback, which catches this under its
garbage-filled cache pass.
Assisted-by: Claude Opus 5
(cherry picked from commit 5ace37e86d5d448e83ef5dde5632c748185b18cd)
* llama: fix PR #27773 test-save-load-state restore failure
Clear the attention and indexer cache data after a failed hybrid state restore so restored NaNs cannot affect a later sequence.
Assisted-by: Codex
* llama: fix PR #27773 gpu-rocm graph reallocation
Reserve the full GLM5-Next pool capacity and dirty pool count. The gpu-rocm Test step aborts when n_new grows while the graph node count stays fixed; CUDA, Vulkan, Metal, and WebGPU checks report the same error.
Assisted-by: Codex
* llama : fix GLM5-Next k-pool layout staleness after edits and shared teardown
Two defects in the cross-ubatch k-pool layout added by the k-pool commit:
1. Wrong results. An edited sequence only rebuilt its pool layout when its cell
count changed, so if the first ubatch after an edit added back exactly as many
cells as were removed, the stale position-to-cell list survived. With a unified
cache and more than one sequence, where another sequence takes the freed cells,
the reused layout points at the wrong cells (CPU: large logit drift, CUDA: NaN).
Rebuild whenever the sequence is stale, not only on a size mismatch.
2. Slowdown. "shared" mode was assumed to end only with an edit that forces a
rebuild, but sharing also ends when the other sequence is removed. The survivor
kept shared = true, pinning cache_safe off and re-pooling every pool on every
ubatch (server trigger: n>1 completions with -kvu, via the seq_cp in
copy_state_to). In seq_rm, if the layout has shared cells, stale every sequence
so one rebuild re-derives sharing and cache_safe returns to 1.
Assisted-by: Claude Opus 5
* llama : fix build_attn_mha stream stride for non-contiguous q
build_attn_mha split the batch into streams with a stream stride of
q->nb[3]/n_stream. That only equals one stream's span, (ne[2]/n_stream)*nb[2],
when q is contiguous. GLM5-Next is nope-only, so it does not concat a rope part
and passes the permuted q_absorbed straight in, where nb[3] != ne[2]*nb[2]; the
stride was then n_head times too large and every stream s >= 1 read another
head's queries. Split-KV (-np N without --kv-unified) multi-stream prefill was
wrong for every stream past the first. Unified KV and decode were unaffected
(n_stream == 1, and decode takes the gather path). Other MLA models concat rope
so q is contiguous and the computed value is unchanged for them.
Compute the stride from the token dimension, which is identical for a
contiguous q.
Assisted-by: Claude Opus 5
* llama : re-derive GLM5-Next k-pool sharing on state_read/state_drop
The shared-cell teardown added to seq_rm (stale every sequence when the layout
has shared cells, so a survivor does not keep shared = true and pin cache_safe
off) was missing from the other paths that can free shared cells: state_read
and state_drop staled only the one sequence. Apply the same re-derivation there
and correct the comment that claimed sharing ends only via an edit or seq_rm.
Assisted-by: Claude Opus 5
* quant : drop duplicate GLM5-Next hc_ filter
The hc_ name filter was listed twice in the GLM5_NEXT protection block.
Assisted-by: Claude Opus 5
* glm5-next: scope K-pool cache access to indexed operations
* glm5-next: keep K-pool access in hybrid index memory
* glm5-next: keep mHC graph builders model-local
* glm5-next: mark only touched pools per ubatch
---------
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@huggingface.co>
Co-authored-by: Stanisław Szymczyk <sszymczy@gmail.com>
Co-authored-by: Piotr Wilkin <ilintar@gmail.com>
* models: pad on the left with ggml_pad_ext
The Parakeet, LFM2-Audio, Granite Speech and Gemma 4 audio encoders
build a left padding as a right pad followed by a roll, and DFlash2
concatenates a zero filled block in front of the previous tokens.
ggml_pad_ext does both in one node now that every backend supports a
left padding. The Gemma 4 audio embeddings are bit identical.
* models: skip the DFlash2 taps that only read padding
A tap at or past block_size shifts every row out of the block, so its
term is zero. The loop runs min(kernel_size, block_size) taps.
* adapt common
* add common_batch
* wip
* wip: spec
* cont
* common_speculative_process
* server_batch to use common_batch
* rm some stale calls
Assisted-by: Claude Fable 5.1
* migrate mtmd
* handle imrope, handle return val of add()/add_embd()
* add spec zeros vector
* add warning on zero fill path
which resulted in different greedy transcripts for 4.5% of English and 6.5% of Japanese
test utterances. In Japanese, some differences changed entire words.
This change:
* uses `log(x + 2^-24)` instead of clamping to the log floor
* uses a symmetric Hann window, equivalent to `torch.hann_window(periodic=False)`
* adds the normalization epsilon to the standard deviation instead of inside the square root
Only the `lfm2a` preprocessor opts into these behaviors. Other audio preprocessors are unchanged.
Tested on top of 84e76d8 using `llama-server` with CUDA and `temperature=0`, compared against
http://github.com/Liquid4All/liquid-audio fp32.
Test set:
* 200 LibriSpeech `test-clean` utterances (EN)
* 200 Common Voice `ja` test utterances (JP)
* identical 16 kHz audio passed to both implementations
| Greedy transcript identical to `liquid-audio` | Without fix | With fix |
| --------------------------------------------- | ----------: | ----------: |
| EN F16 | 191/200 | 200/200 |
| JP F32 | 187/200 | 200/200 |
| JP F16 | 187/200 | 199/200 |
The remaining JP F16 difference is a comma and matches the reference implementation's own bf16
output.
Mel relative L2 error versus `liquid-audio`:
* EN: 3.2% -> ~2e-6 median
* JP: 3.9% -> ~2e-6 median
* model : fold Ling 3.0 VL into the BailingMoeV3 architecture
Assisted-by: Scout
* model : keep shared NORM rope list intact when gating bailingmoe3 on mrope sections
---------
Co-authored-by: aetherbird <aetherbird@users.noreply.github.com>
This commit removes the precompiled headers that I added in Commit
3bcfeb700 ("cmake : add PCH and unity build to improve build times
(#28091)").
The motivation for this is that this looked good when developing this
but has caused multiple issues that I had taken into consideration and
we have decided to remove it and only keep the unity builds from the
above commit.
Refs: https://github.com/ggml-org/llama.cpp/pull/28882#issuecomment-5662272126
* scripts : add initial profiling script (wip)
* src : add precompile headers (PCH) for models.h
* common : add common.h as PCH
* ggml : add PCH for ggml-impl.h
* mtmd : use PCH for models.h
* scripts : add script to build with Server/Tools/Tests
* server : add PCH for common.h
* docs: add profiling progress notes (wip)
* ggml : add exclude for GCC + SVE on ARM
Refs: https://github.com/ggml-org/llama.cpp/actions/runs/33393906061/job/99493756214?pr=28091
* ggml : attempt to fix use of std::hardware_destructive_inference_size
Refs: https://github.com/ggml-org/llama.cpp/actions/runs/33396221677/job/99501265689?pr=28091
* squash! ggml : attempt to fix use of std::hardware_destructive_inference_size
Add a version check for GCC 12 to conditionally apply the `-Winterference-size`
pragma.
* editorconfig : exclude profiling reports dir
This directory will not be included in the merge later and this commit
can be ignore at that point. Just fixing to keep CI happy.
* ggml : skip PCH for gcc on non-x86 architectures
* tests : add PCH for peg-parser/tests.h
There are 7 peg-parser tests that can share one PCH instead of then each
parsing the full tests.h.
* common : add PCH for chat.h
* docs : update linux build profiling full results
Just updating after a number of PCH additions. These are not exact
figures and will vary a bit from run to run, but they give a general idea
of the performance impact of PCH.
* cmake : introduce unity build for models
This commit introduces a unity build for the models to improve
compilation time.
The improvements were roughly the following:
```console
+------------------------+-----+------------+------------+------------+
| Build | TUs | Frontend | Backend | Total |
+------------------------+-----+------------+------------+------------+
| Full, master | 396 | 811.0 s | 692.2 s | 1,503.2 s |
| Full, with PCH | 405 | 380.0 s | 664.7 s | 1,044.7 s |
| Full, with PCH + UB | 264 | 357.7 s | 635.7 s | 993.4 s |
+------------------------+-----+------------+------------+------------+
TU = Translation Unit.
Full = includes Server, Tools, and Tests.
PCH = precompiled headers.
UB = unity build for models.
```
* docs : update linux profiling table with unitiy build results
* docs : update mac profiling results to include unity build [no ci]
* docs: remove profiling reports
* scripts : merge build profile scripts into one script
I was lazy before and just copied the first script to enable Tests,
Server, and Tools. This now merges them into a single script.
* Revert "editorconfig : exclude profiling reports dir" [no ci]
This reverts commit 2922a12118.
* src : rename ggml_view_2d_slice to gemma3n_view_2d_slice
This is to be consistent with the rename in gemma4.cpp which was
required to avoid a name clash.
* cmake : add build profile script for windows [no ci]
This commit adds a port of the scripts/build-profile.sh script to
windows powershell.
This was developed on Windows on ARM but should work on X64 as well but
needs to be tested there as well.
* mtmd : mark context as const in more methods
Mark `mtmd_context` as `const` in:
- mtmd_bitmap_init_lazy
- mtmd_tokenize
- mtmd_tokenize_from_parts
- mtmd_helper_support_video
- mtmd_helper_bitmap_init_from_file
- mtmd_helper_bitmap_init_from_buf
- mtmd_helper_video_init
- mtmd_helper_video_init_from_buf
- mtmd_helper_model_can_chat
The tokenization functions in particular are useful to have marked
`const`, as that allows more easily telling the compiler that we can
safely tokenize from multiple threads (`mtmd_tokenize` is already
documented as thread-safe, this just reifies that in the signature).
* mtmd : mark tokenization input pointer as const
Mark the `bitmaps` and `parts` pointers in `mtmd_tokenize` and
`mtmd_tokenize_from_parts` as `const`. This allows more easily calling
these with immutable arrays / vectors.
* mtmd : mark llama_context as const in mtmd_helper_model_can_chat
* mtmd: load the qwen3-tts code predictor proj_in as optional
The talker and the code predictor share the hidden size on the 0.6B
checkpoints, so the reference builds no small_to_mtp_projection and
the conversion emits no tensor for it. The graph already falls back
to identity when the weight is missing, the loader now agrees.
* mtmd: keep the qwen3-tts code predictor ffn_down in F32
The code predictor carries a massive activation: its layer 2 FFN
intermediate peaks around 1.5e5, well past the 65504 ceiling of F16.
mul_mat casts its input to the weight type, so an F16 ffn_down turns
that peak into inf, the residual follows, and the next rms_norm yields
NaN. Reference forward in float32 gives 145109 against 145396 measured
in the graph.
* feat: add --mmproj-device arg & backwards compatible MTMD_BACKEND_DEVICE env var
* feat: load mmproj device backend immediately, add -mmdev shortflag
* fix: its a pointer now get the name
* clean up
* gen docs
* nits
---------
Co-authored-by: Xuan Son Nguyen <son@huggingface.co>
* xcframework : fix build
* mtmd : remove unused include path
* vendor : use vendor::hash alias target in cmake
CMake reserves "::" in target names for imported/alias targets, so the real
target keeps the name vendor-hash and a vendor::hash ALIAS target is added.
Consumers (mtmd, llama-gguf-hash) now link against the namespaced alias.
Assisted-by: pi:llama.cpp/Qwen3.8-27B
* vendor : add cmake targets for all vendored libs with vendor:: aliases
Add INTERFACE targets for the header-only vendor libs (miniaudio, nlohmann,
sheredom, stb) and ALIAS targets named vendor::<lib> for all of them,
including cpp-httplib and hash. Each exposes the vendor/ root so includes
are namespaced, e.g. <nlohmann/json.hpp>.
Consolidate the per-lib add_subdirectory calls into a single
add_subdirectory(vendor), keeping the cpp-httplib gate on LLAMA_BUILD_COMMON.
Consumers (llama-common, mtmd) now link the aliases instead of relying on
raw vendor/ include paths.
hash: consumers now include via "hash/hash.h"; the vendor/hash dir is kept
as a PRIVATE include so the synced upstream sources compile unmodified.
Assisted-by: pi:llama.cpp/Qwen3.8-27B
* readme : use foo/bar names in acknowledgements
Assisted-by: pi:llama.cpp/Qwen3.8-27B
* ocd : fix valign
* cmake : introduce semantic versioning (wip)
This commit introduces semantic versioning to llama.cpp.
* squash! cmake : introduce semantic versioning (wip)
* cmake : update test-cmake README notes [no ci]
* include libmtmd in output so show its semversioned
* ci : add make-release workflow
* ci : fix build number check in build-cmake-pkg.yml
* examples : remove trailing whitespace
* ci : abort if upstream ggml version does not exist
* ci : extract step contents into scripts
* ci : add GGML_NATIVE=OFF to ubuntu job
* examples : remove CI build information from test-cmake [no ci]
This commit removes the nightly/release information that I added
previously to keep this focused only on using building and installing
llama.cpp with cmake and being able to quickly verify changes or
troubleshoot issues.
* ci : merge scripts into single script
* remove -dev-build_number support
This commit removes the incremental build number (versioning) support
that I added. This was incorrect and we should only use the semver for
the version. Releases will be tag a nightly build and package
maintainers/managers that build from source can use the tag and it is
therefor important that the correct version is reported. So a
nightly-build will report the semver without the build number. The build
number and commit as availble via cmake and test-cmake has been updated
to include an example of using them:
```console
$ ./build.sh
[test-cmake] version: 0.1.0, build: 10360 (08c69e381)
...
```
Refs: https://github.com/ggml-org/llama.cpp/pull/26839#discussion_r3755836969
* docs: add initial release.md documentation
* cmake : clean-up and add LLAMA_BUILD_IS_DEV option
* ci : remove version input from make-release job
* ci : add LLAMA_BUILD_IS_DEV=OFF to build-cmake-pkg.yml
Refs: https://github.com/danbev/llama.cpp/actions/runs/31576801921/job/94050639145
* docs : update release notes with LLAMA_BUILD_IS_DEV info [no ci]
* ci : add TODO to winget workflow [no ci]
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* adapt the api
* text model ok
* working impl, need verify and clean up
* mtmd: build the pocket-tts transposed convolutions as GEMM + col2im
ggml_conv_transpose_1d has no grouped mode, so the depthwise upsample
was built as one convolution and one concat per channel, which floods
the graph with small nodes and makes kernel launches dominate the
decoder.
Fold both cases into the column form the seanet decoder already needs:
the general case reshapes the kernel to [IC, K * OC] and matmuls it
with the input, the depthwise case batches a matmul over the channels
so a step scales its own kernel. A single col2im_1d then scatter-adds
the columns back to the signal, with the same shape as before, so the
overlap-add tail, the streaming state and the bias are untouched.
Generation time per frame drops by 80% on CUDA and by 50% on CPU. The
output matches the previous implementation sample for sample, with a
correlation of 0.999994 and identical frame counts.
* flow_temp + frames_after_eos
* chunking
* mtmd: carry the remaining pocket-tts per-pack settings
The language packs also tune the end-of-speech padding and the padding
of short prompts, next to the temperature already carried in the
mmproj: french_24l asks for 8 tail frames instead of the guessed 3,
english_2026-01 asks for short prompts to be padded with spaces.
Write both in the mmproj as clip.gen.audio.frames_after_eos and
clip.gen.audio.pad_short_text, keyed on the pack in the conversion
script like the temperature. The loader keeps them optional, so a
mmproj without them behaves as before. Map semicolons to commas for
every pack instead, the reference only asks for it on three of them and
it costs nothing elsewhere.
Existing mmproj files must be converted again to carry the two keys.
On a long french text the port now lands within 2% of the reference:
22.96s against 23.44s, with the same peak level and the same amount of
silence.
* clip.gen.audio.model_variant
* clean up code comments
* nit: drop the dead flow_temp hparam, the pack table holds the default
* update docs
* address security problems
* less invasive base.py
* lint
* add mtmd_gen_inp_default
* add docs
* rm gen_flow_temp
---------
Co-authored-by: Pascal <admin@serveurperso.com>
* Get started with Onyx
* Add architecture
* Skip keys handled in super()
* Loading tensors
* Shorten
* Graph
* Apply suggestion from @pcuenca
* Remove norm now embedding in transformers weights
* Add eot
* Explicit output_multiplier
* Handle post_norm_eps
* No super call; unhardcode eot.
The pattern `self._set_vocab_gpt2()` seems preferred throughout the
codebase, and it allows `set_vocab()` to be called from a different part
of the Python class hierarchy: the drafter model converter that we may
need eventually.
* Register for drafting
* DFlash: inherit rope type from the linked target.
Another option would be to store it in the gguf file itself.
* mmproj conversion
Note: some fields to be renamed after the implementation works. We are
keeping compatibility with the reference Meta gguf for testing purposes.
* "clip" header declarations
* Load mmproj
* Pre-processing
* Graph
* Go back to using delimiters.
Otherwise our generations are worse.
Transformers does not use them. We need to trace inputs to verify
whether they are equivalent.
* downsample_factor -> merge_size
* Add vision graph
lol, forgot from a previous commit
* Additional renames, align with llama.cpp / transformers
* Prefer _size instead of independent _h and _w
* Fix token layout
Co-authored-by: Young Han <younghan@fb.com>
* onyx: bring the chat parser onto the onyx branch
common/chat.cpp on this branch has no Onyx handling, so a converted model
serves malformed chat: the assistant preamble leaks into content
("to=self<|message|>...") and tool calls fail with
HTTP 500 "The model produced output that does not match the expected
peg-native format"
common_chat_params_init_onyx exists on onyx-fair-patch, added there by
8bb73dd3d. It was never on this branch, so this is not a regression --
the two lines developed independently.
The code here is taken verbatim from that commit. It is the clean side of
`git merge origin/onyx-fair-patch`: chat.cpp is one of the files that
merges without conflict. The full merge is not viable -- it produces 13
conflicts, including add/add on conversion/onyx.py and src/models/onyx.cpp
where the q_norm-folding and metadata-scale approaches contradict each
other, and #4/#7 are stacked on this branch's side of that.
Verified on this branch: builds with 0 errors, converts an Onyx checkpoint,
and serving it gives "4" for "What is 2+2?" plus a correct
get_weather {"city":"Paris"} tool call, where the unported branch gives the
two failures above.
No converter or runtime changes are included, so this should not interact
with the q_norm work.
Co-authored-by: Beto de Paola <betodepaola@meta.com>
* Less params, bilinear pos-emb interpolation as a graph op instead of CPU
* Map to symbolic V_MMPROJ instead of strings
* Make a couple params explicit
* Patchify via build_inp()
* No param for rope_theta
* Small cleanup
* Restore blank line
* Unpermute, to adapt to the latest transformers checkpoint
* Apply norm after token embeddings
This follows the latest transformers approach.
* Remove duplicated function
* build_vit
* onyx: use the model rope theta on sliding-window layers
* DFlash: conversion from transformers drafter
* Revert rope_type derivation from target
NOTE: this breaks compatibility with Meta's distributed DFlash GGUFs, as
the Q/K are stored in "NEOX" (rotated half) format, like in
transformers.
* Apply suggestion from @pcuenca
* Set model type
* Remove comment that will become obsolete
* Hardcode post_norm_rms_eps instead of new param
* Derive SWA+RoPE pattern from gguf array or scalar
* Fix model type <-> number of layers
* Reorder
* Rename
* Fix typo
* DFlash: seed the draft KV cache from multimodal embedding batches
`common_speculative_impl_draft_dflash::process()` returned early on any batch carrying embeddings, so an image prefill never had its target-layer features fused through the DFlash encoder and injected into the draft's KV cache. That left a hole spanning the image's positions, and the next injection at a post-image position failed to initialize its batch:
```
decoding image batch 1/1, n_tokens_batch = 256
decode: failed to initialize batch
llama_decode: failed to decode, ret = -1
process: llama_decode(ctx_dft) failed rc=-1 (n_tokens=17, offset=0)
srv decode: failed to process speculative batch
```
Every image request with `--spec-type draft-dflash` failed with HTTP 500. Text-only was unaffected, since those batches carry token ids and were let through.
Restore the earlier condition, which admits a batch that is either tokens or embeddings and skips only the degenerate neither/both cases. The rest of `process()` is already layout-agnostic -- it gathers features via `llama_get_embeddings_layer_inp()` and indexes `batch_in.pos[]` / `batch_in.seq_id[]`, none of which assume token ids -- so this is the whole fix.
Validated against `muse-glimmer-30B-bf16.gguf` + `mmproj-muse-glimmer-30B-bf16.gguf` + a DFlash draft head, on an image describe-the-shapes request:
- before: HTTP 500, `failed to process speculative batch`
- after: HTTP 200, draft acceptance 0.34012 (167 accepted / 491 generated), mean len 3.04
Output equivalence holds, which is the property that matters: at temperature 0 the drafted response is byte-identical to the same request served with no draft attached (1213/1213 chars), so the draft is drafting correctly through the image context rather than merely not crashing.
* Conversion: prefer rewrite to mapping
* Revert "Conversion: prefer rewrite to mapping"
This reverts commit a92d0ac584.
* fix lint
* sliding_window metadata is not optional
* disable state save/load
* Apply suggestion from @pcuenca
---------
Co-authored-by: Young Han <younghan@fb.com>
Co-authored-by: Beto de Paola <betodepaola@meta.com>
Co-authored-by: Daniel Han <michaelhan2050@gmail.com>
Co-authored-by: ruanrms <ruanslv@gmail.com>
Co-authored-by: Xuan Son Nguyen <son@huggingface.co>
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@huggingface.co>
The reference implementation has two mutually exclusive prompt layouts.
In non streaming mode the prefill carries the whole utterance text plus
tts_eos summed with codec_pad, and the trailing text hidden collapses to
a single tts_pad row. In streaming mode the prefill carries only the
first text token and the trailing rows stream the rest of the text
followed by tts_eos.
The pipeline built the non streaming prefill but the streaming overlay,
so the talker saw the utterance a second time during generation and read
it twice before emitting codec_eos.
The overlay is now the single tts_pad row that matches the prefill.
* ggml: add ggml_build_forward_order
ggml_build_forward_expand marks the tensor and all its ancestors for
compute, so using it as a pure ordering hint (keeping q, k and v
together) defeats ggml_build_forward_select: the unselected branch is
forced to run with inputs that were never uploaded. In the mtmd audio
graph this makes GEN_WAV calls execute the GEN_CODE branch with a
stale inp_code0, hitting the get_rows bound assert on CPU.
Add ggml_build_forward_order, which inserts nodes without the compute
flag; the flag is restored when the branch is actually selected.
Switch the q/k/v hints in clip_graph::build_attn to it.
* nit: reduce comments (AGENTS.md)
* mtmd: support multi-row batching for deepseek-ocr
* mtmd: weave deepseek-ocr rows in one shot instead of per row (#26615)
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Co-authored-by: Saba Fallah <sabafallah@gmail.com>
