Gemma4 MTP#17
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May 19, 2026
| // of streams (one per active draft seq); q->ne[2] is not divisible by the full | ||
| // n_stream and the view collapses tokens. Slice k/v down to exactly the streams | ||
| // referenced by this ubatch. Requires those streams to form a contiguous range. | ||
| if (k->ne[3] > 1 && (uint32_t) k->ne[3] != ubatch.n_seqs_unq) { |
|
@am17an Are these AIME results with default thinking, or did you set a reasoning budget? |
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|
Just the default, no budget |
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I tried this branch with mtp model from here (merged to latest release) Well, model on |
* webui: added single line reasoning preview. * patch: reduce width slightly for the previewing section * refactor: move formatter constants to the right file * feat: reimplement reasoning preview with throttled dynamic per-line rendering * chore: fix spacing Co-authored-by: Aleksander Grygier <aleksander.grygier@gmail.com> * chore: refactor to requested changes * refactor: grouped by capture pattern instead of block-level + inline * ui: fax interrupt state only trigger for 1st reasoning message * chore: make reasoning preview respects showThoughtInProgress setting * chore; newline at EOF Co-authored-by: Aleksander Grygier <aleksander.grygier@gmail.com> * fix: thread rawContent so collapsible content can handle compute preview * patch: showThoughtInProgress accidentally blocks rawContent being passed * chore: fix lint * chore: change smoke test --------- Co-authored-by: Aleksander Grygier <aleksander.grygier@gmail.com>
* chore(ui): pin package versions to currently installed - Update all dependencies and devDependencies to match exactly what's in package-lock.json - This ensures reproducible builds by locking to specific versions rather than semver ranges * chore: Update packages * chore: Move remaining dependencies to devDependencies * fix: Add missing `mermaid` package * chore: Update `cookie` package to `v1.1.1` * chore: Formatting * test: Update test configs
…gml-org#22445) * Deduplicate imatrix loading code * Add back LLAMA_TRACE, early exit on quantize missing metadata
…debar (ggml-org#23132) * use child snippets for landing and chat message elements * make ... icon visible in conversation history menu * conversation history forward tab fix * add snippet fix for fork icon in conversation history * focus/keyboard fix for attachment x icon and scroll left/right * formatting * fix scroll down issue * simply Statistics and pointer events in scrolldown * create storybook tests and move to folder * improve tests to actually assert on element
mmvq: Port the ncols_dst optimization from ggml-cuda/mmvq.cu to SYCL. Read weights once per dispatch instead of once per column. Covers all standard quant types + reorder paths for Q4_0, Q8_0, Q3_K, Q4_K, Q5_K, Q6_K. IQ types (except IQ4_XS) excluded due to incompatible vec_dot signatures. ggml-sycl: The weight reorder was only bootstrapped on single-token mat-vec (ne[1] == 1). Speculative / MTP verify issues only multi-column mat-vec, so it never triggered the reorder and ran on the slower non-reorder kernel. Bootstrap it on small multi-column batches (ne[1] <= 8) too.
This PR attempts to slim down the dependencies for build-msys jobs making the same changes that we applied in whisper.cpp to reduce the size of the github actions cache, and should also improve the run time due to fewer dependencies that need to be installed. I realize this is a scheduled job but I think it would still make sense to apply these changes. Refs: ggml-org/whisper.cpp#3858
* Enroll mul_mat_vec_q_moe into PDL, boosting MTP performance on BW
Data collected on a B4500:
Before
```
(llama.cpp) ➜ llama.cpp git:(master) ✗ python mtp-bench.py
code_python pred= 192 draft= 150 acc= 116 rate=0.773 tok/s=202.8
code_cpp pred= 192 draft= 147 acc= 117 rate=0.796 tok/s=212.8
explain_concept pred= 192 draft= 161 acc= 110 rate=0.683 tok/s=196.4
summarize pred= 192 draft= 138 acc= 122 rate=0.884 tok/s=226.6
qa_factual pred= 192 draft= 138 acc= 121 rate=0.877 tok/s=225.1
translation pred= 192 draft= 158 acc= 112 rate=0.709 tok/s=201.5
creative_short pred= 192 draft= 160 acc= 110 rate=0.688 tok/s=197.2
stepwise_math pred= 192 draft= 150 acc= 115 rate=0.767 tok/s=209.2
long_code_review pred= 192 draft= 148 acc= 116 rate=0.784 tok/s=208.9
```
After
```
(llama.cpp) ➜ llama.cpp git:(master) ✗ python mtp-bench.py
code_python pred= 192 draft= 150 acc= 116 rate=0.773 tok/s=211.9
code_cpp pred= 192 draft= 147 acc= 117 rate=0.796 tok/s=224.6
explain_concept pred= 192 draft= 161 acc= 110 rate=0.683 tok/s=207.8
summarize pred= 192 draft= 138 acc= 122 rate=0.884 tok/s=240.2
qa_factual pred= 192 draft= 138 acc= 121 rate=0.877 tok/s=238.5
translation pred= 192 draft= 158 acc= 112 rate=0.709 tok/s=213.4
creative_short pred= 192 draft= 160 acc= 110 rate=0.688 tok/s=208.8
stepwise_math pred= 192 draft= 150 acc= 115 rate=0.767 tok/s=221.7
long_code_review pred= 192 draft= 148 acc= 116 rate=0.784 tok/s=220.7
```
Server launched with:
```
➜ llama.cpp git:(osimons/enroll_mul_mat_vec_q_moe_into_PDL) ✗ ./build-x64-linux-gcc-reldbg/bin/llama-server \
-m /mnt/share/gguf/unsloth/Qwen3.6-35B-A3B-MTP-GGUF/Qwen3.6-35B-A3B-UD-Q4_K_M.gguf -dio \
--spec-type draft-mtp \
--spec-draft-n-max 2 \
-ngl all \
-fa on \
--host 0.0.0.0 \
--port 8080 -np 1 --chat-template-kwargs "{\"preserve_thinking\": true}"
```
* LC to overlap with following kernels
* hparams : refactor hparams.n_layer * cont : remove `n_layer_kv()`, use n_layer_all instead * cont : type consistency * pi : update SYSTEM.md * models : fix Step3.5 MTP * cont : remove duplicate switch cases * cont : explicitly set `false` to extra layers for `is_swa` and `is_recr` * cont : fix nextn layer count handling Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com> --------- Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* Update quantization readme * install requirements * Apply suggestions from code review Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com> * dos2unix suggestions --------- Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
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Works with both gemma-31B and gemma-26B but the MoE model is slower. I see a good speed up on my DGX spark (~2-2.5x speedup) on the dense model. The main problem is sharing the memory ctx between the two llama_contexts, so currently it's pretty hacky plus also the ubatch splitting is not super clean.
Replicated the AIME-26 results for Gemma-31B with
-np 4