Update oneMath commit to merged PR https://github.com/uxlfoundation/oneMath/pull/669
which adds SYCL-Graph support for recording CUDA BLAS commands.
With this change the `MUL_MAT` tests now pass on DPC++ CUDA backends with SYCL-Graph
enabled. Prior to this change, an error would be thrown.
```
$ GGML_SYCL_DISABLE_GRAPH=0 ./bin/test-backend-ops -b SYCL0 -o MUL_MAT -p type_a=f16,type_b=f32,m=16,n=1,k=256,bs=\\[1,1\\],nr=\\[2
UR CUDA ERROR:
Value: 700
Name: CUDA_ERROR_ILLEGAL_ADDRESS
Description: an illegal memory access was encountered
Function: operator()
Source Location: $HOME/dpcpp/unified-runtime/source/adapters/cuda/queue.cpp:154
Native API failed. Native API returns: 2147483646 (UR_RESULT_ERROR_UNKNOWN)
Exception caught at file:$HOME/llama.cpp/ggml/src/ggml-sycl/ggml-sycl.cpp, line:3598, func:operator()
SYCL error: CHECK_TRY_ERROR((stream)->wait()): Meet error in this line code!
in function ggml_backend_sycl_synchronize at $HOME/llama.cpp/ggml/src/ggml-sycl/ggml-sycl.cpp:3598
$HOME/llama.cpp/ggml/src/ggml-sycl/../ggml-sycl/common.hpp:118: SYCL error
Could not attach to process. If your uid matches the uid of the target
process, check the setting of /proc/sys/kernel/yama/ptrace_scope, or try
again as the root user. For more details, see /etc/sysctl.d/10-ptrace.conf
ptrace: Operation not permitted.
No stack.
The program is not being run.
```
* ggml-cpu: Factor out feature detection build from x86
* ggml-cpu: Add ARM feature detection and scoring
This is analogous to cpu-feats-x86.cpp. However, to detect compile-time
activation of features, we rely on GGML_USE_<FEAT> which need to be set
in cmake, instead of GGML_<FEAT> that users would set for x86.
This is because on ARM, users specify features with GGML_CPU_ARM_ARCH,
rather than with individual flags.
* ggml-cpu: Implement GGML_CPU_ALL_VARIANTS for ARM
Like x86, however to pass around arch flags within cmake, we use
GGML_INTERNAL_<FEAT> as we don't have GGML_<FEAT>.
Some features are optional, so we may need to build multiple backends
per arch version (armv8.2_1, armv8.2_2, ...), and let the scoring
function sort out which one can be used.
* ggml-cpu: Limit ARM GGML_CPU_ALL_VARIANTS to Linux for now
The other platforms will need their own specific variants.
This also fixes the bug that the the variant-building branch was always
being executed as the else-branch of GGML_NATIVE=OFF. The branch is
moved to an elseif-branch which restores the previous behavior.
This change moves the command pool/buffer tracking into a vk_command_pool
structure. There are two instances per context (for compute+transfer) and
two instances per device for operations that don't go through a context.
This should prevent separate contexts from stomping on each other.
Use the same descriptor set layout for all pipelines (MAX_PARAMETER_COUNT == 8)
and move it to the vk_device. Move all the descriptor pool and set tracking to
the context - none of it is specific to pipelines anymore. It has a single vector
of pools and vector of sets, and a single counter to track requests and a single
counter to track use.
* ggml : disable warnings for tests when using MSVC
This commit disables warnings for tests on windows when using MSVC.
The motivation for this is that this brings the build output more
inline with what Linux/MacOS systems produce.
There is still one warning generated for the tests which is:
```console
Building Custom Rule C:/ggml/tests/CMakeLists.txt
cl : command line warning D9025: overriding '/DNDEBUG' with '/UNDEBUG'
[C:\ggml\build\tests\test-arange.vcxproj]
test-arange.cpp
test-arange.vcxproj -> C:\ggml\build\bin\Release\test-arange.exe
```
* ggml : fix typo in tests disable list
This commit removes the unused `ggml_context_container` structure from
the ggml library. It looks like the usage of this struct was removed in
Commit 4757fe18d56ec11bf9c07feaca6e9d5b5357e7f4 ("ggml : alloc
ggml_contexts on the heap (whisper/2525)").
The motivation for this changes is to improve code clarity/readability.
This commit adds the examples in the "list" of targets to ignore MSVC
warnings.
The motivation for this is that currently the examples generate a number
of warnings that are ignore/disabled for the core ggml project. This
makes for a cleaner output when building.
* Simplify the environment variable setting to specify the memory pool type.
* Adjust the GGML_CANN_ASYNC_MODE setting to accept yes, enable, 1, or on (case-insensitive) as valid options.
* update
* fix CI
* update
* delete whitespace
* fix according to review
* update CANN.md
* update CANN.md
* Add Reorder to Q6_K mmvq implementation
* Address PR comments: clean up comments
* Remove unused parameter after refactoring q4_k
* Adding inline to function and removing unnecessary reference to int
---------
Signed-off-by: nscipione <nicolo.scipione@codeplay.com>
* SYCL: Implement few same quantized type copy kernels
* Use memcpy for copying contiguous tensors
ggml-ci
* feat(sycl): add contiguous tensor copy support and device checks
Adds a memcpy path for contiguous tensors of the same type to optimize data transfer. Updates device support checks to recognize contiguous tensor operations, improving compatibility and performance.
* refactor: replace specific block copy functions with template
The changes replace multiple redundant block copy functions (e.g., cpy_block_q8_0_q8_0, cpy_block_q5_0_q5_0) with a single templated function cpy_blck_q_q. This reduces code duplication by using a generic template that works for any block type, improving maintainability while preserving the same functionality. The template is instantiated with specific block types (e.g., block_q8_0) where needed.
* Exclude BF16 support for COPY tensors for now
ggml-ci
* perf: adjust SYCL copy kernel block sizes for efficiency
Use ceil_div to ensure full element coverage and update nd_range parameters to better align with SYCL block sizes, improving parallelism and device utilization in copy operations.
* * ggml-vulkan: adds op CONV_TRANSPOSE_1D
* test-backend-ops: adds more spohisticated tests for CONV_TRANSPOSE_1D
* Missing barrier added to shader.
Number of additional tests reduced to 108.
* * Fixes typo in variable name.
* Removes extra whitespaces.
* Adds int64->int32 casts to prevent possible warnings.
* Problem size reduced in tests to pass tests with llvmpipe.
* supports_op condition moved from unintended position
* This is not needed by the normal use where the result is read
using `tensor_get`, but it allows perf mode of `test-backend-ops`
to properly measure performance.
Some systems report the CPU implementation as "Power11" instead of "POWER11".
The existing CMake logic uses a case-sensitive regular expression to extract
the CPU generation, which fails when the casing doesn't exactly match "POWER".
This patch provides a fix by first converting the string to uppercase before applying the regex.
Signed-off-by: root <root@rheldb2v.pperf.tadn.ibm.com>
Co-authored-by: root <root@rheldb2v.pperf.tadn.ibm.com>
* threading: support for GGML_SCHED_PRIO_LOW, update thread info on Windows to avoid throttling
We talked about adding LOW priority for GGML threads in the original threadpool PR.
It might be useful for some cases to avoid contention.
Latest Windows ARM64 releases started parking (offlining) the CPU cores
more aggresively which results in suboptimal performance with n_threads > 4.
To deal with that we now disable Power Throttling for our threads for the NORMAL
and higher priorities.
Co-authored-by: Diego Devesa <slarengh@gmail.com>
* threading: disable SetThreadInfo() calls for older Windows versions
* Update tools/llama-bench/llama-bench.cpp
Co-authored-by: Diego Devesa <slarengh@gmail.com>
---------
Co-authored-by: Diego Devesa <slarengh@gmail.com>
* 1. add "integrated" in ggml_cuda_device_info for distinguish whether it is Intergrate_gpu or discrete_gpu
2. Adjust the func:"ggml_backend_cuda_device_supports_buft" for this new feature
* Update ggml/src/ggml-cuda/ggml-cuda.cu
Adjusted code indentation
Co-authored-by: Johannes Gäßler <johannesg@5d6.de>
* Update ggml/src/ggml-cuda/ggml-cuda.cu
Fixed incorrect setting of variable types
Co-authored-by: Johannes Gäßler <johannesg@5d6.de>
* Update ggml/src/ggml-cuda/ggml-cuda.cu
Adjusted the judgment logic
Co-authored-by: Johannes Gäßler <johannesg@5d6.de>
* add a host_buft assert in case of integrated_cuda_device with func:'evaluate_and_capture_cuda_graph()'
* Update ggml/src/ggml-cuda/ggml-cuda.cu
Add a defensive security assert
Co-authored-by: Johannes Gäßler <johannesg@5d6.de>
* Update ggml/src/ggml-cuda/ggml-cuda.cu
Adjusted the support judgment logic.
Co-authored-by: Johannes Gäßler <johannesg@5d6.de>
* revoke the suggest commit changes due to it's not applicable in jetson_device
* Update ggml/src/ggml-cuda/ggml-cuda.cu
Add parentheses to enforce operator precedence
Co-authored-by: Diego Devesa <slarengh@gmail.com>
* Update ggml/src/ggml-cuda/ggml-cuda.cu
Fix ci bug: add a spaces
Co-authored-by: Johannes Gäßler <johannesg@5d6.de>
---------
Co-authored-by: yangxiao <yang_xl@tju.edu.cn>
Co-authored-by: Johannes Gäßler <johannesg@5d6.de>
Co-authored-by: yangxiao <yangxl_zz@qq.com>
Co-authored-by: Diego Devesa <slarengh@gmail.com>
* SYCL: Add mrope kernel
* feat: Optimize rope operations with vectorization
Uses `sycl::vec` to load and store two elements at a time,
significantly improving performance in `rope_norm`,
`rope_neox`, and `rope_multi`. This reduces the number of memory
accesses and leverages SIMD instructions for faster execution.
* Use ceil_div
* cmake: Define function for querying architecture
The tests and results match exactly those of src/CMakeLists.txt
* Switch arch detection over to new function
* SYCL: Add non contiguous input support to norm kernel
* refactor and add RMS_NORM non contiguous input support
ggml-ci
* restore subgroup reduction for multi-subgroup thread blocks in norm kernels
* Swap grid dims of nsamples and nrows
ggml-ci
* Revert "Swap grid dims of nsamples and nrows"
This reverts commit 43be2d657fec7f7fba54e2cd154106bc0fc45adf.
* restore not required changes
ggml-ci
* address review comments: change it to more like SYCL
* Use a common function to calculate offset
* remove wrap around logic for handling broadcasts
* remove static from calculate_offset fn and use ceil_div
* cann: add the basic FA support
* cann: update the readme
* cann: update the FlashAttention with PSEShift
* cann: update the input parameters in FA
* cann: update the alibi with max_bias
* cann: add the constrints of softcap
* cann: update the docs CANN.md
* cann: update the docs CANN.md
* cann: fix typo of CANN.md
* cann: add some comments and update the CANN.md
* cann: update the CANN.md
* cann: update the inner precise for fusedInferAttention
* cann: update the constraints of flash_attn_ext on ggml-cann.cpp
* cann: clean the whitespace
* cann: clean the whitespace
* cann: add a new endline
* 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.
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
* ggml : remove MSVC warnings pragmas
This commit removes the MSVC-specific pragmas as these are now handled
in CMakeLists.txt.
* whisper : remove MSVC warning pragmas
This commit removes the MSVC-specific pragmas. These are now handled in
the CMakeLists.txt file.
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.
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.
* 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.
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.
Ewan Crawford
Anton Mitkov
Georgi Gerganov
Christian Kastner
Jeff Bolz
lhez
0cc4m
Isaac McFadyen
Daniel Bevenius
xctan
Diego Devesa
Xinpeng Dou
Nicolò Scipione
Akarshan Biswas
Masato Nakasaka
Ervin Áron Tasnádi
Johannes Gäßler
rmatif
shalinib-ibm
Atharva Dubey
Aaron Teo
Kai Pastor
Max Krasnyansky
Shawn yang
Yibo Cai
Vineel Abhinav
leo-pony
Xuan-Son Nguyen
Radoslav Gerganov
Daniel Tang
Simon Booth
xctan
Romain Biessy
Bizhao Shi
Chenguang Li
Judd
Henry Linjamäki
R0CKSTAR
Eve
Svetlozar Georgiev
Gilad S.
Łukasz Ślusarczyk
Yibo Cai
bandoti
Dan Johansson
David Huang
Alberto Cabrera Pérez
R0CKSTAR
Justin Santa Barbara
Jesse Gross
Acly
Jared Tweed
Aaron Teo
Ville Vesilehto
SXX
Neo Zhang Jianyu
Jeffrey Morgan
Alan Gray
hipudding
kimminsu
Srihari-mcw