* wip: port MTP architecture
Ports the Multi-Token Prediction (MTP) architecture to the older `llama.cpp` codebase used by `ikllama`.
Changes include:
- Updating `llama_batch` to support `mtp_params`.
- Modifying `llama_decode_internal` (and `encode`) to handle MTP operations (Warmup, Update, Draft).
- Adding public APIs for MTP state management (`llama_set_draft_input_hidden_state`).
- Adapting the embedding extraction logic to skip MTP update passes.
* Refactors `server_slot` to support generic speculative decoding (MTP or Draft Model).
* core: enable hybrid outputs (logits + embeddings) for MTP support
* fix(mtp): correct KV-cache slot finding for updates
* fix(mtp): persist hidden states to prevent context corruption during drafting
* refactor(mtp): clean unused code
* fix(mtp): update server to new functions name
* fix(mtp): fix graph and save hidden state
* mtp: refactor integration, context params and kv cache search
* mtp: fix hidden state extraction and speculative acceptance flow
* server: fix MTP warmup for long prompts and reset token buffer
* llama: refactor MTP operation state to context parameters
* server: fix n_past calculation in MTP acceptance
* llama: fix mtp enable flags
* speculative: refactor MTP to use common_speculative interface
* context: remove unused signatures
* clip: fix deprecated enum-enum conversion warning
* common: fix format string crash in help message
* context: fix mtp activation logic
* llamat: always use the extracted embedding
* llama: get all embeddings to kv cache
* llama: revert logit to not run mtp for not supported arch
* llama: allocate all the n_outputs for MTP
* wip
* server-context: get only the last embedding for hidden state
* ggml-backend: fix array of bounds in debug build
* server-context: run mt kv update to each prompt batch
* revert segmentation fault fixes
* glm-mtp(feat): optimize graph embedding and recursive drafting
* glm5-mtp(feat): add glm 5 mtp logic
* glm-mtp: standardize the MTP graph
* glm 5 mtp: apply post-layer cvec
* glm 5 mtp: mark head as mandatory
* get normed embeddings for glm 5
* Fix GLM5 MTP
* GLM5 MTP: just reuse the layer attention implementation
* Make MTP work with split mode graph
---------
Co-authored-by: samuel <samueloliveira32df@gmail.com>
* MLA tensor parallelism under -sm graph (DEEPSEEK2/GLM_DSA/MISTRAL4)
Extends -sm graph (split-mode graph) to MLA-style attention across the
DEEPSEEK2, GLM_DSA, and MISTRAL4 architectures. Previously these archs
fell back to -sm layer regardless of the user's flag.
Implementation:
- Per-rank attention build in build_deepseek2_tp_attention with
view-sliced FlashAttention, split-buffer output projection, and
ggml_reduce across devices
- wk_b / wv_b absorbed weights replicated per device via materialize()
in llm_prepare_mla (these can't live in a split buffer)
- KV cache replication path (replicated_k_l) for graph-mode TP
- distribute_mla_tensors_for_split_mode_graph routes attention/norm
tensors into ctx_split; expert tensors stay per-layer
- Implements ggml_backend_cuda_split_buffer_get_tensor for the
replicated / row-split / col-split inverse paths
- Early-reject guard in src/llama.cpp that auto-downgrades -sm graph
to -sm layer (with a warning) when incompatible loader flags are set:
-ncmoe, -cmoe, -ot, -rtr, -muge
New CLI flag:
- -gap | --graph-attn-precision <f16|f32> (default f16)
See the PR description for the full validation matrix (3 archs x 2/4/8
GPU counts), perf numbers, VRAM accounting, and known limitations.
* Some tweaks
* materialize lambda: per-head split for graph-mode tp_replicate
7dd19e19 changed wk_b/wv_b distribution from mirror to per-head split
(split_dim=2) via prepare_split_tensors. That path only fires when
wk_b/wv_b are loaded from GGUF.
Models that store only wkv_b in GGUF derive wk_b/wv_b at load via
llm_prepare_mla, going through the materialize lambda, which was
untouched and still produced mirror replicas (split_dim=-1, full n_head
per device).
build_deepseek2_tp_attention now does mul_mat(wk_b_local, q_nope_perm)
without the prior view_3d slice, so a mirror replica passes an n_head
tensor where the kernel expects n_head_local. Result: silent SIGSEGV
right after model load.
Mirror logic in materialize is replaced with the same per-head split as
prepare_split_tensors: head_offsets derived from wo split, each rank
gets a tensor with ne[2]=n_head_local, data copied from the appropriate
source byte slice. Singular `computed` tensor keeps full metadata for
tensors_by_name lookups.
Tested: 8x3090, -sm graph -mla 3 -fa on now boots cleanly and
sweep-benches without crash. Log confirms new path: "Computed
blk.X.attn_k_b.weight ... split across N devices on dim=2".
* cleanup: indent fix + remove dead view_3d slicing and debug printf
- build_deepseek2.cpp: re-indent the self_attention block in
build_deepseek2_layer_attention (lines 253-670). Block was at column 0
inside a function body; now at the expected 4/8-space indent.
- build_deepseek2.cpp: drop the commented-out view_3d slicing and debug
printfs left over after 7dd19e19's switch to direct mul_mat on
per-rank wk_b_local / wv_b_local. Update the stale 'wk_b is
replicated (split_dim=-1)' comment to match the new split_dim=2
reality.
- ggml-cuda.cu: remove the leftover debug printf in
ggml_backend_cuda_split_buffer_get_tensor.
No behavior change. Verified with a clean rebuild and DSV2.5 +
GLM-4.7-Flash sweep-bench runs.
* llm_load_tensors: gate incompatible-flag warning to MLA archs
The -ncmoe / -rtr / -muge / -ot warning under -sm graph currently fires
for all archs that support graph mode. That's an over-reach: the
incompatibility is specific to the MLA TP paths (DEEPSEEK2, GLM_DSA,
MISTRAL4) — Gemma4 graph mode existed pre-PR and works with those flags.
Gate the warning to MLA archs only.
Also refreshes two stale comments left over from the wk_b/wv_b
mirror -> per-head-split rewrite:
- src/llama.cpp llm_prepare_mla: "Replicate wk_b/wv_b ..." now reads
"Per-head split wk_b/wv_b ..." to match what the materialize lambda
actually does post-823a39e2.
- src/llama-load-tensors.cpp distribute_mla_tensors_for_split_mode_graph:
drop the wkv_b row-split mention (wkv_b is no longer created under
graph mode after 7dd19e19) and correct the wk_b/wv_b distribution
description (per-head split, not per-device replicated).
---------
Co-authored-by: Kawrakow <iwankawrakow@gmail.com>
* Use build_std_attention for Gemma4 when possible
It is possible for the 26b MoE and 31b dense models.
It is not possible for the E4B/E2B vaiants because they
don't have KV cache in each layer.
* Standardize Gemma4 dense ffn
* WIP: Gemma4 split mode graph
Runs but produces NaNs
* WIP: Gemma4 split mode graph
Runs but very high PPL. At least it is no longer NaN.
* WIP
* This works!
* Put attn_norm, attn_post_norm, ffn_norm, ffn_post_norm on all GPUs
* Fix crash when saving/loading KV cache
* WIP: split mode graph for Gemma4-MoE - crashes
* Split mode graph for Gemma4-MoE - this works
* Disable SWA optimization
Something goes wrong there
* Consolidate MoE and dense graph parallel
* Use build_std_attention for Gemma4 when possible
It is possible for the 26b MoE and 31b dense models.
It is not possible for the E4B/E2B vaiants because they
don't have KV cache in each layer.
* Standardize Gemma4 dense ffn
* WIP: Gemma4 split mode graph
Runs but produces NaNs
* WIP: Gemma4 split mode graph
Runs but very high PPL. At least it is no longer NaN.
* WIP
* This works!
* Put attn_norm, attn_post_norm, ffn_norm, ffn_post_norm on all GPUs
* Fix crash when saving/loading KV cache
* Gemma4: WIP
* Gemma4: WIP - runs with totally wrong results
* Gemma4: WIP - add CPU 512, 512 FA
* Gemma4: WIP
It gives a meaningful response in llama-cli, but PPL is still much too
high. Is this due to tokenizer issues?
* Gemma4: this works
I had forgotten the softcap on the final output.
* Remove log
* Gemma4: WIP E4B/E2B
* Gemma4: Q4B/E2B appear to work now
* gemma4: tokenizer fixes
* WIP: support pre-merged up/gate experts
Haha, mainline has elected to arrange the merged tensors
the other way around compared to what I had done in the on-the-fly merge.
* Change the order of on-the-fly packed up/gate
* OpenAI
* CUDA TG
* CPU
* Split mode graph for models with pre-merged ffn_up/ffn_gate experts
* WIP: support pre-merged up/gate experts
Haha, mainline has elected to arrange the merged tensors
the other way around compared to what I had done in the on-the-fly merge.
* Change the order of on-the-fly packed up/gate
* OpenAI
* CUDA TG
* CPU
* WIP
* WIP
* WIP
* WIP
* WIP
* WIP
* WIP
Loads and starts running, crashes with illegal memory access in
quantize_mmq_q8_1. This almost always indicates NaNs in the input
to the MoE FFN part.
* WIP
* WIP
Loads and runs, wrong results (very high PPL)
Performance looks promising, around 25% better than previous sm graph.
Needs f32 or bf16 graph reduce type.
* WIP - still wrong
* Fix after rebase
* WIP
* WIP
* This seems to be working for dense Qwen3.5!!!
* WIP: Qwen3-Next is not quite working
* Some cleanup
* Disable Qwen3-Next for now
* Disable graph parallel when mmproj was specified
* Read/write split recurrent state
* That should not crash
* Re-enable vision - it works now
* Recurrent layers should now be counted for split cache
* Display the size of the tensors overriden during the tensor loading
Ex:
`Tensor blk.60.ffn_gate_exps.weight buffer type overriden to CPU
Tensor blk.60.ffn_up_exps.weight buffer type overriden to CPU`
become
`Tensor blk.60.ffn_up_exps.weight (size = 668467200 bytes) buffer type overriden to CPU
Tensor blk.60.ffn_gate_exps.weight (size = 668467200 bytes) buffer type overriden to CPU`
And pass in debug the later displayed size of the unnamed buffer overrides.
Ex : `llm_load_tensors: CPU buffer size = XXX.XX MiB`
That double display is cluttering the screen without being very informative.
* change bytes display to MiB.
Co-authored-by: Kawrakow <iwankawrakow@gmail.com>
---------
Co-authored-by: Kawrakow <iwankawrakow@gmail.com>
Yap Sok Ann
empty-quiver
Kawrakow
SamuelOliveirads
Jun Yamog
Joel Farthing
David Young
Samuel Oliveira Alves
Nexes the Elder
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