3bf9691dfd
Fixes #12823 (llama/12830)
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* Including limits file on AIX
* Fixes #12823
2025-04-24 20:39:16 +03:00
ba444e9c23
ggml-cpu-impl.h: do not redefine bool on POWER9 (llama/12856)
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error: unknown type name '_Bool'
2025-04-24 20:39:16 +03:00
c6caf8eef2
ggml-impl.h: fix build on POWER9 (llama/12855)
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error: ISO C++17 does not allow 'register' storage class specifier
2025-04-24 20:39:16 +03:00
6cae79a1d7
CANN: Support Opt CONV_TRANSPOSE_1D and ELU (llama/12786)
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* [CANN] Support ELU and CONV_TRANSPOSE_1D
* [CANN]Modification review comments
* [CANN]Modification review comments
* [CANN]name adjustment
* [CANN]remove lambda used in template
* [CANN]Use std::func instead of template
* [CANN]Modify the code according to the review comments
---------
Signed-off-by: noemotiovon <noemotiovon@gmail.com >
2025-04-24 20:39:16 +03:00
b9bfe0c693
vulkan: In coopmat2 mmq, load q4_k/q5_k scales through shared memory (llama/12833)
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q4_k and q5_k had a lot of redundant global loads where the same 16B of
scale information is repeatedly loaded and decoded during each loop iteration.
This change restructures the loops to more explicitly iterate over whole
blocks in the outer loop (with unrolled inner loop) and to copy/decode the
scale data into shared memory once at the start of each outer loop. The copy
is pipelined so the scale load from global memory is relatively cheap.
This improves q4_k/q5_k model prompt processing performance by around 5-7%.
I briefly tried applying this to q6_k and q4_0, and it didn't help for q6_k
and hurt for q4_0.
The big "else" path in mul_mm_cm2.comp that had all the clamped/unclamped
variants isn't used as often as it originally was (e.g. due to the padded_N
change), so I trimmed it down to offset some of the new complexity of the
semi-manual loop unrolling.
2025-04-24 20:39:16 +03:00
1d50c6ac22
vulkan: Use fp16 for the flash attention P*V multiplication (llama/12783)
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This is consistent with the ggml-cuda behavior and the mul_mat fallback.
2025-04-24 20:39:16 +03:00
79f23d9132
cuda : add f32 to bf16 copy op (llama/12806)
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This allows BF16 KV-cache on CUDA.
2025-04-24 20:39:16 +03:00
ee2cbeeb74
llama : fix FA when KV cache is not used (i.e. embeddings) (llama/12825)
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* ggml : FA supports F32 V
* graph : cast KV to F16 when the KV cache is not used
ggml-ci
* server : add test that exercises embeddings with FA enabled
ggml-ci
2025-04-24 20:39:16 +03:00
868a5ce310
ggml: don't include arm_neon.h when using CUDA 12 with ARM Neon (ggml/1187)
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fix #1186
2025-04-24 20:39:16 +03:00
b9c71fae5a
ggml : add bilinear upscale support (ggml/1185)
2025-04-24 20:39:16 +03:00
6d67c6d93d
ggml : add more generic custom op, remove deprecated custom ops (ggml/1183)
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* ggml : add more generic ggml_custom op
* ggml : remove deprecated custom ops
2025-04-24 20:39:16 +03:00
12cade118e
Revert "sycl:remove redundant memcopy in function ggml_backend_sycl_buffer_set_tensor" (llama/12812)
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* Revert "sycl: remove redundant memcopy in function ggml_backend_sycl_buffer_s…"
This reverts commit 518a01480eb3a7c80a4951b430db9dee55428310.
* Update ggml/src/ggml-sycl/ggml-sycl.cpp
* Update ggml/src/ggml-sycl/ggml-sycl.cpp
* rm tail space
2025-04-24 20:39:16 +03:00
fd1c725e65
opencl: better identify Adreno GPU (llama/12760)
2025-04-24 20:39:16 +03:00
d33fd00cfe
cuda : fix HIP and MUSA BF16 (llama/0)
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ggml-ci
2025-04-24 20:39:16 +03:00
3e0d89782a
sycl: remove redundant memcopy in function ggml_backend_sycl_buffer_set_tensor (llama/12734)
2025-04-24 20:39:16 +03:00
7074b622eb
CANN: fix typo in ggml-cann (llama/12733)
2025-04-24 20:39:16 +03:00
b8d3e45342
CANN: Refactor to reduce duplicate code (llama/12731)
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* CANN: Refactor to reduce duplicate code
* CANN: fix review comment
2025-04-24 20:39:16 +03:00
1901505138
musa: fix compilation warnings in mp_22/31 (llama/12780)
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Signed-off-by: Xiaodong Ye <xiaodong.ye@mthreads.com >
2025-04-24 20:39:16 +03:00
3c26dd3353
vulkan: fix NaN issue in flash attention shader (llama/12776)
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Use -FLT_MAX/2 rather than -inf as the initial value for computing the maximum.
2025-04-24 20:39:16 +03:00
d792d2a2dc
vulkan: Use unclamped loads for flash attention mask (llama/12720)
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nem1 must be a multiple of GGML_KQ_MASK_PAD, and GGML_KQ_MASK_PAD is a multiple
of the number of rows in the matrix. The KV dim is a multiple of the number of
columns for the aligned shader.
2025-04-24 20:39:16 +03:00
8add58aa5e
Vulkan: Tune Vulkan mmq int dot shader for performance (llama/12767)
2025-04-24 20:39:16 +03:00
8f8ede1b12
sycl: allow ggml-sycl configuration and compilation using Visual Studio project/solution (llama/12625)
2025-04-24 20:39:16 +03:00
3a6fe8d767
cmake: fix ggml-shaders-gen compiler paths containing spaces (llama/12747)
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fixes error for compiler paths with spaces
2025-04-24 20:39:16 +03:00
76231bda56
vulkan: Hybrid waitForFences/getFenceStatus to reduce fence latency (llama/12630)
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There seems to be a bubble waking up from waitForFences, which costs a few
percent performance and also increased variance in performance. This change
inserts an "almost_ready" fence when the graph is about 80% complete and we
waitForFences for the almost_ready fence and then spin (with _mm_pauses) waiting
for the final fence to be signaled.
2025-04-24 20:39:16 +03:00
785437c253
vulkan: set cmake minimum and project name in vulkan-shaders (llama/12744)
2025-04-24 20:39:16 +03:00
2f0612cb1c
CUDA: Prefer vector flash decoding kernel for Gemma models (llama/12738)
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* Prefer vector flash decoding kernel for Gemma models
Vector flash decoding kernel was not being picked for models with head dimension 256. Gemma models are in this category.
Removing this limit improves e2e performance by upto 12% in gen phase throughput for Gemm models.
* Update ggml/src/ggml-cuda/fattn.cu
Co-authored-by: Johannes Gäßler <johannesg@5d6.de >
---------
Co-authored-by: Johannes Gäßler <johannesg@5d6.de >
2025-04-24 20:39:16 +03:00
e944065d5b
vulkan: Fix missing cmake logic for dot product extension (llama/12721)
2025-04-24 20:39:16 +03:00
ccc7b5df0b
fix MUSA compiler warning (llama/12704)
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* fix MUSA compiler warning
* replace (void) with GGML_UNUSED
2025-04-24 20:39:16 +03:00
fbed36851e
CANN: Support operator SIN COS ARGMAX (llama/12709)
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* [CANN]support sin cos argmax
Signed-off-by: noemotiovon <noemotiovon@gmail.com >
* [CANN]codestyle adjustment
Signed-off-by: noemotiovon <noemotiovon@gmail.com >
* [CANN]Remove redundant code
Signed-off-by: noemotiovon <noemotiovon@gmail.com >
---------
Signed-off-by: noemotiovon <noemotiovon@gmail.com >
Co-authored-by: noemotiovon <noemotiovon@gmail.com >
2025-04-24 20:39:16 +03:00
d1d847f184
Simplify and improve CUDA graphs through use of indirect copy pointers (llama/9017)
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* CUDA: Simplify and improve CUDA graphs through use of indirect copy pointers
Previously there was complexity in the CUDA graphs implementation due
frequently changing parameters to copy kernels associated with K and V
cache pointers. This patch simplifies by using indirection to avoid
such parameters frequently changing, avoiding the need for frequent
graph updates.
Fixes #12152
* Addressed comments
* fix HIP builds
* properly sync to stream
* removed ggml_cuda_cpy_fn_ptrs
* move stream sync before free
* guard to only use indirection with graphs
* style fixes
* check for errors
---------
Co-authored-by: slaren <slarengh@gmail.com >
2025-04-24 20:39:16 +03:00
337f91d4a6
CANN: Fix failed test cases (llama/12708)
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* CANN: Fix memory waste in aclnn_tensor
* CANN: fix backend ops fail
* CANN: fix acl_tensor memory alloc.
* CANN: format
* CANN: remove trailing whitespace
2025-04-24 20:39:16 +03:00
317a0031f9
opencl: use max_alloc_size in backend ctx instead of querying again (llama/12705)
2025-04-24 20:39:16 +03:00
b243416918
vulkan: Implement split_k for coopmat2 flash attention. (llama/12627)
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When using group query attention, we have one workgroup per KV batch and this
can be very few workgroups (e.g. just 8 in some models). Enable split_k to
spread the work across SMs. This helps a lot when the KV cache is large.
2025-04-24 20:39:16 +03:00
6e532c7187
cmake: remove caching from vulkan coopmat checks (llama/12719)
2025-04-24 20:39:16 +03:00
2105b110d3
vulkan: Implement grouped query attention in the coopmat2 FA shader (llama/12559)
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When adjacent batches of Q share the same batches of K/V, batch them into
the same workgroup. For example, when:
dst(128,32,1,1) = FA(q(128,1,32,1), k(128,16640,8,1), v(128,16640,8,1))
previously we would run 32 workgroups computing 1 result each, now we will
run 8 workgroups computing 4 results each.
This doesn't directly translate to better performance (at least when you have
>=32 SMs), but in a subsequent change I'll enable split_k which will scale much
better with 4x fewer workgroups.
2025-04-24 20:39:16 +03:00
f82622180f
Vulkan: Fix mmq int dot float cache size (llama/12722)
2025-04-24 20:39:16 +03:00
a71c64512a
llama : add option to override model tensor buffers (llama/11397)
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* llama : add option to override tensor buffers
* ggml : fix possible underflow in ggml_nbytes
2025-04-24 20:39:16 +03:00
1e9c2f87f1
ggml : simplify Arm fp16 CPU logic (ggml/1177)
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* ggml : simlpify Arm fp16 CPU logic
ggml-ci
* cont : bring back CUDA/MUSA checks
ggml-ci
2025-04-24 20:39:16 +03:00
06ce8f83e6
CUDA: don't convert BF16 weights to FP32 (ggml/1174)
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* add bf16 support
* use convert_from_bf16_cuda instead of convert_unary_cuda for f32
* revert 7ec5085
* move functionality into convert_unary with constexpr
2025-04-24 20:39:16 +03:00
8b92060a10
coreml : set convert_to="mlprogram" in convert
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* coreml : skip model load in convert-whisper-to-coreml.py
This commit updates the conversion process for Whisper models to use the
"mlprogram" format instead of "neuralnetwork".
The motivation for this change is that when using the "neuralnetwork"
format the underlying model produced is based on protobuf and my
understanding is that there are limitations to this format, such as
sizes of strings and the complexity of the model.
Currently when trying to convert larger models such as large-v3 the
conversion fails but succeeds for smaller models.
The "mlprogram" format is a more recent addition to CoreML and is
designed to be more flexible and powerful, allowing for more complex
models and larger data types. This seems to work for larger and smaller
models alike and unless I'm there are considerations that I'm not aware
of I think this is what we should be using moving forward.
The error that is generated for large models is the following:
```console
Running MIL backend_neuralnetwork pipeline: 100%|█████████| 9/9 [00:00<00:00, 35.44 passes/s]
Translating MIL ==> NeuralNetwork Ops: 100%|███████████| 5641/5641 [03:31<00:00, 26.65 ops/s]
Traceback (most recent call last):
File "/Users/danbev/work/ai/whisper-work/models/convert-whisper-to-coreml.py", line 322, in <module>
encoder = convert_encoder(hparams, encoder, quantize=args.quantize)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/danbev/work/ai/whisper-work/models/convert-whisper-to-coreml.py", line 255, in convert_encoder
model = ct.convert(
^^^^^^^^^^^
File "/Users/danbev/work/ai/whisper-work/venv/lib/python3.11/site-packages/coremltools/converters/_converters_entry.py", line 635, in convert
mlmodel = mil_convert(
^^^^^^^^^^^^
File "/Users/danbev/work/ai/whisper-work/venv/lib/python3.11/site-packages/coremltools/converters/mil/converter.py", line 186, in mil_convert
return _mil_convert(
^^^^^^^^^^^^^
File "/Users/danbev/work/ai/whisper-work/venv/lib/python3.11/site-packages/coremltools/converters/mil/converter.py", line 245, in _mil_convert
return modelClass(
^^^^^^^^^^^
File "/Users/danbev/work/ai/whisper-work/venv/lib/python3.11/site-packages/coremltools/models/model.py", line 489, in __init__
self.__proxy__, self._spec, self._framework_error = self._get_proxy_and_spec(
^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/danbev/work/ai/whisper-work/venv/lib/python3.11/site-packages/coremltools/models/model.py", line 550, in _get_proxy_and_spec
_MLModelProxy(
ValueError: basic_string
```
Refs: https://github.com/ggml-org/whisper.cpp/issues/3012
2025-04-23 08:24:38 +02:00
7858eddd10
ci : disable freeBSD job in build.yml ( #3064 )
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This commit disables the FreeBSD job in build.yml of the GitHub Actions
workflow.
The motivation for this is that this job seems to stall and timeout from
time to time, taking up to 6 hours to complete/cancel.
2025-04-22 11:07:54 +02:00
3a88f1e504
examples : add HEAPU8 to exported runtime methods ( #3062 )
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This commit adds `HEAPU8` to the list of exported methods.
The motivation for this commit is that currently this is causing an
error on Window systems where HEAPU8 in undefined, which results in the
following error message in the web console:
```console
main.js:1 Uncaught TypeError:
Cannot read properties of undefined (reading 'buffer') at __emval_get_property
(main.js:1:1363125) at 003a453a:0xc4a47 at 003a453a:0xc51cd at
Object.full_default (eval at craftInvokerFunction (main.js:1:1347011),
<anonymous>:9:10) at whisper.cpp/:647:42
```
Resolves: https://github.com/ggml-org/whisper.cpp/issues/3059
2025-04-20 19:40:25 +02:00
f0d2bfbfb7
ruby : make Ruby bindings installed with build options ( #3056 )
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* Fix signature of URI.new7s return value
* Use path instead of string | _ToPath
* Add document comment to RBS
* Remove unnecessary build flags
* Remove unnecessary line
* Remove files have become unnecessary
* Make gem install accept build options for whisper.cpp
* Add instraction for build options in README
* Add methods for check to Options
* Test build options
* Rename: configs -> options
* Add assert_installed assertion
* Use assert_installed
* Remove unused attribute
* Extract dependency check logic as Dependencies class
* Update README
* Add WHISPER_FFMPEG option
* Test extra build options only on local test
* Bump version to 1.3.2 [skip ci]
2025-04-17 18:49:58 +09:00
170b2faf75
whisper : add no_context parameter to whisper_params ( #3045 )
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2025-04-16 06:24:38 +02:00
f8a3509b6d
examples : add FFmpeg v7.0 support to ffmpeg-transcode.cpp ( #3038 )
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FFmpeg introduced a new channel layout API that uses `AVChannelLayout`
interface in v6.0. It subsequently dropped the old bitmask-based API
in v7.0.
This updates decode_audio() to support the new channel layout API,
so that we can compile `whisper-cli` and `whisper-server` with FFmpeg
v7.0 or later.
Tested on on Ubuntu 24.10 with FFmpeg v7.0.2.
Signed-off-by: Fujimoto Seiji <fujimoto@ceptord.net >
2025-04-15 06:09:00 +02:00
2a2d21c75d
ruby: use CMake in build process ( #3043 )
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* Use CMake to build shared object
* Make Rakefile follow change of build process
* Add test for packaging
* Run CI for Ruby bindings almost always
because each CMakeLists.txt might affect Ruby bindings
* Enable PIC
* Bump Ruby version to 3.2 on CI
* Check libgomp
* Check dependency of whisper.cpp accurately
2025-04-14 18:18:27 +09:00
9cfcd6cc45
docs : update README.md to note newer nvidia gpus ( #3031 )
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Resolves: https://github.com/ggml-org/whisper.cpp/issues/3030
2025-04-11 08:54:51 +02:00
e853620270
addon.node : support max_context api for addon.node ( #3025 )
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* feat: support max content
* feat: show api in test file
---------
Co-authored-by: linxiaodong <calm.lin@wukongsch.com >
2025-04-11 06:36:38 +02:00
549db9376f
whisper : reduce delta_min from 1000ms to 100ms ( #3028 )
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ggml-ci
2025-04-11 06:23:02 +02:00
33a25e4dda
docs : document how to use 'WHISPER_FFMPEG' build option ( #3029 )
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FFmpeg integration was introduced in 1b51fdffujimoto@ceptord.net >
2025-04-10 18:21:38 +02:00
