* ggml: add env var GGML_OP_OFFLOAD_MIN_BATCH
* makes the min_batch_size for triggering op offload configurable via env var, defaulting to the prior hardcoded value of 32
* ggml: read GGML_OP_OFFLOAD_MIN_BATCH once and store to dev ctx
* cann: forward declaration of device context struct
* cann: move offload op check after device context declaration
* cuda: fix whitespace
Co-authored-by: Aman Gupta <amangupta052@gmail.com>
---------
Co-authored-by: Aman Gupta <amangupta052@gmail.com>
* 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>
* CUDA: Fixed obj byte size instead of obj count being passed to pool alloc (fattn-common, dst_tmp_meta)
* CUDA: Explicitly casted some of the int alloc counts before multiplication in argsort
---------
Co-authored-by: pl752 <maximpl752@gmail.com>
* ggml-cuda: fixed assertion in ggml_cuda_cpy (#18140)
* ggml-cuda: changes in data types to int64_t
* ggml-cuda: added asserts for CUDA block numbers
* ggml-cuda: changed the condition for y and z dimension
* ggml-cuda: fix blackwell native builds
Replace 12x in native architectures by 12xa
* replace for GGML_NATIVE=OFF too
* only replace for native
* remove 120f-virtual for default compilation
---------
Co-authored-by: Aman Gupta <aman>
I updated test_topk_moe to more closely match llm_graph_context::build_moe_ffn
and added coverage for exp_probs_b and some other missing combinations. This
exposed a bug in both CUDA and Vulkan backends where they were assuming the
input to argsort and the input to get_rows are the same. I'd like to optimize
this graph in another change, but for now just get it functional.
CUDA also had a bug where it got n_experts from the wrong place, leading to
GGML_ASSERT failures in some of the new tests.
* enable mmf for RDNA3
* disable mmf for some shape
* move some mmvf to mmf
* more mmfv to mmf
* 3 is good in mmvf
---------
Co-authored-by: zhang hui <you@example.com>
* Extended TRI
* Fix whitespace
* chore: update webui build output
* Just use cuBLAS for everything...
* Merge both versions
* Remove incorrect imports causing failures for CI
* Still failing... remove all direct cublas imports and rely on common imports from "common.cuh"
* Defines for hipBlas
* Aaaand MUSA defines...
* I hate this job...
* Stupid typo...
* Update ggml/src/ggml-cuda/solve_tri.cu
Co-authored-by: Johannes Gäßler <johannesg@5d6.de>
---------
Co-authored-by: Johannes Gäßler <johannesg@5d6.de>
* ggml-cuda: optimize solve_tri_f32_fast and fix stride handling
- Switch from using shared memory for the RHS/solution matrix to a register-based approach (x_low, x_high), reducing shared memory pressure and bank conflicts.
- Implement explicit `fmaf` instructions for the reduction loop.
- Update kernel arguments to pass strides in bytes rather than elements to align with standard ggml tensor arithmetic (casting to `char *` before addition).
- Remove unused `MAX_K_FAST` definition.
* Small cleanup
* Remove comments in solve_tri.cu
* Update ggml/src/ggml-cuda/solve_tri.cu
Co-authored-by: Johannes Gäßler <johannesg@5d6.de>
* Update ggml/src/ggml-cuda/solve_tri.cu
Co-authored-by: Johannes Gäßler <johannesg@5d6.de>
* Update ggml/src/ggml-cuda/solve_tri.cu
Co-authored-by: Johannes Gäßler <johannesg@5d6.de>
* Use const for variables in solve_tri.cu
* Replace fmaf with more readable code
* remove last fmaf
---------
Co-authored-by: Johannes Gäßler <johannesg@5d6.de>
* Feat: Added vulkan circular tiling support
* Feat: Added cpu circular
* Feat: Added cuda kernels
* Added tests
* Added tests
* Removed non-pad operations
* Removed unneded changes
* removed backend non pad tests
* Update test-backend-ops.cpp
* Fixed comment on pad test
* removed trailing whitespace
* Removed unneded test in test-backend-ops
* Removed removed test from calls
* Update ggml/src/ggml-vulkan/vulkan-shaders/pad.comp
Co-authored-by: Ruben Ortlam <picard12@live.de>
* Fixed alignment
* Formatting
Co-authored-by: Aman Gupta <amangupta052@gmail.com>
* Format pad
* Format
* Clang format
* format
* format
* don't change so much stuff
* clang format and update to bool
* fix duplicates
* don't need to fix the padding
* make circular bool
* duplicate again
* rename vulkan to wrap around
* Don't need indent
* moved to const expr
* removed unneded extra line break
* More readable method calls
* Minor wording changes
* Added final newline
* Update ggml/include/ggml.h
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* Update ggml/include/ggml.h
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* Added circular pad ext tests
* Gate non circular pad devices
* Cleaned gating of non-circular pad devices
---------
Co-authored-by: Phylliida <phylliidadev@gmail.com>
Co-authored-by: Ruben Ortlam <picard12@live.de>
Co-authored-by: Aman Gupta <amangupta052@gmail.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* enabled wmma instructions for most quantizations other than q2k
* fixed the last q2_k test case failure
* address comments: fix out of bound write for RDNA4, add comments after #endif
* clean up rebase: fix ne error in half2
* fix the EditorConfig CI
* Add support for CUMSUM and TRI for CUDA.
* Minor optimizations.
* Correct warp_prefix_inclusive_sum in float2 variant to return float2
* Optimize TRI
* Whitespace
* Fix strides.
* Implement double loop
* Whitespace
* Fix HIP compilation bugs
* Optimizations + big case performance tests
* Implement using CUB with fallback to custom kernel
* Remove error message.
* Fixes from code review
* Comment out CPU-unsupported F16/BF16 cases to fix CI
* Fine, you win :P
* Fix last cast, use NO_DEVICE_CODE and GGML_UNUSED_VARS
* Vary warp-size based on physical warp size
* Add GGML_UNUSED_VARS in tri as well
* Use constexpr and call prefix_inclusive with warp_size template param
* Update ggml/src/ggml-cuda/cumsum.cu
Co-authored-by: Johannes Gäßler <johannesg@5d6.de>
* Apply suggestions from code review
Co-authored-by: Johannes Gäßler <johannesg@5d6.de>
* Change to tid % warp_size
* Fix strides; hardcode mask; add ggml_lane_mask_t
* Missing renames, remove unused get_warp_mask(), explicit calls to ggml_cuda_info()
* Too hasty...
---------
Co-authored-by: Johannes Gäßler <johannesg@5d6.de>
* Adjust to pytorch
* Add antialiasing upscale
* Increase number of patches to 1024
* Handle default marker insertion for LFM2
* Switch to flag
* Reformat
* Cuda implementation of antialias kernel
* Change placement in ops.cpp
* consistent float literals
* Pad only for LFM2
* Address PR feedback
* Rollback default marker placement changes
* Fallback to CPU implementation for antialias implementation of upscale
* enable mmf for rdna4
* move some mmvf to mmf
* revert lds128 for wmma loading
* Revert "revert lds128 for wmma loading"
This reverts commit db9ae8b6b4.
* Revert "enable mmf for rdna4"
This reverts commit 698c9f2418.
* Revert "move some mmvf to mmf"
This reverts commit 99b92bd665.
* enable mul_mat for rdna4
---------
Co-authored-by: zhang hui <you@example.com>
* patch failed test case MUL_MAT(type_a=q4_0,type_b=f32,m=576,n=512,k=576,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1) for enabling WMMA on RDNA4
* Quick clean up on mma.cuh to add ggml_cuda_memcpy_1 back in for half2 and bfloat162
* first commit naive test to enable mmq for RDNA4
* adding appropriate WMMA instructions
* git rebase on top of master: fixing the correctness of the mat mul operations, updating layout mappings for RDNA4
* clean up merge conflicts
* add comments and code clean up
* PR clean up, addressed comments
* enable MMQ fallback on RDNA4
* addressed comments: add guards in load generic, separate wmma branch for use_mmq function
* Revert build-xcframework.sh
* Formating: remove trailing whitespace
* revert CMake files
* clean up after rebase: remove duplicated change, revert cmake files
* clean up after rebase: revert changes from build-xcframework.sh
* clean up: remove extra space line in mma.cuh
* Revert "clean up: remove extra space line in mma.cuh"
This reverts commit b39ed57c45.
* mmf for rdna4
* align the padding for rdna4
* forbit mul_mat_f for rdna4
* fix as comment
* remove device kernels
* add constexpr for early return
* update based on review comment
* change based on the review comment
* pass compile error
* keep code consistency
---------
Co-authored-by: zhang hui <you@example.com>
* Fix too relaxed check on CUDA "fast copy" (can_be_transposed) condition
* Argh.
* Making CISC happy ;)
* Integrate CONT tests
* Use loopy loop
* Skip new tests for (B)F16 for now.
* CUDA: add fused rope
* move k forward_expand up
* create helper function instead of re-using params
* make assert statement more in line with comment
* rope_norm: coalesced writes to global mem
* vulkan : implement upscale with bicubic interpolation
* cuda : implement upscale with bicubic interpolation
* tests : add ggml_interpolate with GGML_SCALE_MODE_BICUBIC to backend tests
* adapt OpenCL backend to not support the OP in that case so tests don't fail
* print scale mode & flags in test-backend-ops
* WIP
* added a cpy kernel specific to transposed tensor which uses smem to avoid uncoalesced access; test cases also added shwoing improved memory bandwidth
* added BF16 support
* more strict check to make sure src0 is a transpose
* reformulated to handle more complicated transpose cases
* bring back 2D transpose for higher performance
* allow build on windows
* tranpose copy more shapes
* minor tweak
* final clean up
* restore some test cases
* keep only the kernel for true tranposed case; updated with review suggestions
* make CI happy
* remove headers not needed
* reduced bank conflicts for fp16 and bf16
* add missing const*
* now bank conflicts free
* use padding instead of swizzling
---------
Co-authored-by: bssrdf <bssrdf@gmail.com>
* CUDA: Remove unneded bias/gate dims in fused mmvq
Pointed out
[here](https://github.com/ggml-org/llama.cpp/pull/16847#discussion_r2476798989)
that only a single value is needed per target col per thread
* Apply suggestions from code review
Co-authored-by: Johannes Gäßler <johannesg@5d6.de>
* Fix "Error 991-D: extra braces are nonstandard" during compilation
---------
Co-authored-by: Johannes Gäßler <johannesg@5d6.de>
* CUDA: Volta tensor core support for MMF
* more generic checks for hardware support
* Update ggml/src/ggml-cuda/mmf.cuh
Co-authored-by: Aman Gupta <amangupta052@gmail.com>
---------
Co-authored-by: Aman Gupta <amangupta052@gmail.com>
This is realised by loading them into registers before computation of
the dot-product, effectively batching them together with said
dot-product. As a lot of threads are alive here, the warp scheduler has
enough threads available to effectively hide the cost of additionally
loading those two floats.
* CUDA: Fix bug in topk-moe for gpt-oss
When using ggml_can_fuse_subgraph, the output nodes which are passed are wrong. This causes `test-backend-ops` to still fuse ndoes (because the nodes are not used elsewhere in the graph),
but it actually doesn't fuse in the actual gpt-oss
* fix for qwen3 too
* change ifndef to ifdef
* ggml : fix interpolate with align-corners and ne=1
* avoid division by zero if one of the spatial dimensions is 1
* cpu, cuda, opencl returned correct result anyway due to clamp
* vulkan didn't clamp for align-corners so results were broken
* fix clang warning
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