* 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>
* Add Maincoder model support
* Removed SPM model vocabulary setting and MOE related GGUF parameters
Removed trailing spaces from maincoder.cpp
* removed set_vocab
* added new line
* Fix formatting
* Add a new line for PEP8
* model: add Solar-Open model
* vocab: add solar-open to end eog blacklist
* model: add proper llm type
* chat: basic template for solar open
* typo: fix comment about vocab
* convert: sugested changes
* convert: suggested changes
* chat: change reasoning end tag for solar-open
* llama-chat: add solar-open template
ModernBERT but without `head.norm` so will currently fail to convert and run any other ModernBERT models, PRs with `head.norm` support welcome!
* constants and tensor mappings for modern bert support, model not supported yet but working on getting conversion to work for encoder only
* conversion now working, hf -> gguf
* working on support, now working on building graph
* some cleanup
* cleanup
* continuing
* correct tensor shape for qkv
* fixed tensor mappings and working on buildin graph
* tensor debugging now works -> (llama-eval-callback), instead of simulated gate split with views, GEGLU is now used which does exactly this
* cleanup
* cleanup
* cleanup
* more cleanup
* ubatch issues, the assert for checking equal seqs in llama-graph.cpp when building attention keeps failing, setting ubatch size to 1 when running llama-embedding with --ubatch-size 1 makes it work, but needs to be looked into more
* added cls token per previous modern bert attempt, still working on checking out the rest
* fixed pre tokenizer and still working through previous pr
* working through previous attemp, implimented more accurate conversion per previous attempt, added local sliding window attention that alternates every third layer
* fixed pre tokenizer
* working on swa with local and global alternating attention
* some cleanup and now fails on build attn
* starting to work, and some cleanup, currently failing on last layer construction in graph build
* alternating rope implemented and modern bert graph build succeeds
* fixed asser for equal ubatch seq
* cleanup
* added mask check in vocab
* fixed alternating rope, the hparams.rope_freq_base_train and hparams.rope_freq_base_train_swa were the same and i set them to correct values
* reuse variable
* removed repeat
* standard swa method can be used instead of a new enum being LLAMA_SWA_TYPE_LOCAL
* correct swa layer indexing, is supposed to be 0, 3, 6 ... instead of 1, 4, 7 ...
* more modular hparam setting
* replaced attn out norm with ffn_norm and cosine similarity between hf embds and llama.cpp embds went way up, from 0.05 to 0.24, replaced the cacheless kv with swa todo per the previous conversion
* Update gguf-py/gguf/tensor_mapping.py
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* Update convert_hf_to_gguf_update.py
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* Update src/llama-model.cpp
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* Update src/llama-vocab.cpp
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* Update src/llama-model.cpp
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* Update gguf-py/gguf/tensor_mapping.py
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* Update convert_hf_to_gguf.py
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* Update gguf-py/gguf/tensor_mapping.py
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* Update gguf-py/gguf/tensor_mapping.py
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* Update convert_hf_to_gguf.py
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* Update gguf-py/gguf/tensor_mapping.py
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* Update gguf-py/gguf/tensor_mapping.py
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* Update gguf-py/gguf/tensor_mapping.py
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* Update gguf-py/gguf/tensor_mapping.py
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* Update gguf-py/gguf/tensor_mapping.py
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* Update gguf-py/gguf/tensor_mapping.py
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* Update src/llama-graph.cpp
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* Update src/llama-arch.cpp
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* Update src/llama-model.cpp
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* Update src/llama-model.cpp
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* Update src/llama-model.cpp
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* Update src/llama-model.cpp
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* Update src/llama-model.cpp
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* removed redundant hparam set
* enums for model sizes
* conversion for modern-bert model supported rather than just granite-small
* Update src/llama-model.cpp
Co-authored-by: Gabe Goodhart <ghart@us.ibm.com>
* Update src/llama-model.cpp
Co-authored-by: Gabe Goodhart <ghart@us.ibm.com>
* fixed ordering of enum for freq_base_swa
* fixed where I added residual, now gives much much better embeddings~
* readded cacheless logic
* removing whitespace
* conversion now working for swa pattern - dense every n layers
* modern bert put into seperate src file
* removing whitespace
* fixed whitespace and newline errors in editorconfig job
* Update convert_hf_to_gguf.py
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* better naming convention, n_swa_pattern -> swa_period
* reusing sliding_window_pattern key rather than making new dense_every_n_layers key, and adding writing and reading support
* fixing pyright type-check fail
* Update convert_hf_to_gguf.py
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* Update gguf-py/gguf/gguf_writer.py
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* Update src/llama-hparams.h
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* Update src/llama-model-saver.cpp
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* Update src/models/modern-bert.cpp
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* Update src/models/modern-bert.cpp
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* Update src/models/modern-bert.cpp
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* Update gguf-py/gguf/gguf_writer.py
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* Update src/models/modern-bert.cpp
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* Update src/models/modern-bert.cpp
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* Update src/llama-model.cpp
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* Update src/llama-model-loader.cpp
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* Update src/llama-model-loader.cpp
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* Update src/llama-model-loader.cpp
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* added descriptions in llama-model
* fixed tensor mappings for conversion
* Update src/llama-model.cpp
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* Update src/llama-model.cpp
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* mapping name for size
* nits
* unused
---------
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
Co-authored-by: Gabe Goodhart <ghart@us.ibm.com>
* convert ok
* no deepstack
* less new tensors
* cgraph ok
* add mrope for text model
* faster patch merger
* add GGML_ROPE_TYPE_MRNORM
* add support for metal
* move glm4v do dedicated graph
* convert: add norm_embd
* clip: add debugging fn
* working correctly
* fix style
* use bicubic
* fix mrope metal
* improve cpu
* convert to neox ordering on conversion
* revert backend changes
* force stop if using old weight
* support moe variant
* fix conversion
* fix convert (2)
* Update tools/mtmd/clip-graph.h
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* process mrope_section on TextModel base class
* resolve conflict merge
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* llama : add support for NVIDIA Nemotron Nano 3
This commit adds support for the NVIDIA Nemotron Nano 3 model, enabling
the conversion and running of this model.
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* llama: automatically fit args to free memory
llama-fit-params tool
* fix CI
* hints for bug reports, ensure no reallocation
* fix segfault with Vulkan
* add llama-fit-params to CI
* fix CI
* fix CI
* fix CI
* minor adjustments
* fix assignment of 1 dense layer
* fix logger not being reset on model load failure
* remove --n-gpu-layer hint on model load failure
* fix llama-fit-params verbosity
* fix edge case
* fix typo [no ci]
* Qwen3 Next - cleaned up version
* Whitespaces and stuff
* Correct minor errors
* Update src/llama-model.cpp
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* Misc. fixes.
* Clean up code, add missing hybrid qualifier
* Did someone transpose the SOLVE_TRI result matrix? Perhaps...
* Whitespace
* Proper tensors for cb calls
* Use llama-graph.h vertical alignment
* BROKEN: chunking
* Set new tensors as inputs.
* Proper chunk logic
* It's the circle of life...
* More shenanigans for n_seq > 1
* Nail in the coffin?
* Fix Windows build
* Eh, one fails on Windows, the other fails on Mac... just use general capture.
* quant : cleanup
* model : cleanup
* qwen3 : cleanup
* cont : cleanup
* cont : cleanup
* ggml : revert change
* qwen3 : cleanup
* cont : cleanup
* Readd cmath
* qwen3 : fix typo
* Update convert_hf_to_gguf.py
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* Usual suspects
* fix my bad suggestion
---------
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* Detect GigaChat3-10-A1.8B as deepseek lite
Hardcodes checking number of layers to detect if lite version of deepseek.
* Add commnent identifying deepseek lite variants
deepseek lite variants include DeepSeek-V2-Lite, GigaChat3-10B-A1.8B
* server : support unified context across slots
* cont : fix speculative decoding initialization
* context : fix n_ctx_per_seq computation
* server : purge slots one by one
* tests : add unified cache server tests
* llama : update per-seq context computation
* test-thread-safety : handle tiny training context of the input model
* server : fix server_tokens clear()
* server : use 4 slots + unified KV by default
* llama : add note about context size queries
* cont : update todos [no ci]
* context : do not cap the size of the context
* tests : adjust parameters to be CI friendlier
* context : add warning
* Added GGUF mappings for CogVLM model
* Add tensor mapping for CogVLM visual encoder
* Add CogVLM to conversion script, no vision part yet
* Added CogVLM vision model to conversion script
* Add graph for CogVLM CLIP model
* Add graph for CogVLM
* Fixes for CogVLM. Now compiles.
* Model now runs
* Fixes for cogvlm graph
* Account for graph context change after rebase
* Changes for whitespace
* Changes in convert script according to comments
* Switch CogVLM LLM graph to merged QKV tensor
* Use rope_type variable instead of direct definition
* Change CogVLM CLIP encoder to use SWIGLU
* Switch CogVLM CLIP to use merged QKV
* Apply rebase edits and remove ggml_cont call that is now unnecessary
* clean up
---------
Co-authored-by: Xuan Son Nguyen <son@huggingface.co>
* 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>
* add BailingMoeV2 support
* update llm types
* undo
* undo
* update llm types
* add model collection link
* update
* almost working
* correct group selection and rename n_group_exp
* avoid large top_k and use argmax instead for now
if we had something like argmax2 that would be equivalent, but this works fine until then
* poke
* skip group selection when there are no tokens
* fix 1T conversion
* hopefully fixed expert group selection
third time's the charm?
* make expert group selection generally available
The new LLaDA2Moe model uses this method too, make it generally available regardless of architecture.
* allow n_expert_groups to be 1 (Kimi K2)
* address review suggestions
* llama-quant: add support for mmproj
* Update src/llama.cpp
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* check prefix instead
* small fix
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* model: EmbeddingGemma sentence-transformers dense linear projections support
* model: add support for EmbeddingGemma SentenceTransformers dense linear projections
Adding support for the Dense modules used in EmbeddingGemma models.
EmbeddingGemma is a SentenceTransformers model with additional modules beyond the base Transformer backbone.
See: https://developers.googleblog.com/en/gemma-explained-embeddinggemma-architecture-and-recipe/
* model: add support for EmbeddingGemma SentenceTransformers dense linear projections
- converting model with dense-layers is optional
- introduced dense config params
* Update convert_hf_to_gguf.py
Co-authored-by: Daniel Bevenius <daniel.bevenius@gmail.com>
* fixed formatting issues
* Update src/llama-graph.cpp
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* - removed pooling_type_opt, always allow overriding pooling_type
- asserts checking dense features dims
* fix python lint
* fix ubuntu gcc build warning
* - fixed thread-safety test
- moved asserts to load_hparams
* - tidying up code
- simplifying graph-context expecting both dense weights
* minor : add TODO
---------
Co-authored-by: Daniel Bevenius <daniel.bevenius@gmail.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* implement --no-host to disable host buffer
* fix equal_mparams
* move no-host enumeration order together with other model params
---------
Co-authored-by: slaren <slarengh@gmail.com>
* initial commit for branch 3
* generalize `swa_checkpoint` to `ctx_checkpoint`
this extends `llama-server`'s SWA checkpointing logic to include
hybrid/recurrent models such as Jamba, Granite
* oops
* disable debug prints
* keep backwards compat with `--swa-checkpoints`
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* update prompt re-processing message
* fix off-by-one error per GG
* keep `seq_rm` log per GG
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* server : fix checkpoint logic to support recurrent caches
* server : cleanup and fixes
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* First attempt
* No permute during convert (fixes qk tensors), proper norm application.
* RoPE = NeoX
* Coherence!
* Migrate xielu params from tensors to hyperparameters
* Simple CUDA kernel
* Revert stupid LLM refactorings
* Chat template support
* configchecker / flake8 errors
* Reorder unary.cu
* I do conclude that LLMs are, in fact, stupid.
* Fix after merge
* Final newline
* Make xIELU an UNARY_OP
* Final newline
* Correctly account for parameter shift
* Argh.
* Update ggml/src/ggml-cpu/unary-ops.cpp
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* Refactor: remove unused methods, inline and factorize softplus, add const modifiers
* Revert CUDA changes, implement xIELU as a separate OP
* Pesky newline
* Add float2half / half2float for F16 inputs/outputs
* CUDA variants, attempt 2
* Actually, attempt 3
* Update ggml/src/ggml-cuda/unary.cu
Co-authored-by: Johannes Gäßler <johannesg@5d6.de>
* Missing convert header
* Proper formula and reference for xIELU in the comments.
* Modify unary-ops.cpp to add the functor-based logic besides the template system to retain optimizations
* Apply suggestions from code review
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* Add tensor mappings for Apertus to global list instead
* Fix lazy on scalars
* Update ggml/src/ggml-cuda/unary.cu
Co-authored-by: Johannes Gäßler <johannesg@5d6.de>
* Add comment about the constraints on positive/negative alpha
* Change `softplus` to `ggml_softplus`
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
Co-authored-by: Johannes Gäßler <johannesg@5d6.de>
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* Fix to use hidden_size_per_head
* Fix num heads
* Fix array
* Fix loading weights
* Support old GGUF converted by the previous version of llama.cpp
* Update src/llama-model.cpp
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* Move shared parameter definitions to the outside of loop
* Not calculating n_embd_head_k,v by n_embd / n_head
---------
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* Make a few GLM tensors not required
layer.nextn.shared_head_head and layer.nextn.embed_tokens are both excluded from GLM 4.6 resulting in the model not loading after conversion/quantization, this marks those tensors as not required which makes it work
* Update llama-model.cpp
layer.nextn.shared_head_norm also not required in case of future models
* minicpm: make GGUF scaling keys optional with legacy defaults
Older MiniCPM GGUFs do not include the scaling metadata keys (minicpm.embedding_scale, minicpm.residual_scale, minicpm.logit_scale). The loader currently treats these as required, so quantization fails with:
key not found in model: minicpm.embedding_scale
This change restores backward compatibility by treating these keys as optional in the loader and using the older MiniCPM scaling values:
embedding_scale = 12.0f
residual_scale = 1.4f / sqrt(n_layer)
logit_scale = 256.0f / n_embd
When the GGUF provides the keys, their values override the defaults; otherwise the legacy defaults are used. Newer GGUFs that already include these keys are unaffected.
Fixes: #16192
Signed-off-by: Vinkal Chudgar <vinkal.chudgar@gmail.com>
* Update src/llama-model.cpp
Committed as suggested. Thanks!
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
---------
Signed-off-by: Vinkal Chudgar <vinkal.chudgar@gmail.com>
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* add grok-2 support
* type fix
* type fix
* type fix
* "fix" vocab for invalid sequences
* fix expert tensor mapping and spaces in vocab
* add chat template
* fix norm tensor mapping
* rename layer_out_norm to ffn_post_norm
* ensure ffn_post_norm is mapped
* fix experts merging
* remove erroneous FFN_GATE entry
* concatenate split tensors and add more metadata
* process all expert layers and try cat instead of hstack
* add support for community BPE vocab
* fix expert feed forward length and ffn_down concat
* commit this too
* add ffn_up/gate/down, unsure if sequence is right
* add ffn_gate/down/up to tensor names
* correct residual moe (still not working)
* mess--
* fix embedding scale being applied twice
* add built in chat template
* change beta fast for grok if default value
* remove spm vocab in favor of community bpe vocab
* change attention temp length metadata type to integer
* update attention temp length metadata
* remove comment
* replace M_SQRT2 with std::sqrt(2)
* add yarn metadata, move defaults to hparams
This commit add support for the EmbeddingGemma 300m. This model supports
sliding window attention (SWA) and a new swq_type is introduced to
support symmetric SWA masking.
This commit also extracts the code from the function
llama_is_masked_swa in llama-impl.h, so that the logic can be shared
by both llm_graph_input_attn_no_cache::set_input and
llama_kv_cache::set_input_kq_mask.
With this commit the EmbeddingGemma 300m model can be converted to
to GGUF and used with llama.cpp.
Once the model has been uploaded to HuggingFace it can be used like
this:
```console
./build/bin/llama-cli -hf ggml-org/embeddinggemma-300m-GGUF:Q8_0
```
This commit fixes the model type for the Gemma 270M model in
llama_model.cpp which should be LLM_TYPE_270M. I incorrectly added this
previously as LLM_TYPE_537M which was wrong.
The motivation for this is that it causes the model to not be identified
properly when using tools like llama-bench. For example:
```console
$ ./build/bin/llama-bench -m models/gemma-3-270m-Q8_0.gguf
| model | size | ...
| ------------------------------ | ---------: | ...
| gemma3 ?B Q8_0 | 271.81 MiB | ...
| gemma3 ?B Q8_0 | 271.81 MiB | ...
```
With the changes in this commit the output will be:
```console
$ ./build/bin/llama-bench -m models/gemma-3-270m-Q8_0.gguf
| model | size | ...
| ------------------------------ | ---------: | ...
| gemma3 270M Q8_0 | 271.81 MiB | ...
| gemma3 270M Q8_0 | 271.81 MiB | ...
```
* feat: Add NEMOTRONH to python arch enum
https://github.com/ggml-org/llama.cpp/issues/nemotron-nano-15409
Branch: gabe-l-hart/nvidia-nemotron-nano-15409
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* feat: Add NEMOTRONH to c++ arch enum
https://github.com/ggml-org/llama.cpp/issues/nemotron-nano-15409
Branch: gabe-l-hart/nvidia-nemotron-nano-15409
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* feat: Add NEMOTRONH to llama-arch layer map
https://github.com/ggml-org/llama.cpp/issues/nemotron-nano-15409
Branch: gabe-l-hart/nvidia-nemotron-nano-15409
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* feat: First pass at conversion for nemotronh
https://github.com/ggml-org/llama.cpp/issues/nemotron-nano-15409
Branch: gabe-l-hart/nvidia-nemotron-nano-15409
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* feat: Add a verbose log for each tensor loaded
This is really helpful for diagnosing mismatches between the expected and
received tensors
https://github.com/ggml-org/llama.cpp/issues/nemotron-nano-15409
Branch: gabe-l-hart/nvidia-nemotron-nano-15409
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* feat: First (broken) pass at nemotronh model architecture
It generates tokens, just not valid ones!
https://github.com/ggml-org/llama.cpp/issues/nemotron-nano-15409
Branch: gabe-l-hart/nvidia-nemotron-nano-15409
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix: Explicitly enable add_bos_token during conversion
The `tokenizer.json`/`tokenizer_config.json` in the model are a bit
contradictory. In the config, add_bos_token is set to False, but the
tokenizer model itself has a post_processor that adds the BOS token via
type: TemplateProcessing
https://github.com/ggml-org/llama.cpp/issues/nemotron-nano-15409
Branch: gabe-l-hart/nvidia-nemotron-nano-15409
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix: Use relu2 (LLM_FFN_RELU_SQR) for activation in FFN layers
https://github.com/ggml-org/llama.cpp/issues/nemotron-nano-15409
Branch: gabe-l-hart/nvidia-nemotron-nano-15409
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix: Only allocate attention cache for attention layers (not non-recurrent)
https://github.com/ggml-org/llama.cpp/issues/nemotron-nano-15409
Branch: gabe-l-hart/nvidia-nemotron-nano-15409
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix: Move residual add to after every block
https://github.com/ggml-org/llama.cpp/issues/nemotron-nano-15409
Branch: gabe-l-hart/nvidia-nemotron-nano-15409
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix: Use the correct norm tensor for the MLP blocks
https://github.com/ggml-org/llama.cpp/issues/nemotron-nano-15409
Branch: gabe-l-hart/nvidia-nemotron-nano-15409
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* Nemotron-H: MLP gate cleanup (pass NULL for unused gate)
This model does not use a gate in MLP blocks; pass NULLs for gate tensors to make intent clear and avoid unused-pointer noise.
* SSM: respect ssm_dt_rank for dt_dim when provided
Use GGUF-provided time_step_rank (ssm_dt_rank) to set dt_dim when > 0; fallback to max(64, n_embd/16).
* fix: plamo2 - revert dt_dim to default (remove ssm_dt_rank usage)
* Rename nemotronh to nemotron_h for consistency
- Update architecture name from NEMOTRONH to NEMOTRON_H in constants.py
- Change architecture string from 'nemotronh' to 'nemotron_h' in all files
- Update enum LLM_ARCH_NEMOTRONH to LLM_ARCH_NEMOTRON_H
- Update class name llm_build_nemotronh to llm_build_nemotron_h
- Consistent naming with underscore convention (nemotron_h vs nemotronh)
* feat: Support conversion for older NemotronH models
https://github.com/ggml-org/llama.cpp/issues/nemotron-nano-15409
Branch: gabe-l-hart/nvidia-nemotron-nano-15409
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
---------
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
Co-authored-by: Maicon Domingues <dominguesm@outlook.com>
Co-authored-by: weatherman <fxdstudios@gmail.com>
This commit adds support for the 18-layer model type in the Gemma3
series, which is the size of the Gemma3-270m model.
The motivation for this commit is was the only change required for
Gemma3-270m to be converted to GGUF format and used with llama.cpp.
Once the model has been converted and uploaded to Huggingface it can be
used like this:
```console
$ ./build/bin/llama-cli -hf ggml-org/gemma-3-270m-GGUF:Q8_0
```
* Add support for Llada-8b: diffusion model
* Add README
* Fix README and convert_hf_to_gguf
* convert_hf_to_gguf.py: address review comments
* Make everything in a single example
* Remove model-specific sampling
* Remove unused argmax
* Remove braced initializers, improve README.md a bit
* Add diffusion specific gguf params in set_vocab, remove setting rope_theta and rms_norm_eps
* Remove adding the mask token
* Move add_add_bos_token to set_vocab
* use add_bool in gguf_writer.py
Sigbjørn Skjæret
Daniel Bevenius
Prabod
tt
o7si
HelloKS
momonga
Johannes Gäßler
Xuan-Son Nguyen
Saba Fallah
Alessandro98-git
Ryan Mangeno
Georgi Gerganov
Eric Zhang
philip-essential
Herman Semenoff
Piotr Wilkin (ilintar)
Gilad S.
Aaron Teo
william pan
ubergarm
Bartowski
Li Pengzhan
Giuseppe Scrivano
JJJYmmm
Tianyue-Zhao
Shunta Saito
Max Krasnyansky
Tarek Dakhran
Gadflyii
ddh0
anavp-nvidia
Vinkal
Douglas Hanley
Tarek Dakhran
Shane A
Aman Gupta
Jie Fu (傅杰)
Gabe Goodhart
Juk Armstrong
Sam
compilade
stevenkuang
Diego Devesa
Dongliang Wei