* ggml-webgpu: add CEIL operation support
Add support for the CEIL unary operation in the WebGPU backend:
- Add CEIL_FUNC shader template in unary_op.wgsl
- Add 4 shader variants (f32, f16, inplace versions)
- Initialize CEIL pipelines in ggml-webgpu.cpp
- Register CEIL in supports_op function
* docs: update WebGPU ops support for CEIL
This commit implements operator fusion for ADD + RMS_NORM operations
in the CANN backend to reduce memory access overhead and improve
performance. The fusion is controlled by the GGML_CANN_OPERATOR_FUSION
environment variable (default: false).
Changes:
- Implement ggml_cann_op_add_rms_norm_fused() using ACLNN AddRmsNorm
- Add ggml_cann_can_fuse() to check fusion eligibility
- Integrate fusion logic into computation graph evaluation
- Add test cases for ADD + RMS_NORM fusion
- Update documentation with new environment variable
The fusion combines ADD and RMS_NORM into a single kernel call,
which is more efficient than executing them separately.
* 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
```
* 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>
* vulkan: Optimize GGML_OP_CUMSUM
There are two paths: The preexisting one that does a whole row per workgroup
in a single shader, and one that splits each row into multiple blocks and does
two passes. The first pass computes partials within a block, the second adds
the block partials to compute the final result. The multipass shader is used
when there are a small number of large rows.
In the whole-row shader, handle multiple elements per invocation.
* use 2 ELEM_PER_THREAD for AMD/Intel
* address feedback
* ggml-cuda: fixed assertion in ggml_cuda_cpy (llama/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
* vulkan: extend topk_moe to handle sigmoid w/exp_probs_b for nemotron
Also handle GGML_OP_SCALE at the end (nemotron, deepseek2).
Fewer pipeline variants and spec constants, just use push constants.
In test_topk_moe, change exp_probs_b to be 1D, matching real networks.
Update test-backend-ops and ggml-backend to allow verifying multiple outputs
in a fusion test (topk_moe has two outputs). Previously only the final node
was verified.
* change test_topk_moe to allow results in arbitrary order
* disable sigmoid fusion for moltenvk
* add count equal for metal
* remove trailing whitespace
* updated doc ops table
* changed shmem to i32
* added multi tg and templating
* removed BLAS support from Metal docs
* Apply suggestions from code review
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* add memset to set dst to 0
* metal : cleanup
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* cmake: work around broken IntelSYCLConfig.cmake in oneAPI 2025.x
* [AI] sycl: auto-detect and skip incompatible IntelSYCL package
Automatically detect compiler versions with incompatible IntelSYCL
CMake configuration files and fall back to manual SYCL flags instead
of requiring users to set options manually.
Fixes build failures with oneAPI 2025.x where IntelSYCLConfig.cmake
has SYCL_FEATURE_TEST_EXTRACT invocation errors.
* refactor: improve SYCL provider handling and error messages in CMake configuration
* refactor: enhance SYCL provider validation and error handling in CMake configuration
* ggml-sycl: wrap find_package(IntelSYCL) to prevent build crashes
* vulkan: Use BK=32 for coopmat2 mul_mat_id
* vulkan: optimize decodeFuncB in coopmat2 mul_mat_id shader
Disable robustness, remove the OOB check in decodeFuncB, and initialize the
row_ids to zero to avoid OOB access.
Don't slice/offset the B matrix to ic * BN, only to adjust the coord back down
to the range [0, BN) in decodeFuncB. Instead just slice with a row offset of
zero and remove the '& (BN - 1)'. This allows the compiler to common some of
the shared memory loads.
* 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>
* CONV_TRANSPOSE_1D kernel_size>255
* remove condition check
* fix the bug of type conversion
* removing trailing whitespaces
* fix: return true in the switch case
* refactor: replace ggml_hexagon_mul_mat with template-based binary operation for improved flexibility
* refactor: replace ggml_hexagon_mul_mat_id with template-based binary operation for improved flexibility
* refactor: initialize buffer types and streamline dspqueue_buffers_init calls for clarity
* add comment
* refactor: remove redundant buffer checks in hexagon supported operations
* wip
* add missing include to fix weak symbol warning
* add ggml_hexagon_op_generic
* refactor: simplify tensor operation initialization and buffer management in hexagon implementation
* refactor: streamline hexagon operation initialization and buffer management
* refactor: update function signatures and streamline request handling in hexagon operations
* wip
* ggml-hexagon: clean up code formatting and improve unary operation handling
* wip
* rename
* fix: add support for permuted F16 tensors and enhance quantization checks in matrix operations
* refactor: replace ggml_hexagon_mul_mat with template-based binary operation for improved flexibility
refactor: replace ggml_hexagon_mul_mat_id with template-based binary operation for improved flexibility
refactor: initialize buffer types and streamline dspqueue_buffers_init calls for clarity
refactor: remove redundant buffer checks in hexagon supported operations
add missing include to fix weak symbol warning
add ggml_hexagon_op_generic
refactor: simplify tensor operation initialization and buffer management in hexagon implementation
refactor: streamline hexagon operation initialization and buffer management
refactor: update function signatures and streamline request handling in hexagon operations
ggml-hexagon: clean up code formatting and improve unary operation handling
fix: add support for permuted F16 tensors and enhance quantization checks in matrix operations
# Conflicts:
# ggml/src/ggml-hexagon/ggml-hexagon.cpp
* hexagon: fix merge conflicts
* hexagon: minor cleanup for buffer support checks
* hexagon: factor out op_desc and the overal op logging
* hexagon: further simplify and cleanup op dispatch logic
* snapdragon: update adb scripts to use llama-cli and llama-completion
* fix pipeline failure
---------
Co-authored-by: Max Krasnyansky <maxk@qti.qualcomm.com>
* feat: working gelu with src0 put on vtcm
* feat: gelu ping-pong for both in and out
* fix: fixu compile error
* break: distinguish dma ddr->vtcm and vtcm->ddr operation
* fix: fix dma queue size
* break: update dma api to either pop src or dst ptr
* fix: fix activation vtcm allocation issue for src1 when swapperd
* refactor: ping-pong gelu logic to avoid unnecessary if else
* dma: improved queue interface and prefetch handling
* gelu: fix N+2 block prefetch
---------
Co-authored-by: Max Krasnyansky <maxk@qti.qualcomm.com>
The goal is to enable the async loading code paths in
llama_model_loader::load_all_data, originally from #7896. This works and the
loads themselves are faster, but with host visible vidmem I think the cost of
allocating/mapping vidmem moves and becomes more expensive, and I don't see a
benefit by default. But with GGML_VK_DISABLE_HOST_VISIBLE_VIDMEM=1 I do see a
significant improvement in model loading time.
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.
* Some improvement on mul_mat_iq2_xs
Refactor calculations for db values and grid data to optimize performance and reduce redundancy.
* Fix trailing whitespace
This implements a variation of the perf logger where rather than timing each
operation individually with effectively a barrier in between, we put the
timing boundaries where we already synchronize and time the groups of work
that normally overlap. This can be useful to help understand whether
individual operations need to be optimized, or if the group is already running
efficiently.
GGML_VK_PERF_LOGGER_CONCURRENT=1 enables the new mode (when
GGML_VK_PERF_LOGGER is also set).
GGML_VK_SYNC_LOGGER=1 replaces the ENABLE_SYNC_LOGGING compile time switch.
* UI: implement basic UI components
* util: implement performance monitor; wrap it with a viewmodel
* util: implement user preferences utility
* UI: implement core flow's screens
* UI: add a new MainActivity; update manifest
* [WIP] DI: implement simple local vm factory provider
* UI: disable triggering drawer via gesture; enable alert dialog on back navigation inside conversation and benchmark
* UI: allow drawer's gesture control only on Home and Settings screens; enable alert dialog on back navigation inside conversation and benchmark
* UI: split a nested parent settings screen into separate child settings screens
* UI: polish system prompt setup UI
* Deps: bump Kotlin plugin; introduce KSP; apply in :app subproject
* DB: setup Room database
* data: introduce repo for System Prompt; flow data from Room to VM
* bugfix: properly handle user's quitting conversation screen while tokens in generation
* UI: rename `ModeSelection` to `ModelLoading` for better clarity
* UI: update app name to be more Arm
* UI: polish conversation screen
* data: code polish
* UI: code polish
* bugfix: handle user quitting on model loading
* UI: locks user in alert dialog when model is unloading
* vm: replace token metrics stubs with actual implementation
* UI: refactor top app bars
* nit: combine temperatureMetrics and useFahrenheit
* DI: introduce Hilt plugin + processor + lib dependencies
* DI: make app Hilt injectable
* DI: make viewmodels Hilt injectable
* DI: replace manual DI with Hilt DI
* UI: optimize AppContent's composing
* bugfix: wait for model to load before navigating to benchmark screen; use NavigationActions instead of raw navController
* UI: navigation with more natural animated transitions
* DI: Optimize AppModule
* Feature: Introduce ModelRepository and ModelsManagementViewModel; update AppModule
* UI: polish UI for ModelsManagementScreen; inject ModelsManagementVieModel
* DI: abstract the protocol of SystemPromptRepository; update AppModule
* data: [WIP] prepare for ModelRepository refactor & impl
* data: introduce Model entity and DAO; update DI module
* UI: replace Models Management screen's stubbing with instrumentation
* UI: polish sort order menu
* data: import local model with file picker
* bugfix: use List instead of Collection for ModelDao's deletion
* data: add a util file for extracting file name & size and model metadata
* UI: enrich ModelManagementState; extract filename to show correct importing UI
* UI: implement multiple models deletion; update Models Management screen
* UI: handle back navigation when user is in multi-selection mode
* util: extract file size formatting into ModelUtils
* UI: add a confirmation step when user picks a file; refactor model import overlay into AlertDialog
* UI: extract a shared ModelCard component
* UI: replace model selection screen's data stubbing; add empty view
* nit: tidy SystemPromptViewModel
* Util: split FileUtils from ModelUtils; extract copy methods into FileUtils
* data: pass through getModelById from ModelDao into ModelRepository
* core: extract conversation and benchmark logics into InferenceManager; add logs and missing state updates in stub InferenceEngine
* vm: split mono MainViewModel into separate individual ViewModels
* vm: merge SystemPromptViewModel into ModelLoadingViewModel
* core: break down InferenceManager due to Interface Segregation Principle
* UI: show model card in Model Loading screen
* UI: show model card in Conversation screen
* UI: unify Model Card components
* core: swap in LLamaAndroid and mark stub engine for testing only
* data: allow canceling the ongoing model import
* UI: update UI ongoing model import's cancellation
* LLama: update engine state after handling the cancellation of sendUserPrompt
* VM: handle the cancellation of ongoing token generation
* LLama: refactor loadModel by splitting the system prompt setting into a separate method
* feature: check for available space before copying local model
* UI: centralize the AppScaffold and modularize its configs
* UI: refactor BottomBarConfig.ModelsManagement APIs
* UI: combine TopBarConfig and BottomBarConfig into each route's ScaffoldConfig
* UI: replace ugly optional as casts in AppScaffold with extension functions
* UI: fix the typo `totalGb` in `StorageMetrics`
* UI: remove code duplication in sort menu
* LLama: add ModelUnloadingState to engine State; add missing state checks in stub engine; fix instrumentation engine's error messages
* UI: refactor back handling by removing centralized BackHandlerSetup and UnloadModelConfirmationDialog from AppContent
* UI: implement BenchmarkScreen's individual back handling
* LLama: add a new Initializing state; ; add two extension properties; rename LibraryLoaded state to Initialized
* UI: Introduce an abstract ViewModel to handle additional model unloading logics
* UI: expose a single facade ModelUnloadDialogHandler; move UnloadModelState into ModelUnloadingViewModel.kt
* UI: migrate ModelLoadingScreen onto ModelLoadingViewModel; update & refine ModelLoadingScreen
* UI: migrate ConversationViewModel onto ModelLoadingViewModel; update & refine ConversationScreen
* nit: extract app name into a constant value; remove unused onBackPressed callbacks
* UI: update AppContent to pass in correct navigation callbacks
* nit: polish ModelLoadingScreen UI
* core: throw Exception instead of returning null if model fails to load
* navigation: sink model loading state management from AppContent down into ModelLoadingScreen; pass ModelLoadingMetrics to Benchmark and Conversation screens
* gguf: add GGUF metadata data holder and its corresponding extractor implementation
* DB: introduce Kotlin serialization extension's library and plugin; add Room runtime library
* GGUF: make GgufMetadata serializable in order to be compatible with Room
* nit: refactor data.local package structure
* nit: rename lastUsed field to dateLastUsed; add dateAdded field
* UI: refactor ModelCard UI to show GGUF metadata
* UI: update ModelSelectionScreen with a preselect mechanism
* UI: polish model card
* nit: allow deselect model on Model Selection screen
* nit: revert accidental committing of debug code
* UI: polish ModelLoading screen
* util: extract formatting helper functions from FileUtils into a new FormatUtils
* UI: polish model cards on Benchmark and Conversation screens to show model loading metrics
* UI: show a Snack bar to warn user that system prompt is not always supported
* UI: handle back press on Model Selection screen
* UI: finally support theme modes; remove hardcoded color schemes, default to dynamic color scheme implementation
* feature: support searching on Model Selection screen
* nit: move scaffold related UI components into a separate package
* UI: extract InfoView out into a separate file for reusability
* data: move Model related actions (query, filter, sort) into ModelInfo file
* UI: animate FAB on model preselection states
* feature: support filtering in Model Management screen
* ui: show empty models info in Model Management screen
* ui: add filter off icon to "Clear filters" menu item
* [WIP] ui: polish Benchmark screen; implement its bottom app bar
* ui: polish Benchmark screen; implement its bottom app bar's rerun and share
* nit: disable mode selection's radio buttons when loading model
* feature: implement Conversation screen's bottom app bar
* pkg: restructure BottomAppBars into separate files in a child package
* pkg: restructure TopBarApps into separate files in a child package
* pkg: restructure system metrics into a separate file
* UI: polish Conversation screen
* data: update system prompt presets
* UI: allow hide or show model card on Conversation & Benchmark screens; fix message arrangement
* data: update & enhance system prompt presets
* deps: introduce Retrofit2
* data: implement HuggingFace data model, data source with Retrofit API
* data: update Model data repository to support fetching HuggingFace models
* [WIP] UI: replace the HuggingFace stub in Model Management screen with actual API call
* UI: map language codes into country Emojis
* ui: add "clear results" action to Benchmark screen
* nit: print current pp & tg in llama-bench
* UI: disable landscape mode; prevent duplicated benchmark running
* llama: migrate C/CXX flags into CMakeList
* [WIP] llama: ABI split builds five .so artifacts.
However, all .so are performing on SVE level
* [WIP] llama: ABI split where five tiers are built sequentially.
* [WIP] llama: disable OpenMP in ABI split since most SoCs are big.LITTLE
* [WIP] llama: enable KleidiAI and disable tier 4 due to `+sve+sve2` bug caused by `ggml_add_cpu_backend_variant_impl` as explained below
```CMake
if (NOT SME_ENABLED MATCHES -1)
...
set(PRIVATE_ARCH_FLAGS "-fno-tree-vectorize;${PRIVATE_ARCH_FLAGS}+sve+sve2")
...
```
* core: add Google's cpu_features as a submodule
* core: implement cpu_detector native lib
* core: swap out hardcoded LlamaAndroid library loading
* core: add back OpenMP due to huge perf loss on TG128
* misc: reorg the pkg structure
* misc: rename LlamaAndroid related class to InferenceEngine prefixes
* [WIP] lib: move GgufMetadata into the lib submodule
* lib: expose GgufMetadataReader as interface only
* lib: replace the naive & plain SharedPreferences with DataStore implementation
* lib: hide the internal implementations, only expose a facade and interfaces
* lib: expose Arm features
* di: add a stub TierDetection; provide both actual impl and stub in AppModule
* UI: add visualizer UI for Arm features
* misc: UI polish
* lib: refactored InferenceEngineLoader; added a `NONE` Llama Tier
* UI: support `NONE` Llama Tier in general settings
* lib: optimize engine loader; always perform a fresh detection when cache is null
* remote: add HuggingFaceModelDetails data class
* remote: refine HuggingFaceModel data class
* nit: remove `trendingScore` field from HuggingFace model entities, weird...
* remote: refactor HuggingFaceApiService; implement download feature in HuggingFaceRemoteDataSource
* remote: fix the incorrect parse of HuggingFace's inconsistent & weird JSON response
* UI: scaffold Models Management screen and view model
* UI: implement a dialog UI to show fetched HuggingFace models.
* UI: use a broadcast receiver to listen for download complete events and show local import dialog.
* data: handle network exceptions elegantly
* pkg: restructure `data`'s packages
* data: extract local file info, copy and cleanup logics into LocalFileDataSource
* nit: minor UI patch; add missing comments
* bugfix: tapping "Home" in navigation drawer should simply close it without any navigation action.
* UI: improve autoscroll during token generation
* lib: tested on JFrog Artifactory for Maven publishing
* UI: show RAM warning if model too large
* UI: polish model management screen's error dialog
* util: add more items into the mapping table of ISO 639-1 language code to ISO 3166-1 country code
* llm: properly propagate error to UI upon failing to load selected model
* UI: avoid duplicated calculation of token metrics
* lib: read & validate the magic number from the picked source file before executing the import
* UI: add "Learn More" hyperlinks to Error dialog upon model import failures
* lib: refactor the GgufMetadataReader to take InputStream instead of absolute path as argument
* lib: fix the `SIMD` typo in Tier description
* core: verify model file path is readable
* lib: add UnsupportedArchitectureException for triaged error message
* util: split FormatUtils into multiple utils for better readability
* UI: change benchmark screen from raw markdown to table view
* bugfix: reset preselection upon running the preselected model
* misc: linter issue
* bugfix: fix the malfunctioning monitoring switch
* UI: update Arm features indicator; fix the broken hyperlinks
* UI: add quick action buttons to benchmark screen's result card
* UI: hide share fab after clearing all benchmark results
* UI: fix the model unload dialog message; elevate the model card and hide it by default on Conversation screen;
* UI: hide the stubbing actions in Conversation screen
* UI: add show/hide stats control to conversation screen's assistant message bubble; fix placeholder
* UI: add a info button to explain token metrics
* misc: remove the redundant `Companion` added due to refactoring
* UI: show corresponding system metrics detailed info upon tapping RAM / storage / temperature indicator
* UI: add info button to System Prompt switch; expand the model card by default
* UI: disable tag & language chips; add section headers to explain what they are
* misc: replace top bar indicator's spacer with padding
* UI: merge the Model Selection and Model Management into a unified Models screen
* UI: split the ModelsManagementViewModel from a unified ModelsViewModel due to huge complexity
* UI: add model loading in progress view; polish the empty model info view
* UI: polish the bottom bars and info view when no models found; show loading in progress while fetching models
* build: [BREAKING] bump the versions of libraries and plugins
* UI: fix the breaking build
* UI: add Tooltip on Import FAB for user onboarding
* UI: adds AppPreferences to track user onboarding status
* UI: tracks user's first success on importing a model
* data: add hand crafted rules to filter the models fetched from HuggingFace API
* UI: update app name & about; polish top bars' indicators & buttons
* UI: polish Hugging Face download dialog UI
* UX: implement onboarding tooltips for model import and onboarding
* misc: use sentence case for CTA button labels
* [WIP] UI: add Arm color palette from Philip.Watson3
* UI: address Rojin's UX feedbacks
* UI: address Rojin's UX feedbacks - part 2
* UI: update Arm color palette from Philip.Watson3
* data: make sure fetch preselected models in the same order of their IDs
* UI: fix UI issues in the generic settings screen and navigation drawer
* nit: address Rojin's feedbacks on model import message again
* nit: append `®` to all `Arm` labels
* UI: extract a reusable InfoAlertDialog
* core: support GGML_CPU_ALL_VARIANTS on Android!
* core: restructure Kleidi-Llama library
* core: organizing cmake arguments
* data: sort preselected models according to device's available RAM
* app: update adaptive + themed + legacy icons and app name
* UI: fix the font size auto scaling for ArmFeaturesVisualizer
* core: further improve the performance on native methods
* UI: minor color palette changes; emphasize the bottom bar FABs; fix Settings Screen menu item label
* UI: make more room for assistant message bubble's width
* UI: better usage of tertiary colors to highlight model cards but not for warnings
* UI: fix the layout issue on large font sizes
* lib: support x86-64 by dynamically set Arm related definitions
* lib: replace the factory pattern for deprecated tiered lib loading with single instance pattern
* llama: update the library name in JNI and CMake project
* llama: update the library's package name and namespace
* llama: update the app's package name and namespace
* app: bump ksp version
* app: remove deprecated SystemUIController from accompanist by migrating to EdgeToEdge
* app: extract AppContent from MainActivity to a separate file in ui package
* lib: add File version for GGUF Magic number verification
* lib: perform engine state check inclusively instead of exclusively
* lib: change `LlamaTier` to `ArmCpuTier`
* lib: remove kleidi-llama related namings
* cleanup: remove Arm AI Chat/Playground app source code; replace with the basic sample app from https://github.com/hanyin-arm/Arm-AI-Chat-Sample
Note: the full Google Play version of AI Chat app will be open will be open sourced in another repo soon, therefore didn't go through the trouble of pruning the history using `git filter-repo` here.
* [WIP] doc: update main and Android README docs; add self to code owners
* lib: revert System.load back to System.loadLibrary
* jni: introduce a logging util to filter different logging levels on different build types
* lib: enable app optimization
* doc: replace stub Google Play app URL with the actual link add screenshots; add my GitHub ID to maintainer list
* Remove cpu_features
* Fix linters issues in editorconfig-checker job
https://github.com/ggml-org/llama.cpp/actions/runs/19548770247/job/55974800633?pr=17413
* Remove unnecessary Android CMake flag
* purge include/cpu_features directory
---------
Co-authored-by: Han Yin <han.yin@arm.com>
* feat: add run_mtmd script for hexagon
* fix: fix issue in fp16xfp32 mm
* fix: remove opt_experiment for fp16xfp32 mm
* fix: ggml-hexagon: matmul fp16xfp32 support non-contigious src0
* fix: fix syntax check for run-mtmd.sh for cli
* metal: use shared buffers on eGPU
With #15906, I noticed on important regression when using metal backend on eGPU.
This commit restore the previous behavior and add an option to force its activation.
* metal: use shared buffers on eGPU
* metal: use shared buffers on eGPU
* llama: automatically fit args to free memory
llama-fit-params tool
* fix CI
* hints for bug reports, ensure no reallocation
* fix segfault with Vulkan
* add llama-fit-params to CI
* fix CI
* fix CI
* fix CI
* minor adjustments
* fix assignment of 1 dense layer
* fix logger not being reset on model load failure
* remove --n-gpu-layer hint on model load failure
* fix llama-fit-params verbosity
* fix edge case
* fix typo [no ci]
When the number of cols is large, split each row across multiple workgroups.
There are three phases that communicate partial results through temp buffers:
(1) compute max partials
(2) take max of partials, compute sum(exp(x-max)) partials
(3) sum partials, compute scaled result
* ggml-cpu:fix RISC-V Q4_0 repack select and RVV feature reporting
Signed-off-by: Wang Yang <yangwang@iscas.ac.cn>
* using the name VLEN instead of CNT
* Update ggml/include/ggml-cpu.h
---------
Signed-off-by: Wang Yang <yangwang@iscas.ac.cn>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* 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>
Some backend depends on CMAKE_RUNTIME_OUTPUT_DIRECTORY to create temporary file like metal backened.
Missing CMAKE_RUNTIME_OUTPUT_DIRECTORY will cause some cmake error like permission denied (try to copy file to root).
This PR wants to setup a default path for CMAKE_RUNTIME_OUTPUT_DIRECTORY when it does not exist.
* fix test failure
* fix: correct scaling calculations in rope_cache_init
* fix: optimize element copying in rope_hex_f32 using memcpy
* fix: optimize loop boundaries in rope_hex_f32 for better performance
* feat: add profiling macros for performance measurement in operations
* tests: update barrier test to check for race condition in active threads
* cpu: combine n_graph and n_threads into a single atomic update
* tests: add multi-graph test for test_barrier
* feat: Add a batched version of ssm_conv
This was done using Claude Code. It found a number of optimizations around
how the threads were organized, resulting in a huge performance boost!
Branch: Mamba2SSD
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* feat: Optimized SSM_SCAN kernel for metal
This used Claude Code and resulted in a modest performance improvement
while maintaining correctness.
Branch: Mamba2SSD
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* test: Add test-backend-ops perf tests for SSM_CONV
Branch: SSMKernelImprovements
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* test: Real representitive tests for SSM_CONV
Branch: SSMKernelImprovements
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* refactor: Use function constant for ssm_conv batch size
Branch: SSMKernelImprovements
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* test: backend op tests for ssm_scan from granite4 1b-h
Branch: SSMKernelImprovements
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* style: remove commented out templates
Branch: SSMKernelImprovements
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* feat: float4 version of ssm_conv_batched
Branch: SSMKernelImprovements
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix: Add missing ggml_metal_cv_free
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
---------
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* fix: Provide macos-specific backtrace printing to avoid terminal death
Branch: MacOSSafeBacktrace
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix: Add GGML_BACKTRACE_LLDB env var to enable using lldb for backtrace
Branch: MacOSSafeBacktrace
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
---------
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* cann: add support for partial RoPE and Vision mode
Add support for two important RoPE variants: partial rotation (rope_dims < ne0)
and Vision mode rotation.
1. Support for partial RoPE (rope_dims < ne0):
- Split tensor into head (first rope_dims dimensions) and tail portions
- Apply rotation only to head portion using RotaryPositionEmbedding operator
- Copy unrotated tail portion directly from source to destination
- Handle both contiguous and non-contiguous tensor layouts
2. Support for Vision mode (GGML_ROPE_TYPE_VISION):
- Set rope_dims = ne0 for Vision mode to rotate entire tensor
- Vision mode pairs dimension i with dimension i+n_dims (where n_dims = ne0/2)
- No tail handling needed since entire tensor is rotated
Implementation details:
- Use has_tail flag to determine execution path: head/tail splitting when
rope_dims < ne0, or full tensor rotation when rope_dims == ne0
- Support both F32 and F16 data types with intermediate F32 conversion
- Copy non-contiguous tensors to contiguous buffers before calling
RotaryPositionEmbedding operator for compatibility
- Improve cache invalidation logic to include rope_dims and indep_sects
parameters
These enhancements enable CANN backend to handle various RoPE configurations
used in modern vision-language models and models with partial rotation.
* cann: fix review comment
* 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>
* ggml-cpu: add ggml_thread_cpu_relax with Zihintpause support
Signed-off-by: Wang Yang <yangwang@iscas.ac.cn>
* cmake: enable RISC-V zihintpause extension for Spacemit builds
* readme : add ZIHINTPAUSE support for RISC-V
---------
Signed-off-by: Wang Yang <yangwang@iscas.ac.cn>
* Optimize Vulkan shader for matrix-vector multiplication
* Revert changes on compute_outputs and main
Refactor compute_outputs to handle remaining rows correctly.
* Fix trailing whitespace
* vulkan: perf_logger improvements
- Move perf_logger from device to ctx.
- Add an env var to control the frequency we dump the stats. If you set a very
large value, it just dumps when the ctx is destroyed.
- Add a fusion info string to the tracking, only log one item per fused op.
- Fix MUL_MAT_ID flops calculation.
* fix vector sizes
* 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>
* Improve error handling for search path existence checks
Refactor existence checks for search paths using std::error_code to handle potential errors.
* Improve cache file existence check with error code
Update fs::exists to use std::error_code for error handling.
* Simplify existence check for search paths
Simplify existence check for search paths
* Fix logging path in error message for posix_stat
* Update ggml/src/ggml-backend-reg.cpp
Co-authored-by: Aman Gupta <amangupta052@gmail.com>
* Adapt to the coding standard
---------
Co-authored-by: Aman Gupta <amangupta052@gmail.com>
The MoE models have a mul_mat_vec with very small m (32, 64, 128) right before
the topk_moe selection. Running multiple rows per wg doesn't utilize the SMs
well. I think even for larger m, f32 is so bandwidth-limited that running
multiple rows doesn't help.
* Fix shader to support 2D workgroup mapping to a single subgroup
* Set required_subgroup_size
topk_moe shader requires static WARP_SIZE and actual subgroup size to match
* vulkan: Reduce temporary memory usage for TOP_K
- Compute row size for the temp buffer based on the output of the first pass.
- Update shader addressing math to use the output row size
- Pass the output row size as "ncols_output", what used to be "ncols_output" is now "k"
For the common case of K=40 and src0=(200000,1,1,1), this reduces the temporary buffer
from about 3.2MB to 500KB.
* vulkan: fix top_k bug when there are ties in the input
I noticed by inspection a bug in the vulkan top_k shader where if the least
value in the top_k appears multiple times we could end up writing those extra
copies out rather than some larger values (if the larger values are on higher
numbered threads).
I rewrote the test verification to handle this case, where the final index set
is not necessarily the same.
* Update tests/test-backend-ops.cpp
Co-authored-by: Georgi Gerganov <ggerganov@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>
* feat(wip): Port initial TRI impl from pervious work
The kernel does not work and is not optimized, but the
code compiles and runs, so this will be the starting point
now that the core op has been merged.
Branch: ggml-cumsum-tri
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix: Remove argument for constant val override
This was added in the original draft, but later removed. With this, the
kernel now passes tests.
Branch: ggml-cumsum-tri
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* feat: Move the ttype conditional to templating to avoid conditional in kernel
Branch: ggml-cumsum-tri
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix: Type fixes
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* feat: Add softplus for metal
Branch: ggml-cumsum-tri
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* feat: Add EXPM1 for metal
Branch: ggml-cumsum-tri
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* feat: Add FILL for metal
Branch: ggml-cumsum-tri
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* refactor: Branchless version of tri using _ggml_vec_tri_cmp as a mask
Branch: ggml-cumsum-tri
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix: Remove unused arguments
Branch: ggml-cumsum-tri
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* refactor: Use select instead of branch for softplus non-vec
Branch: ggml-cumsum-tri
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
---------
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
Previously, cmake was forcing `_WIN32_WINNT=0x0A00` for MinGW builds,
This caused "macro redefined" warnings with toolchains that define the version.
This also removes the `GGML_WIN_VER` variable as it is no longer needed.
Signed-off-by: Adrien Gallouët <angt@huggingface.co>
* Faster tensors (llama/8)
Add fast matrix and matrix/vector multiplication.
* Use map for shader replacements instead of pair of strings
* Wasm (llama/9)
* webgpu : fix build on emscripten
* more debugging stuff
* test-backend-ops: force single thread on wasm
* fix single-thread case for init_tensor_uniform
* use jspi
* add pthread
* test: remember to set n_thread for cpu backend
* Add buffer label and enable dawn-specific toggles to turn off some checks
* Intermediate state
* Fast working f16/f32 vec4
* Working float fast mul mat
* Clean up naming of mul_mat to match logical model, start work on q mul_mat
* Setup for subgroup matrix mat mul
* Basic working subgroup matrix
* Working subgroup matrix tiling
* Handle weirder sg matrix sizes (but still % sg matrix size)
* Working start to gemv
* working f16 accumulation with shared memory staging
* Print out available subgroup matrix configurations
* Vectorize dst stores for sg matrix shader
* Gemv working scalar
* Minor set_rows optimization (llama/4)
* updated optimization, fixed errors
* non vectorized version now dispatches one thread per element
* Simplify
* Change logic for set_rows pipelines
---------
Co-authored-by: Neha Abbas <nehaabbas@macbookpro.lan>
Co-authored-by: Neha Abbas <nehaabbas@ReeseLevines-MacBook-Pro.local>
Co-authored-by: Reese Levine <reeselevine1@gmail.com>
* Comment on dawn toggles
* Working subgroup matrix code for (semi)generic sizes
* Remove some comments
* Cleanup code
* Update dawn version and move to portable subgroup size
* Try to fix new dawn release
* Update subgroup size comment
* Only check for subgroup matrix configs if they are supported
* Add toggles for subgroup matrix/f16 support on nvidia+vulkan
* Make row/col naming consistent
* Refactor shared memory loading
* Move sg matrix stores to correct file
* Working q4_0
* Formatting
* Work with emscripten builds
* Fix test-backend-ops emscripten for f16/quantized types
* Use emscripten memory64 to support get_memory
* Add build flags and try ci
---------
Co-authored-by: Xuan Son Nguyen <son@huggingface.co>
* Remove extra whitespace
* Move wasm single-thread logic out of test-backend-ops for cpu backend
* Disable multiple threads for emscripten single-thread builds in ggml_graph_plan
* Fix .gitignore
* Add memory64 option and remove unneeded macros for setting threads to 1
---------
Co-authored-by: Xuan Son Nguyen <son@huggingface.co>
- Compute row size for the temp buffer based on the output of the first pass.
- Update shader addressing math to use the output row size
- Pass the output row size as "ncols_output", what used to be "ncols_output" is now "k"
For the common case of K=40 and src0=(200000,1,1,1), this reduces the temporary buffer
from about 3.2MB to 500KB.
This commit removes a redundant check for sched->n_copies > 1 when
setting input and output flags on tensor copies in
ggml_backend_sched_split_graph.
The motivation for this change is to clarify the code as the outer if
statement already performs this check.
* 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
* ggml : add GGML_SCHED_NO_REALLOC option to disable reallocations in ggml_backend_sched
Enabled in ggml-ci for testing.
* llama : update worst-case graph for unified cache
* ci : disable op offload in some tests
* fix spelling
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* Qwen3 Next - cleaned up version
* Whitespaces and stuff
* Correct minor errors
* Update src/llama-model.cpp
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* Misc. fixes.
* Clean up code, add missing hybrid qualifier
* Did someone transpose the SOLVE_TRI result matrix? Perhaps...
* Whitespace
* Proper tensors for cb calls
* Use llama-graph.h vertical alignment
* BROKEN: chunking
* Set new tensors as inputs.
* Proper chunk logic
* It's the circle of life...
* More shenanigans for n_seq > 1
* Nail in the coffin?
* Fix Windows build
* Eh, one fails on Windows, the other fails on Mac... just use general capture.
* quant : cleanup
* model : cleanup
* qwen3 : cleanup
* cont : cleanup
* cont : cleanup
* ggml : revert change
* qwen3 : cleanup
* cont : cleanup
* Readd cmath
* qwen3 : fix typo
* Update convert_hf_to_gguf.py
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* Usual suspects
* fix my bad suggestion
---------
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
Store the last computed graph and reuse it when possible.
Also do not return response from GRAPH_COMPUTE and assume it always
completes successfully. If this this is not the case, the server closes
the connection. This saves us a network round trip to the server.
* enable mmf for rdna4
* move some mmvf to mmf
* revert lds128 for wmma loading
* Revert "revert lds128 for wmma loading"
This reverts commit db9ae8b6b4738a5def5b393caa1611d52133e9b5.
* Revert "enable mmf for rdna4"
This reverts commit 698c9f24187b990e35c3b73a8067e5387e6ddbd4.
* Revert "move some mmvf to mmf"
This reverts commit 99b92bd6653cc8593607f641e44606391691792f.
* enable mul_mat for rdna4
---------
Co-authored-by: zhang hui <you@example.com>
* Enabled q4_K_4x8 path
* Fixed generic Q4_K 8x4 implementation
* wip: dotprod gemm
* Working arm q4_K dotprod gemm
Signed-off-by: Alberto Cabrera <alberto.cabrera@liquid.ai>
* Undo acc rename
Signed-off-by: Alberto Cabrera <alberto.cabrera@liquid.ai>
* Q4_K arm dotprod gemm
Signed-off-by: Alberto Cabrera <alberto.cabrera@liquid.ai>
* Fix: q4_qs reinterpret from uint to int
Signed-off-by: Alberto Cabrera <alberto.cabrera@liquid.ai>
* Removed comments
* Fixed macro guards
* Fixed unused vars in generic implementation
* Fixed unused vars in 8x4 repack
* Fixed unused vars in generic implementation, unneeded comment
* Missing arch fallback for x86
* minor : style
---------
Signed-off-by: Alberto Cabrera <alberto.cabrera@liquid.ai>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* vulkan: Implement top-k
Each pass launches workgroups that each sort 2^N elements (where N is usually 7-10)
and discards all but the top K. Repeat until only K are left. And there's a fast
path when K==1 to just find the max value rather than sorting.
* fix pipeline selection
* vulkan: Add N-ary search algorithm for topk
* microoptimizations
On arm64 with `cmake` version 3.31.6, the final feature verification fails:
-- ARM detected flags: -mcpu=neoverse-v2+crc+sve2-aes+sve2-sha3+nossbs
-- Performing Test GGML_MACHINE_SUPPORTS_dotprod
-- Performing Test GGML_MACHINE_SUPPORTS_dotprod - Success
-- Performing Test GGML_MACHINE_SUPPORTS_i8mm
-- Performing Test GGML_MACHINE_SUPPORTS_i8mm - Success
-- Performing Test GGML_MACHINE_SUPPORTS_sve
-- Performing Test GGML_MACHINE_SUPPORTS_sve - Success
-- Performing Test GGML_MACHINE_SUPPORTS_sme
-- Performing Test GGML_MACHINE_SUPPORTS_sme - Failed
-- Performing Test GGML_MACHINE_SUPPORTS_nosme
-- Performing Test GGML_MACHINE_SUPPORTS_nosme - Success
-- Checking for ARM features using flags:
-- -U__ARM_FEATURE_SME
-- -mcpu=neoverse-v2+crc+sve2-aes+sve2-sha3+nossbs+dotprod+i8mm+sve+nosme
-- Performing Test HAVE_DOTPROD
-- Performing Test HAVE_DOTPROD - Failed
-- Performing Test HAVE_SVE
-- Performing Test HAVE_SVE - Failed
-- Performing Test HAVE_MATMUL_INT8
-- Performing Test HAVE_MATMUL_INT8 - Failed
-- Performing Test HAVE_FMA
-- Performing Test HAVE_FMA - Success
-- Performing Test HAVE_FP16_VECTOR_ARITHMETIC
-- Performing Test HAVE_FP16_VECTOR_ARITHMETIC - Failed
-- Performing Test HAVE_SME
-- Performing Test HAVE_SME - Failed
-- Adding CPU backend variant ggml-cpu: -U__ARM_FEATURE_SME;-mcpu=neoverse-v2+crc+sve2-aes+sve2-sha3+nossbs+dotprod+i8mm+sve+nosme
We need to explicitly replace `;` with spaces from the list to make
`CMAKE_REQUIRED_FLAGS` work correctly...
Signed-off-by: Adrien Gallouët <angt@huggingface.co>
* 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
* CANN: ROPE supports both MROPE and IMROPE.
1. Optimize the caching logic of rope_cache_init.
2. Add support for mRoPE and i-mRoPE.
Note that on Ascend 910B devices, it is necessary to disable FA
in CLIP and disable NZ-format conversion. These two issues are
still under investigation.
* Resolve review comments
* 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 b39ed57c4529906466bd0bc7c2a86e08fc2f8bee.
* ggml: add RISC-V cpu-feats
Signed-off-by: Wang Yang <yangwang@iscas.ac.cn>
* fix comment[1]
---------
Signed-off-by: Wang Yang <yangwang@iscas.ac.cn>
**Description of the problem**
`cann_graph_update_required` is redundantly defined and
initialized as `false` inside two mutually exclusive macro branches.
**Proposed solution**
Define it right before the macro so that it could serve both
branches.
* ggml-hexagon: fix build error with GCC
Add stdexcept include to fix GCC build errors
Signed-off-by: Mohamed Mediouni <mohamed@unpredictable.fr>
* ggml-hexagon: check VTCM acquire failures
Signed-off-by: Mohamed Mediouni <mohamed@unpredictable.fr>
* ggml-hexagon: disable destination bypass on older than v73
v68 errors out if having bypass enabled when the VTCM is the destination.
At least on v68 this made things actually work... not a proper fix though, so to look at later...
Signed-off-by: Mohamed Mediouni <mohamed@unpredictable.fr>
* ggml-hexagon: add initial v68/v69 support
v68 is the Hexagon revision notably used on the Snapdragon 8cx
Gen 3 and the QCM6490.
Also add support for v69.
8MB isn't a supported page size, so relax asked for page size constraint
for HAP_compute_res_attr_set_vtcm_param_v2 to optimal.
Signed-off-by: Mohamed Mediouni <mohamed@unpredictable.fr>
---------
Signed-off-by: Mohamed Mediouni <mohamed@unpredictable.fr>
* hexagon: add buffer support checks for hexagon sessions
* refactor: simplify buffer support checks in hexagon operations
* hexagon: update buffer support checks to use tensor structure
* refactor: streamline buffer initialization for DSP queue in hexagon operations
* refactor: simplify buffer initialization in DSP queue for hexagon operations
* refactor: optimize hex_supported_buffer function by fold expression
* wip
* refactor: simplify dspqueue_buffers_init function and its usage in hexagon operations
* fix: improve nan handling at hvx_vec_fast_sigmoid_fp32_guard
* refactor: optimize hvx_vec_inverse_fp32_guard for better nan handling
* refactor: update hvx_vec_fast_sigmoid_fp32_guard to use adjusted exponent limits
* refactor: modify hvx_vec_fast_sigmoid_fp32_guard to accept parameters for improved flexibility
* refactor: update hvx_vec_exp_fp32_guard to accept max_exp and inf parameters to save some instructions
* refactor: move hvx_vec_inverse_fp32_guard implementation to hvx-inverse.c for better perf
* 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>
* CANN: Refactor `evaluate_and_capture_cann_graph`
**Description of the problem**
* `matched_graph` is obtained even if graph mode is disabled.
* End of graph capture and graph replay are unnecessarily placed in different `if` blocks.
**Proposed solution**
* Obtain `matched_graph` only if graph mode is enabled.
* Place end of graph capture and graph reply inside the same `if` block.
* Unify graph related comments.
* Remove trailing whitespace
* vulkan: support larger argsort
This is an extension of the original bitonic sorting shader that puts the
temporary values in global memory and when more than 1024 threads are needed
it runs multiple workgroups and synchronizes through a pipelinebarrier.
To improve the memory access pattern, a copy of the float value is kept with
the index value. I've applied this same change to the original shared memory
version of the shader, which is still used when ncols <= 1024.
* Reduce the number of shader variants. Use smaller workgroups when doing a single pass, for a modest perf boost
* reduce loop overhead
* run multiple cols per invocation, to reduce barrier overhead
* 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.
* cann: fix acl_tensor_ptr usage in ASCEND_310P ROPE implementation
Fix compilation errors in the ASCEND_310P-specific ROPE operation code
by adding .get() calls when passing acl_tensor_ptr smart pointers to
functions expecting raw aclTensor* pointers.
This fixes the code that was missed in the previous refactoring commit
(8981848) which changed ggml_cann_create_tensor() return type from
aclTensor* to acl_tensor_ptr.
* cann: format code
This commit removes the "-dirty" suffix from the GGML version string.
The motivation for this change is to ensure that the version string
works with different ways of checking out ggml and using it in projects.
By removing the dirty flag from the version string, we avoid potential
artifacts like shared libraries getting a -dirty suffix in their names.
Instead, if the project is built from a dirty git state, the dirty flag
will be appended to the commit hash in the GGML_BUILD_COMMIT variable.
This will enable users to still identify that the build was made from
from a modified/dirty state even though the version might match a "real"
version.
For example, the commit can be produces as follows:
```c++
printf("commit: %s\n", ggml_commit());
```
Which would print the following for a dirty build:
```console
commit: 781baf2a-dirty
```
Refs: https://github.com/ggml-org/ggml/pull/1363#issuecomment-3569691546
* CANN: Use smart pointers to manage ACL objects
Previously, ACL objects were managed via manual destruction, which
led to multiple memory-leak issues during runtime. This patch replaces
manual memory management with smart pointers so that ACL objects
are properly released and ownership is clearly defined.
Note that the ownership of an ACL object belongs to the function
that creates it. Other internal functions should operate on these ACL
objects using raw pointers to avoid unintended ownership transfers.
Additionally, since aclTensorList automatically frees its contained
aclTensor objects, any aclTensor added to a tensor list must release
ownership to avoid double free operations.
This PR also removes the asynchronous task submission mechanism.
Due to changes in recent CANN versions, tiling time has significantly
decreased. Even with a dual-thread submission model, the dispatch
overhead still falls on the critical path, making async submission
less beneficial. Moreover, aclGraph support provides a much better
path to reducing operator dispatch latency.
* CANN: resolve review comments
* vulkan: add LOG operation support for F32 and F16
Part of #14909.
* vulkan: Fix LOG operation types
* docs: Update operation support documentation for Vulkan LOG operation
* vulkan: fix log_f16 shader
* docs: restore missing LOG test cases and regenerate ops.md
* SYCL: add generic unary op implementation for multiple ops (ABS/SGN/…); unify non-contiguous access
* SYCL: update documentation and sycl.csv to reflect new unary op support
* update ops.md after syncing SYCL.csv changes
* Fix SYCL.csv merge conflict
* Update ops.md after fixing SYCL.csv conflicts
* Fix SYCL.csv tail after merge conflict and regenerate ops.md
* Fix line endings and final newline in SYCL.csv
* Remove TOPK_MOE entries from SYCL.csv as requested
* Update ops.md after removing TOPK_MOE from SYCL.csv
* Regenerated SYCL.csv and synced ops.md with upstream
* Update ops.md using create_ops_docs.py
* vulkan: change graph_compute to be async and enable get_tensor_async
This allows some additional CPU/GPU overlap for large pp workloads. Also seems
to help a bit for token gen, maybe getting rid of a small bubble between
graph_compute and get_tensor.
Async set and copy functions seem to be very rarely used, so I didn't enable
them because I didn't have a good way to test them.
The async commands need to be ordered against each other, so put them all on
the compute queue. The non-async commands still use the transfer queue.
The fence for graph_compute/get_tensor_async is submitted and waited on in
ggml_vk_synchronize.
* fix thread safety errors
* teardown context cleanly
* Handle async read to non-pinned dst
* ggml-cpu: handle 3d tensors in repack mul_mat
* Removed unnecessary branch, removed need for <algorithm>
* Fixed dst_ptr pointer in chunk + clang_format
* GGML_ASSERT to check wdata within bounds
* Accidental ggml.h inclusion
* Improved GGML_ASSERT on wdata boundaries
* Address performance regression in Qwen and llama.cpp due to chunking
* vulkan: remove shell call from vulkan-shaders-gen tool
* use string vector for command execution
* Fix condition
* use string, remove const_cast
* Fix dependency file quotation on Windows
---------
Co-authored-by: Jeff Bolz <jbolz@nvidia.com>
* update L2_NORM op support
* update L2_NORM op support
* remove extra whitespace
* cann: update cross_entropy_loss op support
* remove trailing whitespaces
* rebase the latest code in the main repository and remove the l2_norm operator that already exists in another pull request.
* undo the l2_norm operator deletion
* 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
* hexagon: explicitly check for ops with zero nrows
llm_graph_context::build_inp_out_ids() can generate tensors with zero nrows.
Somehow other backends seems to handle this without obvious explicit checks.
In the hexagon case we need to check explicitly and skip them.
* hexagon: introduce fastdiv, fix test-backend-ops for ADD/SUB/MUL
Co-authored-by: chraac <chraac@gmail.com>
* hexagon: use fastdiv in ADD_ID
* hexagon: use ggml_op_is_empty and ggml_is_empty to check for NOPs
---------
Co-authored-by: chraac <chraac@gmail.com>
* extract rotate_pairs logic from ggml_compute_forward_rope_f32
* templateify ggml_compute_forward_rope_f32 and _f16
* abort when rope type not supported, remove GLM from test-rope
* add imrope branch to switch
* add rope tests for perf
* Update ggml/src/ggml-cpu/ops.cpp
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* Update ggml/src/ggml-cpu/ops.cpp
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
When compiling llama.cpp in Yocto, it fails QA checks because the generated so files aren't versioned. This applies a version to all generated so files, allowing the package to build without errors.
* add i8mm route with SVE ggml_vec_dot_q4_K_q8_K and ggml_vec_dot_q6_K_q8_K
* Surround SVE function with compiler directive
* fix compile switch
* fix coding style
* ggml : fix indent
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* 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
* vulkan: use all device-local heaps for memory availability reporting
Co-authored-by: Giuseppe Scrivano <gscrivan@redhat.com>
* use all available heaps for iGPU memory reporting
* Allow multiple memory types per buffer request for devices with split heaps
---------
Co-authored-by: Giuseppe Scrivano <gscrivan@redhat.com>
This change combines the rms_norm+mul and rope+view+set_rows fusions to
allow fusing the whole sequence together. This comes up in Qwen3, Bailing,
and some other models.
The std::map pipeline_flash_attn_f32_f16 could be searched and inserted at the
same time, which needs to hold the lock. To be safe, hold the lock for all of
ggml_vk_load_shaders.
When using GCC 9 and GCC 12 on the arm64 platform of ubuntu 2004,
the command "gcc -mcpu=native -E -v -" fails to detect the correct CPU flags,
which results in compilation failures for certain extended instructions,
but the correct CPU flags can be obtained by using gcc -march.
Signed-off-by: lizhenneng <lizhenneng@kylinos.cn>
Co-authored-by: lizhenneng <lizhenneng@kylinos.cn>
* metal : rework mat-mat multiplication
* metal : initial Metal4 support
* cont
* metal : detect tensor support
* cont : better ifdefs
* metal : support tensors in mul_mm_id
* metal : add env for disabling tensor API
* tests : restore
* metal : remove unused constants
* metal : fix check for bfloat tensor support
* cont : handle API incompatibilities
* cont : handle even more incompatibilities
* metal : use tensor API only on M5 and later
* support older socs where FASTRPC_GET_URI is unsupported
* added graceful fallback when FASTRPC_GET_URI call fails
* use weak symbols instead of loading libcdsprpc.so dynamically
* Add weak pragma for rpcmem_alloc2
* Remove weak declaration for rpcmem_alloc2 in ggml-hexagon.cpp
Removed weak declaration for rpcmem_alloc2.
* Enforce ndev to 1 for archs below v75
Force ndev to 1 for SoCs architectures lower than v75.
* 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>
* Add buffer label and enable dawn-specific toggles to turn off some checks
* Minor set_rows optimization (ggml/4)
* updated optimization, fixed errors
* non vectorized version now dispatches one thread per element
* Simplify
* Change logic for set_rows pipelines
---------
Co-authored-by: Neha Abbas <nehaabbas@macbookpro.lan>
Co-authored-by: Neha Abbas <nehaabbas@ReeseLevines-MacBook-Pro.local>
Co-authored-by: Reese Levine <reeselevine1@gmail.com>
* Comment on dawn toggles
* Remove some comments
* Implement overlap binary operators
* Revert "Implement overlap binary operators"
This reverts commit ed710b36f51ab3f53fa13db15c1685dc8678a32a.
* Disable support for non-contiguous binary_op tensors and leave note for future support
---------
Co-authored-by: neha-ha <137219201+neha-ha@users.noreply.github.com>
Co-authored-by: Neha Abbas <nehaabbas@macbookpro.lan>
Co-authored-by: Neha Abbas <nehaabbas@ReeseLevines-MacBook-Pro.local>
* vulkan: remove the need for the dryrun
Allocate pipelines and descriptor sets when requested.
Reallocate the prealloc buffers when needed, and flush any pending work
before reallocating.
For rms_partials and total_mul_mat_bytes, use the sizes computed the last time
the graph was executed.
* remove dryrun parameters
* Fix garbled output with REPACK at high thread counts
Fixed a race condition in the REPACK matrix multiplication code that caused garbled output when using 26+ threads (model-dependent threshold). The issue occurred because with high thread counts, the code forced chunk count to equal thread count, creating many small chunks. After aligning these chunks to NB_COLS boundaries, adjacent chunks could overlap, causing data corruption and race conditions. The fix enforces minimum chunk sizes based on NB_COLS and caps maximum chunk count to prevent creating too many tiny chunks, ensuring proper alignment without overlaps.
* Update ggml/src/ggml-cpu/repack.cpp
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* Update ggml/src/ggml-cpu/repack.cpp
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* SYCL repeat_back v1 — add core op + switch case
* Implement repeat_back SYCL operation and minor fixes
* SYCL: optimize repeat_back kernel
* Remove Hebrew comment from repeat_back.cpp
* Remove comments for code clarity
Removed comments to clean up the code.
* Fix formatting in ggml-sycl.cpp
* Formatted lambda according to legacy style. No logic changes
* Remove blank line in repeat_back.cpp
Remove unnecessary blank line before assigning acc to dst_dd.
* clip : use FA
* cont : add warning about unsupported ops
* implement "auto" mode for clip flash attn
* clip : print more detailed op support info during warmup
* cont : remove obsolete comment [no ci]
* improve debugging message
* trailing space
* metal : remove stray return
---------
Co-authored-by: Xuan Son Nguyen <son@huggingface.co>
* vulkan: fuse mul_mat+add and mul_mat_id+add_id
The fusion is only applied for the mat-vec mul paths.
* Apply suggestions from code review
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* fix 32b build
---------
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.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>
* Fix test-conv2d-dw failure on ARM SVE by using runtime vector length
The ggml_compute_forward_conv_2d_dw_cwhn function was using a hardcoded GGML_F32_EPR (8) for SIMD vectorization, but on ARM SVE the actual vector length varies by hardware. This caused incorrect computation when processing CWHN layout tensors on ARM machines.
Fix by using svcntw() to get the runtime SVE vector length instead of the compile-time constant.
Co-authored-by: ggerganov <1991296+ggerganov@users.noreply.github.com>
* ci : reduce sam score threshold
* ci : update bbox checks for sam test
---------
Co-authored-by: copilot-swe-agent[bot] <198982749+Copilot@users.noreply.github.com>
Co-authored-by: ggerganov <1991296+ggerganov@users.noreply.github.com>
* Experimenting crash fix
* added assert for aborting and fixed comment
* changed to check if a pipeline is empty or not
* Moved function in class definition
* replaced with is_empty
* Modified is_empty to check only unaligned pipelines
* respect input size when getting/setting tensor data
allows partial repacking/copying when get tensor size is smaller than the actual tensor
* Removed duplicate repack_mxfp4_mxfp4x4x2 function
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.
This pattern appears in a lot of models, the rope operation is applied right
before storing into the KV cache (usually on the K tensor).
Add a path to some of the rope shaders that computes the destination address
based on the set_rows tensor. Compile variants of the shader with D_TYPE of
f16 (the usual KV cache type).
Add a src3 operand to ggml_vk_op_f32 - sometimes rope uses three srcs and needs
the fourth for the row indices.
Add fused_ops_write_mask to indicate which intermediate tensors need to write
their results to memory. Skipping writing the roped K value helps to allow more
nodes to run concurrently.
Add logic to ggml_vk_graph_optimize to make ROPE+VIEW+SET_ROWS consecutive. It
rarely starts out that way in the graph.
Add new backend tests.
* vulkan: Update topk_moe fusion to handle gpt's late softmax
Based on #16649.
* Add ggml_check_edges
* Add sync logging to show fusion effects
* handle clamp added in #16655
* Update ggml/src/ggml-impl.h
Co-authored-by: Diego Devesa <slarengh@gmail.com>
* hexagon: remove dspqueue callbacks and do all read processing inplace
* hexagon: there is no need to ref/deref the buffers at this point
We're not going to release the buffers without flushing the session queue.
So there is no need to inc/dec the refcounts for every request.
We also don't need to include those bufs in the response.
* hexagon: bump the thread count in the adb wrapper scripts
We can use more CPU cores now that the dedicated dspqueue polling threads are not used (ie no contention).
Also enable more agressive polling for now since we still map Flash Attention (and a few other kernels) to
the CPU and those dspqueue threads were keeping the CPU cores are higher clock freqs.
* hexagon: add lhez as the second code owner
* 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
* cann: improve device ID handling and aclnnArange checks
- Stop relying on CANN's internal device ID retrieval; use a global variable instead.
- Enforce stricter dimension validation in aclnnArange for better compatibility across CANN versions.
* cann: use thread local var
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