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Author SHA1 Message Date
Andrew Marshall
84b0a98319 webui: Update Svelte to fix effect_update_depth_exceeded errors (#19144) 
The upstream fix is first available in 5.38.2, so constrain to at least
that version.

Rebuild pre-compiled webui index.html.gz based on these changes.

See also:
https://github.com/ggml-org/llama.cpp/issues/16347
https://github.com/huntabyte/bits-ui/issues/1687
https://github.com/sveltejs/svelte/issues/16548
 2026-01-29 15:56:39 +01:00
Hendrik Erz
3802d3c78f fix: Use tabular-nums for chat message statistics (#18915) 
* fix: Use `tabular-nums` for chat message statistics

* fix: Rebuild WebUI
 2026-01-21 18:46:01 +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
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
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
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
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
Aleksander Grygier
acb73d8340 webui: Add editing attachments in user messages (#18147) 
* feat: Enable editing attachments in user messages

* feat: Improvements for data handling & UI

* docs: Update Architecture diagrams

* chore: update webui build output

* refactor: Exports

* chore: update webui build output

* feat: Add handling paste for Chat Message Edit Form

* chore: update webui build output

* refactor: Cleanup

* chore: update webui build output
 2025-12-19 11:14:07 +01:00
Pascal
f9ec8858ed webui: display prompt processing stats (#18146) 
* webui: display prompt processing stats

* feat: Improve UI of Chat Message Statistics

* chore: update webui build output

* refactor: Post-review improvements

* chore: update webui build output

---------

Co-authored-by: Aleksander Grygier <aleksander.grygier@gmail.com>
 2025-12-18 17:55:03 +01:00
Aleksander Grygier
9ce64aed7d webui: Fix selecting generated output issues during active streaming (#18091) 
* draft: incremental markdown rendering with stable blocks

* refactor: Logic improvements

* refactor: DRY Markdown post-processing logic

* refactor: ID generation improvements

* fix: Remove runes

* refactor: Clean up & add JSDocs

* chore: update webui static output

* fix: Add tick to prevent race conditions for rendering Markdown blocks

Suggestion from @ServeurpersoCom

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

* chore: Run `npm audit fix`

* chore: update webui static output

* feat: Improve performance using global counter & id instead of UUID

* refactor: Enhance Markdown rendering with link and code features

* chore: update webui static output

* fix: Code block content extraction

* chore: update webui static output

* chore: update webui static output

---------

Co-authored-by: Pascal <admin@serveurperso.com>
 2025-12-18 11:13:52 +01:00
Kim S.
900316da4e webui: fix chat screen shadow width (#18010) 
* webui: fix chat screen shadow width

* chore: add index.html.gz
 2025-12-18 11:08:42 +01:00
Pascal
6ce3d85796 server: (webui) add --webui-config (#18028) 
* server/webui: add server-side WebUI config support

Add CLI arguments --webui-config (inline JSON) and --webui-config-file
(file path) to configure WebUI default settings from server side.

Backend changes:
- Parse JSON once in server_context::load_model() for performance
- Cache parsed config in webui_settings member (zero overhead on /props)
- Add proper error handling in router mode with try/catch
- Expose webui_settings in /props endpoint for both router and child modes

Frontend changes:
- Add 14 configurable WebUI settings via parameter sync
- Add tests for webui settings extraction
- Fix subpath support with base path in API calls

Addresses feedback from @ngxson and @ggerganov

* server: address review feedback from ngxson

* server: regenerate README with llama-gen-docs
 2025-12-17 21:45:45 +01:00
Kim S.
d37fc93505 webui: fix chat header width when sidebar is closed (#17981) 
* webui: fix chat header width when sidebar is closed

* chore: add index.html.gz
 2025-12-17 20:05:45 +01:00
Aleksander Grygier
3034836d36 webui: Improve copy to clipboard with text attachments (#17969) 
* feat: Create copy/paste user message including "pasted text" attachments

* chore: update webui build output

* chore: update webui static output

* fix: UI issues

* chore: update webui static output

* fix: Decode HTML entities using `DOMParser`

* chore: update webui build output

* chore: update webui static output
 2025-12-16 07:38:46 +01:00
Aleksander Grygier
a20979d433 webui: Add setting to always show sidebar on Desktop (#17809) 
* feat: Add setting to always show Sidebar on Desktop

* chore: update webui build output

* feat: Add auto-show sidebar setting

* fix: Mobile settings dialog UI

* chore: update webui build output

* feat: UI label update

* chore: update webui build output

* chore: update webui build output

* chore: update webui build output

* refactor: Cleanup

* chore: update webui build output
 2025-12-16 07:31:37 +01:00
Darius Lukas
40d9c394f4 Webui: Disable attachment button and model selector button when prompt textbox is disabled. (#17925) 
* Pass disabled state to the file attachments button and the model
selector button.

* Update index.html.gz

* Fix model info card in non-router mode.

* Update index.html.gz
 2025-12-16 07:15:49 +01:00
Pascal
0f4f35e7be Fix unreadable user markdown colors and truncate long texts in deletion dialogs (#17555) 
* webui: limit conversation name length in dialogs

* webui: fix unreadable colors on links and table cell hover in user markdown

* webui: keep table borders visible in user markdown

* webui: updating unified exports

* Update tools/server/webui/src/lib/components/app/chat/ChatAttachments/ChatAttachmentThumbnailFile.svelte

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

* chore: update webui build output

* chore: update webui build output

* chore: update webui build output

---------

Co-authored-by: Aleksander Grygier <aleksander.grygier@gmail.com>
 2025-12-15 16:34:53 +01:00
Thomas Jarosch
e73d548659 webui: add "delete all conversations" button to import/export tab (#17444) 
* webui: add "delete all conversations" button to import/export tab

- Add 'Delete all conversations' functionality with confirmation dialog
- Add Trash icon and destructive styling for clear visual indication
- Redirects to "?new_chat=true#/" by using conversationsStore.deleteAll()

* chore: update webui build output
 2025-12-15 11:29:29 +01:00
Aleksander Grygier
12280ae905 webui: Fix parsing non-LaTeX occurrencies of \( or \) (#17810) 
* fix: Improve latex protection logic to prevent turning non-latex `\(` into `$`

* chore: update webui build output
 2025-12-12 15:13:36 +01:00
Pascal
a81a569577 Add a search field on model selector / improve mobile display (#17765) 
* webui: add search field to model selector and fixes mobile viewport overflow

* webui: simplify model search style and code

* refacor: Search Input component & consistent UI for Models Selector search

* feat: Use Popover component + improve interactions

* fix: Fetching props for only loaded models in ROUTER mode

* webui: prevent models selector popover from overflowing viewport

Use Floating UI's auto-positioning with 50dvh height limit and proper
collision detection instead of forcing top positioning. Fixes overflow
on desktop and mobile keyboard issues

* webui: keep search field near trigger in models selector

Place search at the 'near end' (closest to trigger) by swapping layout
with CSS flexbox order based on popover direction. Prevents input from
moving during typing as list shrinks

* chore: update webui build output

---------

Co-authored-by: Aleksander Grygier <aleksander.grygier@gmail.com>
 2025-12-11 18:21:21 +01:00
Aleksander Grygier
a28e3c7567 webui: Stop generation from chat sidebar (#17806) 
* feat: Add stop generation button for Conversation Item

* chore: update webui build output
 2025-12-06 13:29:15 +01:00
Aleksander Grygier
e31b5c55c3 webui: Fix context available value in Multi-model Router mode (#17804) 
* fix: Use context size from `/props?model=...` in ROUTER mode

* chore: update webui build output
 2025-12-06 13:23:29 +01:00
Aleksander Grygier
21f24f27a9 webui: Per-conversation system message with UI displaying, edition & branching (#17275) 
* feat: Per-conversation system message with optional display in UI, edition and branching (WIP)

* chore: update webui build output
 2025-12-06 13:19:05 +01:00
Piotr Wilkin (ilintar)
c6d1a00aa7 Add a couple of file types to the text section (#17670) 
* Add a couple of file types to the text section

* Format + regenerate index

* Rebuild after rebase
 2025-12-03 21:45:06 +01:00
Aleksander Grygier
e9f9483464 Use OpenAI-compatible /v1/models endpoint by default (#17689) 
* refactor: Data fetching via stores

* chore: update webui build output

* refactor: Use OpenAI compat `/v1/models` endpoint by default to list models

* chore: update webui build output

* chore: update webui build output
 2025-12-03 20:49:09 +01:00
Andika Wasisto
41c5e02f42 webui: Fix zero pasteLongTextToFileLen to disable conversion being overridden (#17445) 
* webui: Fix zero pasteLongTextToFileLen to disable conversion being overridden

Zero pasteLongTextToFileLen should disable the conversion, but it was
overwritten with 2500.

* Apply suggestions from code review

* Update webui build
 2025-12-03 20:45:17 +01:00
Aleksander Grygier
cee92af553 Add context info to server error (#17663) 
* fix: Add context info to server error

* chore: update webui build output
 2025-12-02 09:20:57 +01:00
Xuan-Son Nguyen
ec18edfcba server: introduce API for serving / loading / unloading multiple models (#17470) 
* server: add model management and proxy

* fix compile error

* does this fix windows?

* fix windows build

* use subprocess.h, better logging

* add test

* fix windows

* feat: Model/Router server architecture WIP

* more stable

* fix unsafe pointer

* also allow terminate loading model

* add is_active()

* refactor: Architecture improvements

* tmp apply upstream fix

* address most problems

* address thread safety issue

* address review comment

* add docs (first version)

* address review comment

* feat: Improved UX for model information, modality interactions etc

* chore: update webui build output

* refactor: Use only the message data `model` property for displaying model used info

* chore: update webui build output

* add --models-dir param

* feat: New Model Selection UX WIP

* chore: update webui build output

* feat: Add auto-mic setting

* feat: Attachments UX improvements

* implement LRU

* remove default model path

* better --models-dir

* add env for args

* address review comments

* fix compile

* refactor: Chat Form Submit component

* ad endpoint docs

* Merge remote-tracking branch 'webui/allozaur/server_model_management_v1_2' into xsn/server_model_maagement_v1_2

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

* feat: Add copy to clipboard to model name in model info dialog

* feat: Model unavailable UI state for model selector

* feat: Chat Form Actions UI logic improvements

* feat: Auto-select model from last assistant response

* chore: update webui build output

* expose args and exit_code in API

* add note

* support extra_args on loading model

* allow reusing args if auto_load

* typo docs

* oai-compat /models endpoint

* cleaner

* address review comments

* feat: Use `model` property for displaying the `repo/model-name` naming format

* refactor: Attachments data

* chore: update webui build output

* refactor: Enum imports

* feat: Improve Model Selector responsiveness

* chore: update webui build output

* refactor: Cleanup

* refactor: Cleanup

* refactor: Formatters

* chore: update webui build output

* refactor: Copy To Clipboard Icon component

* chore: update webui build output

* refactor: Cleanup

* chore: update webui build output

* refactor: UI badges

* chore: update webui build output

* refactor: Cleanup

* refactor: Cleanup

* chore: update webui build output

* add --models-allow-extra-args for security

* nits

* add stdin_file

* fix merge

* fix: Retrieve lost setting after resolving merge conflict

* refactor: DatabaseStore -> DatabaseService

* refactor: Database, Conversations & Chat services + stores architecture improvements (WIP)

* refactor: Remove redundant settings

* refactor: Multi-model business logic WIP

* chore: update webui build output

* feat: Switching models logic for ChatForm or when regenerating messges + modality detection logic

* chore: update webui build output

* fix: Add `untrack` inside chat processing info data logic to prevent infinite effect

* fix: Regenerate

* feat: Remove redundant settigns + rearrange

* fix: Audio attachments

* refactor: Icons

* chore: update webui build output

* feat: Model management and selection features WIP

* chore: update webui build output

* refactor: Improve server properties management

* refactor: Icons

* chore: update webui build output

* feat: Improve model loading/unloading status updates

* chore: update webui build output

* refactor: Improve API header management via utility functions

* remove support for extra args

* set hf_repo/docker_repo as model alias when posible

* refactor: Remove ConversationsService

* refactor: Chat requests abort handling

* refactor: Server store

* tmp webui build

* refactor: Model modality handling

* chore: update webui build output

* refactor: Processing state reactivity

* fix: UI

* refactor: Services/Stores syntax + logic improvements

Refactors components to access stores directly instead of using exported getter functions.

This change centralizes store access and logic, simplifying component code and improving maintainability by reducing the number of exported functions and promoting direct store interaction.

Removes exported getter functions from `chat.svelte.ts`, `conversations.svelte.ts`, `models.svelte.ts` and `settings.svelte.ts`.

* refactor: Architecture cleanup

* feat: Improve statistic badges

* feat: Condition available models based on modality + better model loading strategy & UX

* docs: Architecture documentation

* feat: Update logic for PDF as Image

* add TODO for http client

* refactor: Enhance model info and attachment handling

* chore: update webui build output

* refactor: Components naming

* chore: update webui build output

* refactor: Cleanup

* refactor: DRY `getAttachmentDisplayItems` function + fix UI

* chore: update webui build output

* fix: Modality detection improvement for text-based PDF attachments

* refactor: Cleanup

* docs: Add info comment

* refactor: Cleanup

* re

* refactor: Cleanup

* refactor: Cleanup

* feat: Attachment logic & UI improvements

* refactor: Constants

* feat: Improve UI sidebar background color

* chore: update webui build output

* refactor: Utils imports + move types to `app.d.ts`

* test: Fix Storybook mocks

* chore: update webui build output

* test: Update Chat Form UI tests

* refactor: Tooltip Provider from core layout

* refactor: Tests to separate location

* decouple server_models from server_routes

* test: Move demo test  to tests/server

* refactor: Remove redundant method

* chore: update webui build output

* also route anthropic endpoints

* fix duplicated arg

* fix invalid ptr to shutdown_handler

* server : minor

* rm unused fn

* add ?autoload=true|false query param

* refactor: Remove redundant code

* docs: Update README documentations + architecture & data flow diagrams

* fix: Disable autoload on calling server props for the model

* chore: update webui build output

* fix ubuntu build

* fix: Model status reactivity

* fix: Modality detection for MODEL mode

* chore: update webui build output

---------

Co-authored-by: Aleksander Grygier <aleksander.grygier@gmail.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
 2025-12-01 19:41:04 +01:00
Pascal
b1846f1c8e webui: add rehype plugin to restore HTML in Markdown table cells (#17477) 
* webui: add rehype plugin to restore HTML in Markdown table cells

The remark/rehype pipeline neutralizes inline HTML as literal text
(remarkLiteralHtml) so that XML/HTML snippets in LLM responses display
as-is instead of being rendered. This causes <br> and <ul> markup in
table cells to show as plain text.

This plugin traverses the HAST post-conversion, parses whitelisted HTML
patterns (<br>, <ul><li>) from text nodes, and replaces them with actual
HAST element nodes. For lists, adjacent siblings must be combined first
as the AST fragmentation breaks pattern matching.

Strict validation rejects malformed markup, keeping it as raw text.

* chore: update webui build output
 2025-11-25 08:01:02 +01:00
Pascal
0c7220db56 webui: minor settings reorganization and add disable autoscroll option (#17452) 
* webui: added a dedicated 'Display' settings section that groups visualization options

* webui: added a Display setting to toggle automatic chat scrolling

* chore: update webui build output
 2025-11-23 18:42:00 +01:00
Aleksander Grygier
4c91f2633f Improved file naming & structure for UI components (#17405) 
* refactor: Component iles naming & structure

* chore: update webui build output

* refactor: Dialog titles + components namig

* chore: update webui build output

* refactor: Imports

* chore: update webui build output
 2025-11-20 14:07:31 +01:00
Aleksander Grygier
99c53d6558 webui: Add a "Continue" Action for Assistant Message (#16971) 
* feat: Add "Continue" action for assistant messages

* feat: Continuation logic & prompt improvements

* chore: update webui build output

* feat: Improve logic for continuing the assistant message

* chore: update webui build output

* chore: Linting

* chore: update webui build output

* fix: Remove synthetic prompt logic, use the prefill feature by sending the conversation payload ending with assistant message

* chore: update webui build output

* feat: Enable "Continue" button based on config & non-reasoning model type

* chore: update webui build output

* chore: Update packages with `npm audit fix`

* fix: Remove redundant error

* chore: update webui build output

* chore: Update `.gitignore`

* fix: Add missing change

* feat: Add auto-resizing for Edit Assistant/User Message textareas

* chore: update webui build output
 2025-11-19 14:39:50 +01:00
Aleksander Grygier
22e1ce2f81 webui: Fix clickability around chat processing statistics UI (#17278) 
* fix: Better pointer events handling in chat processing info elements

* chore: update webui build output
 2025-11-15 22:41:41 +01:00
Pascal
1411d9275a webui: add OAI-Compat Harmony tool-call streaming visualization and persistence in chat UI (#16618) 
* webui: add OAI-Compat Harmony tool-call live streaming visualization and persistence in chat UI

- Purely visual and diagnostic change, no effect on model context, prompt
  construction, or inference behavior

- Captured assistant tool call payloads during streaming and non-streaming
  completions, and persisted them in chat state and storage for downstream use

- Exposed parsed tool call labels beneath the assistant's model info line
  with graceful fallback when parsing fails

- Added tool call badges beneath assistant responses that expose JSON tooltips
  and copy their payloads when clicked, matching the existing model badge styling

- Added a user-facing setting to toggle tool call visibility to the Developer
  settings section directly under the model selector option

* webui: remove scroll listener causing unnecessary layout updates (model selector)

* 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>

* chore: npm run format & update webui build output

* chore: update webui build output

---------

Co-authored-by: Aleksander Grygier <aleksander.grygier@gmail.com>
 2025-11-15 21:09:32 +01:00
Aleksander Grygier
f1bad23f88 Better UX for handling multiple attachments in WebUI (#17246) 2025-11-14 01:19:08 +01:00
Aleksander Grygier
8e878f0cb4 Update packages + upgrade Storybook to v10 (#17201) 
* chore: Update packages + upgrade Storybook to v10

* fix: Increase timeout for UI tests
 2025-11-12 19:01:48 +01:00
chansikpark
333f2595a3 webui: fix keyboard shortcuts for new chat & edit chat title (#17007) 2025-11-08 20:52:35 +01:00
Aleksander Grygier
e7da30b584 fix: Viewing multiple PDF attachments (#16974) 2025-11-03 18:53:26 +01:00
Georgi Gerganov
48bd26501b server : add props.model_alias (#16943) 
* server : add props.model_alias

* webui : npm run format
 2025-11-03 14:38:23 +01:00
Sascha Rogmann
bcfa87622a feat(webui): improve LaTeX rendering with currency detection (#16508) 
* webui : Revised LaTeX formula recognition

* webui : Further examples containg amounts

* webui : vitest for maskInlineLaTeX

* webui: Moved preprocessLaTeX to lib/utils

* webui: LaTeX in table-cells

* chore: update webui build output (use theirs)

* webui: backslash in LaTeX-preprocessing

* chore: update webui build output

* webui: look-behind backslash-check

* chore: update webui build output

* Apply suggestions from code review

Code maintenance (variable names, code formatting, string handling)

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

* webui: Moved constants to lib/constants.

* webui: package woff2 inside base64 data

* webui: LaTeX-line-break in display formula

* chore: update webui build output

* webui: Bugfix (font embedding)

* webui: Bugfix (font embedding)

* webui: vite embeds assets

* webui: don't suppress 404 (fonts)

* refactor: KaTeX integration with SCSS

Moves KaTeX styling to SCSS for better customization and font embedding.

This change includes:
- Adding `sass` as a dev dependency.
- Introducing a custom SCSS file to override KaTeX variables and disable TTF/WOFF fonts, relying solely on WOFF2 for embedding.
- Adjusting the Vite configuration to resolve `katex-fonts` alias and inject SCSS variables.

* fix: LaTeX processing within blockquotes

* webui: update webui build output

---------

Co-authored-by: Aleksander Grygier <aleksander.grygier@gmail.com>
 2025-11-03 00:41:08 +01:00
Pascal
2f68ce7cfd webui: auto-refresh /props on inference start to resync model metadata (#16784) 
* webui: auto-refresh /props on inference start to resync model metadata

- Add no-cache headers to /props and /slots
- Throttle slot checks to 30s
- Prevent concurrent fetches with promise guard
- Trigger refresh from chat streaming for legacy and ModelSelector
- Show dynamic serverWarning when using cached data

* fix: restore proper legacy behavior in webui by using unified /props refresh

Updated assistant message bubbles to show each message's stored model when available,
falling back to the current server model only when the per-message value is missing

When the model selector is disabled, now fetches /props and prioritizes that model name
over chunk metadata, then persists it with the streamed message so legacy mode properly
reflects the backend configuration

* fix: detect first valid SSE chunk and refresh server props once

* fix: removed the slots availability throttle constant and state

* webui: purge ai-generated cruft

* chore: update webui static build
 2025-11-01 19:49:51 +01:00
Pascal
e4a71599e5 webui: add HTML/JS preview support to MarkdownContent with sandboxed iframe (#16757) 
* webui: add HTML/JS preview support to MarkdownContent with sandboxed iframe dialog

Extended MarkdownContent to flag previewable code languages,
add a preview button alongside copy controls, manage preview
dialog state, and share styling for the new button group

Introduced CodePreviewDialog.svelte, a sandboxed iframe modal
for rendering HTML/JS previews with consistent dialog controls

* webui: fullscreen HTML preview dialog using bits-ui

* Update tools/server/webui/src/lib/components/app/misc/CodePreviewDialog.svelte

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

* Update tools/server/webui/src/lib/components/app/misc/MarkdownContent.svelte

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

* webui: pedantic style tweak for CodePreviewDialog close button

* webui: remove overengineered preview language logic

* chore: update webui static build

---------

Co-authored-by: Aleksander Grygier <aleksander.grygier@gmail.com>
 2025-11-01 17:14:54 +01:00
Aleksander Grygier
d8b860a219 Add a setting to display message generation statistics (#16901) 
* feat: Add setting to display message generation statistics

* chore: build static webui output
 2025-11-01 15:35:57 +01:00
Jaromír Hradílek
1ae74882f8 webui: recognize AsciiDoc files as valid text files (#16850) 
* webui: recognize AsciiDoc files as valid text files

* webui: add an updated static webui build

* webui: add the updated dependency list

* webui: re-add an updated static webui build

This also reverts commit 742dbb8379.
 2025-11-01 15:02:57 +01:00
Florian Badie
69e9ff0103 webui: support q URL parameter (#16728) 
* webui: support q URL parameter

Fixes #16722
I’ve checked that it works with Firefox’s AI tools

* webui: apply suggestions from code review

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

* chore: update webui static build

---------

Co-authored-by: Aleksander Grygier <aleksander.grygier@gmail.com>
 2025-10-24 14:10:29 +02:00
Pascal
9b9201f65a webui: introduce OpenAI-compatible model selector in JSON payload (#16562) 
* webui: introduce OpenAI-compatible model selector in JSON payload

* webui: restore OpenAI-Compatible model source of truth and unify metadata capture

This change re-establishes a single, reliable source of truth for the active model:
fully aligned with the OpenAI-Compat API behavior

It introduces a unified metadata flow that captures the model field from both
streaming and non-streaming responses, wiring a new onModel callback through ChatService
The model name is now resolved directly from the API payload rather than relying on
server /props or UI assumptions

ChatStore records and persists the resolved model for each assistant message during
streaming, ensuring consistency across the UI and database
Type definitions for API and settings were also extended to include model metadata
and the onModel callback, completing the alignment with OpenAI-Compat semantics

* webui: address review feedback from allozaur

* webui: move model selector into ChatForm (idea by @allozaur)

* webui: make model selector more subtle and integrated into ChatForm

* webui: replaced the Flowbite selector with a native Svelte dropdown

* webui: add developer setting to toggle the chat model selector

* webui: address review feedback from allozaur

Normalized streamed model names during chat updates
by trimming input and removing directory components before saving
or persisting them, so the conversation UI shows only the filename

Forced model names within the chat form selector dropdown to render as
a single-line, truncated entry with a tooltip revealing the full name

* webui: toggle displayed model source for legacy vs OpenAI-Compat modes

When the selector is disabled, it falls back to the active server model name from /props

When the model selector is enabled, the displayed model comes from the message metadata
(the one explicitly selected and sent in the request)

* Update tools/server/webui/src/lib/components/app/chat/ChatForm/ChatFormActions.svelte

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

* Update tools/server/webui/src/lib/constants/localstorage-keys.ts

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

* Update tools/server/webui/src/lib/components/app/chat/ChatForm/ChatFormModelSelector.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>

* Update tools/server/webui/src/lib/services/chat.ts

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

* Update tools/server/webui/src/lib/services/chat.ts

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

* webui: refactor model selector and persistence helpers

- Replace inline portal and event listeners with proper Svelte bindings
- Introduce 'persisted' store helper for localStorage sync without runes
- Extract 'normalizeModelName' utils + Vitest coverage
- Simplify ChatFormModelSelector structure and cleanup logic

Replaced the persisted store helper's use of '$state/$effect' runes with
a plain TS implementation to prevent orphaned effect runtime errors
outside component context

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

* webui: document normalizeModelName usage with inline examples

* Update tools/server/webui/src/lib/components/app/chat/ChatForm/ChatFormModelSelector.svelte

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

* Update tools/server/webui/src/lib/stores/models.svelte.ts

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

* Update tools/server/webui/src/lib/stores/models.svelte.ts

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

* webui: extract ModelOption type into dedicated models.d.ts

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

* webui: refine ChatMessageAssistant displayedModel source logic

* webui: stabilize dropdown, simplify model extraction, and init assistant model field

* chore: update webui static build

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

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

* chore: npm format, update webui static build

* webui: align sidebar trigger position, remove z-index glitch

* chore: update webui build output

---------

Co-authored-by: Aleksander Grygier <aleksander.grygier@gmail.com>
 2025-10-22 16:58:23 +02:00
Aleksander Grygier
c9c1972e2c Handle legacy 'context' attachments (#16687)
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 2025-10-20 19:49:02 +02:00
Aleksander Grygier
79068501fa Prevent premature submission on IME input (#16673) 
* fix: Prevent premature submission on IME input

* chore: update webui static build

* refactor: Put IME completion checker in a helper function and add checking for `KeyboardEvent.eventKey === 229`

* chore: update webui static build

* chore: update webui static build

* chore: update webui static build
 2025-10-20 14:21:12 +02:00
Aleksander Grygier
0e4a0cf2fa Import/Export UX improvements (#16619) 
* webui : added download action (#13552)

* webui : import and export (for all conversations)

* webui : fixed download-format, import of one conversation

* webui : add ExportedConversations type for chat import/export

* feat: Update naming & order

* chore: Linting

* feat: Import/Export UX improvements

* chore: update webui build output

* feat: Update UI placement of Import/Export tab in Chat Settings Dialog

* refactor: Cleanup

chore: update webui build output

* feat: Enable shift-click multiple conversation items selection

* chore: update webui static build

* chore: update webui static build

---------

Co-authored-by: Sascha Rogmann <github@rogmann.org>
 2025-10-20 13:29:14 +02:00