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Author SHA1 Message Date
Piotr Wilkin (ilintar) d98b548120 Restore clip's cb() to its rightful glory - extract common debugging elements in llama (#17914) 
* Extract common debugging functions; plug eval-callback and mtmd's MTMD_DEBUG_GRAPH with same functionality

* Move to common

* Remove unneeded header

* Unlink from common

* chore: update webui build output

* Cleanup; properly pass params to mtmd without depending on common; factorize debug.cpp to use common debug code.

* Revert change to webapp

* Post-merge adjust

* Apply suggestions from code review

Co-authored-by: Xuan-Son Nguyen <thichthat@gmail.com>

* Apply code review changes

* Remove changes to server-context

* Remove mtmd.h include

* Remove utility functions from header

* Apply suggestions from code review

Co-authored-by: Xuan-Son Nguyen <thichthat@gmail.com>

* Rename functions

* Update tools/mtmd/clip.cpp

Co-authored-by: Xuan-Son Nguyen <thichthat@gmail.com>

* Update tools/mtmd/clip.cpp

Co-authored-by: Xuan-Son Nguyen <thichthat@gmail.com>

* Update tools/mtmd/clip.cpp

Co-authored-by: Xuan-Son Nguyen <thichthat@gmail.com>

---------

Co-authored-by: Xuan-Son Nguyen <thichthat@gmail.com>
 2026-01-14 20:29:35 +01:00
Adrien Gallouët 516a4ca9b5 refactor : remove libcurl, use OpenSSL when available (#18828) 2026-01-14 18:02:47 +01:00
Xuan-Son Nguyen e047f9ee9d mtmd: fix use_non_causal being reported incorrectly (#18793) 
* mtmd: fix use_non_causal being reported incorrectly

* move clip_is_mrope to mtmd_decode_use_mrope

* fix sloppy code ggml_cpy
 2026-01-13 12:19:38 +01:00
Ruben Ortlam db79dc06b1 llama-bench: add direct_io parameter (#18778) 2026-01-13 08:49:10 +01:00
Radoslav Gerganov bcf7546160 server : add arg for disabling prompt caching (#18776) 
* server : add arg for disabling prompt caching

Disabling prompt caching is useful for clients who are restricted to
sending only OpenAI-compat requests and want deterministic
responses.

* address review comments

* address review comments
 2026-01-12 19:21:34 +02:00
Xuan-Son Nguyen ce3bf9b1a4 server: update docs for sleeping [no ci] (#18777) 2026-01-12 13:01:24 +01:00
Georgi Gerganov f307926482 server : adjust unified KV cache tests (#18716) 2026-01-10 17:51:56 +02:00
Xuan-Son Nguyen 9ac2693a30 server: fix n_cmpl not skipping processing prompt (#18663) 
* server: fix n_cmpl not skipping processing

* fix infinite loop on empty batch

* cont : init child samplers + modify child logic

* cont : cleanup

* cont : improve n_cmpl logic

- launch the parent task first so it finds the slot with best cache
- parent task waits for child tasks to be launched
- when a child task finishes - remove its cache

* cont : remove redundant function

* cont : reduce parent checks

* fix : nullptr task dereference

---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
 2026-01-10 00:00:41 +01:00
Simranjeet Singh a61c8bc3bf mtmd: Add Gemma3n multimodal support with MobileNetV5 vision encoder (#18256) 
* Add Gemma3nVisionModel - MobileNetV5 vision encoder convertor to convert_hf_to_gguf.py. Add gemma3n to vision projectors in gguf-py/gguf/constants.py.

* Add mobilenetv5 impl

* Fix comments, remove unused vars

* Fix permute and remove transpose of projection weights

* Fix comments, remove debugging prints from hf_to_gguf

* 1. Hard-code image_mean = 0 and image_std = 1
2. Use available tensor mapping logic
3. Remove redundant chat template replacement of soft tokens placeholder with media placeholder

* 1. Move mobilenetv5 helpers declarations to `clip_graph_mobilenetv5` struct and definitions to mobilenetv5.cpp
2.Remove unused `clip_is_gemma3n` func declarations and definitions
3. Remove redundant `rescale_image_u8_to_f32` func and use `normalize_image_u8_to_f32` with zero mean and unit std
4. Calculate n_patches using image_size / patch_size

* Remove obsolete comments

* - convert_hf_to_gguf.py & constants.py & tensor_mapping.py: Use explicit mapping: Custom map for double indexed blocks and tensor_mapping.py for rest
- convert_hf_to_gguf.py: Unsqueeze Stem Bias and Layer scale tensors to correct shape while converting to gguf
- mobilenetv5.cpp: Remove explicit reshaping of Stem Bias and Layer scale which are now handled while converting to gguf, replace fprintf with LOG_*
- clip.cpp: Remove unused embedding and hard_emb_norm tensor loading

* - Rename tensors to v.conv..., v.blk..., v.msfa... to better align with already existing terminology

* Fix stem conv bias name

* Remove explicit handling of bias term for stem conv

* - Change order of addition in "project_per_layer_inputs" to support broadcasting of vision inp_per_layer
- Simplify the vision embeddings path of "get_per_layer_inputs" to output [n_embd_altup, n_layer, 1], broadcastable

* clean up conversion script

* fix code style

* also preserve audio tensors

* trailing space

* split arch A and V

* rm unused gemma3 func

* fix alignment

---------

Co-authored-by: Xuan Son Nguyen <son@huggingface.co>
 2026-01-09 23:42:38 +01:00
Pascal ec8fd7876b Webui/file upload (#18694) 
* webui: fix restrictive file type validation

* webui: simplify file processing logic

* chore: update webui build output

* webui: remove file picker extension whitelist (1/2)

* webui: remove file picker extension whitelist (2/2)

* chore: update webui build output

* refactor: Cleanup

* chore: update webui build output

* fix: update ChatForm storybook test after removing accept attribute

* chore: update webui build output

* refactor: more cleanup

* chore: update webui build output
 2026-01-09 16:45:32 +01:00
Asbjørn Olling a180ba78c7 cmake: only build cli when server is enabled (#18670) 2026-01-09 16:43:26 +01:00
Georgi Gerganov 53eb9435da server : fix timing of prompt/generation (#18713) 2026-01-09 12:59:50 +02:00
Georgi Gerganov f5f8812f7c server : use different seeds for child completions (#18700) 
* server : use different seeds for child completions

* cont : handle default seed

* cont : note
 2026-01-09 09:33:50 +02:00
Adrien Gallouët 55abc39355 vendor : update cpp-httplib to 0.30.0 (#18660) 
* vendor : update cpp-httplib to 0.30.0
* common : allow custom headers when downloading
 2026-01-08 13:53:54 +01:00
Johannes Gäßler 64848deb18 llama-fit-params: free memory target per device (#18679) 2026-01-08 10:07:58 +01:00
Adrien Gallouët 56d2fed2b3 tools : remove llama-run (#18661) 
* tools : remove llama-run
* Remove licenses/LICENSE-linenoise

Signed-off-by: Adrien Gallouët <angt@huggingface.co>
 2026-01-07 16:18:26 +01:00
Tarek Dakhran ccbc84a537 mtmd: mtmd_audio_streaming_istft (#18645) 
Change is decoupled from https://github.com/ggml-org/llama.cpp/pull/18641.

[LFM2.5-Audio-1.5B](https://huggingface.co/LiquidAI/LFM2.5-Audio-1.5B)
needs streaming istft for generating output audio.

* add streaming ISTFT class (`mtmd_audio_streaming_istft`) with overlap-add for audio reconstruction
* replace global audio cache with per-instance cache, the model requires
  two independent caches, for preprocessing (audio input) and for istft
  (audio output).
* unified templated FFT/IFFT implementation supporting both forward and inverse transforms
 2026-01-06 21:00:29 +01:00
R 3d26a09dc7 server : add thinking content blocks to Anthropic Messages API (#18551) 
* server : add thinking content blocks to Anthropic Messages API

Add support for returning reasoning/thinking content in Anthropic API
responses when using models with --reasoning-format deepseek and the
thinking parameter enabled.

- Non-streaming: adds thinking block before text in content array
- Streaming: emits thinking_delta events with correct block indices
- Partial streaming: tracks reasoning state across chunks via
  anthropic_has_reasoning member variable

Tested with bartowski/DeepSeek-R1-Distill-Qwen-7B-GGUF model.

* server : fix Anthropic API streaming for thinking content blocks

Add signature field and fix duplicate content_block_start events in
Anthropic Messages API streaming responses for reasoning models.

* server: refactor Anthropic streaming state to avoid raw pointer

Replace raw pointer to task_result_state with direct field copies:
- Copy state fields in update() before processing chunk
- Use local copies in to_json_anthropic() instead of dereferencing
- Pre-compute state updates for next chunk in update()

This makes the data flow clearer and avoids unsafe pointer patterns.
 2026-01-06 16:17:13 +01:00
Tarek Dakhran 73d284a250 model : add LFM2-ColBert-350M (#18607) 
* model : add LFM2-ColBert-350M

* llama_model_n_embd_out() - returns `hparams.n_embd_out` if set and fallbacks to `hparams.n_embd`
 2026-01-05 19:52:56 +01:00
Vladislav Sayapin da143b9940 server : fix router child env in containerized environments (#18562) 2026-01-05 14:12:05 +01:00
Daniel Bevenius d3dce4e0a5 sampling : add support for backend sampling (#17004) 
* sampling : add support for backend sampling

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.

* llama-cli : add backend sampler configuration

* server : add backend sampling options/configuration

* webui : add backend sampling options

* ggml : add initial cumsum implementation for CUDA

* sampling : enable all backend sampler tests

This commit enables all exisiting backend sampler tests in the
test-backend-sampler. Previously, some tests were disabled because
there were missing ggml operation implementations.

* graph : do not include llama-model.h

* sampling : always expose sampled_ids

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.

* sampling : ensure at most one output token per seq

This commit adds a check in the batch allocator to ensure that when
backend sampling is enabled, at most one output token is specified per
sequence.

* CUDA: Optimize argsort for gpu-based token sampling

Argsort is used for top-k currently. WE optimize argsort by 2 things:

1. Use `DeviceRadixSort` for single-row/sequence to parallelize it
   across our SMs
2. Use `DeviceSegmentedSort` for multi-row/sequence as this is the
   correct entrypoint (the function chooses different execution paths,
   it contains `DeviceSegmentedRadixSort` as one of the paths and will
   choose the best one according to heuristics.
   https://nvidia.github.io/cccl/cub/api/structcub_1_1DeviceSegmentedSort.html#overview

Some perf numbers for a RTX PRO 6000:

On the kernel level, tested with
`GGML_CUDA_DISABLE_GRAPHS=1 ./test-backend-ops -o ARGSORT perf`
Before:
```
  ARGSORT(type=f32,ne=[65000,16,1,1],order=0):                  4130 runs -   359.24 us/run
  ARGSORT(type=f32,ne=[200000,1,1,1],order=0):                  8192 runs -   861.34 us/run
  ARGSORT(type=f32,ne=[200000,16,1,1],order=0):                 1343 runs -  1020.01 us/run
```

After:
```
  ARGSORT(type=f32,ne=[65000,16,1,1],order=0):                  4130 runs -   312.41 us/run
  ARGSORT(type=f32,ne=[200000,1,1,1],order=0):                 16384 runs -    63.48 us/run
  ARGSORT(type=f32,ne=[200000,16,1,1],order=0):                 1343 runs -   874.36 us/run
```

---
On the model level, tested with
`llama-cli -m gpt-oss-20b-mxfp4.gguf -n 200 -p "What is
the Capital of Sweden?" -no-cnv -fa 1 --backend-sampling`

Before:
```
llama_perf_sampler_print:    sampling time =       0.25 ms /   207 runs   (    0.00 ms per token, 824701.20 tokens per second)
llama_perf_context_print:        load time =   18215.58 ms
llama_perf_context_print: prompt eval time =      28.20 ms /     7 tokens (    4.03 ms per token,   248.19 tokens per second)
llama_perf_context_print:        eval time =     714.79 ms /   199 runs   (    3.59 ms per token,   278.40 tokens per second)
llama_perf_context_print:       total time =     857.62 ms /   206 tokens
```

After
```
llama_perf_sampler_print:    sampling time =       0.25 ms /   207 runs   (    0.00 ms per token, 828000.00 tokens per second)
llama_perf_context_print:        load time =   18366.92 ms
llama_perf_context_print: prompt eval time =      35.92 ms /     7 tokens (    5.13 ms per token,   194.87 tokens per second)
llama_perf_context_print:        eval time =     532.79 ms /   199 runs   (    2.68 ms per token,   373.50 tokens per second)
llama_perf_context_print:       total time =     683.65 ms /   206 tokens
```

* sampling : remove version from sampler chain

This commit removes the version field from the sampler chain and instead
used the sampler pointer itself for change detection.

* sampling : always populate logits for sampled probs

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.

* sampling : simplify backend sampling logic decode

This commit tries to simplify the backend sampling logic in
llama_context::decode.

* squash! sampling : simplify backend sampling logic decode

Fix condition to check if backend actually sampled tokens, not just that
backend samplers are available.

* common : fix regression caused by extra memory allocations during sampling

* squash! sampling : simplify backend sampling logic decode

The commit fixes a variable shadowing issue in the
`llama_context::decode` function which was introduced in a previous
refactoring.

* squash! common : fix regression caused by extra memory allocations during sampling

Apply the same changes to llama-sampling.cpp, llama_sampler_sample as
were applied in commit 38f408c25.

* sampling : introduce sampling_info struct

This commit introduces a sampling_info struct to encapsulate all
backend sampling related data within the llama_context class.

It also updates to use more descriptive names for sampled tokens and
candidates in the backend sampler ggml data structure.

* sampling : return early if backend sampling is disabled

* sampling : use pinned memory for backend sampling buffers

* common, tools : refactor model loading to support backend samplers

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.

* sampling : add stride variable for clarity

* sampling: clarify candidate ids usage in comments

* sampling : fix copying both sampled tokens and logits/probs from backend

This commit fixes the issue where both sampled tokens and logits/probs
were not being copied correctly from the backend to the host when
multiple backend samplers were used.

A test for this scenario has also been added to ensure that both types
of data are copied correctly when different backend samplers are
employed.

* tests : cleanup test-backend-sampler.cpp

* common : remove build-info.cpp from commit [no ci]

This file was generated during the build process and should not be
included in previous commits.

* sampling : cleanup and clarify output_reserve

* sampling : remove redundant checks for stride and size [no ci]

* sampling : add debug log when backend sampler selects token

This commit adds a debug log statement in the llama_sampler_sample
to indicate when a backend sampler has selected a token for a given
index.

The modification helps in tracing the sampling process and understanding
the flow of control when backend samplers are used.

* examples : update batched to use backend sampling

This commit updates the batched example to demonstrate how to use
backend samplers.

* llama-cli : fix dangling reference to sampler config

* common : initialize backend samplers

* samplers : add missing cont

* sampling : add assertions for contiguous tensors in async copy functions

* examples : add info about hybrid sampling in batched [no ci]

* sampling : remove backend-dist option (wip)

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.

* CUDA: Add top-k implementation

* sampling : add min-p backend sampler

* Use `FetchContent` over CPM as it's bundled with CMake

Thanks @ggerganov for the suggestion

* common : add get_active_samplers function to check enabled samplers

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.

* cuda : fix editorconfig-checker warning

* sampling : use argmax for min-p sampling

* sampling : fix temperature check to allow zero temperature

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
```

* cuda : fix top-k compilation when CUB is unavailable

This commit adds a macro guard around argsort_f32_i32_cuda_cub usage
in the top-k fallback path, falling back to bitonic sort when
GGML_CUDA_USE_CUB is not defined.

The motivation for this is that some environments like AMD HIP
do not have CUB available, causing compilation failure.

Refs: https://github.com/ggml-org/llama.cpp/actions/runs/19728226426/job/56523606840#step:6:208

* sampling : add comments about backend sampler [no ci]

This commit adds a comment to llama_context's constructor explaining why
backend samplers are initialized early in the process.

* sampling : remove backend sampling chain from common_sampler

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.

* Fix top-k comp & behavior for non-CUB path

Some changes were made in 5ea3be265b
which were incomplete. In the case of non-CUB, bitonic sort and its
limitations of ncols < 1024 have to apply, similar to argsort.cu

* sampling : support intermixed backend/cpu samplers

This commit updates the backend sampling implementation to support
intermixed usage of backend and CPU samplers within the same batch.

The initial implementation was developed as an all-or-nothing solution:
either perform backend sampling for the entire batch, or perform CPU
sampling for the entire batch.

The motivation for this change is to support batches with mixed
sequences. For example, we may have a backend sampler configured for
sequence 0, while sequence 1 in the same batch uses CPU sampling. This
was not supported in the initial implementation.

This issue manifested in llama-server with the webui: decoding with
backend samplers would work initially, but after changing to CPU
sampling, a slot (sequence) could still be using a backend sampler.
This meant that logits in output_reserve would not be allocated,
resulting in an error.

The solution in this commit inspects the batch to determine which
sampling modes are needed and allocates buffers accordingly. However,
there is a known inefficiency: when we have intermixed backend/CPU
samplers in the same batch, we currently copy all logits to the host,
even for sequences using backend samplers.

Added test_backend_cpu_mixed_batch to verify correct behavior with
mixed backend/CPU samplers in a single batch, including dynamic
sampler switching between decode calls.

* squash! sampling : support intermixed backend/cpu samplers

Add check that logits is not null which is can happen for embeddings.

* squash! sampling : support intermixed backend/cpu samplers

Fix llama-save-load-state which currently fails by handling the case
when batch.logits is nullptr (like when loading state) by allocating
space for all outputs as CPU logits.

* refactor : simplify and improve memory management

* Add initial version for top-p sampling

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)

* sampling : use logits directly for min-p filtering

* sampling : simplify

* llama : simplify

* llama : cleanup + naming

* llama : call backend_init once

* llama : reserve graphs with samplers

* llama : naming

* cont : naming

* sampling : lower log level for output buffer reallocations [no ci]

This commit changes the logging level for output buffer reallocations
in the llama_context::output_reserve function from INFO to DEBUG.

The motivation for this is that it currently logs to info and when
enabling verbose logging for llama-cli this will get mixed with the
output, for example:

```console
What is the capital of Sweden?output_reserve: reallocating output buffer from size 0.58 MiB to 1.74 MiB
 1. Stockholm
2\. Helsinki
Based are the options
1. Stockholm
Explanation: Stockholm is the capital of
...
```

* Fix backend_top_p_sampler

softmax(softmax) will return uniform distribution, so we should not
return the softmax but the logits instead.

* Factor out `ggml_sort` into its own function

* Make backend's top_p sampler inclusive

In addition to match the algorithm proposed in the original
[paper](https://arxiv.org/abs/1904.09751), this resolves the edge-case
where `max_p is > top_p` for a single logit, where the mask would
otherwise be empty (and we thus sample from the whole vocabulary with
equal likelihood)

* common : simplify sampler chain initialization

* sampling : do not create empty samplers

* sampling : fix top_p empty condition

* examples : remove outdated backend sampling section

This commit removes the outdated section about using backend samplers
from the README.md file in the examples/batched.

* sampling : fix backend temp sampler for zero temperature

This commit fixes the implementation of the temperature-based sampler
for the case when the temperature is set to zero. This now correctly
selects the most probable token by masking out all other tokens in the
logits.

* CUDA: Move cccl fetch to after cuda has been enabled in CMakeLists.txt

This will allow cccl to set build flags for the CUDA compiler, required
e.g. for MSVC compat, see also
https://github.com/NVIDIA/cccl/pull/6791

* CUDA: Use standard-compliant preprocessor for MSVC builds

Workarounds of https://github.com/NVIDIA/cccl/pull/6791 will not be
backported to CCCL 3.2, only the diagnostics/error messages will:
https://github.com/NVIDIA/cccl/pull/6827

* CUDA: Update CCCL's rc candidate

* squash! sampling : fix backend temp sampler for zero temperature

This modifies the parent commit to simply return the most probably token
instead of masking the logits.

* sampling : implement temp_ext_backend sampling

This commit implements the apply function for the extended temperature
sampling.

* sampling : minor cleanup

* sampling : stop short if backend sampler sampled a token

This commit modifies the graph building logic to immediately continue
when a token has already been sampled by the backend sampler.

It also updates the test for backend temporary sampling to include
top-k and distribution samplers in the chain to verify that they are not
producing any logits (they are not run).

* Revert "sampling : stop short if backend sampler sampled a token"

This reverts commit 87b2719eca.

* sampling : fix backend temp sampling to use logits masking

* sampling : simplify temp sampling

* sampling : remove redundant calls to ggml_build_forward_expand

* sampling : check backend support during init

* cont : keep backend sampling disabled for now

* sampling : fix outputs and device checks

* sampling : fix candidates logic

* Add perf-tests for CUMSUM

* Readd `cub::DeviceScan::InclusiveSum`-based CumSum

For single rows and large columns doing a for-loop over the function
`cub::DeviceScan::InclusiveSum` offered by CUB outperforms the
`cumsum_cub_kernel` where `cub::BlockScan` is used.

Numbers before this change

  Backend 1/3: CUDA0
  Device description: NVIDIA RTX 6000 Ada Generation
  Device memory: 48510 MB (48039 MB free)

  CUMSUM(type=f32,ne=[128,128,4,4]):                  311258 runs -     3.26 us/run -     2048 kB/run -  599.76 GB/s
  CUMSUM(type=f32,ne=[2048,16,5,4]):                  229390 runs -     4.40 us/run -     5120 kB/run - 1110.23 GB/s
  CUMSUM(type=f32,ne=[20000,10,4,1]):                  37583 runs -    29.63 us/run -     6250 kB/run -  201.18 GB/s
  CUMSUM(type=f32,ne=[128,1,1,1]):                    892819 runs -     1.12 us/run -        1 kB/run -    0.85 GB/s
  CUMSUM(type=f32,ne=[1024,1,1,1]):                   450505 runs -     2.25 us/run -        8 kB/run -    3.39 GB/s
  CUMSUM(type=f32,ne=[4096,1,1,1]):                   155629 runs -     6.61 us/run -       32 kB/run -    4.62 GB/s
  CUMSUM(type=f32,ne=[8192,1,1,1]):                    81910 runs -    12.60 us/run -       64 kB/run -    4.85 GB/s
  CUMSUM(type=f32,ne=[16384,1,1,1]):                   49146 runs -    23.99 us/run -      128 kB/run -    5.09 GB/s
  CUMSUM(type=f32,ne=[32768,1,1,1]):                   24573 runs -    47.10 us/run -      256 kB/run -    5.18 GB/s
  CUMSUM(type=f32,ne=[65536,1,1,1]):                   16382 runs -    93.57 us/run -      512 kB/run -    5.22 GB/s
  CUMSUM(type=f32,ne=[131072,1,1,1]):                   8191 runs -   184.79 us/run -     1024 kB/run -    5.29 GB/s
  CUMSUM(type=f32,ne=[200000,1,1,1]):                   8191 runs -   280.43 us/run -     1562 kB/run -    5.31 GB/s
  CUMSUM(type=f32,ne=[2000000,1,1,1]):                  2148 runs -  2771.23 us/run -    15625 kB/run -    5.38 GB/s
  CUMSUM(type=f32,ne=[128,4,1,1]):                    458696 runs -     2.21 us/run -        4 kB/run -    1.73 GB/s
  CUMSUM(type=f32,ne=[1024,4,1,1]):                   360404 runs -     2.82 us/run -       32 kB/run -   10.83 GB/s
  CUMSUM(type=f32,ne=[4096,4,1,1]):                   147438 runs -     7.12 us/run -      128 kB/run -   17.15 GB/s
  CUMSUM(type=f32,ne=[8192,4,1,1]):                    81910 runs -    12.90 us/run -      256 kB/run -   18.92 GB/s
  CUMSUM(type=f32,ne=[16384,4,1,1]):                   49146 runs -    24.32 us/run -      512 kB/run -   20.08 GB/s
  CUMSUM(type=f32,ne=[32768,4,1,1]):                   24573 runs -    47.28 us/run -     1024 kB/run -   20.66 GB/s
  CUMSUM(type=f32,ne=[65536,4,1,1]):                   16382 runs -    93.21 us/run -     2048 kB/run -   20.96 GB/s
  CUMSUM(type=f32,ne=[131072,4,1,1]):                   8191 runs -   185.04 us/run -     4096 kB/run -   21.11 GB/s
  CUMSUM(type=f32,ne=[200000,4,1,1]):                   5369 runs -   282.08 us/run -     6250 kB/run -   21.13 GB/s
  CUMSUM(type=f32,ne=[2000000,4,1,1]):                   537 runs -  2806.46 us/run -    62500 kB/run -   21.26 GB/s
  CUMSUM(type=f32,ne=[128,8,1,1]):                    458696 runs -     2.20 us/run -        8 kB/run -    3.47 GB/s
  CUMSUM(type=f32,ne=[1024,8,1,1]):                   360404 runs -     2.82 us/run -       64 kB/run -   21.66 GB/s
  CUMSUM(type=f32,ne=[4096,8,1,1]):                   147438 runs -     7.12 us/run -      256 kB/run -   34.28 GB/s
  CUMSUM(type=f32,ne=[8192,8,1,1]):                    81910 runs -    12.90 us/run -      512 kB/run -   37.84 GB/s
  CUMSUM(type=f32,ne=[16384,8,1,1]):                   49146 runs -    24.32 us/run -     1024 kB/run -   40.15 GB/s
  CUMSUM(type=f32,ne=[32768,8,1,1]):                   24573 runs -    47.28 us/run -     2048 kB/run -   41.31 GB/s
  CUMSUM(type=f32,ne=[65536,8,1,1]):                   16382 runs -    93.20 us/run -     4096 kB/run -   41.92 GB/s
  CUMSUM(type=f32,ne=[131072,8,1,1]):                   8194 runs -   185.05 us/run -     8192 kB/run -   42.22 GB/s
  CUMSUM(type=f32,ne=[200000,8,1,1]):                   5370 runs -   282.15 us/run -    12500 kB/run -   42.26 GB/s
  CUMSUM(type=f32,ne=[2000000,8,1,1]):                   269 runs -  4067.61 us/run -   125000 kB/run -   29.36 GB/s
  CUMSUM(type=f32,ne=[128,16,1,1]):                   303067 runs -     3.32 us/run -       16 kB/run -    4.60 GB/s
  CUMSUM(type=f32,ne=[1024,16,1,1]):                  303067 runs -     3.32 us/run -      128 kB/run -   36.76 GB/s
  CUMSUM(type=f32,ne=[4096,16,1,1]):                  147438 runs -     7.17 us/run -      512 kB/run -   68.13 GB/s
  CUMSUM(type=f32,ne=[8192,16,1,1]):                   81910 runs -    12.90 us/run -     1024 kB/run -   75.68 GB/s
  CUMSUM(type=f32,ne=[16384,16,1,1]):                  49146 runs -    24.33 us/run -     2048 kB/run -   80.28 GB/s
  CUMSUM(type=f32,ne=[32768,16,1,1]):                  24573 runs -    47.30 us/run -     4096 kB/run -   82.59 GB/s
  CUMSUM(type=f32,ne=[65536,16,1,1]):                  12291 runs -    93.24 us/run -     8192 kB/run -   83.80 GB/s
  CUMSUM(type=f32,ne=[131072,16,1,1]):                  6147 runs -   185.07 us/run -    16384 kB/run -   84.45 GB/s
  CUMSUM(type=f32,ne=[200000,16,1,1]):                  4029 runs -   282.40 us/run -    25000 kB/run -   84.46 GB/s
  CUMSUM(type=f32,ne=[2000000,16,1,1]):                  270 runs -  4118.40 us/run -   250000 kB/run -   58.11 GB/s
  Backend CUDA0: OK
Backend 2/3: CUDA1
  Device description: NVIDIA RTX PRO 6000 Blackwell Max-Q Workstation Edition
  Device memory: 97250 MB (96677 MB free)

  CUMSUM(type=f32,ne=[128,128,4,4]):                  368595 runs -     2.73 us/run -     2048 kB/run -  715.83 GB/s
  CUMSUM(type=f32,ne=[2048,16,5,4]):                  216282 runs -     4.72 us/run -     5120 kB/run - 1035.32 GB/s
  CUMSUM(type=f32,ne=[20000,10,4,1]):                  32214 runs -    34.33 us/run -     6250 kB/run -  173.64 GB/s
  CUMSUM(type=f32,ne=[128,1,1,1]):                    810909 runs -     1.24 us/run -        1 kB/run -    0.77 GB/s
  CUMSUM(type=f32,ne=[1024,1,1,1]):                   401359 runs -     2.52 us/run -        8 kB/run -    3.03 GB/s
  CUMSUM(type=f32,ne=[4096,1,1,1]):                   139247 runs -     7.44 us/run -       32 kB/run -    4.10 GB/s
  CUMSUM(type=f32,ne=[8192,1,1,1]):                    73719 runs -    14.27 us/run -       64 kB/run -    4.28 GB/s
  CUMSUM(type=f32,ne=[16384,1,1,1]):                   40955 runs -    27.24 us/run -      128 kB/run -    4.48 GB/s
  CUMSUM(type=f32,ne=[32768,1,1,1]):                   24573 runs -    53.46 us/run -      256 kB/run -    4.57 GB/s
  CUMSUM(type=f32,ne=[65536,1,1,1]):                   16382 runs -   105.29 us/run -      512 kB/run -    4.64 GB/s
  CUMSUM(type=f32,ne=[131072,1,1,1]):                   8191 runs -   210.15 us/run -     1024 kB/run -    4.65 GB/s
  CUMSUM(type=f32,ne=[200000,1,1,1]):                   8191 runs -   318.22 us/run -     1562 kB/run -    4.68 GB/s
  CUMSUM(type=f32,ne=[2000000,1,1,1]):                  2148 runs -  3142.23 us/run -    15625 kB/run -    4.74 GB/s
  CUMSUM(type=f32,ne=[128,4,1,1]):                    303067 runs -     3.34 us/run -        4 kB/run -    1.14 GB/s
  CUMSUM(type=f32,ne=[1024,4,1,1]):                   253921 runs -     4.03 us/run -       32 kB/run -    7.58 GB/s
  CUMSUM(type=f32,ne=[4096,4,1,1]):                   122865 runs -     8.20 us/run -      128 kB/run -   14.89 GB/s
  CUMSUM(type=f32,ne=[8192,4,1,1]):                    73719 runs -    14.96 us/run -      256 kB/run -   16.32 GB/s
  CUMSUM(type=f32,ne=[16384,4,1,1]):                   40955 runs -    28.66 us/run -      512 kB/run -   17.04 GB/s
  CUMSUM(type=f32,ne=[32768,4,1,1]):                   24573 runs -    54.21 us/run -     1024 kB/run -   18.01 GB/s
  CUMSUM(type=f32,ne=[65536,4,1,1]):                   16382 runs -   106.49 us/run -     2048 kB/run -   18.34 GB/s
  CUMSUM(type=f32,ne=[131072,4,1,1]):                   8191 runs -   210.88 us/run -     4096 kB/run -   18.52 GB/s
  CUMSUM(type=f32,ne=[200000,4,1,1]):                   5369 runs -   321.77 us/run -     6250 kB/run -   18.53 GB/s
  CUMSUM(type=f32,ne=[2000000,4,1,1]):                   537 runs -  3191.79 us/run -    62500 kB/run -   18.69 GB/s
  CUMSUM(type=f32,ne=[128,8,1,1]):                    376786 runs -     2.67 us/run -        8 kB/run -    2.86 GB/s
  CUMSUM(type=f32,ne=[1024,8,1,1]):                   245730 runs -     4.10 us/run -       64 kB/run -   14.90 GB/s
  CUMSUM(type=f32,ne=[4096,8,1,1]):                   122865 runs -     8.20 us/run -      256 kB/run -   29.79 GB/s
  CUMSUM(type=f32,ne=[8192,8,1,1]):                    65528 runs -    16.38 us/run -      512 kB/run -   29.82 GB/s
  CUMSUM(type=f32,ne=[16384,8,1,1]):                   40955 runs -    28.69 us/run -     1024 kB/run -   34.04 GB/s
  CUMSUM(type=f32,ne=[32768,8,1,1]):                   24573 runs -    55.28 us/run -     2048 kB/run -   35.33 GB/s
  CUMSUM(type=f32,ne=[65536,8,1,1]):                   16382 runs -   108.50 us/run -     4096 kB/run -   36.00 GB/s
  CUMSUM(type=f32,ne=[131072,8,1,1]):                   8194 runs -   213.75 us/run -     8192 kB/run -   36.55 GB/s
  CUMSUM(type=f32,ne=[200000,8,1,1]):                   5370 runs -   326.31 us/run -    12500 kB/run -   36.54 GB/s
  CUMSUM(type=f32,ne=[2000000,8,1,1]):                   538 runs -  3252.68 us/run -   125000 kB/run -   36.72 GB/s
  CUMSUM(type=f32,ne=[128,16,1,1]):                   303067 runs -     3.32 us/run -       16 kB/run -    4.60 GB/s
  CUMSUM(type=f32,ne=[1024,16,1,1]):                  253921 runs -     4.06 us/run -      128 kB/run -   30.09 GB/s
  CUMSUM(type=f32,ne=[4096,16,1,1]):                  122865 runs -     8.20 us/run -      512 kB/run -   59.57 GB/s
  CUMSUM(type=f32,ne=[8192,16,1,1]):                   65528 runs -    16.38 us/run -     1024 kB/run -   59.63 GB/s
  CUMSUM(type=f32,ne=[16384,16,1,1]):                  40955 runs -    28.69 us/run -     2048 kB/run -   68.09 GB/s
  CUMSUM(type=f32,ne=[32768,16,1,1]):                  24573 runs -    55.28 us/run -     4096 kB/run -   70.67 GB/s
  CUMSUM(type=f32,ne=[65536,16,1,1]):                  12291 runs -   108.50 us/run -     8192 kB/run -   72.02 GB/s
  CUMSUM(type=f32,ne=[131072,16,1,1]):                  6147 runs -   213.60 us/run -    16384 kB/run -   73.17 GB/s
  CUMSUM(type=f32,ne=[200000,16,1,1]):                  4029 runs -   326.04 us/run -    25000 kB/run -   73.15 GB/s
  CUMSUM(type=f32,ne=[2000000,16,1,1]):                  270 runs -  5458.69 us/run -   250000 kB/run -   43.84 GB/s

----
Numbers after:

Backend 1/3: CUDA0
  Device description: NVIDIA RTX 6000 Ada Generation
  Device memory: 48510 MB (48039 MB free)

  CUMSUM(type=f32,ne=[128,128,4,4]):                  311258 runs -     3.25 us/run -     2048 kB/run -  601.62 GB/s
  CUMSUM(type=f32,ne=[2048,16,5,4]):                  229390 runs -     4.40 us/run -     5120 kB/run - 1110.14 GB/s
  CUMSUM(type=f32,ne=[20000,10,4,1]):                  37583 runs -    29.67 us/run -     6250 kB/run -  200.89 GB/s
  CUMSUM(type=f32,ne=[128,1,1,1]):                    892819 runs -     1.12 us/run -        1 kB/run -    0.85 GB/s
  CUMSUM(type=f32,ne=[1024,1,1,1]):                   458696 runs -     2.21 us/run -        8 kB/run -    3.45 GB/s
  CUMSUM(type=f32,ne=[4096,1,1,1]):                   376786 runs -     2.66 us/run -       32 kB/run -   11.46 GB/s
  CUMSUM(type=f32,ne=[8192,1,1,1]):                   393168 runs -     2.59 us/run -       64 kB/run -   23.57 GB/s
  CUMSUM(type=f32,ne=[16384,1,1,1]):                  393168 runs -     2.59 us/run -      128 kB/run -   47.15 GB/s
  CUMSUM(type=f32,ne=[32768,1,1,1]):                  376786 runs -     2.69 us/run -      256 kB/run -   90.69 GB/s
  CUMSUM(type=f32,ne=[65536,1,1,1]):                  327640 runs -     3.06 us/run -      512 kB/run -  159.65 GB/s
  CUMSUM(type=f32,ne=[131072,1,1,1]):                 311258 runs -     3.28 us/run -     1024 kB/run -  297.77 GB/s
  CUMSUM(type=f32,ne=[200000,1,1,1]):                 270303 runs -     3.74 us/run -     1562 kB/run -  398.14 GB/s
  CUMSUM(type=f32,ne=[2000000,1,1,1]):                137472 runs -     7.35 us/run -    15625 kB/run - 2026.94 GB/s
  CUMSUM(type=f32,ne=[128,4,1,1]):                    876437 runs -     1.14 us/run -        4 kB/run -    3.33 GB/s
  CUMSUM(type=f32,ne=[1024,4,1,1]):                   442314 runs -     2.28 us/run -       32 kB/run -   13.39 GB/s
  CUMSUM(type=f32,ne=[4096,4,1,1]):                   155629 runs -     6.69 us/run -      128 kB/run -   18.24 GB/s
  CUMSUM(type=f32,ne=[8192,4,1,1]):                    81910 runs -    12.53 us/run -      256 kB/run -   19.49 GB/s
  CUMSUM(type=f32,ne=[16384,4,1,1]):                   49146 runs -    24.18 us/run -      512 kB/run -   20.20 GB/s
  CUMSUM(type=f32,ne=[32768,4,1,1]):                   65528 runs -    15.34 us/run -     1024 kB/run -   63.66 GB/s
  CUMSUM(type=f32,ne=[65536,4,1,1]):                   73719 runs -    14.76 us/run -     2048 kB/run -  132.35 GB/s
  CUMSUM(type=f32,ne=[131072,4,1,1]):                  65528 runs -    16.01 us/run -     4096 kB/run -  244.07 GB/s
  CUMSUM(type=f32,ne=[200000,4,1,1]):                  64428 runs -    16.51 us/run -     6250 kB/run -  360.97 GB/s
  CUMSUM(type=f32,ne=[2000000,4,1,1]):                 33831 runs -    29.59 us/run -    62500 kB/run - 2016.08 GB/s
  CUMSUM(type=f32,ne=[128,8,1,1]):                    868246 runs -     1.16 us/run -        8 kB/run -    6.59 GB/s
  CUMSUM(type=f32,ne=[1024,8,1,1]):                   442314 runs -     2.28 us/run -       64 kB/run -   26.76 GB/s
  CUMSUM(type=f32,ne=[4096,8,1,1]):                   155629 runs -     6.69 us/run -      256 kB/run -   36.48 GB/s
  CUMSUM(type=f32,ne=[8192,8,1,1]):                    81910 runs -    12.53 us/run -      512 kB/run -   38.97 GB/s
  CUMSUM(type=f32,ne=[16384,8,1,1]):                   49146 runs -    24.17 us/run -     1024 kB/run -   40.41 GB/s
  CUMSUM(type=f32,ne=[32768,8,1,1]):                   24573 runs -    47.53 us/run -     2048 kB/run -   41.10 GB/s
  CUMSUM(type=f32,ne=[65536,8,1,1]):                   16382 runs -    61.25 us/run -     4096 kB/run -   63.77 GB/s
  CUMSUM(type=f32,ne=[131072,8,1,1]):                  32776 runs -    31.79 us/run -     8192 kB/run -  245.82 GB/s
  CUMSUM(type=f32,ne=[200000,8,1,1]):                  32220 runs -    32.90 us/run -    12500 kB/run -  362.35 GB/s
  CUMSUM(type=f32,ne=[2000000,8,1,1]):                  6725 runs -   151.99 us/run -   125000 kB/run -  785.77 GB/s
  CUMSUM(type=f32,ne=[128,16,1,1]):                   851864 runs -     1.18 us/run -       16 kB/run -   12.97 GB/s
  CUMSUM(type=f32,ne=[1024,16,1,1]):                  442314 runs -     2.30 us/run -      128 kB/run -   53.13 GB/s
  CUMSUM(type=f32,ne=[4096,16,1,1]):                  155629 runs -     6.68 us/run -      512 kB/run -   73.13 GB/s
  CUMSUM(type=f32,ne=[8192,16,1,1]):                   81910 runs -    12.68 us/run -     1024 kB/run -   77.00 GB/s
  CUMSUM(type=f32,ne=[16384,16,1,1]):                  40955 runs -    24.56 us/run -     2048 kB/run -   79.53 GB/s
  CUMSUM(type=f32,ne=[32768,16,1,1]):                  24573 runs -    47.52 us/run -     4096 kB/run -   82.21 GB/s
  CUMSUM(type=f32,ne=[65536,16,1,1]):                  12291 runs -    93.44 us/run -     8192 kB/run -   83.62 GB/s
  CUMSUM(type=f32,ne=[131072,16,1,1]):                 16392 runs -    63.36 us/run -    16384 kB/run -  246.68 GB/s
  CUMSUM(type=f32,ne=[200000,16,1,1]):                 16116 runs -    65.25 us/run -    25000 kB/run -  365.53 GB/s
  CUMSUM(type=f32,ne=[2000000,16,1,1]):                 3375 runs -   304.46 us/run -   250000 kB/run -  785.98 GB/s
  Backend CUDA0: OK
Backend 2/3: CUDA1
  Device description: NVIDIA RTX PRO 6000 Blackwell Max-Q Workstation Edition
  Device memory: 97250 MB (96677 MB free)

  CUMSUM(type=f32,ne=[128,128,4,4]):                  376786 runs -     2.69 us/run -     2048 kB/run -  727.04 GB/s
  CUMSUM(type=f32,ne=[2048,16,5,4]):                  216282 runs -     4.64 us/run -     5120 kB/run - 1053.30 GB/s
  CUMSUM(type=f32,ne=[20000,10,4,1]):                  32214 runs -    34.21 us/run -     6250 kB/run -  174.27 GB/s
  CUMSUM(type=f32,ne=[128,1,1,1]):                    819100 runs -     1.22 us/run -        1 kB/run -    0.78 GB/s
  CUMSUM(type=f32,ne=[1024,1,1,1]):                   409550 runs -     2.47 us/run -        8 kB/run -    3.09 GB/s
  CUMSUM(type=f32,ne=[4096,1,1,1]):                   303067 runs -     3.31 us/run -       32 kB/run -    9.21 GB/s
  CUMSUM(type=f32,ne=[8192,1,1,1]):                   237539 runs -     4.33 us/run -       64 kB/run -   14.08 GB/s
  CUMSUM(type=f32,ne=[16384,1,1,1]):                  237539 runs -     4.33 us/run -      128 kB/run -   28.17 GB/s
  CUMSUM(type=f32,ne=[32768,1,1,1]):                  188393 runs -     5.37 us/run -      256 kB/run -   45.47 GB/s
  CUMSUM(type=f32,ne=[65536,1,1,1]):                  188393 runs -     5.41 us/run -      512 kB/run -   90.20 GB/s
  CUMSUM(type=f32,ne=[131072,1,1,1]):                 188393 runs -     5.41 us/run -     1024 kB/run -  180.41 GB/s
  CUMSUM(type=f32,ne=[200000,1,1,1]):                 188393 runs -     5.41 us/run -     1562 kB/run -  275.27 GB/s
  CUMSUM(type=f32,ne=[2000000,1,1,1]):                128880 runs -     7.76 us/run -    15625 kB/run - 1920.33 GB/s
  CUMSUM(type=f32,ne=[128,4,1,1]):                    802718 runs -     1.26 us/run -        4 kB/run -    3.03 GB/s
  CUMSUM(type=f32,ne=[1024,4,1,1]):                   401359 runs -     2.51 us/run -       32 kB/run -   12.18 GB/s
  CUMSUM(type=f32,ne=[4096,4,1,1]):                   139247 runs -     7.51 us/run -      128 kB/run -   16.26 GB/s
  CUMSUM(type=f32,ne=[8192,4,1,1]):                    73719 runs -    14.17 us/run -      256 kB/run -   17.23 GB/s
  CUMSUM(type=f32,ne=[16384,4,1,1]):                   40955 runs -    27.37 us/run -      512 kB/run -   17.84 GB/s
  CUMSUM(type=f32,ne=[32768,4,1,1]):                   40955 runs -    26.33 us/run -     1024 kB/run -   37.10 GB/s
  CUMSUM(type=f32,ne=[65536,4,1,1]):                   40955 runs -    26.19 us/run -     2048 kB/run -   74.59 GB/s
  CUMSUM(type=f32,ne=[131072,4,1,1]):                  40955 runs -    26.35 us/run -     4096 kB/run -  148.26 GB/s
  CUMSUM(type=f32,ne=[200000,4,1,1]):                  42952 runs -    24.18 us/run -     6250 kB/run -  246.51 GB/s
  CUMSUM(type=f32,ne=[2000000,4,1,1]):                 32757 runs -    31.01 us/run -    62500 kB/run - 1923.68 GB/s
  CUMSUM(type=f32,ne=[128,8,1,1]):                    786336 runs -     1.28 us/run -        8 kB/run -    5.95 GB/s
  CUMSUM(type=f32,ne=[1024,8,1,1]):                   393168 runs -     2.57 us/run -       64 kB/run -   23.73 GB/s
  CUMSUM(type=f32,ne=[4096,8,1,1]):                   131056 runs -     7.67 us/run -      256 kB/run -   31.82 GB/s
  CUMSUM(type=f32,ne=[8192,8,1,1]):                    73719 runs -    14.43 us/run -      512 kB/run -   33.84 GB/s
  CUMSUM(type=f32,ne=[16384,8,1,1]):                   40955 runs -    27.90 us/run -     1024 kB/run -   35.01 GB/s
  CUMSUM(type=f32,ne=[32768,8,1,1]):                   24573 runs -    54.63 us/run -     2048 kB/run -   35.75 GB/s
  CUMSUM(type=f32,ne=[65536,8,1,1]):                   16382 runs -    72.24 us/run -     4096 kB/run -   54.08 GB/s
  CUMSUM(type=f32,ne=[131072,8,1,1]):                  20485 runs -    52.66 us/run -     8192 kB/run -  148.37 GB/s
  CUMSUM(type=f32,ne=[200000,8,1,1]):                  21480 runs -    48.00 us/run -    12500 kB/run -  248.42 GB/s
  CUMSUM(type=f32,ne=[2000000,8,1,1]):                 16140 runs -    61.99 us/run -   125000 kB/run - 1926.51 GB/s
  CUMSUM(type=f32,ne=[128,16,1,1]):                   786336 runs -     1.28 us/run -       16 kB/run -   11.90 GB/s
  CUMSUM(type=f32,ne=[1024,16,1,1]):                  393168 runs -     2.57 us/run -      128 kB/run -   47.57 GB/s
  CUMSUM(type=f32,ne=[4096,16,1,1]):                  131056 runs -     7.65 us/run -      512 kB/run -   63.83 GB/s
  CUMSUM(type=f32,ne=[8192,16,1,1]):                   73719 runs -    14.42 us/run -     1024 kB/run -   67.74 GB/s
  CUMSUM(type=f32,ne=[16384,16,1,1]):                  40955 runs -    27.87 us/run -     2048 kB/run -   70.09 GB/s
  CUMSUM(type=f32,ne=[32768,16,1,1]):                  24573 runs -    54.54 us/run -     4096 kB/run -   71.63 GB/s
  CUMSUM(type=f32,ne=[65536,16,1,1]):                  12291 runs -   107.53 us/run -     8192 kB/run -   72.66 GB/s
  CUMSUM(type=f32,ne=[131072,16,1,1]):                 10245 runs -   105.10 us/run -    16384 kB/run -  148.70 GB/s
  CUMSUM(type=f32,ne=[200000,16,1,1]):                 10744 runs -    95.36 us/run -    25000 kB/run -  250.11 GB/s
  CUMSUM(type=f32,ne=[2000000,16,1,1]):                 5400 runs -   186.97 us/run -   250000 kB/run - 1279.90 GB/s

* sampling : expand support (wip)

* tests : fix memory leaks

* cont : fixes

* tests : check temp back to 0.0

* sampling : fix top-p

* sampling : handle n_probs case

* server : handle unsupported cases

* metal : print node names for debugging

* ggml : remove redundant src in ggml_cast

* ggml-alloc : fix reuse-parent logic for misaligned sizes

* Revert "ggml : remove redundant src in ggml_cast"

This reverts commit 62d1b0082d.

* CUDA: Add Cooperative-Groups-based parallelization of ncols in softmax

Old implementation parallelizes rows across SMs, which does not fit the
needs of backend-sampling (where we have ncols >> nrows and thus want to
parallelize ncols across SMs)

* Add TODOs to and adjust heuristics of row-wise soft_max in CUDA

Heuristics were selected based on the following numbers:

```
-- Before
Backend 1/2: CUDA0
  Device description: NVIDIA RTX PRO 6000 Blackwell Max-Q Workstation Edition
  Device memory: 97250 MB (96691 MB free)

  SOFT_MAX(type=f32,ne=[4096,4096,5,1],mask=0,sinks=0,m_prec=f32,nr23=[1,1],scale=1.000000,max_bias=0.000000,inplace=0):                2236 runs -   450.34 us/run -   655360 kB/run - 1401.20 GB/s
  SOFT_MAX(type=f32,ne=[12888,256,5,1],mask=0,sinks=0,m_prec=f32,nr23=[1,1],scale=1.000000,max_bias=0.000000,inplace=0):               17748 runs -    56.80 us/run -   128880 kB/run - 2168.19 GB/s
  SOFT_MAX(type=f32,ne=[77,4096,5,1],mask=0,sinks=0,m_prec=f32,nr23=[1,1],scale=1.000000,max_bias=0.000000,inplace=0):                 57204 runs -    18.35 us/run -    12320 kB/run -  640.57 GB/s
  SOFT_MAX(type=f32,ne=[1024,1024,10,1],mask=0,sinks=0,m_prec=f32,nr23=[1,1],scale=1.000000,max_bias=0.000000,inplace=0):               9840 runs -   102.46 us/run -    81920 kB/run -  763.45 GB/s
  SOFT_MAX(type=f32,ne=[77,1024,10,1],mask=0,sinks=0,m_prec=f32,nr23=[1,1],scale=1.000000,max_bias=0.000000,inplace=0):                98064 runs -    10.25 us/run -     6160 kB/run -  573.43 GB/s
  SOFT_MAX(type=f32,ne=[256,256,20,1],mask=0,sinks=0,m_prec=f32,nr23=[1,1],scale=1.000000,max_bias=0.000000,inplace=0):                98310 runs -    10.25 us/run -    10240 kB/run -  953.20 GB/s
  SOFT_MAX(type=f32,ne=[64,64,20,1],mask=0,sinks=0,m_prec=f32,nr23=[1,1],scale=1.000000,max_bias=0.000000,inplace=0):                 172011 runs -     5.99 us/run -      640 kB/run -  101.84 GB/s
  SOFT_MAX(type=f32,ne=[77,64,20,1],mask=0,sinks=0,m_prec=f32,nr23=[1,1],scale=1.000000,max_bias=0.000000,inplace=0):                 172011 runs -     5.97 us/run -      770 kB/run -  123.02 GB/s
  SOFT_MAX(type=f32,ne=[8192,1,1,1],mask=0,sinks=0,m_prec=f32,nr23=[1,1],scale=1.000000,max_bias=0.000000,inplace=0):                 172011 runs -     6.00 us/run -       64 kB/run -   10.16 GB/s
  SOFT_MAX(type=f32,ne=[8192,4,1,1],mask=0,sinks=0,m_prec=f32,nr23=[1,1],scale=1.000000,max_bias=0.000000,inplace=0):                 163820 runs -     6.12 us/run -      256 kB/run -   39.91 GB/s
  SOFT_MAX(type=f32,ne=[8192,16,1,1],mask=0,sinks=0,m_prec=f32,nr23=[1,1],scale=1.000000,max_bias=0.000000,inplace=0):                147438 runs -     6.88 us/run -     1024 kB/run -  141.92 GB/s
  SOFT_MAX(type=f32,ne=[16384,1,1,1],mask=0,sinks=0,m_prec=f32,nr23=[1,1],scale=1.000000,max_bias=0.000000,inplace=0):                122865 runs -     8.20 us/run -      128 kB/run -   14.89 GB/s
  SOFT_MAX(type=f32,ne=[16384,4,1,1],mask=0,sinks=0,m_prec=f32,nr23=[1,1],scale=1.000000,max_bias=0.000000,inplace=0):                114674 runs -     8.87 us/run -      512 kB/run -   55.06 GB/s
  SOFT_MAX(type=f32,ne=[16384,16,1,1],mask=0,sinks=0,m_prec=f32,nr23=[1,1],scale=1.000000,max_bias=0.000000,inplace=0):                98292 runs -    10.24 us/run -     2048 kB/run -  190.82 GB/s
  SOFT_MAX(type=f32,ne=[32768,1,1,1],mask=0,sinks=0,m_prec=f32,nr23=[1,1],scale=1.000000,max_bias=0.000000,inplace=0):                 49146 runs -    21.37 us/run -      256 kB/run -   11.43 GB/s
  SOFT_MAX(type=f32,ne=[32768,4,1,1],mask=0,sinks=0,m_prec=f32,nr23=[1,1],scale=1.000000,max_bias=0.000000,inplace=0):                 49146 runs -    22.54 us/run -     1024 kB/run -   43.33 GB/s
  SOFT_MAX(type=f32,ne=[32768,16,1,1],mask=0,sinks=0,m_prec=f32,nr23=[1,1],scale=1.000000,max_bias=0.000000,inplace=0):                49146 runs -    23.92 us/run -     4096 kB/run -  163.32 GB/s
  SOFT_MAX(type=f32,ne=[65536,1,1,1],mask=0,sinks=0,m_prec=f32,nr23=[1,1],scale=1.000000,max_bias=0.000000,inplace=0):                 32764 runs -    38.94 us/run -      512 kB/run -   12.54 GB/s
  SOFT_MAX(type=f32,ne=[65536,4,1,1],mask=0,sinks=0,m_prec=f32,nr23=[1,1],scale=1.000000,max_bias=0.000000,inplace=0):                 24573 runs -    41.94 us/run -     2048 kB/run -   46.57 GB/s
  SOFT_MAX(type=f32,ne=[65536,16,1,1],mask=0,sinks=0,m_prec=f32,nr23=[1,1],scale=1.000000,max_bias=0.000000,inplace=0):                24582 runs -    43.09 us/run -     8192 kB/run -  181.32 GB/s
  SOFT_MAX(type=f32,ne=[131072,1,1,1],mask=0,sinks=0,m_prec=f32,nr23=[1,1],scale=1.000000,max_bias=0.000000,inplace=0):                16382 runs -    74.56 us/run -     1024 kB/run -   13.10 GB/s
  SOFT_MAX(type=f32,ne=[131072,4,1,1],mask=0,sinks=0,m_prec=f32,nr23=[1,1],scale=1.000000,max_bias=0.000000,inplace=0):                16382 runs -    79.85 us/run -     4096 kB/run -   48.92 GB/s
  SOFT_MAX(type=f32,ne=[131072,16,1,1],mask=0,sinks=0,m_prec=f32,nr23=[1,1],scale=1.000000,max_bias=0.000000,inplace=0):               12294 runs -    82.41 us/run -    16384 kB/run -  189.64 GB/s
  SOFT_MAX(type=f32,ne=[262144,1,1,1],mask=0,sinks=0,m_prec=f32,nr23=[1,1],scale=1.000000,max_bias=0.000000,inplace=0):                 8191 runs -   145.16 us/run -     2048 kB/run -   13.46 GB/s
  SOFT_MAX(type=f32,ne=[262144,4,1,1],mask=0,sinks=0,m_prec=f32,nr23=[1,1],scale=1.000000,max_bias=0.000000,inplace=0):                 8194 runs -   155.46 us/run -     8192 kB/run -   50.26 GB/s
  SOFT_MAX(type=f32,ne=[262144,16,1,1],mask=0,sinks=0,m_prec=f32,nr23=[1,1],scale=1.000000,max_bias=0.000000,inplace=0):                7175 runs -   160.70 us/run -    32768 kB/run -  194.56 GB/s
  SOFT_MAX(type=f32,ne=[524288,1,1,1],mask=0,sinks=0,m_prec=f32,nr23=[1,1],scale=1.000000,max_bias=0.000000,inplace=0):                 8191 runs -   285.81 us/run -     4096 kB/run -   13.67 GB/s
  SOFT_MAX(type=f32,ne=[524288,4,1,1],mask=0,sinks=0,m_prec=f32,nr23=[1,1],scale=1.000000,max_bias=0.000000,inplace=0):                 4098 runs -   306.91 us/run -    16384 kB/run -   50.92 GB/s
  SOFT_MAX(type=f32,ne=[524288,16,1,1],mask=0,sinks=0,m_prec=f32,nr23=[1,1],scale=1.000000,max_bias=0.000000,inplace=0):                3591 runs -   317.06 us/run -    65536 kB/run -  197.32 GB/s

-- After
Backend 1/2: CUDA0
  Device description: NVIDIA RTX PRO 6000 Blackwell Max-Q Workstation Edition
  Device memory: 97250 MB (96691 MB free)

  SOFT_MAX(type=f32,ne=[4096,4096,5,1],mask=0,sinks=0,m_prec=f32,nr23=[1,1],scale=1.000000,max_bias=0.000000,inplace=0):                2236 runs -   450.67 us/run -   655360 kB/run - 1400.15 GB/s
  SOFT_MAX(type=f32,ne=[12888,256,5,1],mask=0,sinks=0,m_prec=f32,nr23=[1,1],scale=1.000000,max_bias=0.000000,inplace=0):               17748 runs -    56.97 us/run -   128880 kB/run - 2161.50 GB/s
  SOFT_MAX(type=f32,ne=[77,4096,5,1],mask=0,sinks=0,m_prec=f32,nr23=[1,1],scale=1.000000,max_bias=0.000000,inplace=0):                 57204 runs -    18.35 us/run -    12320 kB/run -  640.36 GB/s
  SOFT_MAX(type=f32,ne=[1024,1024,10,1],mask=0,sinks=0,m_prec=f32,nr23=[1,1],scale=1.000000,max_bias=0.000000,inplace=0):               9840 runs -   102.46 us/run -    81920 kB/run -  763.42 GB/s
  SOFT_MAX(type=f32,ne=[77,1024,10,1],mask=0,sinks=0,m_prec=f32,nr23=[1,1],scale=1.000000,max_bias=0.000000,inplace=0):                98064 runs -    10.25 us/run -     6160 kB/run -  573.43 GB/s
  SOFT_MAX(type=f32,ne=[256,256,20,1],mask=0,sinks=0,m_prec=f32,nr23=[1,1],scale=1.000000,max_bias=0.000000,inplace=0):                98310 runs -    10.25 us/run -    10240 kB/run -  953.21 GB/s
  SOFT_MAX(type=f32,ne=[64,64,20,1],mask=0,sinks=0,m_prec=f32,nr23=[1,1],scale=1.000000,max_bias=0.000000,inplace=0):                 147438 runs -     7.00 us/run -      640 kB/run -   87.26 GB/s
  SOFT_MAX(type=f32,ne=[77,64,20,1],mask=0,sinks=0,m_prec=f32,nr23=[1,1],scale=1.000000,max_bias=0.000000,inplace=0):                 147438 runs -     6.99 us/run -      770 kB/run -  105.05 GB/s
  SOFT_MAX(type=f32,ne=[8192,1,1,1],mask=0,sinks=0,m_prec=f32,nr23=[1,1],scale=1.000000,max_bias=0.000000,inplace=0):                 172011 runs -     6.02 us/run -       64 kB/run -   10.13 GB/s
  SOFT_MAX(type=f32,ne=[8192,4,1,1],mask=0,sinks=0,m_prec=f32,nr23=[1,1],scale=1.000000,max_bias=0.000000,inplace=0):                 163820 runs -     6.12 us/run -      256 kB/run -   39.87 GB/s
  SOFT_MAX(type=f32,ne=[8192,16,1,1],mask=0,sinks=0,m_prec=f32,nr23=[1,1],scale=1.000000,max_bias=0.000000,inplace=0):                147438 runs -     6.91 us/run -     1024 kB/run -  141.40 GB/s
  SOFT_MAX(type=f32,ne=[16384,1,1,1],mask=0,sinks=0,m_prec=f32,nr23=[1,1],scale=1.000000,max_bias=0.000000,inplace=0):                122865 runs -     8.20 us/run -      128 kB/run -   14.89 GB/s
  SOFT_MAX(type=f32,ne=[16384,4,1,1],mask=0,sinks=0,m_prec=f32,nr23=[1,1],scale=1.000000,max_bias=0.000000,inplace=0):                114674 runs -     8.79 us/run -      512 kB/run -   55.54 GB/s
  SOFT_MAX(type=f32,ne=[16384,16,1,1],mask=0,sinks=0,m_prec=f32,nr23=[1,1],scale=1.000000,max_bias=0.000000,inplace=0):                98292 runs -    10.24 us/run -     2048 kB/run -  190.82 GB/s
  SOFT_MAX(type=f32,ne=[32768,1,1,1],mask=0,sinks=0,m_prec=f32,nr23=[1,1],scale=1.000000,max_bias=0.000000,inplace=0):                131056 runs -     8.11 us/run -      256 kB/run -   30.12 GB/s
  SOFT_MAX(type=f32,ne=[32768,4,1,1],mask=0,sinks=0,m_prec=f32,nr23=[1,1],scale=1.000000,max_bias=0.000000,inplace=0):                 49146 runs -    22.54 us/run -     1024 kB/run -   43.33 GB/s
  SOFT_MAX(type=f32,ne=[32768,16,1,1],mask=0,sinks=0,m_prec=f32,nr23=[1,1],scale=1.000000,max_bias=0.000000,inplace=0):                49146 runs -    23.32 us/run -     4096 kB/run -  167.50 GB/s
  SOFT_MAX(type=f32,ne=[65536,1,1,1],mask=0,sinks=0,m_prec=f32,nr23=[1,1],scale=1.000000,max_bias=0.000000,inplace=0):                122865 runs -     8.19 us/run -      512 kB/run -   59.63 GB/s
  SOFT_MAX(type=f32,ne=[65536,4,1,1],mask=0,sinks=0,m_prec=f32,nr23=[1,1],scale=1.000000,max_bias=0.000000,inplace=0):                 40955 runs -    24.59 us/run -     2048 kB/run -   79.43 GB/s
  SOFT_MAX(type=f32,ne=[65536,16,1,1],mask=0,sinks=0,m_prec=f32,nr23=[1,1],scale=1.000000,max_bias=0.000000,inplace=0):                24582 runs -    43.21 us/run -     8192 kB/run -  180.84 GB/s
  SOFT_MAX(type=f32,ne=[131072,1,1,1],mask=0,sinks=0,m_prec=f32,nr23=[1,1],scale=1.000000,max_bias=0.000000,inplace=0):               122865 runs -     8.19 us/run -     1024 kB/run -  119.25 GB/s
  SOFT_MAX(type=f32,ne=[131072,4,1,1],mask=0,sinks=0,m_prec=f32,nr23=[1,1],scale=1.000000,max_bias=0.000000,inplace=0):                40955 runs -    24.59 us/run -     4096 kB/run -  158.87 GB/s
  SOFT_MAX(type=f32,ne=[131072,16,1,1],mask=0,sinks=0,m_prec=f32,nr23=[1,1],scale=1.000000,max_bias=0.000000,inplace=0):               12294 runs -    82.37 us/run -    16384 kB/run -  189.74 GB/s
  SOFT_MAX(type=f32,ne=[262144,1,1,1],mask=0,sinks=0,m_prec=f32,nr23=[1,1],scale=1.000000,max_bias=0.000000,inplace=0):               122865 runs -     8.20 us/run -     2048 kB/run -  238.28 GB/s
  SOFT_MAX(type=f32,ne=[262144,4,1,1],mask=0,sinks=0,m_prec=f32,nr23=[1,1],scale=1.000000,max_bias=0.000000,inplace=0):                36873 runs -    28.66 us/run -     8192 kB/run -  272.61 GB/s
  SOFT_MAX(type=f32,ne=[262144,16,1,1],mask=0,sinks=0,m_prec=f32,nr23=[1,1],scale=1.000000,max_bias=0.000000,inplace=0):                9225 runs -   108.51 us/run -    32768 kB/run -  288.13 GB/s
  SOFT_MAX(type=f32,ne=[524288,1,1,1],mask=0,sinks=0,m_prec=f32,nr23=[1,1],scale=1.000000,max_bias=0.000000,inplace=0):                98292 runs -    10.24 us/run -     4096 kB/run -  381.65 GB/s
  SOFT_MAX(type=f32,ne=[524288,4,1,1],mask=0,sinks=0,m_prec=f32,nr23=[1,1],scale=1.000000,max_bias=0.000000,inplace=0):                32784 runs -    31.74 us/run -    16384 kB/run -  492.43 GB/s
  SOFT_MAX(type=f32,ne=[524288,16,1,1],mask=0,sinks=0,m_prec=f32,nr23=[1,1],scale=1.000000,max_bias=0.000000,inplace=0):                8721 runs -   121.20 us/run -    65536 kB/run -  516.19 GB/s
```

* Fix compiler warnings by casting `const` away

* llama : require backend samplers to be of type llama_sampler_chain

* sampling : use host buffer type for inputs

* Try fixing HIP build errors by adding corresponding #defines

Will likely have to disable for MUSA as I didn't find any docs online

* Fix launch logic when supports_cooperative_launch=false

* Disable cooperative groups for musa

Didn't find any doc online, so I don't even know if they support this

* server : reconnect the backend_sampling setting in the WebUI

* graph : make the compute graph constant with respect to active samplers

* batch : fix sequence id ownage

* graph : respect sampler order for graph reuse

* HIP/MUSA: fix build for backend sampling

* sampling : optimize logit_bias sampler

* cont : fix build

* sampling : generic ggml op support detection

* sampling : fix greedy

* tests : run backend sampler tests always on the CPU

* Apply suggestions from code review

Co-authored-by: Johannes Gäßler <johannesg@5d6.de>

* webui : fix lint

* Fix data-race in `soft_max_f32_parallelize_cols_single_row`

By using `tmp_vals` to store both max values and exponential
accumulator there was a potential data-race, where the exponential accumulator
for a given CTA may have written to `tmp_vals` before all others CTAs have
read the max value from it.

To avoid a third g.sync(), an additional temporary data-storage was
added. Given that there are syncs in place after writing to gmem, it is
guaranteed that the previous values for sums/max were read by all CTAs now.

* Apply automated code-formating to softmax.cu

* llama : clarify backend_accept/backend_set_input comments [no ci]

* llama : fix typo in comment [no ci]

* tests : use smart pointers for backend samplers

* tests : use smart pointers for model and context

* tests : remove vocab member from test_model_context

Also includes some minor cleanups related to nullptr checks.

* tests : extract batch info update to separate method

* tests : fix batch token position tracking in test_backend_sampler.cpp

* tests : add --device option support to backend sampler tests

This commit adds support for specifying a device to run the test on.

* common : disable backend sampling when grammar is involved

* Fix different RNG-states between backend-sampling and llama-sampling

By default, we perform a warm-up step where the ggml_cgraph is computed
once. For backend-sampling, this graph contains the sampler, and thus
the RNG state of the backend's dist sampler is advanced once.

Solution to this is to reset the samplers after the warmup has finished

* Make backend dist sampler use same rnd's as dist sampler

We sample in double precision and cast to float to match rnd numbers of
llama_dampler_dist which uses double precision (sampling from
std::uniform_real_distribution<double> and
std::uniform_real_distribution<float> with same rng will produce
different sequences).

* Update CCCL version to v3.2.0-rc2

* Build with CCCL 3.2 for CUDA backends

Gives best perf for backend-sampling on CUDA. Flag can be removed once
CCCL 3.2 is bundled within CTK and that CTK version is used in llama.cpp

* tests : revert server test changes (no longer needed)

* ggml : include cub/cub.cuh instead of block_scan.cuh

This commit updates the include directive in cumsum.cu to use
cub/cub.cuh instead of cub/block/block_scan.cuh.

The motivation of this change is that without it compilation fails
with the following error:
```console
/llama.cpp/ggml/src/ggml-cuda/cumsum.cu(196): error: name followed by "::" must be a class or namespace name
      cub::DeviceScan::InclusiveSum(nullptr,
           ^

/llama.cpp/ggml/src/ggml-cuda/cumsum.cu(207): error: name followed by "::" must be a class or namespace name
      cub::DeviceScan::InclusiveSum((void *) tmp_alloc.get(), tmp_size, src, dst, ne, stream);
           ^

2 errors detected in the compilation of "/llama.cpp/ggml/src/ggml-cuda/cumsum.cu".
gmake[2]: *** [ggml/src/ggml-cuda/CMakeFiles/ggml-cuda.dir/build.make:317: ggml/src/ggml-cuda/CMakeFiles/ggml-cuda.dir/cumsum.cu.o] Error 2
```
Commit 83b3b1c271 ("cuda: optimize
cumsum cub path (#18362)") updated the include directive replacing
device_scan.cuh which is causing this issue.

This commit uses cub/cub.cuh umbrella header which is consistent with
other files in the ggml-cuda directory like mean.cu, sum.cu, etc.

* arg : add shorthand for --backend-sampling

* ci : add server workflow with backend sampling

* sampling : fix reshapes

* server : remove printfs

* sampling : zero-initialize input buffers

* minor : add comments + some cleanup

* llama : assert at most one output token per sequence

* tests : add more top_k tests

* CUDA: Fix non-determinism of CUB-based Top-K

DeviceTopK::MaxPairs is an iterative algorithm, where `d_keys_out` is
written after every iteration. As a consequence, it must not overlap
with `d_keys_in`, or otherwise undefined behavior occurs (keys are no
longer unique in d_keys_in and may map to different values between
iterations)

* CUDA: Optimize index of top_k_cub

By using the fancy
[`counting_iterator`](https://nvidia.github.io/cccl/thrust/api/classthrust_1_1counting__iterator.html#classthrust_1_1counting__iterator)
exposed by CCCL, we can avoid materializing the index to GPU memory,
saving VRAM + 1 kernel invocation

* Apply code-formatting to top-k.cu

* CUDA: Remove obsolete temp_keys from CUB

Since we use cuda::discard_iterator to avoid writing out the keys, we
can directly pass in src instead of copying it to `temp_keys`

* minor : cleanup, TODOs, etc.

---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
Co-authored-by: Oliver Simons <osimons@nvidia.com>
Co-authored-by: Johannes Gäßler <johannesg@5d6.de>
 2026-01-04 22:22:16 +02:00
Tarek Dakhran 4974bf53cf model : mtmd : make input norm optional in LFM2-VL (#18594) 
Upcoming LFM2-VL releases will have configurable input norm.
See https://github.com/huggingface/transformers/pull/43087 for details.
 2026-01-04 18:50:02 +01:00
tt ced765be44 model: support youtu-vl model (#18479) 
* Support Youtu-VL Model

* merge code

* fix bug

* revert qwen2 code & support rsplit in minja.hpp

* update warm info

* fix annotation

* u

* revert minja.hpp

* fix

* Do not write routed_scaling_factor to gguf when routed_scaling_factor is None

* fix expert_weights_scale

* LGTM after whitespace fixes

* fix

* fix

* fix

* layers to layer_index

* enum fix

---------

Co-authored-by: Xuan-Son Nguyen <son@huggingface.co>
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
 2026-01-01 19:25:54 +01:00
Anri Lombard d5574c919c webui: fix code copy stripping XML/HTML tags (#18518) 
* webui: fix code copy stripping XML/HTML tags

* webui: update static build
 2026-01-01 13:44:11 +01:00
Anri Lombard 33ded988ba quantize: prevent input/output file collision (#18451) 
Check if input and output files are the same before quantizing to prevent
file corruption when mmap reads from a file being written to.

Fixes #12753
 2025-12-31 23:29:03 +08:00
Henry147147 9b8329de7a mtmd : Adding support for Nvidia Music Flamingo Model (#18470) 
* Inital commit, debugging q5_k_s quant

* Made hf_to_gguf extend whisper to reduce code duplication

* addressed convert_hf_to_gguf pull request issue

---------

Co-authored-by: Henry D <henrydorsey147@gmail.com>
 2025-12-31 12:13:23 +01:00
Jeff Bolz f14f4e421b server: fix files built redundantly (#18474) 2025-12-30 13:11:13 +01:00
Xuan-Son Nguyen 51a48720b8 webui: fix prompt progress ETA calculation (#18468) 
* webui: fix prompt progress ETA calculation

* handle case done === 0
 2025-12-29 21:42:11 +01:00
Pascal c9a3b40d65 Webui/prompt processing progress (#18300) 
* webui: display prompt preprocessing progress

* webui: add percentage/ETA and exclude cached tokens from progress

Address review feedback from ngxson

* webui: add minutes and first chunk (0%) case

* Update tools/server/webui/src/lib/components/app/chat/ChatMessages/ChatMessageAssistant.svelte

Co-authored-by: Aleksander Grygier <aleksander.grygier@gmail.com>

* Update tools/server/webui/src/lib/components/app/chat/ChatMessages/ChatMessageAssistant.svelte

Co-authored-by: Aleksander Grygier <aleksander.grygier@gmail.com>

* webui: address review feedback from allozaur

* chore: update webui build output

* webui: address review feedback from allozaur

* nit

* chore: update webui build output

* feat: Enhance chat processing state

* feat: Improve chat processing statistics UI

* chore: update webui build output

* feat: Add live generation statistics to processing state hook

* feat: Persist prompt processing stats in hook for better UX

* refactor: Enhance ChatMessageStatistics for live stream display

* feat: Implement enhanced live chat statistics into assistant message

* chore: update webui build output

* fix: Proper tab for each stage of prompt processing/generation

* chore: update webui build output

* fix: Improved ETA calculation & display logic

* chore: update webui build output

* feat: Simplify logic & remove ETA from prompt progress

* chore: update webui build output

---------

Co-authored-by: Aleksander Grygier <aleksander.grygier@gmail.com>
 2025-12-29 19:32:21 +01:00
wbtek 5b1248c9af server : Cmdline arg -to changes http read timeout from current 600sec default (#18279) 
* Prevent crash if TTFT >300sec, boosted to 90 days

* server : allow configurable HTTP timeouts for child models

* server : pass needed timeouts from params only

---------

Co-authored-by: Greg Slocum <fromgit@wbtek.slocum.net>
 2025-12-29 17:12:48 +01:00
Georgi Gerganov 2a85f720b8 server : handle closed connection for tasks (#18459) 2025-12-29 15:34:41 +02:00
o7si daa242dfc8 common: fix return value check for setpriority (#18412) 
* common: fix return value check for setpriority

* tools: add logging for process priority setting
 2025-12-29 11:07:49 +02:00
Xuan-Son Nguyen cffa5c46ea mtmd: clarify that we no longer accept AI-generated PRs (#18406) 2025-12-28 09:57:04 +01:00
Johannes Gäßler a52dc60ba3 llama_fit_params: return enum for fail vs. error (#18374) 2025-12-27 09:59:19 +01:00
o7si 4893cc07bb server : fix crash when seq_rm fails for hybrid/recurrent models (#18391) 
* server : fix crash when seq_rm fails for hybrid/recurrent models

* server : add allow_processing param to clear_slot
 2025-12-26 16:35:29 +01:00
Xuan-Son Nguyen f5acfb2ffa server: (router) add stop-timeout option (#18350) 
* server: (router) add stop-timeout option

* also allow stop while loading

* add docs

* unload_lru: also wait for unload to complete
 2025-12-24 23:47:49 +01:00
Aadeshveer Singh c184284230 fit-params : fix race condition in fit-params output (#18276) 2025-12-24 15:57:38 +01:00
Xuan-Son Nguyen 5ee4e43f26 server: return_progress to also report 0% processing state (#18305) 2025-12-23 21:49:05 +01:00
Pascal 5b6c9bc0f3 webui: apply webui_settings on first load (#18223) 
* webui: apply webui_settings on first load

The webui_settings from /props were not applied on initial load
when default_generation_settings.params was null

Now syncs whenever serverProps is available, regardless of params,
works for both single-model and router modes

* chore: update webui build output
 2025-12-23 15:48:03 +01:00
Xuan-Son Nguyen 849d021104 server: fix crash with model not having BOS/EOS (#18321) 2025-12-23 14:39:36 +01:00
Xuan-Son Nguyen 179fd82a72 gen-docs: automatically update markdown file (#18294) 
* gen-docs: automatically update markdown file

* also strip whitespace

* do not add extra newline

* update TOC
 2025-12-22 19:30:19 +01:00
Xuan-Son Nguyen 6ce863c803 server: prevent data race from HTTP threads (#18263) 
* server: prevent data race from HTTP threads

* fix params

* fix default_generation_settings

* nits: make handle_completions_impl looks less strange

* stricter const

* fix GGML_ASSERT(idx < states.size())

* move index to be managed by server_response_reader

* http: make sure req & res lifecycle are tied together

* fix compile

* fix index handling buggy

* fix data race for lora endpoint

* nits: fix shadow variable

* nits: revert redundant changes

* nits: correct naming for json_webui_settings
 2025-12-22 14:23:34 +01:00
Xuan-Son Nguyen 3997c78e33 server: fix data race in to_json_anthropic (#18283) 2025-12-22 13:21:43 +01:00
Xuan-Son Nguyen 86af848153 server: (docs) remove mention about extra_args (#18262) 2025-12-22 12:22:01 +01:00
Johannes Gäßler 147a521636 tool/ex/tests: consistently free ctx, then model (#18168) 2025-12-22 11:00:37 +01:00
Xuan-Son Nguyen ddcb75dd8a server: add auto-sleep after N seconds of idle (#18228) 
* implement sleeping at queue level

* implement server-context suspend

* add test

* add docs

* optimization: add fast path

* make sure to free llama_init

* nits

* fix use-after-free

* allow /models to be accessed during sleeping, fix use-after-free

* don't allow accessing /models during sleep, it is not thread-safe

* fix data race on accessing props and model_meta

* small clean up

* trailing whitespace

* rm outdated comments
 2025-12-21 02:24:42 +01:00
Oleksandr Kuvshynov 408616adbd server : [easy] fix per round speculative decode logging (#18211) 
Currently we always log 0, as we clear slot.drafted before.

To reproduce:
Run llama-server with devstral-2 as main model and devstral-2-small as
md, and verbose logging:

```
% ./build/bin/llama-server -v  \
  -m ~/llms/Devstral-2-123B-Instruct-2512-UD-Q6_K_XL-00001-of-00003.gguf \
  -md ~/llms/Devstral-Small-2-24B-Instruct-2512-UD-Q2_K_XL.gguf \
  -c 8192 2> /tmp/llama.cpp.debug

Check the log:

slot update_slots: id  3 | task 0 | accepted 11/0 draft tokens, new
n_tokens = 741
slot update_slots: id  3 | task 0 | accepted 4/0 draft tokens, new
n_tokens = 746
slot update_slots: id  3 | task 0 | accepted 16/0 draft tokens, new
n_tokens = 763
slot update_slots: id  3 | task 0 | accepted 11/0 draft tokens, new
n_tokens = 775
slot update_slots: id  3 | task 0 | accepted 2/0 draft tokens, new
n_tokens = 778
slot update_slots: id  3 | task 0 | accepted 4/0 draft tokens, new
n_tokens = 783
slot update_slots: id  3 | task 0 | accepted 8/0 draft tokens, new
n_tokens = 792
slot update_slots: id  3 | task 0 | accepted 2/0 draft tokens, new
n_tokens = 795
slot update_slots: id  3 | task 0 | accepted 1/0 draft tokens, new
n_tokens = 797
slot update_slots: id  3 | task 0 | accepted 1/0 draft tokens, new
n_tokens = 799
slot update_slots: id  3 | task 0 | accepted 0/0 draft tokens, new
n_tokens = 800
slot update_slots: id  3 | task 0 | accepted 2/0 draft tokens, new
n_tokens = 803
slot update_slots: id  3 | task 0 | accepted 1/0 draft tokens, new
n_tokens = 805
slot update_slots: id  3 | task 0 | accepted 6/0 draft tokens, new
n_tokens = 812
slot update_slots: id  3 | task 0 | accepted 3/0 draft tokens, new
n_tokens = 816
```

After the fix, get correct per round logging:

```
slot update_slots: id  3 | task 0 | accepted 7/8 draft tokens, new
n_tokens = 654
slot update_slots: id  3 | task 0 | accepted 1/2 draft tokens, new
n_tokens = 656
slot update_slots: id  3 | task 0 | accepted 2/16 draft tokens, new
n_tokens = 659
slot update_slots: id  3 | task 0 | accepted 1/16 draft tokens, new
n_tokens = 661
slot update_slots: id  3 | task 0 | accepted 2/16 draft tokens, new
n_tokens = 664
slot update_slots: id  3 | task 0 | accepted 16/16 draft tokens, new
n_tokens = 681
slot update_slots: id  3 | task 0 | accepted 16/16 draft tokens, new
n_tokens = 698
slot update_slots: id  3 | task 0 | accepted 3/4 draft tokens, new
n_tokens = 702
slot update_slots: id  3 | task 0 | accepted 5/12 draft tokens, new
n_tokens = 708
slot update_slots: id  3 | task 0 | accepted 16/16 draft tokens, new
n_tokens = 725
slot update_slots: id  3 | task 0 | accepted 1/1 draft tokens, new
n_tokens = 727
slot update_slots: id  3 | task 0 | accepted 8/16 draft tokens, new
n_tokens = 736
```
 2025-12-20 10:57:40 +01:00
Xuan-Son Nguyen 9e39a1e6a9 server: support load model on startup, support preset-only options (#18206) 
* server: support autoload model, support preset-only options

* add docs

* load-on-startup

* fix

* Update common/arg.cpp

Co-authored-by: Pascal <admin@serveurperso.com>

---------

Co-authored-by: Pascal <admin@serveurperso.com>
 2025-12-20 09:25:27 +01:00
Pascal 14931a826e arg: fix order to use short form before long form (#18196) 
* arg: fix order to use short form before long form

* arg: update doc

* arg: update test-arg-parser

* arg: address review feedback from ngxson

simplified to check first.length() <= last.length() only
fixed: --sampler-seq, --rerank, --draft ordering
note: middle positions in 3+ arg sets are not verified

* arg: update doc
 2025-12-19 18:01:56 +01:00
Aman Gupta cc0a04343e server: friendlier error msg when ctx < input (#18174) 
* llama-server: friendlier error msg when ctx < input

This PR adds formatted strings to the server's send_error function

* llama-server: use string_format inline

* fix test
 2025-12-19 12:10:00 +01:00