This change limits progress updates to approximately every 0.1% of the
file size to minimize stdio overhead.
Also fixes compiler warnings regarding __func__ in lambdas.
Signed-off-by: Adrien Gallouët <angt@huggingface.co>
As we only support static graphs for the time and we don't know the size
of the output of top-p, we have to do value-scaling same as for min-p
operator.
Further improvements can be applied to the unit-test (i.e. check for
equivalence of top_p happening on backend with top_p happening on cpu)
and also by constructing candidates and sorting those as opposed to
reversing the sort of the logits (this would be arange +
get_rows instead of argsort + get_rows)
This commit removes the backend sampling chain from the common_sampler
structure and related functions.
The motivation for this change is that the backend samplers are not
currently set on the context, and if they are they would cause the
a graph reallocation to occur. Instead, the intialization is handled
like it currently is by llama_context's constructor.
This commit modifies the temperature sampling check to allow a
temperature value of zero. Previously, the check only allowed
positive temperature values, which excluded the valid case of
zero temperature.
The motivation for this is to enable a zero temperature setting which is
also currently causing the following test to fail:
```console
(venv) $ cd tools/server/tests
(venv) $ ./tests.sh unit/test_basic.py::test_load_split_model
```
This commit adds a function to check if a sampler is actually enabled,
meaning that it does not have values that disables its effect. This is
then used by the backend samplers initialization to avoid considering
samplers that are not enabled when determining the split point between
them.
The motivation for this is that this allows the default sampler chain
for `--samplers` to be used and any sampler that is not enabled will not
cause the backend samplers to be skipped.
For example, before this change if the penalties sampler was included in
the samplers list but had default values that disable it, it would cause
the backend samplers to be skipped entirely.
This commit also contains some refactoring to remove some code
duplication.
This commit removes the `--backend-dist` option and instead uses the
configured --samplers chain to determine which samplers run on the
backend.
Backend sampling is still enabled using With `--backend_sampling`, and
the sampler chain, either explictly specified using `--samplers` or the
default, is automatically analyzed to determine which samplers can run
on the backend. The system finds the longest contiguous chain of
backend supported samplers from the start of the sampler sequence.
For example:
* If the chain is `top-k -> temperature -> top-p`, and both `top-k` and
`temperature` are backend-supported but `top-p` is not, then `top-k`
and `temperature` will run on the backend, while `top-p` and
subsequent samplers run on the CPU.
* If all configured samplers are supported, the final distribution
sampling will also happen on the backend, transferring only the
sampled token IDs back to the host.
* If the sampler chain starts with an unsupported sampler (e.g.,
`penalties`), all sampling runs on the CPU. Note that this is
currently the case with the default sampler so to use backend sampling
it is required to specify a sampler chain. See below for an example.
The following shows how llama-cli can be run with backend sampling:
```console
$ llama-cli -m models/Qwen2.5-VL-3B-Instruct-Q8_0.gguf \
--prompt 'What is the capital of Sweden?' \
-n 20 \
-no-cnv \
--verbose-prompt \
-ngl 40 \
--backend-sampling \
--samplers 'top_k;temperature'
```
In this case the all sampling will happen on the backend since both
`top_k` and `temperature` are supported backend samplers.
To enable a partial backend sampling (hybrid sampling), for example
running `top_k` and `temperature` on the backend and `typ_p` on the CPU
the following sampler chain could be specified:
```console
$ llama-cli -m models/Qwen2.5-VL-3B-Instruct-Q8_0.gguf \
--prompt 'What is the capital of Sweden?' \
-n 20 \
-no-cnv \
--verbose-prompt \
-ngl 40 \
--backend-sampling \
--samplers 'top_k;temperature;top_p'
```
If this looks good then I'll follow up with updates the llama-cli and
llama-server documentation to reflect these changes.
This commit refactors the model loading process in common/common.cpp
to enable backend sampler to be configure prior to the llama_context
creation.
The motivation for this change is that just being able to set/reset the
backend samplers after the llama_context has been created will cause a
resize to occur in llama_context::output_reserve which we want to avoid.
This commit updates common/sampler.cpp set_logits and
src/llama-sampling.cpp llama_sampler_sample to always populate the
logits field when backend sampled probabilities are available.
The motivation for this is that this ensure that CPU sampler always have
access to the logits values even when probabilites have been produced by
backend samplers.
* Add files via upload
* fix unit test
* fix crashes for --reasoning-format=none
* Patch buggy official MiniMax-M2 chat template
* add upstream minja fix: https://github.com/ochafik/minja/pull/7
* Fix <think> token not generated
* add test copied from https://github.com/ggml-org/llama.cpp/pull/16946
* cleanup
* Hopes to fix the compilation error on CI
* Delete chat template patching since it’s fixed by upstream Minja
* Remove undeeded Minimax-M2 template patch
https://github.com/ochafik/minja/pull/7#issuecomment-3480356100
* Add proper handling of optional parameters with test
merged tests from: 23d4bb75c4
* Fix making all tool parameters optional
* Move xml tool parser to separate file
* cleanup & add tests for GLM4.5
* add streaming tests & enhancement & cleanups
Add streaming test for both GLM 4.5 and minimax-m2.
Cleanup for preserved_tokens.
Cleanup for grammar rule name.
Enhance the parser's stability.
* cleanup & add support for Kimi-K2 Qwen3-Coder Apriel-1.5 Xiaomi-MiMo
* apply suggestions from reviewers
* fix a misuse for data.grammar_lazy
* fix grammar when tool have no argument
* Fix `no triggers set for lazy grammar!` for GLM4.5/4.6. Insert additional stops for Kimi-K2
* update chat.cpp
* fix grammar for GLM 4.5/4.6
* Try fix Jinja template for GLM
* Try fix GLM-4.6.jinja
* Update common/chat-parser-xml-toolcall.cpp
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* Update tests/test-chat.cpp
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* improve chat template for GLM, rename Kimi-K2 template to Kimi-K2-Thinking
* Improve Kimi-K2 chat template
* Fix unit test
* Fix "Invalid tool call arguments passed" in a rare case.
In a rare case, the model may emit a raw string that begins with a valid JSON string. This commit adds unit tests to cover that scenario and fixes the regression introduced during the Kimi-K2 adaptation.
---------
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
This commit precomputes and caches the full-vocab token id list in
llama_context's constructor, so llama_get_backend_sampled_token_ids_ith
always returns a valid pointer.
The motivation for this is that this enables both common/sampling.cpp
and src/llama-sampling.cpp can simplify their logic.
Not all backends samplers that process logits need to set the
sampled_tokens_id as they may not change the order of the logits, for
example the temperature sampler only scales the logits but does not
change their order. Simliar the logit bias sampler only adds bias to
specific token ids but does not change the order of the logits. In
these cases there will not be a device to host copy of the sampled
token ids, and this is the use case where having this precomputed
list is useful.
This commit adds support for performing sampling operations on the
backend (e.g. GPU) as part of the model computation graph.
The motivation for this feature is to enable sampling to be performed
directly on the backend as part of the computation graph being executed,
allowing for some or all of the sampling to be done on the backend.
For example, the backend sampler chain might select/sample a token
directly in which case only the sampled token needs to be transferred
from device memory to host memory.
It is also possible for the backend samplers to perform filtering of
the logits, or compute and filter the probability distribution, in
which case only the filtered logits or probabilites need to be
transferred back to system memory for further processing by CPU
samplers.
Currently the backend sampling works in a similar manner to how
pooling works, it is a function that is called by build_graph and the
sampler operations become part of the models computation graph.
* arg: add --cache-list argument to list cached models
* new manifest naming format
* improve naming
* Update common/arg.cpp
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* common: move download functions to download.(cpp|h)
* rm unused includes
* minor cleanup
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
Daniel Bevenius
Xuan-Son Nguyen
Georgi Gerganov
Adrien Gallouët
Aaron Teo
ddh0
Igor Smirnov
DAN™
Oliver Simons
hksdpc255
Aldehir Rojas