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249 Commits

Author SHA1 Message Date
Adrien Gallouët
516a4ca9b5 refactor : remove libcurl, use OpenSSL when available (#18828) 2026-01-14 18:02:47 +01:00
Adrien Gallouët
f709c7a33f ci, tests : use cmake to download models and remove libcurl dependency (#18791) 
* ci, tests : use cmake to download models and remove libcurl dependency
* llama_dl_model -> llama_download_model
* use EXPECTED_HASH for robust model downloading
* Move llama_download_model to cmake/common.cmake

Signed-off-by: Adrien Gallouët <angt@huggingface.co>
 2026-01-14 07:46:27 +01:00
Adrien Gallouët
36c5913c45 ci : use openssl for openEuler-latest-cmake-cann (#18779) 
Signed-off-by: Adrien Gallouët <angt@huggingface.co>
 2026-01-12 17:29:00 +01:00
Reese Levine
15bff84bf5 ggml webgpu: initial flashattention implementation (#18610) 
* FlashAttention (#13)

* Add inplace softmax

* Move rms_norm to split row approach

* Update debug for supports_op

* clean up debug statements

* neg f16xf32xip builds and runs, havent actually ran a model that uses neg kernel yet though

* neg passes backend test

* unary operators pass ggml tests

* rms_norm double declaration bug atoned

* abides by editor-config

* removed vestigial files

* fixed autoconfig

* All operators (inlcluding xielu) working

* removed unnecesarry checking if node->src[1] exists for unary operators

* responded and dealt with PR comments

* implemented REPL_Template support and removed bug in unary operators kernel

* formatted embed wgsl and ggml-webgpu.cpp

* Faster tensors (#8)

Add fast matrix and matrix/vector multiplication.

* Use map for shader replacements instead of pair of strings

* Wasm (#9)

* webgpu : fix build on emscripten

* more debugging stuff

* test-backend-ops: force single thread on wasm

* fix single-thread case for init_tensor_uniform

* use jspi

* add pthread

* test: remember to set n_thread for cpu backend

* Add buffer label and enable dawn-specific toggles to turn off some checks

* Intermediate state

* Fast working f16/f32 vec4

* Working float fast mul mat

* Clean up naming of mul_mat to match logical model, start work on q mul_mat

* Setup for subgroup matrix mat mul

* Basic working subgroup matrix

* Working subgroup matrix tiling

* Handle weirder sg matrix sizes (but still % sg matrix size)

* Working start to gemv

* working f16 accumulation with shared memory staging

* Print out available subgroup matrix configurations

* Vectorize dst stores for sg matrix shader

* Gemv working scalar

* Minor set_rows optimization (#4)

* updated optimization, fixed errors

* non vectorized version now dispatches one thread per element

* Simplify

* Change logic for set_rows pipelines

---------

Co-authored-by: Neha Abbas <nehaabbas@macbookpro.lan>
Co-authored-by: Neha Abbas <nehaabbas@ReeseLevines-MacBook-Pro.local>
Co-authored-by: Reese Levine <reeselevine1@gmail.com>

* Comment on dawn toggles

* Working subgroup matrix code for (semi)generic sizes

* Remove some comments

* Cleanup code

* Update dawn version and move to portable subgroup size

* Try to fix new dawn release

* Update subgroup size comment

* Only check for subgroup matrix configs if they are supported

* Add toggles for subgroup matrix/f16 support on nvidia+vulkan

* Make row/col naming consistent

* Refactor shared memory loading

* Move sg matrix stores to correct file

* Working q4_0

* Formatting

* Work with emscripten builds

* Fix test-backend-ops emscripten for f16/quantized types

* Use emscripten memory64 to support get_memory

* Add build flags and try ci

---------

Co-authored-by: Xuan Son Nguyen <son@huggingface.co>

* Remove extra whitespace

* Move wasm single-thread logic out of test-backend-ops for cpu backend

* Disable multiple threads for emscripten single-thread builds in ggml_graph_plan

* Refactored pipelines and workgroup calculations (#10)

* refactored pipelines

* refactored workgroup calculation

* removed commented out block of prior maps

* Clean up ceiling division pattern

---------

Co-authored-by: Neha Abbas <nehaabbas@eduroam-169-233-141-223.ucsc.edu>
Co-authored-by: Reese Levine <reeselevine1@gmail.com>

* Start work on flash attention

* Shader structure set up (many bugs still)

* debugging

* Working first test

* Working with head grouping, head sizes to 128, logit softcap, mask/sinks enabled, f32

* Generalize softmax to work with multiple subgroups, f16 accumulation, mask shared memory tiling

* Start work on integrating pre-wgsl

* Separate structs/initial shader compilation library into separate files

* Work on compilation choices for flashattention

* Work on subgroup matrix/tile size portability

* subgroup size agnostic online softmax

* Cleanups, quantization types

* more cleanup

* fix wasm build

* Refactor flashattention to increase parallelism, use direct loads for KV in somce cases

* Checkpoint

* formatting

* Update to account for default kv cache padding

* formatting shader

* Add workflow for ggml-ci webgpu

* Try passing absolute path to dawn in ggml-ci

* Avoid error on device destruction, add todos for proper cleanup

* Fix unused warning

* Forgot one parameter unused

* Move some flashattn computation to f32 for correctness
 2026-01-08 08:23:39 -08:00
Sigbjørn Skjæret
9dfa8ee950 ci : run cann build unconditionally [no ci] (#18659) 2026-01-07 13:07:08 +01:00
Ali Tariq
8e3a761189 ci : init git lfs in every build for RISC-V (#18590) 
* Initialized git lfs in every test

* Added git-lfs in dependencies to instal
 2026-01-05 02:18:33 +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
Sigbjørn Skjæret
f74747d886 ci : only save ccache on master (#18207) 2025-12-19 22:29:37 +01:00
Eve
d15d177f43 vulkan: faster q6_k matmul (#17813) 
* q6_k faster mul mat

* 8 values

* fix comment

* switch to two at a time

* start ci for .glsl files
 2025-12-14 08:29:37 +01:00
jiahao su
a8c7f33d79 ci : change the cann version and the container pull method (#17953) 
fix error format

Update build.yml

Remove unnecessary zip files

fix

update
 2025-12-12 20:43:00 +01:00
Sigbjørn Skjæret
45e350e3d3 ci: fix riscv64-native build (#17916) 2025-12-10 23:24:31 +01:00
Xuan-Son Nguyen
6c2131773c cli: new CLI experience (#17824) 
* wip

* wip

* fix logging, add display info

* handle commands

* add args

* wip

* move old cli to llama-completion

* rm deprecation notice

* move server to a shared library

* move ci to llama-completion

* add loading animation

* add --show-timings arg

* add /read command, improve LOG_ERR

* add args for speculative decoding, enable show timings by default

* add arg --image and --audio

* fix windows build

* support reasoning_content

* fix llama2c workflow

* color default is auto

* fix merge conflicts

* properly fix color problem

Co-authored-by: bandoti <bandoti@users.noreply.github.com>

* better loading spinner

* make sure to clean color on force-exit

* also clear input files on "/clear"

* simplify common_log_flush

* add warning in mtmd-cli

* implement console writter

* fix data race

* add attribute

* fix llama-completion and mtmd-cli

* add some notes about console::log

* fix compilation

---------

Co-authored-by: bandoti <bandoti@users.noreply.github.com>
 2025-12-10 15:28:59 +01:00
Sigbjørn Skjæret
7dba049b07 ci : disable ggml-ci-x64-amd-* (#17753) 2025-12-04 11:25:08 +01:00
Reese Levine
7ca5991d2b ggml webgpu: add support for emscripten builds (#17184) 
* Faster tensors (#8)

Add fast matrix and matrix/vector multiplication.

* Use map for shader replacements instead of pair of strings

* Wasm (#9)

* webgpu : fix build on emscripten

* more debugging stuff

* test-backend-ops: force single thread on wasm

* fix single-thread case for init_tensor_uniform

* use jspi

* add pthread

* test: remember to set n_thread for cpu backend

* Add buffer label and enable dawn-specific toggles to turn off some checks

* Intermediate state

* Fast working f16/f32 vec4

* Working float fast mul mat

* Clean up naming of mul_mat to match logical model, start work on q mul_mat

* Setup for subgroup matrix mat mul

* Basic working subgroup matrix

* Working subgroup matrix tiling

* Handle weirder sg matrix sizes (but still % sg matrix size)

* Working start to gemv

* working f16 accumulation with shared memory staging

* Print out available subgroup matrix configurations

* Vectorize dst stores for sg matrix shader

* Gemv working scalar

* Minor set_rows optimization (#4)

* updated optimization, fixed errors

* non vectorized version now dispatches one thread per element

* Simplify

* Change logic for set_rows pipelines

---------

Co-authored-by: Neha Abbas <nehaabbas@macbookpro.lan>
Co-authored-by: Neha Abbas <nehaabbas@ReeseLevines-MacBook-Pro.local>
Co-authored-by: Reese Levine <reeselevine1@gmail.com>

* Comment on dawn toggles

* Working subgroup matrix code for (semi)generic sizes

* Remove some comments

* Cleanup code

* Update dawn version and move to portable subgroup size

* Try to fix new dawn release

* Update subgroup size comment

* Only check for subgroup matrix configs if they are supported

* Add toggles for subgroup matrix/f16 support on nvidia+vulkan

* Make row/col naming consistent

* Refactor shared memory loading

* Move sg matrix stores to correct file

* Working q4_0

* Formatting

* Work with emscripten builds

* Fix test-backend-ops emscripten for f16/quantized types

* Use emscripten memory64 to support get_memory

* Add build flags and try ci

---------

Co-authored-by: Xuan Son Nguyen <son@huggingface.co>

* Remove extra whitespace

* Move wasm single-thread logic out of test-backend-ops for cpu backend

* Disable multiple threads for emscripten single-thread builds in ggml_graph_plan

* Fix .gitignore

* Add memory64 option and remove unneeded macros for setting threads to 1

---------

Co-authored-by: Xuan Son Nguyen <son@huggingface.co>
 2025-12-03 10:25:34 +01:00
Ali Tariq
4eba8d9451 ci : RVV1.0 builds with tests (#16682) 
* Added RISC-V supported tests

* Added default value for LLAMA_FATAL_WARNINGS and option to specify by user

* Added RISC-V supported tests

* Added default value for LLAMA_FATAL_WARNINGS and option to specify by user

* Removed apt prompt

* Added RISC-V specific tests with corrections

Corrections included:
1. Changed the test names from debian to ubuntu as it is more stable than Debian Trixie
2. Added explicit compiler in cmake command as GCC compiler below version 14 have been recorded
to throw errors with rvv1.0 and some other extensions
3. Added dependencies which are not installed by default in the RISC-V Ubuntu 24.04
4. Separate ccache directory for all jobs as all the ccache results are not the same and may cause ccache to not work

* Resolved the merge conflict and cleaned up run.sh

* Update ci/run.sh

Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>

* Removed previously added build ci for RISC-V

* Removed trailing whitespaces

* corrected build name

Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>

* cleanup

* Enabled build tests (1)

Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>

* Enabled build tests (2)

Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>

* enable openssl

---------

Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
 2025-12-02 21:46:10 +01:00
Adrien Gallouët
28175f857d cmake : add option to build and link BoringSSL (#17205) 
* cmake: add option to build and link BoringSSL

Signed-off-by: Adrien Gallouët <angt@huggingface.co>

* cmake : fix typo

Signed-off-by: Adrien Gallouët <angt@huggingface.co>

* cmake : disable boringssl test and asm by default

Signed-off-by: Adrien Gallouët <angt@huggingface.co>

* cmake : skip bssl

Signed-off-by: Adrien Gallouët <angt@huggingface.co>

* cmake : disable fips

Signed-off-by: Adrien Gallouët <angt@huggingface.co>

* cmake : fix cmake --install

Signed-off-by: Adrien Gallouët <angt@huggingface.co>

* ci : use boringssl for windows and mac

Signed-off-by: Adrien Gallouët <angt@huggingface.co>

---------

Signed-off-by: Adrien Gallouët <angt@huggingface.co>
 2025-11-21 11:46:45 +01:00
Adrien Gallouët
9cc4080441 ci : start using OpenSSL (#17235) 
Signed-off-by: Adrien Gallouët <angt@huggingface.co>
 2025-11-21 11:45:00 +01:00
jiahao su
561a3e2788 ci : change the openEuler-310p image to fix release (#17361) 2025-11-18 18:10:23 +01:00
jiahao su
ffa277a54c CANN: Add openEuler-cann in build and release (#17192) 
Update openEuler version

Remove variable ASCEND_SOC_TYPE

Modify the chip type

Fix case in zip filename

Change "device" to "chip_type"

Modify the value of chip_type
 2025-11-18 16:08:55 +08:00
Eve
8b1c339bd2 ci : revert #16249 (#17303) 
* Delete .github/workflows/build-amd.yml

* Update build.yml
 2025-11-16 19:09:17 +01:00
sudhiarm
3fe36c3238 ci: add Arm-hosted Graviton4 runner (#17021) 
* ci: add Arm-hosted Graviton4 runner

* ci: add missing dependencies for graviton4 build

* ci: enable LFS checkout on graviton4

* ci: move git-lfs install to dependencies in Graviton4 workflow
 2025-11-11 17:58:05 +02:00
Reese Levine
647b960bd8 ggml webgpu: faster matrix multiplication/matrix-vector multiplication (#17031) 
* Faster tensors (#8)

Add fast matrix and matrix/vector multiplication.

* Use map for shader replacements instead of pair of strings
 2025-11-07 19:27:20 -08:00
Max Krasnyansky
63d2fc46e1 Add experimental ggml-hexagon backend for the Hexagon NPU (#16547) 
* model: add support for extra bufs for all devices

* hexagon: add experimental ggml-hexagon backend for the Hexagon NPU

This commit introduces a new experimental backend `ggml-hexagon` with support for the Hexagon NPU.

Highlights:
- Supports Hexagon versions: v73, v75, v79, and v81
- Targets Android devices based on Snapdragon SoCs: Gen3, 8-Elite, and 8-Elite Gen5
- Supports Q4_0, Q8_0, MXFP4, and FP32 data types
- Implements core LLM ops: MUL_MAT/MUL_MAT_ID, ADD/SUB/MUL/ADD_ID, RMS_NORM, ROPE, GLU/SWIGLU, SOFTMAX

**Note:** This backend is experimental and may exhibit instability or limited performance across supported devices.
It is intended for early testing and feedback from llama.cpp/ggml developer and user community.

Co-Authored-By: Rajdeep Ganguly <rganguly@qti.qualcomm.com>
Co-Authored-By: Todor Boinovski <todorb@qti.qualcomm.com>

* hexagon: fix format checker errors

* hexagon: update readme and cmake presets

* ci: add android-ndk-build jobs that build plain ARM64 and Snapdragon versions

* hexagon: add simple graph optimizer for stacking MUL_MAT ops with the same input

* hexagon: move ADB helper scripts into scripts/snapdragon/adb

* hexagon: replace all f/printfs with GGML_LOG_...

* readme: add hexagon to the list supported backends

* hexagon: stack malmuts with quantized inputs only

* hexagon: add TODO for fixing issues in hexagon_graph_optimize

* hexagon: update to hex-sdk 6.4.0 and add scripts for running on QDC

* scripts: fix lint errors

* scripts: update qdc pytest script to make linter happy

* hexagon: add reduce sum in fp32

* hexagon: reduce number of vector stores in matmul output

* hexagon: remove the need for vdelta in reduce-multiply-x8

* hexagon: consistent use of reduce_sum_fp32 for row_sums

* hexagon: some more matmul optimizations and comments

Optimize cases where tensor dims are not multiple of 1024 (e.g in Qwen models).
We've handled those cases already but at a higher overhead.

* hexagon: update cmake presets

* hexagon: add OPMASK support for run-bench.sh wrapper

* hexagon: update to use GGML_BACKEND_API

* hexagon: remove unused logic for setting tensor flags for the views

* hexagon: add asserts to set/get_tensor to make sure we handle complete tensors

Same asserts as the CPU backend.

* hexagon: use cpy_tensor slow path for non-host buffers

* hexagon: error checks in the buffer allocator

* cmake: move include(extProj) under ggml-hexagon

* hexagon: don't forget to delete the backend on free

* hexagon: set/get_tensor size assert apply only to quantized tensors

* hexagon: reintroduce HEX_VERBOSE wrapper for GGML_LOG_DEBUG for now

GGML_LOG_DEBUG is always enabled for test-backend-ops and the output gets in the way.
Ideally we need a bit more finer log levels.

* docs: typos in hexagon developer docs (libggm-...)

* hexagon: overhaul error handling in the session/device allocation

this should handle all failure paths in the session allocation.

* hexagon: update cmake presets to enable fp16 vectors

* hexagon: remove unused time_usec function

* hexagon: don't forget to release buffer contexts

* hexagon: fixed indents in hvx-utils (missed clang-format auto-format failure)

* hexagon: remove custom can_repeat function and use ggml_can_repeat

---------

Co-authored-by: Rajdeep Ganguly <rganguly@qti.qualcomm.com>
Co-authored-by: Todor Boinovski <todorb@qti.qualcomm.com>
 2025-10-22 13:47:09 -07:00
Mathieu Baudier
8415f61e23 ci : add Vulkan on Ubuntu with default packages build (#16532) 
* ci: build Vulkan on Ubuntu with default packages

* ci: disable tests in Vulkan build with default Ubuntu packages
 2025-10-12 15:48:03 +02:00
sudhiarm
2c0d875ae6 ci: add ARM64 Kleidiai build and test support (#16462) 2025-10-09 10:13:18 +02:00
Reese Levine
74b8fc17f9 ggml webgpu: profiling, CI updates, reworking of command submission (#16452) 
* Add profiling

* More detailed profiling

* Rework command submission to avoid global locks

* Update wait handling

* try new method of waiting on futures

* Add serializing of command submission in some cases

* Add new pool for timestamp queries and clean up logging

* Serialize command submission in CI and leave a TODO note

* Update webgpu CI

* Add myself as WebGPU codeowner

* Deadlock avoidance

* Leave WebGPU/Vulkan CI serialized

* Fix divide by 0

* Fix logic in division by inflight_threads

* Update CODEOWNERS and remove serialize submit option
 2025-10-07 13:48:56 -07:00
Sigbjørn Skjæret
3a002afafa ci : refactor sdk caching to minimize storage (#16414) 
* refactor sdk caching to minimize storage

* use correct action

* add myself as owner to /.github/actions/ [no ci]
 2025-10-06 17:40:21 +02:00
Daniel Bevenius
ad126479c2 ci : change macos-13 to macos-15-intel (#16401) 
This commit updates the macos-13 runners to macos-15-intel.

The motivation for this changes is the macos-13 runners are scheduled
to be retired on 2025-12-04.

Refs: https://github.blog/changelog/2025-09-19-github-actions-macos-13-runner-image-is-closing-down/
 2025-10-03 11:45:16 +02:00
Sigbjørn Skjæret
72ee736c44 ci : fix ubuntu-latest-cmake-rpc (disable ccache) (#16388) 2025-10-02 13:51:36 +02:00
Eve
f09aefaa84 ci: update vulkan ci (#16294) 2025-10-02 10:10:07 +02:00
Neo Zhang Jianyu
2be72c2b12 SYCL: Update to oneAPI 2025.2 (#16371) 
* update oneapi to 2025.2, use deep-learning-essentials to replace base-tool

* update to 2025.2 use deeplearn essi to replace base toolkit

* add missed dll

* add deep learning essentials

* add sycl-ls

---------

Co-authored-by: Zhang Jianyu <zhang.jianyu@outlook.com>
 2025-10-02 10:16:25 +03:00
uvos
1fe4e38cc2 ci: Properly install rocwmma for hip builds (#16305) 
* CI: Properly install rocwmma for hip builds

on windows we now windows install rocwmma from ubuntu pacakges

* CI: update linux rocm docker build to use rocm 7.0
 2025-10-01 20:18:03 +02:00
Sigbjørn Skjæret
b2ba81dbe0 ci : fix ccache key for ubuntu-cpu-cmake (#16355) 
* fix ccache key for ubuntu-cpu-cmake

* set it for release as well [no ci]
 2025-09-30 21:41:42 +02:00
Sigbjørn Skjæret
2df5bcf357 ci : disable ccache for android (#16348) 2025-09-30 15:38:01 +02:00
Georgi Gerganov
d72f5f7ba2 ci : add AMD runners and workflows (#16249) 
* ci : add AMD runners and workflows

* ci : move AMD jobs to separate workflow

* cont : fix paths
 2025-09-29 17:51:48 +03:00
Aaron Teo
624207e676 devops: add s390x & ppc64le CI (#15925) 
* devops: move s390x and ppc64le ci build

we have access to ubuntu-24.04-s390x and ppc64le images now

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* devops: disable ppc64le for now since they have compiler errors

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* devops: stop warnings as errors

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* devops: switch to non-macro flag

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* devops: going the llama macro route

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* devops: add big-endian gguf test models

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* devops: disable ppc64le to test s390x, check test build

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* devops: dup .gguf.inp files for big-endian tests

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* devops: dup .gguf.out files for big-endian too

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* devops: add python setup and endian byteswap

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* devops: pooring thing does not have s390x python3

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* devops: add missing rust compiler for s390x

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* devops: try rust actions runner

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* Revert "devops: try rust actions runner"

This reverts commit 3f8db04356033d6c1d7eccc75ca396bc5298250c.

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* devops: try a different path for rust

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* devops: dump home directory and user info

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* devops: install gguf-py only

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* devops: missed relative path

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* devops: remove big-endian files since local swapping is working

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* devops: revert test-tokenizer-0 cmakelists

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* Fix unicode flags conversion from and to uint16_t

Bitfields are allocated in different order on s390x

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* Simplify byteswap command

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* Add byteswapping and git-lfs for test-tokenizers-ggml-vocabs

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* Fix endianness detection in vocab loader

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* Disable test-thread-safety on s390x

In this test a model is downloaded,
then immediately loaded to check if more downloads are needed,
and then used for test.

There is no clean way to separate all those steps
 to add byteswapping between them, so just skip this test.

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* Fix q8_0 test in test-quantize-fns

vec_signed uses unexpected rounding mode.
Explicitly use different rounding function.

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* devops: add big-endian stories260K

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* devops: add s390x test-eval-callback

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* devops: fix test does not exist

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* devops: fix model not found llama-eval-callback

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* Fix q3_K dot product error in test-quantize-fns on s390x

Array q8bytes had only 4 elements allocated, but 8 elements accessed.
This lead to write out of bounds and later read of overwritten values out of bounds
and incorrect result.

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* devops: re-enable ppc64le for testing

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* devops: activate test-thread-safety for s390x

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* devops: disable ppc64le tests

for some reason it keeps failing test-thread-safety tests and I do not
    have a machine that is able to replicate the tests.

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* devops: LLAMA_FATAL_WARNINGS=ON

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* Correct repository URL for s390x for test-thread-safety model

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* Fix fs_get_cache_directory

Ensure it works even if both XDG_CACHE_HOME and HOME are unset.
This might happen in containers.

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* Re-enable CI for ppc64le

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* Fortify ggml_rope_impl

Only memcpy data from sections argument if it's non-NULL.

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* Add TODO in struct unicode_cpt_flags to reimplement it in endian-independent way

* Update URL for big-endian model

* Update .github/workflows/build.yml

Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>

* Update remaining mentions of BE models to ggml-org/models repo

---------

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>
Co-authored-by: Aleksei Nikiforov <aleksei.nikiforov@linux.ibm.com>
Co-authored-by: Aleksei Nikiforov <103434461+AlekseiNikiforovIBM@users.noreply.github.com>
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
 2025-09-27 02:03:33 +08:00
R0CKSTAR
a86a580a66 musa: upgrade musa sdk to 4.3.0 (#16240) 
Signed-off-by: Xiaodong Ye <xiaodong.ye@mthreads.com>
 2025-09-26 02:56:38 +02:00
Eve
bee378e098 ci: run the x64 and arm ci on the github machines instead (#16183) 
* run the x64 ci on regular machines

* set up the same thing for arm

fix test-quantize-perf just like #12306

* try to disable sve

* add another sve run
 2025-09-25 08:06:06 +03:00
Georgi Gerganov
f505bd83ca ci : disable AMD workflows + update NVIDIA workflows (#16200) 
* ci : disable AMD workflows + update NVIDIA workflows

* cont : fixes

* cont : update nvidia vulkan workflows
 2025-09-23 20:41:40 +03:00
Georgi Gerganov
0889589dbe ci : enable Vulkan workflow on Mac (#16194) 2025-09-23 13:44:25 +03:00
Georgi Gerganov
ec65fb52f0 ci : remove vulkaninfo calls (#16169) 2025-09-22 10:16:05 +03:00
Georgi Gerganov
4d0a7cbc61 ci : adjust params for less runtime (#16167) 
* ci : adjust params for less runtime

* ci : gate BF16 on some hardware

* ci : move extra tests to Arm runner
 2025-09-22 08:31:40 +03:00
Georgi Gerganov
28baac9c9f ci : migrate ggml ci to self-hosted runners (#16116) 
* ci : migrate ggml ci to a self-hosted runners

* ci : add T4 runner

* ci : add instructions for adding self-hosted runners

* ci : disable test-backend-ops from debug builds due to slowness

* ci : add AMD V710 runner (vulkan)

* cont : add ROCM workflow

* ci : switch to qwen3 0.6b model

* cont : fix the context size
 2025-09-21 16:50:45 +03:00
Daniel Bevenius
a91d035b90 ci : revert back to macos-13 for macOS-latest-cmake-x64 (#16040) 
This commit reverts the change of the runs-on parameter for the
macOS-latest-cmake-x64 job back to macos-13 that was make in
Commit 51abc96bdc ("ci : update
macos-latest* jobs to use macos-latest (#15938)").

The motivation for this is that using macos-latest will cause an ARM
based runner to be used, and not an x64 based runner.

Refs: https://github.com/ggml-org/llama.cpp/pull/15938#issuecomment-3300805127
 2025-09-17 09:34:09 +02:00
Daniel Bevenius
77475530b8 ci : use macos-latest for arm64 webgpu build (#16029) 
This commit updates the runs-on field for the macOS arm64 webgpu build
job to use macos-latest instead of just latest.

The motivation for this is that this job can wait for a runner to pick
up the job for a very long time, sometimes over 7 hours. This is an
attempt to see if this change can help reduce the wait time.

Refs: https://github.com/ggml-org/llama.cpp/actions/runs/17754163447/job/50454257570?pr=16004
 2025-09-16 15:27:52 +02:00
Daniel Bevenius
76888d202e ci : upload xcframework artifact from ios-xcode-build job (#16010) 
This commit updates the github workflows build.yml file to include steps
for uploading and downloading the xcframework artifact. The
macos-latest-swift job now depends on the ios-xcode-build job and
downloads the xcframework artifact produced by it.

The motivation for this changes is that it takes a long time to build
the xcframework and we are currently doing this twice in the workflow.
With this change, we only build it once and reuse the artifact.
 2025-09-16 13:41:38 +02:00
Daniel Bevenius
51abc96bdc ci : update macos-latest* jobs to use macos-latest (#15938) 
* ci : update macos-latest* jobs to use macos-latest

This commit updates the jobs that are named macos-latest* to use the
macos-latest label instead explicit versions.

The motivation for this is that there is currently a mixuture of
versions in this workflow and there are jobs that are failing because
they require a newer version.

Refs: https://github.com/ggml-org/llama.cpp/actions/runs/17644792595/job/50140010907#step:5:1759

* ci : add xcodebuild -downloadPlatform iOS command
 2025-09-16 05:57:16 +02:00
lcy
a0e13dcbe5 build: fix the build failures of Windows HIP release job (#15984) 
* build: fix the cache keys for Windows HIP release job

Update the cache keys to include the HIP SDK version, preventing the
use of outdated ROCm installation caches.

* build: sync changes from release.yml to build.yml

- Update HIP SDK version to 25.Q3 and ROCm version to 6.4.2
- Update the cache keys to reflect the new versions

* build: remove Windows HIP release for gfx1151
since the current stable rocWMMA does not support gfx1151.
 2025-09-14 07:20:35 -07:00
Georgi Gerganov
55758b00ca metal : refactor kernel loading (#15964) 
* metal : refactor bin kernels loading

ggml-ci

* metal : refactor rms kernel loading

ggml-ci

* ci : try to add memory leaks check

ggml-ci

* ci : try to enable memory leak detection for Mac

* cont : seems to be working
 2025-09-13 16:24:22 +03:00
Daniel Bevenius
ff02caf9ee ci : cache ROCm installation in windows-latest-cmake-hip (#15887) 
This commit adds caching of the ROCm installation for the windows-latest-cmake-hip job. 

The motivation for this is that the installation can sometimes hang and/or not complete properly leaving an invalid installation which later fails the build. By caching the installation hopefully we can keep a good installation available in the cache and avoid the installation step.

Refs: https://github.com/ggml-org/llama.cpp/pull/15365
 2025-09-10 05:23:19 +02:00