commit 912ed2cd9339d1b2875d98744ca5b51fa62e581e
Author: samuel <samueloliveira32df@gmail.com>
Date: Sun Dec 7 23:00:29 2025 -0300
speculative (feat): implement recursive MTP drafting for GLM-4.5
commit bdf72d9552e3da64ffc85f175664713388752914
Author: samuel <samueloliveira32df@gmail.com>
Date: Sat Dec 6 16:10:16 2025 -0300
sampling (feat): optimize speculative drafting with fast-path selection
commit a91980a8f3475a6bbac0a64d8be06dd4b613020e
Author: samuel <samueloliveira32df@gmail.com>
Date: Sat Dec 6 15:18:19 2025 -0300
mtp (chore): clean old code
commit 6de0ecf55db8567db4faa99b0152b72c9e854548
Author: samuel <samueloliveira32df@gmail.com>
Date: Sat Dec 6 14:40:13 2025 -0300
mtp (feat): add mtp arg
commit ea77394183b8e6c368af969b8274039a54b11486
Author: samuel <samueloliveira32df@gmail.com>
Date: Sat Dec 6 13:47:54 2025 -0300
mtp-graph (fix): move llama_get_logits_ith outside the loop
commit 15dff208958fb66802f20ec53ce5fcaff133edb7
Merge: 171346c74 cae85fe53
Author: samuel <samueloliveira32df@gmail.com>
Date: Thu Oct 16 13:44:41 2025 -0300
Merge branch 'glm4-mtp-batch' of https://github.com/SamuelOliveirads/llama.cpp into glm4-mtp-graph-cache
commit cae85fe531
Author: samuel <samueloliveira32df@gmail.com>
Date: Thu Oct 16 13:42:31 2025 -0300
mtp-batch(fix): avoid logits for mtp kv cache operations
commit 171346c742c310bbcfbd786b61250638ccf8b44d
Author: samuel <samueloliveira32df@gmail.com>
Date: Sun Oct 12 16:33:01 2025 -0300
mtp-graph(feat): Reactivate graph reuse only for main model path
commit 0127c6beeb
Author: samuel <samueloliveira32df@gmail.com>
Date: Sat Oct 11 22:20:54 2025 -0300
mtp-batch(chore): Remove final MTP debug logs and dead code
commit 4bcc9e261e
Author: samuel <samueloliveira32df@gmail.com>
Date: Sat Oct 11 18:51:22 2025 -0300
mtp-batch(fix): Correctly advance cache head and add MTP documentation
commit b4cbe030ac
Author: samuel <samueloliveira32df@gmail.com>
Date: Sat Oct 11 18:37:40 2025 -0300
mtp-batch(chore): Fix logit flags for speculative sampling and remove debug logs
commit a99709d0c1
Author: samuel <samueloliveira32df@gmail.com>
Date: Fri Oct 10 17:24:34 2025 -0300
mtp-batch(refactor): Extract decode context and MTP input logic into helper methods
commit 913af8f48d
Author: samuel <samueloliveira32df@gmail.com>
Date: Fri Oct 10 16:44:28 2025 -0300
mtp-batch(refactor): Replace MTP boolean flags with an explicit operation enum
commit 6f74ba3807
Author: samuel <samueloliveira32df@gmail.com>
Date: Thu Oct 9 22:27:18 2025 -0300
mtp-batch (fix): prevent mtp draft from polluting the cache
commit 5e1d719bef
Author: samuel <samueloliveira32df@gmail.com>
Date: Thu Oct 9 15:21:23 2025 -0300
mtp-batch (feat): Create and manage sinfo for MTP
commit febd8235d2
Author: samuel <samueloliveira32df@gmail.com>
Date: Sun Oct 5 14:43:40 2025 -0300
mtp-batch (wip): fix how to warmup kv cache for MTP
commit 67c6c069e0
Author: samuel <samueloliveira32df@gmail.com>
Date: Sat Sep 27 19:42:32 2025 -0300
mtp-batch (wip): Isolate MTP graph to prevent host embedding buffer corruption
commit 75dc25e6fe
Author: samuel <samueloliveira32df@gmail.com>
Date: Sat Sep 27 17:17:00 2025 -0300
mtp-batch (wip): organize batch for mtp cache
commit 3da7e7f330
Author: samuel <samueloliveira32df@gmail.com>
Date: Tue Sep 23 22:45:11 2025 -0300
mtp-batch (fix): warm mtp cache for small batch size
commit df64508b93
Author: samuel <samueloliveira32df@gmail.com>
Date: Sun Sep 21 21:55:41 2025 -0300
mtp-batch (wip): merge glm graphs
commit 042eb8a829
Author: samuel <samueloliveira32df@gmail.com>
Date: Sun Sep 21 21:29:00 2025 -0300
mtp-batch (wip): merge mtp and model graph
commit 1318b2de82
Author: samuel <samueloliveira32df@gmail.com>
Date: Sun Sep 14 10:22:59 2025 -0300
mtp-batch (wip): move mtp execution to batch format
commit c6237c71ff
Merge: 9fab53e438742ce0e3
Author: Aaron Lee <lee.aaron.65@gmail.com>
Date: Sat Sep 13 02:57:01 2025 -0400
Merge pull request #1 from SamuelOliveirads/glm4-moe-mtp
feat: implemented sampling for MTP
commit 8742ce0e39
Author: samuel <samueloliveira32df@gmail.com>
Date: Sat Sep 6 00:21:18 2025 -0300
feat: apply logits + greedy sampler
commit 5a5bce8577
Author: samuel <samueloliveira32df@gmail.com>
Date: Wed Sep 3 17:56:14 2025 -0300
fix: add sample acceptance
commit 07670a22c6
Author: samuel <samueloliveira32df@gmail.com>
Date: Wed Sep 3 13:25:21 2025 -0300
feat: implemented sampling for MTP
commit 9fab53e438
Author: Aaron Lee <lee.aaron.65@gmail.com>
Date: Tue Sep 2 17:14:09 2025 -0400
fixed mtp kv cache update step in cases where prompt size > n_batch and n_ubatch
commit 98bc0c6bf2
Author: Aaron Lee <lee.aaron.65@gmail.com>
Date: Tue Aug 26 01:26:51 2025 -0400
replace standard sampler with greedy sampler for mtp draft
commit 471e026327
Author: Aaron Lee <lee.aaron.65@gmail.com>
Date: Tue Aug 19 23:10:56 2025 -0400
fixed vram leak
commit d72f9d5691
Author: Aaron Lee <lee.aaron.65@gmail.com>
Date: Tue Aug 19 01:50:34 2025 -0400
kludge-y kv cache management of mtp layer
commit 382135aa36
Author: Aaron Lee <lee.aaron.65@gmail.com>
Date: Sun Aug 17 21:54:45 2025 -0400
fixed mtp kv cache update sequencing after prompt processing
commit 6870f9790c
Author: Aaron Lee <lee.aaron.65@gmail.com>
Date: Sun Aug 17 04:59:36 2025 -0400
added proper KV cache management for MTP layers and slightly refactored
commit 6e9bafc7a7
Author: Aaron Lee <lee.aaron.65@gmail.com>
Date: Fri Aug 15 23:13:56 2025 -0400
failed attempt to implement MTP; outputs tokens but KV cache management is unreasonable
commit cf0f7c0448
Author: Aaron Lee <lee.aaron.65@gmail.com>
Date: Wed Aug 13 02:21:17 2025 -0400
broad thrust of the mtp implementation
commit 03231da69e
Author: Aaron Lee <lee.aaron.65@gmail.com>
Date: Tue Aug 12 01:03:59 2025 -0400
add model member function to build mtp graph, to be called from speculative.cpp
commit 1f477b3755
Author: Aaron Lee <lee.aaron.65@gmail.com>
Date: Mon Aug 11 20:54:45 2025 -0400
make nextn weights loadable without a crash
commit e434f87cc7
Author: Aaron Lee <lee.aaron.65@gmail.com>
Date: Mon Aug 11 01:21:47 2025 -0400
some work towards building mtp layer graph
commit db60623e79
Author: Aaron Lee <lee.aaron.65@gmail.com>
Date: Sun Aug 10 23:52:54 2025 -0400
added getter for nextn layer count and server slot has_mtp property
* llama: automatically fit args to free memory
llama-fit-params tool
* fix CI
* hints for bug reports, ensure no reallocation
* fix segfault with Vulkan
* add llama-fit-params to CI
* fix CI
* fix CI
* fix CI
* minor adjustments
* fix assignment of 1 dense layer
* fix logger not being reset on model load failure
* remove --n-gpu-layer hint on model load failure
* fix llama-fit-params verbosity
* fix edge case
* fix typo [no ci]
* Make graph_max_nodes vary by ubatch size for models where chunking might explode the graph
* Update src/llama-context.h
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* Add missing const
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* ggml : add GGML_SCHED_NO_REALLOC option to disable reallocations in ggml_backend_sched
Enabled in ggml-ci for testing.
* llama : update worst-case graph for unified cache
* ci : disable op offload in some tests
* fix spelling
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* Qwen3 Next - cleaned up version
* Whitespaces and stuff
* Correct minor errors
* Update src/llama-model.cpp
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* Misc. fixes.
* Clean up code, add missing hybrid qualifier
* Did someone transpose the SOLVE_TRI result matrix? Perhaps...
* Whitespace
* Proper tensors for cb calls
* Use llama-graph.h vertical alignment
* BROKEN: chunking
* Set new tensors as inputs.
* Proper chunk logic
* It's the circle of life...
* More shenanigans for n_seq > 1
* Nail in the coffin?
* Fix Windows build
* Eh, one fails on Windows, the other fails on Mac... just use general capture.
* quant : cleanup
* model : cleanup
* qwen3 : cleanup
* cont : cleanup
* cont : cleanup
* ggml : revert change
* qwen3 : cleanup
* cont : cleanup
* Readd cmath
* qwen3 : fix typo
* Update convert_hf_to_gguf.py
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* Usual suspects
* fix my bad suggestion
---------
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
This commit adds a check to skip the output reordering logic when
n_outputs == 1. With a single output token, the data is trivially
sorted and the reordering code is currently doing unnecessary work
(resetting and rebuilding output_ids to the same values).
The motivation for this change is improved code clarity and avoiding
confusion when debugging. While the performance impact is probably
negligible, this unnecessary work happens on every decode call in
llama-server when processing batches with single-token outputs.
* kv-cache : pad the size of the small SWA cache for performance
* context : pad the total context to 256
* cont : future-proof the swa pad
* server : adjust test params to new logic
* server : support unified context across slots
* cont : fix speculative decoding initialization
* context : fix n_ctx_per_seq computation
* server : purge slots one by one
* tests : add unified cache server tests
* llama : update per-seq context computation
* test-thread-safety : handle tiny training context of the input model
* server : fix server_tokens clear()
* server : use 4 slots + unified KV by default
* llama : add note about context size queries
* cont : update todos [no ci]
* context : do not cap the size of the context
* tests : adjust parameters to be CI friendlier
* context : add warning
The unexpeced pooling_type warning was incorrectly shown when users did not
specify the --pooling-type parameter. In this case, the parameter
defaults to `LLAMA_POOLING_TYPE_UNSPECIFIED (-1)`, and the code
automatically applies the model's default pooling type.
Example of spurious warning:
```
$ llama-embedding -hf ggml-org/bge-m3-Q8_0-GGUF -p "hello"
...
llama_init_from_model: model default pooling_type is [2], but [-1] was specified
...
```
This fix ensures the warning only appears when users explicitly specify
a pooling type that differs from the model's default (e.g., using
--pooling-type mean on a model that expects CLS pooling).
* model: EmbeddingGemma sentence-transformers dense linear projections support
* model: add support for EmbeddingGemma SentenceTransformers dense linear projections
Adding support for the Dense modules used in EmbeddingGemma models.
EmbeddingGemma is a SentenceTransformers model with additional modules beyond the base Transformer backbone.
See: https://developers.googleblog.com/en/gemma-explained-embeddinggemma-architecture-and-recipe/
* model: add support for EmbeddingGemma SentenceTransformers dense linear projections
- converting model with dense-layers is optional
- introduced dense config params
* Update convert_hf_to_gguf.py
Co-authored-by: Daniel Bevenius <daniel.bevenius@gmail.com>
* fixed formatting issues
* Update src/llama-graph.cpp
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* - removed pooling_type_opt, always allow overriding pooling_type
- asserts checking dense features dims
* fix python lint
* fix ubuntu gcc build warning
* - fixed thread-safety test
- moved asserts to load_hparams
* - tidying up code
- simplifying graph-context expecting both dense weights
* minor : add TODO
---------
Co-authored-by: Daniel Bevenius <daniel.bevenius@gmail.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* add grok-2 support
* type fix
* type fix
* type fix
* "fix" vocab for invalid sequences
* fix expert tensor mapping and spaces in vocab
* add chat template
* fix norm tensor mapping
* rename layer_out_norm to ffn_post_norm
* ensure ffn_post_norm is mapped
* fix experts merging
* remove erroneous FFN_GATE entry
* concatenate split tensors and add more metadata
* process all expert layers and try cat instead of hstack
* add support for community BPE vocab
* fix expert feed forward length and ffn_down concat
* commit this too
* add ffn_up/gate/down, unsure if sequence is right
* add ffn_gate/down/up to tensor names
* correct residual moe (still not working)
* mess--
* fix embedding scale being applied twice
* add built in chat template
* change beta fast for grok if default value
* remove spm vocab in favor of community bpe vocab
* change attention temp length metadata type to integer
* update attention temp length metadata
* remove comment
* replace M_SQRT2 with std::sqrt(2)
* add yarn metadata, move defaults to hparams
This commit adds check for two function pointers returned from
ggml_backend_reg_get_proc_address.
The motivation for this is that the function pointer could be nullptr if
the get proc address function changes in the future. This is also
consistent with all the other calls to ggml_backend_reg_get_proc_address
in the code base.
* llama : set n_outputs to 1 to avoid 0 outputs mean-pooling
This commit modifies the llama_context constructor to set n_outputs to
1.
The motivation for this is that when using pooling, and specifically
mean pooling, for embeddings having n_outputs set to 0 can lead to the
following error:
```console
$ build/bin/llama-embedding -m models/nomic-embed-text-1.5-Q4_K_M.gguf \
--pooling mean -p "Hello, how are you?"
...
llama_context: CPU output buffer size = 0.12 MiB
/home/danbev/work/ai/llama.cpp/ggml/src/ggml.c:3023: GGML_ASSERT(ggml_can_mul_mat(a, b)) failed
0x0000743c96d107e3 in __GI___wait4 (pid=292978, stat_loc=0x0, options=0, usage=0x0) at ../sysdeps/unix/sysv/linux/wait4.c:30
warning: 30 ../sysdeps/unix/sysv/linux/wait4.c: No such file or directory
30 in ../sysdeps/unix/sysv/linux/wait4.c
196 waitpid(child_pid, NULL, 0);
230 ggml_print_backtrace();
3023 GGML_ASSERT(ggml_can_mul_mat(a, b));
1823 cur = ggml_mul_mat(ctx0, ggml_cont(ctx0, ggml_transpose(ctx0, inp)), inp_mean);
18983 llm->build_pooling(cls, cls_b, cls_out, cls_out_b);
1399 auto * gf = model.build_graph(gparams);
292 auto * gf = graph_reserve(1, n_seqs, n_outputs, mctx.get(), true);
2329 auto * ctx = new llama_context(*model, params);
913 llama_context * lctx = llama_init_from_model(model, cparams);
105 common_init_result llama_init = common_init_from_params(params);
[Inferior 1 (process 292976) detached]
Aborted (core dumped)
```
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* add comment about not reserving graphs with zero outputs
* add assert in graph_reserve to ensure n_outputs >= 1
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
Exposes ggml_backend_sched_split_graph() to allow splitting the graph without allocating compute buffers and uses it to split the graph for the automatic Flash Attention check.
* server : add SWA checkpoints
ggml-ci
* cont : server clean-up
* server : handle state restore fails
* llama : add extended llama_state_seq_ API
* server : do not make checkpoints if --swa-full
ggml-ci
* llama : remove flags value for NONE
* server : configure number of SWA checkpoints with CLI arg
ggml-ci
* args : fix scope of new argument
* examples/finetune -opt SGD (stochastic gradient descent) memory opt
add unit tested GGML_OPT_OPTIMIZER_SGD to ggml - avoids allocating
m, v tensors.
support finetune.cpp arg -opt SGD (or sgd). (default adamw as before)
llama 3.2-1b-F32 result: observed 11gb gpu ram (41 sec/epoch)
when using SGD instead of 19gb (55 sec/epoch) using adamw.
(wikipedia 100 lines finetune)
(
using the same GPU memory, adamw can only do before OOM 512
batch/context, reaching:
train: [███████▉] data=0000140/0000140 loss=0.02575±0.00099 acc=99.52±0.03% t=00:00:47 ETA=00:00:00
val: [███████▉] data=0000008/0000008 loss=4.76565±0.28810 acc=41.46±0.77% t=00:00:00 ETA=00:00:00
SGD is superior, though it converges slower, with max before OOM 1728
batch/context (esp see the better validation perf):
train: [███████▉] data=0000039/0000039 loss=0.00371±0.00010 acc=99.96±0.01% t=00:00:41 ETA=00:00:00
val: [███████▉] data=0000003/0000003 loss=5.11406±0.76034 acc=48.01±0.69% t=00:00:01 ETA=00:00:00
)
note: when finetuning long enough (or w/ enough -lr),
validation accuracy *eventually* drops ('catastrophic forgetting')
-lr-half (halflife) option useful for SGD to avoid oscillation or
super slow underdamped learning (makes setting -lr more forgiving).
terminal -lr for now is set by lr-halvings i.e. if you want at most
1/8 the inital -lr you set -lr-halvings 3.
note: objective loss not directly comparable between adamw, sgd? -
check perplexity or accuracy or consider relative improvements
for convergence
new finetune args -wd 1e-9 to enable weight decay in sgd or adamw,
and max -epochs N (default 2 as before)
cache (1 - wd*alpha) in 'adamw' opt struct -
no noticeable perf benefit, disabled (still done
for new SGD though)
since opt. memory is pre-allocated, the ggml_opt_get_optimizer_params
would probably be able to change between SGD and AdamW with each epoch
but would need to use adamw for the first (unconfirmed - no cmdline arg
to set such a policy yet)
test-opt checks adamw as before and now sgd (except for a few disabled
tests for sgd only; probably just needs logging values and adding
alternate reference values); tolerance on the 'regression'
test is broader for sgd (so we don't need many more epochs)
* Vulkan: Implement GGML_OP_OPT_STEP_SGD
* tests: Fix OPT_STEP_SGD test-backend-ops
* SGD op param store weight-decay and not 1-alpha*wd
* minor + cosmetic changes
* fix vulkan sgd
* try CI fix
---------
Co-authored-by: 0cc4m <picard12@live.de>
Co-authored-by: Johannes Gäßler <johannesg@5d6.de>
* llama : clarify comment about pp and tg graphs [no ci]
This commit clarifies the comment in `llama-context.cpp` regarding the
prefill prompt (pp), and token generation (tg) graphs.
The motivation for this is that I've struggled to remember these and had
to look them up more than once, so I thought it would be helpful to add
a comment that makes it clear what these stand for.
* squash! llama : clarify comment about pp and tg graphs [no ci]
Change "pp" to "prompt processing".
samuel
Johannes Gäßler
Georgi Gerganov
Jeff Bolz
Piotr Wilkin (ilintar)
Diego Devesa
Daniel Bevenius
Sigbjørn Skjæret
takuya kodama
Saba Fallah
Haiyue Wang
Jonathan Graehl
compilade
Aman Gupta