* wip: separate llama_context for MTP with graph reuse
* wip: fix KV cache desync with separate MTP context
* refactor: remove dead mtp logic code, encapsulate KV mirroring
* mtp-context: derive args directly from the main model's context
* mtp: fix kv cache positions
* clean small comments
* minor refactor for context shift
* 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
* Little maintenance
* llama-quantize : Add the missing items in the help
* Add GGML_MAX_CONTEXTS define in the general cmakelist.txt
* Make the KV cache (CPU) based warnings clearer
* Correct placement of GGML_MAX_CONTEXTS definition
* Revert wrong indents
This reverts commit d0728cbb6c6d4d6d8dc13f062e542d232647a38d.
* Moving the GGML_MAX_CONTEXTS definition to src/CMakeLists.txt
* Update warning message for unsupported KV cache types
* forgotten antislash
* 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: 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
* Allow using -rtr and -muge together
* Various Qwen-3.5 tweaks
* No need to make v, g, beta contiguous
* Adjust NEON delta-net to non-contiguous v, g, b
* Cleanup
---------
Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
* simpler n_rewind logic, delete old comments
* use more consistent names, add updt_w_cur to json schema
* align comments
* refactor review logic, update struct/variable names
* revert cosmetic changes
* check enable/disable in llama_prep_adaptive_p_impl()
* delete extra whitespaces after statement
* show target in debug prints
* more concise debug print
* delete old comments
* update with loop instead of move()
* comment out all adaptive p debug prints
* more debug prints
* move review() variables: common_sampler struct -> common_sampler_review() args
* match n_unsent type
* fix merge bugs, delete adaptive p references in buffer_and_check_string_ban()
* restore accidental erasure
* Revert "adaptive p: collect probability before logit bias"
This reverts commit 1434878461c49d1a2a9047fc15d5e7b78421fd2a.
* server : support multi-modal context checkpoints and prompt caching
do not create checkpoint right after image processing
improve mtmd check for slot ops
fix context shift
do not abort if template parse failed
* change to debug message when detecting ban token
---------
Co-authored-by: firecoperana <firecoperana>
* 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
Kawrakow
Samuel Oliveira Alves
Nexes the Elder
gapeleon
firecoperana
dungquixote42