* opencl: Add support for multiple devices
... but limited to one platform. A platform with a GPU will be preferred.
Additionally:
* Filter out devices that lack capabilities needed by the backend
implementation (half support, OpenCL 2.0+, etc).
* Make ggml_backend_opencl_reg() thread-safe.
* fixup: fix an error in sync_with_other_backends
... when there is only one OpenCL device available.
* opencl: fix couple crashes
* fix kernel launches failed on devices which do not support
non-uniform work-groups. When non-uniform work-groups are not
supported, set `local_work_size` to NULL (= let driver choose the
work-group sizes). This patch does not cover everything - just the
cases tested by test-backend-ops.
* fix sub-buffer creation failed due to `cl_buffer_region::origin` not
being aligned to `CL_DEVICE_MEM_BASE_ADDR_ALIGN`.
* OpenCL: query non-uniform WG sizes only on OpenCL 3.0+
* musa: fix build warning (unused parameter)
Signed-off-by: Xiaodong Ye <xiaodong.ye@mthreads.com>
* musa: upgrade MUSA SDK version to rc4.0.1
Signed-off-by: Xiaodong Ye <xiaodong.ye@mthreads.com>
* musa: use mudnn::Unary::IDENTITY op to accelerate D2D memory copy
Signed-off-by: Xiaodong Ye <xiaodong.ye@mthreads.com>
* Update ggml/src/ggml-cuda/cpy.cu
Co-authored-by: Johannes Gäßler <johannesg@5d6.de>
* musa: remove MUDNN_CHECK_GEN and use CUDA_CHECK_GEN instead in MUDNN_CHECK
Signed-off-by: Xiaodong Ye <xiaodong.ye@mthreads.com>
---------
Signed-off-by: Xiaodong Ye <xiaodong.ye@mthreads.com>
Co-authored-by: Johannes Gäßler <johannesg@5d6.de>
* Remove mmap workaround on windows
After some testing I found that mmap is supported on windows and for
many GPUs on Linux. Therefore I remove the workaround for windows since
it is not necessary.
* Update llama-bench README
SYCL backend introduced a workaround that allows execution of
llama-bench also without specifying `--mmp 0` flag
This shader uses coopmat1 to do the Q*K^T multiply. The P*V multiply is more
difficult for various reasons so I haven't done it. Performance for this
shader is around 2.5x better than for the scalar shader when doing prompt
processing. Some of the benefit may be from other optimizations like staging
through shared memory, or splitting by rows.
* batched-bench : fix pp batch contents
* metal : optimize multi-sequence FA vec kernel
ggml-ci
* metal : use FA-vec kernel up to batch size 20
ggml-ci
* llama/ggml: add LLM training support
more compact progress bar
llama_save_model_to_file
llama_opt_param_filter
ggml_graph_dup force_grads
refactor ggml_opt, fix test-opt
* remove logits_all
* refactor CUDA implementation for ACC
* reset graph at beginning of opt period
* ggml-cpu: Integrate fp32=bf16xbf16 SME KleidiAI kernel
Signed-off-by: Dan Johansson <dan.johansson@arm.com>
* * code review fixes
Signed-off-by: Dan Johansson <dan.johansson@arm.com>
* * adds a comment that clarifies barrier usage
Signed-off-by: Dan Johansson <dan.johansson@arm.com>
---------
Signed-off-by: Dan Johansson <dan.johansson@arm.com>
Co-authored-by: Charles Xu <charles.xu@arm.com>
* vulkan: scalar flash attention implementation
* vulkan: always use fp32 for scalar flash attention
* vulkan: use vector loads in scalar flash attention shader
* vulkan: remove PV matrix, helps with register usage
* vulkan: reduce register usage in scalar FA, but perf may be slightly worse
* vulkan: load each Q value once. optimize O reduction. more tuning
* vulkan: support q4_0/q8_0 KV in scalar FA
* CI: increase timeout to accommodate newly-supported tests
* vulkan: for scalar FA, select between 1 and 8 rows
* vulkan: avoid using Float16 capability in scalar FA
* sycl : Implemented reorder Q4_0 mmvq
Signed-off-by: Alberto Cabrera <alberto.cabrera@codeplay.com>
* sycl : Fixed mmvq being called when reorder is disabled
* sycl : Improved comments in the quants header
Signed-off-by: Alberto Cabrera <alberto.cabrera@codeplay.com>
* Use static_assert
* safe_div -> ceil_div
* Clarify qi comment
* change the reorder tensor from init to execute OP
* dbg
* Undo changes to test-backend-ops
* Refactor changes on top of q4_0 reorder fix
* Missing Reverts
* Refactored opt_for_reorder logic to simplify code path
* Explicit inlining and unroll
* Renamed mul_mat_algo enum for consistency
---------
Signed-off-by: Alberto Cabrera <alberto.cabrera@codeplay.com>
Co-authored-by: romain.biessy <romain.biessy@codeplay.com>
This assert fired running Qwen_Qwen3-30B-A3B-Q2_K.gguf:
GGML_ASSERT(nei0 * nei1 <= 3072);
The tensor is 8 x 512. Increase this array size to accommodate.
* ggml : remove MSVC warnings pragmas
This commit removes the MSVC-specific pragmas as these are now handled
in ggml/CMakeLists.txt.
* whisper : remove MSVC warning pragmas
This commit removes the MSVC-specific pragmas. These are now handled in
the ggml/CMakeLists.txt file.
This patch upstreams llamafile's cpu matrix multiplication kernels for ppc64le using MMA builtins for BF16 data type.
This change results in 9x - 40x gains
in total speed S t/s (ie all tokens/total time), across various batch sizes tested using llama-batched-bench benchmark.
The patch is tested with Meta-Lllama-3-8B,
and Mistral-7B models (BF16 models generated by using llama-quantize from corresponding FP32 models) on an IBM POWER10 machine.
Signed-off-by: Shalini Salomi Bodapati <Shalini.Salomi.Bodapati@ibm.com>
The following scenario will cause an assertion failure in the graph
allocator:
- Build and allocate a graph containing a tensor with a non-NULL data
pointer
- Build and allocate a new graph where that data is NULL
Result:
ggml-alloc.c:819: GGML_ASSERT(talloc->buffer_id >= 0) failed
This happens during revalidation because we think that memory should
have been previously allocated based on the current graph but in
reality the previous graph was different. In this situation, we
should do a full reallocation pass.
* vulkan: Add bfloat16 support
This adds bfloat16 matrix multiply support based on VK_KHR_shader_bfloat16.
The extension is required for coopmat multiply support, but matrix-vector
multiply trivially promotes bf16 to fp32 and doesn't require the extension.
The copy/get_rows shaders also don't require the extension.
It's probably possible to fall back to non-coopmat and promote to fp32 when
the extension isn't supported, but this change doesn't do that.
The coopmat support also requires a glslc that supports the extension, which
currently requires a custom build.
* vulkan: Support bf16 tensors without the bf16 extension or coopmat support
Compile a variant of the scalar mul_mm shader that will promote the bf16
values to float, and use that when either the bf16 extension or the coopmat
extensions aren't available.
* vulkan: bfloat16 fixes (really works without bfloat16 support now)
* vulkan: fix spirv-val failure and reenable -O
This commit adds a check to makes sure that the target exists before
trying to add compile options to ignore warnings when using MSVC.
The motivation for this is currently the build is broken depending on
the cmake options provided. With this fix it should be possible to build
even if the targets are not actually available.
Refs: https://github.com/ggml-org/whisper.cpp/pull/3090#issuecomment-2842760104
* whisper: suppress Windows compiler warnings
This commit disables compiler warnings on window using MSVC.
The motivation for these changes is that some compilers generate
warnings for these conversion, for example Windows MSVC, and
there are quite a few of them. This makes it a little difficult to
spot new warnings that may be introduced and also can be difficult
for users/embedders of ggml where these warnings are hard to separate
from their own warnings.
* squash! whisper: suppress Windows compiler warnings
Move ggml related warnings into ggml. This commit also fixes the
indentation and adds a missing whitespace to the if statement.
Build fails with compilation error on power pc.
This patch fixes the same.
Tested with unit tests run via
--build <build_dir> && cd <build_dir> && make test
Signed-off-by: Shalini Salomi Bodapati <Shalini.Salomi.Bodapati@ibm.com>
* fix(rpc): Improve input validation and error handling
The `rpc-server` was vulnerable to Denial of Service attacks via
several RPC commands (`SET_TENSOR`, `GRAPH_COMPUTE`, etc.). Malformed
messages could trigger failed assertions (e.g., invalid `ggml_type`)
or out-of-bounds reads/writes leading to `GGML_ABORT` calls,
crashing the server process.
This PR introduces robust input validation and replaces `abort()`
calls with graceful error handling:
- **Type Validation:** `deserialize_tensor` now checks if the
`tensor->type` is within the valid `GGML_TYPE_COUNT` range
*before* calling `ggml_new_tensor_4d`. Returns `nullptr` on
invalid type.
- **Bounds Checks:** Replaced `GGML_ABORT` in `set_tensor`,
`set_tensor_hash`, and `get_tensor` handlers with error
logging and returning `false` when data/offset parameters
are out of buffer bounds.
- **Size Checks:** Added safe arithmetic checks (for overflow) in
`graph_compute` when calculating required message sizes based
on client-provided `n_nodes` and `n_tensors`. Returns early
if the reported sizes conflict with the actual message size or
would lead to overflow.
- **Error Propagation:**
- `create_node` now checks for `nullptr` return values from
`deserialize_tensor` and its recursive calls, propagating
`nullptr` upwards on failure. Uses `find` instead of `at`
for safer map access.
- `copy_tensor` now checks for `nullptr` from `deserialize_tensor`
and sets the response status to failure if deserialization
or bounds checks fail.
- `graph_compute` now checks for `nullptr` return from
`create_node` and returns failure status correctly. The final
return value now reflects the actual computation status.
These changes improve the RPC server's resilience
against malformed client requests, preventing crashes and ensuring
errors are handled more gracefully.
Signed-off-by: Ville Vesilehto <ville@vesilehto.fi>
* refactor(rpc): address pr comments
removed comments and unnecessary returns
Signed-off-by: Ville Vesilehto <ville@vesilehto.fi>
* refactor(rpc): ambiguous nullptr from create_node
rpc_server::create_node could previously return nullptr if the input ID
was 0 (valid) or if an internal error (deserialization, recursion
failure) occurred (invalid). This ambiguity made error handling
difficult for the caller (`graph_compute`).
This commit clarifies the meaning of nullptr:
- `graph_compute` now checks if the input 'id' was non-zero when
`create_node` returns nullptr, correctly identifying failures
versus intentional null links.
- `create_node` avoids recursive calls for zero IDs and propagates
nullptr unambiguously on failure during recursion.
Signed-off-by: Ville Vesilehto <ville@vesilehto.fi>
* refactor(rpc): initial zero check in create_node
The caller (`graph_compute`) already checks `id != 0` when handling
a `nullptr` return from `create_node`, correctly distinguishing
intentional null links from actual errors. This makes the initial
`if (id == 0)` check redundant.
Also removes the log message when a tensor ID is not found in the
provided map which was added in this branch.
Signed-off-by: Ville Vesilehto <ville@vesilehto.fi>
* fix(rpc): Handle get_alloc_size failure in server
Check the return value of `server.get_alloc_size` in the RPC server
loop. If the call fails, return early to close the connection.
Signed-off-by: Ville Vesilehto <ville@vesilehto.fi>
* refactor(rpc): input size validation in graph_compute
Removes detailed, step-by-step size calculations and overflow
checks in favor of simpler direct comparisons, assuming 64-bit
overflow is unlikely.
Signed-off-by: Ville Vesilehto <ville@vesilehto.fi>
* refactor(rpc): remove extra status code setting
Removes the explicit setting of `response.result = GGML_STATUS_FAILED`
when `create_node` returns `nullptr` within `graph_compute`.
Primary signal is the `false` return value in case of failure.
Signed-off-by: Ville Vesilehto <ville@vesilehto.fi>
* refactor(rpc): remove redundant check for tensor->type
Breaks CI on ubuntu-cpu-make. Tensor type is uint32_t, thus
the check is not needed.
Signed-off-by: Ville Vesilehto <ville@vesilehto.fi>
---------
Signed-off-by: Ville Vesilehto <ville@vesilehto.fi>
* SYCL: Add all missing unary kernels
ggml-ci
* decouple kernel launch range from data size using strided loop
* use ciel_div helper for num_blocks
ggml-ci
* clean auto imported header files
RPC_CMD_SET_TENSOR always returns an empty response and we send this 4
times per token. We can improve TG speed if we don't wait for this empty
response.
The performance impact of this change depends on the network latency.
* tune matmul for gcn
* this one is more power efficient
* Update ggml/src/ggml-vulkan/ggml-vulkan.cpp
Co-authored-by: 0cc4m <picard12@live.de>
* disable this tune for the proprietary driver
---------
Co-authored-by: 0cc4m <picard12@live.de>
Add RPC_CMD_HELLO for getting the version of the protocol implemend by
the server. Follow the semantic versioning rules at https://semver.org
Hopefully this bring better user experience when we make breaking
changes at the protocol level and avoid issues like #12465
* graph : make mla compatible with FA
* metal : add exp FA kernels for DeepSeek models
ggml-ci
* llama : minor naming updates
ggml-ci
* ggml : disable FA for DS head sizes
* tests : add FA tests for MLA shapes
ggml-ci
Submit operators using asynchronous threads to improve performance.
Use the environment variable GGML_CANN_ASYNC_MODE to control whether
asynchronous submission is enabled. It is disabled by default.
Testing shows a 10%–20% performance improvement in scenarios with
small parameter sizes, especially in quantized models.
The grouped query attention optmization doesn't require a power of two ratio,
the only thing relying on it was the modulo operation written as bitwise &.
split_k need not depend on gqa_ratio - enable it any time there's only one
workgroup in the X dimension. The shader gets the split index from the x coord,
and multiple workgroups in the X dimension (pre-split) indicates a larger
FA operation that wouldn't need splitting.
* opencl: refactor - split the kernel files
---------
Co-authored-by: Shangqing Gu <quic_shawngu@quicinc.com>
* opencl: split more kernels into separate files
* opencl: specify subgroup size instead of querying it
* opencl: refine Adreno cl compiler version parsing
* opencl: skip some kernels not used by Adreno on old compilers
* opencl: refine logic for selecting Adreno kernels
* opencl: refine Adreno cl compiler version
* opencl: cleanup preprocessor for kernels
* opencl: consider Adreno CL compiler on Windows
* opencl: add final newline for `mul_mv_f16_f16.cl`
---------
Co-authored-by: Shangqing Gu <quic_shawngu@quicinc.com>
Replace compile-time `GGML_HIP_UMA` with environment variable `GGML_CUDA_ENABLE_UNIFIED_MEMORY`. This unifies the usage on NVIDIA and AMD GPUs, and allows a single binary to be shared between integrated and dedicated GPUs.
Multiple optional memory pools are provided for CANN, including VMM,
priority queue-based, and traditional memory pools.
1.When the memory pool is available and GGML_CANN_DISABLE_VMM_POOL
is not defined, the VMM pool is selected by default.
2.Otherwise, if GGML_CANN_ENABLE_BUF_PRIO_POOL is defined,
the priority queue-based memory pool is used.
3.If neither condition is met, the default memory pool is used.
The current usage of the SYCL-Graph extension checks for
the `sycl_ext_oneapi_graph` device aspect. However, it is also
possible to support `sycl_ext_oneapi_limied_graph` devices that
don't support update
* SYCL: Add fp16 support to some elementwise OP kernels
* remove comment
ggml-ci
* Use static_cast directly
* remove not needed cast from tanh
* Use static cast and remove unneeded castings
* Adjust device_support_op for unary OPs
* Use cast_data and typed_data struct to deduplicate casting code
* [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>
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.
* 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
* add bf16 support
* use convert_from_bf16_cuda instead of convert_unary_cuda for f32
* revert 7ec5085
* move functionality into convert_unary with constexpr
* cpu: refactor SIMD mappings and vectorized op functions into separate files
* Fix warning for ggml_float to float
* Fix warnings
* cpu: move all the operations (except mul_mat) to a separate c++ file
* fix whitespace
* Update ggml/src/ggml-cpu/vec.h
Co-authored-by: Diego Devesa <slarengh@gmail.com>
* Fix PR comments - use GGML_UNUSED, use cassert in ops.cpp
* Reverse the order of import for ops.h and vec.h, to match what was present in ggml-cpu.c previously
---------
Co-authored-by: Diego Devesa <slarengh@gmail.com>
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.
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.
* 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>
* 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>
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.
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.
* Rename oneMKL Interface to oneMath
* Use oneMath for Intel vendor
* Rename occurences to mkl
* clang-format
* Silence verbose warnings
* Set oneMath HIP_TARGETS
* Fix silence warnings
* Remove step to build oneMath from build instructions
* Use fixed oneMath version
* Remove INTEL_CPU
* Fold CMake oneDNN conditions
* Use Intel oneMKL for Intel devices
* Improve CMake message
* Link against MKL::MKL_SYCL::BLAS only
* Move oneMath documentation to Nvidia and AMD sections
This commit adds debug level logging for the native build options and
variables to ggml/CMakeLists.txt.
The motivation for this is that it can be useful to see the effective
result of `GGML_NATIVE`, `GGML_NATIVE_DEFAULT`, and `INS_ENB` for a
cmake build. I've found myself adding similar logging a few times now,
so I thought it might be a good idea to add this.
Example output, specifying `-DCMAKE_MESSAGE_LOG_LEVEL=DEBUG` when
running cmake produces the following output:
```console
-- GGML_NATIVE : OFF
-- GGML_NATIVE_DEFAULT : OFF
-- INS_ENB : OFF
```
This commit updates the command.wasm example by adding a server.py script to make it easy to start a local http server to try out the example, updates the build instructions, and also addresses some of the compiler warnings that were being generated.
* emscripten : fix TOTAL_STACK for wasm
This commit moves the TOTAL_STACK setting from the compile flags to the
linker flags. This is because the TOTAL_STACK setting is a linker
setting.
The motivation for this change is that currently the following warnings
are generated when building:
```console
em++: warning: linker setting ignored during compilation: 'TOTAL_STACK' [-Wunused-command-line-argument]
em++: warning: linker setting ignored during compilation: 'TOTAL_STACK' [-Wunused-command-line-argument]
em++: warning: linker setting ignored during compilation: 'TOTAL_STACK' [-Wunused-command-line-argument]
em++: warning: linker setting ignored during compilation: 'TOTAL_STACK' [-Wunused-command-line-argument]
em++: warning: linker setting ignored during compilation: 'TOTAL_STACK' [-Wunused-command-line-argument]
em++: warning: linker setting ignored during compilation: 'TOTAL_STACK' [-Wunused-command-line-argument]
```
* examples : suppress C++17 deprecation warning for std::codecvt_utf8
This commit suppresses the C++17 deprecation warning for
std::codecvt_utf8 similar to what is done in
examples/talk-llama/unicode.cpp.
The motivation for this change is to suppress these warnings:
```console
/Users/danbev/work/ai/whisper-work/examples/common.cpp:251:31: warning: 'codecvt_utf8<wchar_t>' is deprecated [-Wdeprecated-declarations]
251 | std::wstring_convert<std::codecvt_utf8<wchar_t>> converter;
| ^
/Users/danbev/work/wasm/emsdk/upstream/emscripten/cache/sysroot/include/c++/v1/codecvt:193:28: note: 'codecvt_utf8<wchar_t>' has been explicitly marked deprecated here
193 | class _LIBCPP_TEMPLATE_VIS _LIBCPP_DEPRECATED_IN_CXX17 codecvt_utf8 : public __codecvt_utf8<_Elem> {
| ^
/Users/danbev/work/wasm/emsdk/upstream/emscripten/cache/sysroot/include/c++/v1/__config:723:41: note: expanded from macro '_LIBCPP_DEPRECATED_IN_CXX17'
723 | # define _LIBCPP_DEPRECATED_IN_CXX17 _LIBCPP_DEPRECATED
| ^
/Users/danbev/work/wasm/emsdk/upstream/emscripten/cache/sysroot/include/c++/v1/__config:688:49: note: expanded from macro '_LIBCPP_DEPRECATED'
688 | # define _LIBCPP_DEPRECATED __attribute__((__deprecated__))
| ^
/Users/danbev/work/ai/whisper-work/examples/common.cpp:251:10: warning: 'wstring_convert<std::codecvt_utf8<wchar_t>>' is deprecated [-Wdeprecated-declarations]
251 | std::wstring_convert<std::codecvt_utf8<wchar_t>> converter;
| ^
/Users/danbev/work/wasm/emsdk/upstream/emscripten/cache/sysroot/include/c++/v1/locale:3145:28: note: 'wstring_convert<std::codecvt_utf8<wchar_t>>' has been explicitly marked deprecated here
3145 | class _LIBCPP_TEMPLATE_VIS _LIBCPP_DEPRECATED_IN_CXX17 wstring_convert {
| ^
/Users/danbev/work/wasm/emsdk/upstream/emscripten/cache/sysroot/include/c++/v1/__config:723:41: note: expanded from macro '_LIBCPP_DEPRECATED_IN_CXX17'
723 | # define _LIBCPP_DEPRECATED_IN_CXX17 _LIBCPP_DEPRECATED
| ^
/Users/danbev/work/wasm/emsdk/upstream/emscripten/cache/sysroot/include/c++/v1/__config:688:49: note: expanded from macro '_LIBCPP_DEPRECATED'
688 | # define _LIBCPP_DEPRECATED __attribute__((__deprecated__))
| ^
/Users/danbev/work/ai/whisper-work/examples/common.cpp:257:31: warning: 'codecvt_utf8<wchar_t>' is deprecated [-Wdeprecated-declarations]
257 | std::wstring_convert<std::codecvt_utf8<wchar_t>> converter;
| ^
/Users/danbev/work/wasm/emsdk/upstream/emscripten/cache/sysroot/include/c++/v1/codecvt:193:28: note: 'codecvt_utf8<wchar_t>' has been explicitly marked deprecated here
193 | class _LIBCPP_TEMPLATE_VIS _LIBCPP_DEPRECATED_IN_CXX17 codecvt_utf8 : public __codecvt_utf8<_Elem> {
| ^
/Users/danbev/work/wasm/emsdk/upstream/emscripten/cache/sysroot/include/c++/v1/__config:723:41: note: expanded from macro '_LIBCPP_DEPRECATED_IN_CXX17'
723 | # define _LIBCPP_DEPRECATED_IN_CXX17 _LIBCPP_DEPRECATED
| ^
/Users/danbev/work/wasm/emsdk/upstream/emscripten/cache/sysroot/include/c++/v1/__config:688:49: note: expanded from macro '_LIBCPP_DEPRECATED'
688 | # define _LIBCPP_DEPRECATED __attribute__((__deprecated__))
| ^
/Users/danbev/work/ai/whisper-work/examples/common.cpp:257:10: warning: 'wstring_convert<std::codecvt_utf8<wchar_t>>' is deprecated [-Wdeprecated-declarations]
257 | std::wstring_convert<std::codecvt_utf8<wchar_t>> converter;
| ^
/Users/danbev/work/wasm/emsdk/upstream/emscripten/cache/sysroot/include/c++/v1/locale:3145:28: note: 'wstring_convert<std::codecvt_utf8<wchar_t>>' has been explicitly marked deprecated here
3145 | class _LIBCPP_TEMPLATE_VIS _LIBCPP_DEPRECATED_IN_CXX17 wstring_convert {
| ^
/Users/danbev/work/wasm/emsdk/upstream/emscripten/cache/sysroot/include/c++/v1/__config:723:41: note: expanded from macro '_LIBCPP_DEPRECATED_IN_CXX17'
723 | # define _LIBCPP_DEPRECATED_IN_CXX17 _LIBCPP_DEPRECATED
| ^
/Users/danbev/work/wasm/emsdk/upstream/emscripten/cache/sysroot/include/c++/v1/__config:688:49: note: expanded from macro '_LIBCPP_DEPRECATED'
688 | # define _LIBCPP_DEPRECATED __attribute__((__deprecated__))
| ^
4 warnings generated.
```
* ggml : suppress double-promotion warning in GGML_F16x4_REDUCE
This commit adds a cast to `ggml_float` in the `GGML_F16x4_REDUCE` macro
to suppress a double-promotion warning.
Currently the following warning is generated when compiling the
command.wasm example:
```console
/whisper-work/src/ggml-cpu/ggml-cpu.c:1592:5: warning: implicit conversion increases floating-point precision: 'float' to 'ggml_float' (aka 'double') [-Wdouble-promotion]
1592 | GGML_F16_VEC_REDUCE(sumf, sum);
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
/Users/danbev/work/ai/whisper-work/src/ggml-cpu/ggml-cpu.c:932:37: note: expanded from macro 'GGML_F16_VEC_REDUCE'
932 | #define GGML_F16_VEC_REDUCE GGML_F16x4_REDUCE
| ^
/Users/danbev/work/ai/whisper-work/src/ggml-cpu/ggml-cpu.c:920:44: note: expanded from macro 'GGML_F16x4_REDUCE'
918 | res = wasm_f32x4_extract_lane(x[0], 0) + \
| ~ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
919 | wasm_f32x4_extract_lane(x[0], 1) + \
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
920 | wasm_f32x4_extract_lane(x[0], 2) + \
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~~~~~~~~
921 | wasm_f32x4_extract_lane(x[0], 3); \
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
/whisper-work/src/ggml-cpu/ggml-cpu.c:1640:9: warning: implicit conversion increases floating-point precision: 'float' to 'ggml_float' (aka 'double') [-Wdouble-promotion]
1640 | GGML_F16_VEC_REDUCE(sumf[k], sum[k]);
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
/Users/danbev/work/ai/whisper-work/src/ggml-cpu/ggml-cpu.c:932:37: note: expanded from macro 'GGML_F16_VEC_REDUCE'
932 | #define GGML_F16_VEC_REDUCE GGML_F16x4_REDUCE
| ^
/Users/danbev/work/ai/whisper-work/src/ggml-cpu/ggml-cpu.c:920:44: note: expanded from macro 'GGML_F16x4_REDUCE'
918 | res = wasm_f32x4_extract_lane(x[0], 0) + \
| ~ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
919 | wasm_f32x4_extract_lane(x[0], 1) + \
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
920 | wasm_f32x4_extract_lane(x[0], 2) + \
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~~~~~~~~
921 | wasm_f32x4_extract_lane(x[0], 3); \
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
2 warnings generated.
```
wasm_f32x4_extract_lane returns a 32-bit float and this is what the
addition is performed on. But there is an implicit conversion from
32-bit float to 64-bit double when the result is assigned to `res`,
which is of type `ggml_float`. My understanding here is that this is
intentional and adding a cast to `ggml_float` should suppress the
warning.
* emscripten : add -Wno-deprecated to for emscripten
This commit adds -Wno-deprecated to the CMAKE_CXX_FLAGS for emscripten
builds.
The motivation for this is that currently there a number of warnings
generated like the following:
```console
warning: JS library symbol '$print' is deprecated. Please open a bug if you have a continuing need for this symbol [-Wdeprecated]
warning: JS library symbol '$printErr' is deprecated. Please open a bug if you have a continuing need for this symbol [-Wdeprecated]
em++: warning: warnings in JS library compilation [-Wjs-compiler]
em++: warning: linker setting ignored during compilation: 'ENVIRONMENT' [-Wunused-command-line-argument]
warning: JS library symbol '$print' is deprecated. Please open a bug if you have a continuing need for this symbol [-Wdeprecated]
warning: JS library symbol '$printErr' is deprecated. Please open a bug if you have a continuing need for this symbol [-Wdeprecated]
em++: warning: warnings in JS library compilation [-Wjs-compiler]
warning: JS library symbol '$print' is deprecated. Please open a bug if you have a continuing need for this symbol [-Wdeprecated]
warning: JS library symbol '$printErr' is deprecated. Please open a bug if you have a continuing need for this symbol [-Wdeprecated]
em++: warning: warnings in JS library compilation [-Wjs-compiler]
em++: warning: linker setting ignored during compilation: 'ENVIRONMENT' [-Wunused-command-line-argument]
em++: warning: linker setting ignored during compilation: 'ENVIRONMENT' [-Wunused-command-line-argument]
```
The downside of this is that we might miss other deprecation warnings
in the future so I'm not sure if this is acceptable. But it make the
wasm examples cleaner without the warnings.
* examples : fix tautological-compare warning in stb_vorbis.c [no ci]
This commit applies a fix to address a tautological-compare warning
in stb_vorbis.c.
The motivation for this is that currently the following warning is
generated when compiling the commmand-wasm example:
```console
/Users/danbev/work/ai/whisper-work/examples/stb_vorbis.c:1404:75: warning: pointer comparison always evaluates to false [-Wtautological-compare]
1404 | if (f->stream_start + loc >= f->stream_end || f->stream_start + loc < f->stream_start) {
| ^
1 warning generated.
```
This fix was taken from an open pull request on the stb repository
that addreses this issue:
https://github.com/nothings/stb/pull/1746
* squash! examples : update command.wasm instructions [no ci]
This commit adds a Python script to serve the the wasm examples build
in the `build-em` directory. Initially I thought that it would be enough
to start a simple python server but I did not notice that there was an
error in the browser console when I did that:
```console
command.js:1 Uncaught (in promise) DataCloneError: Failed to execute 'postMessage' on 'Worker': SharedArrayBuffer transfer requires self.crossOriginIsolated.
at command.js:1:1206224
at new Promise (<anonymous>)
at loadWasmModuleToWorker (command.js:1:1204981)
at Array.map (<anonymous>)
at Object.loadWasmModuleToAllWorkers (command.js:1:1206428)
at command.js:1:1204318
at callRuntimeCallbacks (command.js:1:1202062)
at preRun (command.js:1:6136)
at run (command.js:1:1294094)
at removeRunDependency (command.js:1:7046)
```
We need a few CORS headers to be set and in order hopefully make this
easy for users a Python script is added to the examples directory.
This should be able to server all the wasm examples provided they have
been built. command.wasm's README.md is updated to reflect this change.
* examples : remove unused functions
This commit removed the unused functions convert_to_utf8 and
convert_to_wstring from examples/common.cpp.
* Revert "examples : fix tautological-compare warning in stb_vorbis.c [no ci]"
This reverts commit 8e3c47d96141c7675c985562ebdc705e839e338a.
We should not make this change here and instead when the upstream PR is
merged we can sync with it.
Refs: https://github.com/ggerganov/whisper.cpp/issues/2784
If users already set CMAKE_C_COMPILER_LAUNCHER globally, setting it in
cmake again will lead to conflict and compile fail.
Signed-off-by: Jay <BusyJay@users.noreply.github.com>
* ggml : FA with different K, V head sizes (CPU)
ggml-ci
* metal : add FA with HS=192
* metal : extend FA to support different K and V head sizes
ggml-ci
* metal : add FA vector kernels for heads K 192 and V 128
ggml-ci
* ggml : restrict op on other backends to equal head sizes
ggml-ci
* metal : optimize FA-vec kernel
ggml-ci
* metal : FA remove mq registers
* metal : improve MoE mul_mat_id condition
ggml-ci
* metal : fix comments + remove unnecessary addition
ggml-ci
* metal : avoid too much shared memory usage with mul_mat_id
ggml-ci
* vulkan: fix coopmat shader generation when cross-compiling
Previously the status of coopmat{,2} support isn't passed to the
vulkan-shaders-gen project building on the host, which leads to build
failure because of the cross-compiling code expecting coopmat{,2}
shaders that didn't get generated.
Fix this by passing the coopmat{,2} support status to vulkan-shaders
subproject.
Signed-off-by: Icenowy Zheng <uwu@icenowy.me>
* Only call coop-mat shaders once
* Fix whitespace
---------
Signed-off-by: Icenowy Zheng <uwu@icenowy.me>
Co-authored-by: bandoti <141645996+bandoti@users.noreply.github.com>
This patch enables usage of MMA when one of the
dimensions of the matrix(ie either M or N) is 1. This
is useful in case of token generation where N < 2.
The concept of 'GEMV Forwarding' is used where when one
of the matrix has a single row/column, the elements are
broadcasted, instead of using packing routine to prepack
the matrix elements.
This change results in 5% - 15% improvement in total
speed(ie all tokens/total time), across various batch
sizes. This is in comparision with the corresponding
dot product implementation.
The patch is tested with FP32 models of Meta-Lllama-3-8B,
Mistral-7B, Llama-2-7B-chat-hf on a IBM POWER10 machine.
Signed-off-by: Amrita H S <amritahs@linux.vnet.ibm.com>
* rpc : send hash when tensor data is above some fixed threshold
ref #10095
* rpc : put cache under $HOME/.cache/llama.cpp
* try to fix win32 build
* another try to fix win32 build
* remove llama as dependency
This change upstreams llamafile's cpu matrix
multiplication kernels for ppc64le ISA using MMA
builtins. This patch handles matrix multiplication
between quantised datatypes, block_q4_0 and
block_q8_0.
This change results in 5% - 50% improvement
in total speed(ie all tokens/total time), across
various batch sizes.
The patch is tested with Meta-Lllama-3-8B,
Mistral-7B, Llama-2-7B-chat-hf models on a
IBM POWER10 machine.
Signed-off-by: Amrita H S <amritahs@linux.vnet.ibm.com>
The OOB calculation could be wrong if the last iteration was during one of
the unrolled loops. Adjust the unrolling counts to avoid this. Add a couple
new backend tests that hit this failure on NVIDIA GPUs.
* tests: add mul_mat perf/functional tests for p021/nc vulkan shaders
* vulkan: Optimize mul_mat_vec p021 and nc shaders.
These shaders are used in attention calculations, and when the KV cache grows
large they start to dominate the run time. For the nc shader (which is called
with large 'k' dimension), use unrolling and vector loads. For the p021 shader
(which is called with large 'm' and small 'k' dimensions), take advantage of
grouped query attention to reuse loads from the A matrix for the whole group,
and reduce the number of workgroups (too much overhead from tiny dispatches).
Using subgroupAdd in the p021 shader also helps, use that conditionally.
* [SYCL] Fix build on Windows when ccache enabled (#9954)
* take effect only on windows and force it to icl
---------
Co-authored-by: Romain Biessy <romain.biessy@codeplay.com>
* Add block interleaving support for Q4_K quantization
* Remove whitespaces and fix CI/CD issues
* Update pointer of bsums from int16_t to const int16_t
* Add vector version of quantize_q8_K_4x8 function
* Update code formatting based on review comments
- Find out active blocks per SM using cudaOccupancyMaxActiveBlocksPerMultiprocessor API. Use this value to determine the optimal parallel_blocks value.
- Prefer vector flash attention kernels over MMA kernel for BS=1
Fixes Issue: #12182
---------
Co-authored-by: Johannes Gäßler <johannesg@5d6.de>
* ci: add visionOS build workflow
Add a new GitHub Actions workflow for building on visionOS with CMake and Xcode.
* ggml: Define _DARWIN_C_SOURCE for visionOS to fix missing u_xxx typedefs
* ci: remove define hacks for u_xxx system types
---------
Co-authored-by: Giovanni Petrantoni <7008900+sinkingsugar@users.noreply.github.com>
I've been seeing significantly worse performance for tg with flash attention
enabled vs disabled, and it seems to be related to the submit heuristic.
Change the heuristic to check how many bytes worth of weight matrix are
used and flush every 100MB, and ramp up after the first few submits.
This seems to resolve the issue, and also increases perf for non-FA a bit.
* opencl: more profiling timing
* opencl: generate trace for profiling
* opencl: reduce profiling overhead
* Populate profiling timing info at the end rather than after each
kernel run
* opencl: fix for chrome tracing
* Enable CUDA Graph on CTK < 12.x
`cudaGraphExecUpdate` API was changed on 12.x. For this reason CUDA graph support was disabled on older CUDA toolkit. This change enables CUDA support in CTK version < 12.x by using older API if CTK < 12.x.
* Fix compilation errors with MUSA
* Disable CUDA Graph for MUSA
* cmake: Factor out compiler flag function from ggml
llama.cpps's build requires it, too, and we may want to make use of it
without add_subdirectory(ggml).
* cmake: Enable building against system ggml
This facilitates package maintenance for Linux distributions, where the
libggml library most likely will be shipped as an individual package
upon which a llama.cpp package depends.
When fattn-wmma was ported over to warp64 various bits that also touch fattn-vec where converted to
selectable warp size, however the fattn-vec kernels dont work with 64 wide warps for now, so we need
to avoid launching them with parameters for warp64
refactor mmqv to unify the calculation of nwarps and rows per block between host and device code.
---------
Co-authored-by: Johannes Gäßler <johannesg@5d6.de>
This patch nudges the llama.cpp a bit to be supported on PoCL which
doesn't support OpenCL C CL2.0. The issue is solved by querying the
device for the supported OpenCL C versions and using the highest one
available.
Jeff Bolz
Judd
Nicolò Scipione
Ewan Crawford
Henry Linjamäki
Xuan-Son Nguyen
R0CKSTAR
Eve
Johannes Gäßler
Svetlozar Georgiev
Georgi Gerganov
0cc4m
Diego Devesa
Daniel Tang
Chenguang Li
Gilad S.
Łukasz Ślusarczyk
Atharva Dubey
Yibo Cai
bandoti
Dan Johansson
lhez
David Huang
Alberto Cabrera Pérez
Radoslav Gerganov
Daniel Bevenius
Jared Tweed
R0CKSTAR
Akarshan Biswas
Aaron Teo
shalinib-ibm
Justin Santa Barbara
Jesse Gross
Aaron Teo
Ville Vesilehto
SXX
Neo Zhang Jianyu
Acly
Jeffrey Morgan
Alan Gray
hipudding
kimminsu
Srihari-mcw
Xinpeng Dou
cmdr2
Ewan Crawford
Prajwal B Mehendarkar
Piotr Kubaj
Sigbjørn Skjæret
zhouwg
Ronny Brendel
Gaurav Garg
a3sh
Junil Kim
Wagner Bruna
Romain Biessy
Sandro Hanea
Jay
Icenowy Zheng
amritahs-ibm
xctan
Slobodan Josic
stduhpf
蕭澧邦
Guus Waals
fj-y-saito
Molly Sophia
Daniele
Christian Kastner
aubreyli
fairydreaming
uvos
jklincn
BB-fat
Jason C.H