* flash attn: add auto mode for llama engine
If the user does not specify fa in the environment, use auto-mode.
* review comments
* ensure kv cache quantized types have FA explicitly enabled
additional review comments
* feat: Bump llama.cpp to the latest master (17f7f4b)
This brings in significant improvements to prefill performance for all
models using the SSM_CONV and SSM_SCAN ops (granite4, jamba, falcon-h,
nemotron-h, Qwen3 Next) on Apple Metal.
See https://github.com/ggml-org/llama.cpp/pull/17876
Branch: LlamaCPPMetalSSMImprovements
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* feat: Update patches 1-4
Branch: LlamaCPPMetalSSMImprovements
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix: Update patches 5-12
Branch: LlamaCPPMetalSSMImprovements
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* feat: Update patches 13-18
Branch: LlamaCPPMetalSSMImprovements
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* feat: Update patch 20
Branch: LlamaCPPMetalSSMImprovements
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* feat: Update patches 21-31
Branch: LlamaCPPMetalSSMImprovements
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* feat: Sync vendored code
The two files I'm not sure about here are the swap from gemma3-iswa.cpp to
gemma3.cpp (I chose to include this because I think it's required), and the
inclusion of `ggml-zendnn.h` which I chose to omit.
Branch: LlamaCPPMetalSSMImprovements
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
---------
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
Although the vision component of multimodal models typically already
call the optimized nn.Attention, it is converted into non-fused
operations. That is because the backend-specific fused kernels may
have requirements, such as padding, and they is performed by the
cache, which vision encoders don't use.
This implements a fallback path in the backend, softening the
requirements into optimizations. In turn, this allows flash attention
to be used for vision encoders, saving a significant amount of VRAM
and improving performance.
We currently use cache padding of 32 when not using flash attention
and 256 with flash attention, which is based on the historic alignment
requirements of these kernels. The restrictions have since been
loosened but there are still performance benefits, such as better
CUDA graph reuse.
Since the requirement is no longer kernel-specific, set the padding
uniformly to 256, as llama.cpp has.
* Revert "vulkan: temporary cary of vulkan fixes (#12971)"
This reverts commit 3a9e8e9fd4.
* ggml update to b7087
* fix argsort on metal
* update to b7108
* fix bakllava regression
This model lacks the metadata for the projector type.
* update to b7209
* fix TopK perf
* only build arm code on arm
We now do a deeper probe of CUDA devices to verify the library version has
the correct compute capability coverage for the device. Due to ROCm also
interpreting the CUDA env var to filter AMD devices, we try to avoid setting
it which leads to problems in mixed vendor systems. However without setting
it for this deeper probe, each CUDA library subprocess discovers all CUDA GPUs
and on systems with lots of GPUs, this can lead to hitting timeouts. The fix is
to turn on the CUDA visibility env var just for this deeper probe use-case.
We currently copy data into the KV cache in contiguous buffers using
ggml_cpy(). ggml_set_rows() was introduced to allow scatter operation
so that contiguous buffers are no longer required. The direct primary
benefit of this is that we no longer need to perform defragmentation.
However, GGML recently removed an optimization for ggml_cpy() and
we picked it up in 544b673 "ggml update to b6840 (#12791)". This
caused a roughly 40% drop in token generation performance on CUDA
due to CUDA graphs no longer being used. By switching to
ggml_set_rows(), the original optimization is no longer necessary
and CUDA performance is restored.
Fixes#13112
GGML requires tensors to be contiguous for reshape and if
this is not the case, it will assert fail. Contiguous is an
expensive operation, so it's best to do it lazily when it is
actually required rather than ahead of time when it may not
be needed.
Calling abort on windows triggers the C++ runtime to attempt a debugger
attach, which causes the crashed runners to hang instead of exit, leading
to a timeout instead of a fast failure during discovery.
* build: optimize dockerfile context for iterating
This moves the copy of the source into the layer AFTER
doing software installs so we don't have to go through
the RPM install for cuda, etc. every time you touch a
source file.
* amd: implement linux sysfs based VRAM lookup
This adds a C++ implementation of sysfs DRM VRAM discovery
for more accurate free VRAM data on linux for AMD GPUs.
We currently assign model layers to GPUs according to free VRAM,
which assumes that GPU performance is roughly equal. This does not
work well for mixed dGPU and iGPU systems because iGPUs typically
use system memory which is large but their performance is slow.
This instead assigns layers to dGPUs first and then iGPUs.
In the future, this could be generalized to have a more fine grained
notion of GPU performance but dGPU vs. iGPU performance is the most
extreme.
We used to control the way that llama.cpp saw devices using
CUDA_VISIBLE_DEVICES or similar. This would ensure that the layers
offloaded to a device were actually the ones intended. This is
particularly important because we might reorder devices based on
free memory or performance.
When we started explicitly scheduling layers, this logic went
away but the llamarunner didn't have any way to set the correct
order of devices. This meant that the correct number of layers
would be assigned to a device but not necessarily the layers
that were expected. This change sets up the devices correctly
based on the offload information.
* discovery: only retry AMD GPUs
CUDA and Vulkan don't crash on unsupported devices, so retry isn't necessary.
This also refactors the code to shift the Library specific logic into the ml
package.
* review comments
* PDH free memory skeleton
* Add PDH printing
* Add LUID support for Vulkan
* wire luid from ggml-vulkan to mem-dxgi-pdh file
* Fix to ggml-impl
* Continue skeleton
* Implemented ggml_dxgi_pdh_get_device_memory
* fix comments
* Fix - change value GB to bytes
* add ifdefs to only support windows and not linux
* modify error codes
* Finished ggml_dxgi_pdh_init() function
* completed ggml_dxgi_pdh_release()
* Formatting changes, add static to functions
* fix build errors
* fix go build error
* fix luid - now should match between dxgi and vulkan
* Fix the free memory reporting (was using copy by value, change to reference)
* keep only dxgi1_2.h
* Modifications based on PR feedback
* fix merge conflicts (2) and fix desc1.description printout
* move dxgi + pdh api calls to before the vendor specific library calls
* change from 3 samples to 1 sample for PDH
* modify when old_mode is set
* add fix for building MacOS
* fix release and returns for other vendors
* add patch file
The initial implementation of qwen3-vl:235b exceeded the maximum graph
size based on the number of tensors. Although this was later fixed
through the use of the mrope operation, we are close to the limit in
some cases. This updates to track the current llama.cpp usage of GGML.
We pass invalid pointers when we check the size of the required
compute graph before fitting. Some CUDA APIs validate these pointers
but we can just skip them during this phase. cudaMemsetAsync is one
of these that we weren't skipping but never took the code path that
used it. Now that we have enabled op_offload, we can hit it in
memory pressured situations.
When a model is partially offloaded to system RAM, we can either
do the calculations on the CPU or we can temporarily transfer the
data to the GPU to do the calculations there. Small batches tend
to be better on the CPU, large batches on the GPU.
The llamarunner used the GPU in most cases and the ollamarunner
used the CPU. Although the ollamarunner saw an improvement in
token generation performance, there was a large performance hit
in prompt processing (3-10x).
There is an existing heuristic to dynamically switch between these
two modes but in practice it doesn't have enough information to
accurately make that decision. This adds authoritative data to make
the check work to get the best of both worlds.
Fixes#12037
* Fix vulkan PCI ID and ID handling
Intel GPUs may not report PCI IDs which was leading to incorrect overlap
detection. Switch to using the existing PCI IDs, however AMD GPUs claim not to
report PCI IDs, but actually do, so try anyway, as this is required for ADLX to
find the GPUs on Windows. Numeric IDs lead to scheduling problems, so this also
switches Vulkan to use UUID based IDs. The GPU discovery patches have been
squashed into a single patch to simplify future rebases.
* review comments
* DRY out the runner lifecycle code
Now that discovery uses the runners as well, this unifies the runner spawning code
into a single place. This also unifies GPU discovery types with the newer ml.DeviceInfo
* win: make incremental builds better
Place build artifacts in discrete directories so incremental builds don't have to start fresh
* Adjust sort order to consider iGPUs
* handle cpu inference oom scenarios
* review comments
Users on Windows without GPUs are reporting errors relating to
cudaDriverGetVersion with the device set to -1. This ensures we only grab the
driver once we're enumerating actual devices.
When loading the dynamic libraries, if something goes wrong report some
details. Unfortunately this wont explain which dependencies are missing,
but this breadcrumb in the logs should help us diagnose GPU discovery
failures.
* Simplify NVML fallback for unified memory GPUs
Remove device-specific checks and environment variable dependency for
NVML_ERROR_NOT_SUPPORTED fallback. When NVML doesn't support memory
queries, unconditionally use /proc/meminfo instead of checking device
names or OLLAMA_UNIFIED_MEMORY environment variable.
This provides better memory reporting by using MemAvailable which
accounts for reclaimable memory, avoiding the underreporting issue
described in NVIDIA support article a_id/5728.
Tested on NVIDIA GB10 unified memory iGPU with consistent and accurate
memory reporting across multiple model load/unload cycles.
* Add NVML fallback patch for unified memory GPUs
* implement the vulkan C backend
* add support in gpu.go
* add support in gen_linux.sh
* it builds
* fix segfault
* fix compilation
* fix free memory monitor
* fix total memory monitor
* update gpu.go
* fix build
* fix check_perfmon len
* remove cap_get_bound check
* fix vulkan handle releasing
* fix build on federa 40
* fix vulkan on windows
* making amdgpu work on arm achitecutre with vulkan
* add x86_64 lines in VulkanGlobs and capLinuxGlobs
* add aarch64 lines in vulkanGlobs and capLinuxGlobs
* Fix variable name
* Add vulkan build patch from @jmorganca
* Sync vendored ggml to add Vulkan support
* Updated dockerfile
https://github.com/whyvl/ollama-vulkan/issues/7#issuecomment-2660836871
Signed-off-by: Vadim Grinco <vadim@grinco.eu>
* Installing rocm library
Signed-off-by: Vadim Grinco <vadim@grinco.eu>
* This version works well
built based on this: https://github.com/whyvl/ollama-vulkan/issues/7#issuecomment-2660836871
Signed-off-by: Vadim Grinco <vadim@grinco.eu>
* Applied 00-fix-vulkan-building.patch
Work done by McBane87 here: https://github.com/whyvl/ollama-vulkan/issues/7#issuecomment-2660836871
Signed-off-by: Vadim Grinco <vadim@grinco.eu>
* Fixed the "detached head" issues
Signed-off-by: Vadim Grinco <vadim@grinco.eu>
* Merged in the right direction
Signed-off-by: Vadim Grinco <vadim@grinco.eu>
* Merging the latest stable (#2)
* Applied 00-fix-vulkan-building.patch
* Implemented vulkan backend based on the work done by whyvl, Dts0, McBane87 and others
Tested on AMD Ryzen 7 8845HS w/ Radeon 780M Graphics with ROCm disabled
```
[GIN-debug] POST /v1/chat/completions --> github.com/ollama/ollama/server.(*Server).ChatHandler-fm (6 handlers)
[GIN-debug] POST /v1/completions --> github.com/ollama/ollama/server.(*Server).GenerateHandler-fm (6 handlers)
[GIN-debug] POST /v1/embeddings --> github.com/ollama/ollama/server.(*Server).EmbedHandler-fm (6 handlers)
[GIN-debug] GET /v1/models --> github.com/ollama/ollama/server.(*Server).ListHandler-fm (6 handlers)
[GIN-debug] GET /v1/models/:model --> github.com/ollama/ollama/server.(*Server).ShowHandler-fm (6 handlers)
time=2025-03-11T13:00:40.793Z level=INFO source=gpu.go:199 msg="vulkan: load libvulkan and libcap ok"
time=2025-03-11T13:00:40.877Z level=INFO source=gpu.go:421 msg="error looking up vulkan GPU memory" error="device is a CPU"
time=2025-03-11T13:00:40.878Z level=WARN source=amd_linux.go:443 msg="amdgpu detected, but no compatible rocm library found. Either install rocm v6, or follow manual install instructions at https://github.com/ollama/ollama/blob/main/docs/linux.md#manual-install"
time=2025-03-11T13:00:40.878Z level=WARN source=amd_linux.go:348 msg="unable to verify rocm library: no suitable rocm found, falling back to CPU"
time=2025-03-11T13:00:40.879Z level=INFO source=types.go:137 msg="inference compute" id=0 library=vulkan variant="" compute=1.3 driver=1.3 name="AMD Radeon Graphics (RADV GFX1103_R1)" total="15.6 GiB" available="15.6 GiB"
```
```
# ollama run phi4:14b
>>> /set verbose
Set 'verbose' mode.
>>> how's it going?
Hello! I'm here to help you with any questions or tasks you have. How can I assist you today? 😊
total duration: 3.341959745s
load duration: 18.165612ms
prompt eval count: 15 token(s)
prompt eval duration: 475ms
prompt eval rate: 31.58 tokens/s
eval count: 26 token(s)
eval duration: 2.846s
eval rate: 9.14 tokens/s
>>>
```
* This is no longer needed
Signed-off-by: Vadim Grinco <vadim@grinco.eu>
* Fixes SIGSEGV: segmentation violation running gemma3 models on ollama 0.6.0 #21
Patch provided by McBane87 on https://github.com/whyvl/ollama-vulkan/issues/21
Signed-off-by: Vadim Grinco <vadim@grinco.eu>
* Applied 04-disable-mmap-vulkan.patch
From: https://github.com/whyvl/ollama-vulkan/issues/7#issuecomment-2660836871
Signed-off-by: Vadim Grinco <vadim@grinco.eu>
* Pulled new upstream code for ggml-bulkan backend
Signed-off-by: Vadim Grinco <vadim@grinco.eu>
* Merged latest ollama 0.6.2 and nasrally's Flash Attention patches (#5)
* readme: add Ellama to list of community integrations (#9800)
* readme: add screenpipe to community integrations (#9786)
* Add support for ROCm gfx1151 (#9773)
* conditionally enable parallel pipelines
* sample: make mutations in transforms explicit (#9743)
* updated minP to use early exit making use of sorted tokens
* ml/backend/ggml: allocate memory with malloc when loading model (#9822)
* runner: remove cache prompt flag from ollama runner (#9826)
We do not need to bypass the prompt caching in the ollama runner yet, as
only embedding models needed to bypass the prompt caching. When embedding
models are implemented they can skip initializing this cache completely.
* ollamarunner: Check for minBatch of context space when shifting
Models can specify that a group of inputs need to be handled a single
batch. However, context shifting didn't respect this and could trigger
a break anyways. In this case, we should instead trigger a context
shift earlier so that it occurs before the grouped batch.
Note that there still some corner cases:
- A long prompt that exceeds the context window can get truncated
in the middle of an image. With the current models, this will
result in the model not recognizing the image at all, which is
pretty much the expected result with truncation.
- The context window is set less than the minimum batch size. The
only solution to this is to refuse to load the model with these
settings. However, this can never occur with current models and
default settings.
Since users are unlikely to run into these scenarios, fixing them is
left as a follow up.
* Applied latest patches from McBane87
See this for details: https://github.com/whyvl/ollama-vulkan/issues/7#issuecomment-2708820861
Signed-off-by: Vadim Grinco <vadim@grinco.eu>
* Add ability to enable flash attention on vulkan (#4)
* discover: add flash attention handling for vulkan
* envconfig: fix typo in config.go
As part of the process some code was refactored and I added a new field
FlashAttention to GpuInfo since the previous solution didn't allow for a
granular check via vulkan extensions. As a side effect, this now allows
for granular per-device FA support checking in other places
---------
Signed-off-by: Vadim Grinco <vadim@grinco.eu>
Co-authored-by: zeo <108888572+zeozeozeo@users.noreply.github.com>
Co-authored-by: Louis Beaumont <louis.beaumont@gmail.com>
Co-authored-by: Daniel Hiltgen <dhiltgen@users.noreply.github.com>
Co-authored-by: Michael Yang <mxyng@pm.me>
Co-authored-by: Parth Sareen <parth.sareen@ollama.com>
Co-authored-by: Jeffrey Morgan <jmorganca@gmail.com>
Co-authored-by: Bruce MacDonald <brucewmacdonald@gmail.com>
Co-authored-by: Jesse Gross <jesse@ollama.com>
Co-authored-by: Nikita <50599445+nasrally@users.noreply.github.com>
* Revert Readme changes
* Revert
* Revert changes in amd_linux.go
* Revert changes in amd_linux.go
* Remove flashattention setting gpu.go
* Revert whitespace changes in gpu.go
* Revert changes in transforms_test.go
* Revert changes in runner.go
* Revert changes in Makefile.sync
* Revert some unintented changes in Dockerfile
* Revert vulkan copy changes in Dockerfile
* Update Vulkan Code to de4c07f93783a1a96456a44dc16b9db538ee1618
* Fixed duplicate sync in ggml.go
* Revert changes in ggml.go
* Revert chnages in ggml.go
* enable falsh attention on vulkan
* revert remove parenthesis
* fixed flash attention logic enabling
* vk_check_flash_attention 0 means supported
* Update gpu.go
* Add vulkan to Windows Build script
* Remove commented out code
* Enable Vulkan Flash attention in FlashAttentionSupported
* Fix logging
* Update Vulkan backend to e54d41befcc1575f4c898c5ff4ef43970cead75f
* Removed libcap related code
libcap is not directly related to Vulkan and should be added by its own PR. It adds additional library dependencies for building and also requires users to run setcap or run ollama as root, which is not ideal for easy use
* Fix Unit Test (Add Vulkan Library)
* Add vulkan to TestHomogeneousGPUs
Test
* vulkan: get GPU ID (ollama v0.11.5)
Signed-off-by: Xiaodong Ye <xiaodong.ye@mthreads.com>
* disable mmap for vulkan
* Reduce Changes remove TestHomogeneousGPUs (doesn't exist on master)
* Update vulkan version to the version used in llama.cpp
* rename gpu patch to correct number
* added Vulkan API to get correct Device UUID
current UUID from pipelineCacheUUID does not match CUDA
* Fix GPU ID Patch
* Remove Code not in llama.cpp
* modified UUID code inside ggml
* Fix Patch
* Copied minimal definition from vulkan header
* Fix compile error in Mac
Metal is preferred so we're disabling Vulkan for now
* Removed unused code
Fix linter error in CI
* Fix patches apply
* fixing lint error
* Removed unneeded function call
Somehow removing this call fixed the crashing when Vulkan header was removed
* added missing NL
* Fixed missing members in Vulkan header
also added zero clear for some structs
* Fixed wrong structure ID
* Fixed Vulkan header
More aligned with official header definition now
* buildvulkanAsSeperateFunction
* Vulkan on Windows Test
* temporarly comment out gate to run windows task
* use temporarly windows-latest for build
* Commenting out other presets to build vulkan
* reenable cpu
* commenting out error action stop
* temporarly commenting out rocm
* set vulkan path
* comment out cude for faster turnaround
* correct vulkan install
* correct vulkan silent install
* fixed install command
* revert debugging changes (vulkan builds on windows)
* revert windows-latest
* trying to build vulkan for linux
* temporarly disable cuda and rocm
* try again linux build
* fix version
* trying to fix
* trying again
* trying again
* fix version
* fixed vulkan-sdk name
* try again
* trying again
* try without version number
* try again
* add some more extra
* trying to use version 1.4.313
* revert debugging changes
* Filter out already supported gpus
* revert debug code
* Use runners for GPU discovery
This revamps how we discover GPUs in the system by leveraging the Ollama
runner. This should eliminate inconsistency between our GPU discovery and the
runners capabilities at runtime, particularly for cases where we try to filter
out unsupported GPUs. Now the runner does that implicitly based on the actual
device list. In some cases free VRAM reporting can be unreliable which can
leaad to scheduling mistakes, so this also includes a patch to leverage more
reliable VRAM reporting libraries if available.
Automatic workarounds have been removed as only one GPU leveraged this, which
is now documented. This GPU will soon fall off the support matrix with the next
ROCm bump.
Additional cleanup of the scheduler and discovery packages can be done in the
future once we have switched on the new memory management code, and removed
support for the llama runner.
* timing info for runner
* WIP - wire up Vulkan with the new engine based discovery
Not a complete implementation - free VRAM is better, but not accurate on
windows
* fix - trust the library paths from discovery when starting runner
* fix index bug
* fix vulkan ids to be underlying
* fix - give bootstrapping more time on slow systems
* Test if Vulkan device is supported
* vk_check_flash_attention is not needed (coompat2 coopmapt and scalar implementation exist)
* Handle GGML_VK_VISIBLE_DEVICES
* ask for supported first
* win: fix CPU query buffer handling
Try in a short loop until we get the size right.
* test: harden integration tests for slow start
If the server takes a while to start up, block
tests from starting until it's online to avoid
setting large timeouts in individual test cases.
* gofumpt fix
* fix build
* merge fixes
* merge fixes
* fixed build
* merge fixes
* fixing build
* fixed build
* fixed formatting
* fixed build
* fix vulkan gpu id patch
* sync llama.cpp vulkan code
* update build windows script
* merge fixes
* fix format
* fixed vulkan casing
* handle igpu as gpu
* improve case
* print out unknown library
* rturn Vulkan for vulkan library
* Revert "rturn Vulkan for vulkan library"
This reverts commit 690461a12f.
* fixed patch number
* return Library Name
* remvoe debug code
* return integrated in vulkan backend
* Return pci Properties
* update patch
* directly get pci proeprties without parsing
* workaround for filtering devices. Correct way is to have a LibraryPosition Parameter in the deviceInfo
* Revert "directly get pci proeprties without parsing"
This reverts commit 8e0624851f.
* Set FilteredID for Environment Filtering
* ROCm Library is named ROCm
* revert changes in patch
* Create 0028-vulkan-pci-and-memory.patch
* vulkan memory patch
* casing fix
* Add more pci properties
* Added better memory management
* Added better memory managament
* fixed patch
* Fixed patch
* FilterID creation group by library
* filter out vulkan supported by other gpu
* fixing deviceid compare
* Vulkan Fix FA coopmat1 invalid array indexing
* Use everywhere the same Vulkan Version 1.4.321.1
* Remove unneeded patch
* vulkan update
* sync vulkan glsl files
* only use for vulkan the filteredid (numeric device number)
* simplify code
---------
Signed-off-by: Vadim Grinco <vadim@grinco.eu>
Signed-off-by: Xiaodong Ye <xiaodong.ye@mthreads.com>
Co-authored-by: pufferffish <github@bandersnatch.anonaddy.com>
Co-authored-by: KOISHI KOMEIJI FROM TOUHOU 11 <fuck>
Co-authored-by: DSLstandard <qgeneral35@gmail.com>
Co-authored-by: pufferffish <me@windtfw.com>
Co-authored-by: yeongbba <yeongmo.lee@logpresso.com>
Co-authored-by: tomaThomas <tomathomas@mailbox.org>
Co-authored-by: Antoine Viallon <antoine@lesviallon.fr>
Co-authored-by: Vadim Grinco <vadim@grinco.eu>
Co-authored-by: zeo <108888572+zeozeozeo@users.noreply.github.com>
Co-authored-by: Louis Beaumont <louis.beaumont@gmail.com>
Co-authored-by: Daniel Hiltgen <dhiltgen@users.noreply.github.com>
Co-authored-by: Michael Yang <mxyng@pm.me>
Co-authored-by: Parth Sareen <parth.sareen@ollama.com>
Co-authored-by: Jeffrey Morgan <jmorganca@gmail.com>
Co-authored-by: Bruce MacDonald <brucewmacdonald@gmail.com>
Co-authored-by: Jesse Gross <jesse@ollama.com>
Co-authored-by: Nikita <50599445+nasrally@users.noreply.github.com>
Co-authored-by: Masato Nakasaka <masato.nakasaka@intel.com>
Co-authored-by: Xiaodong Ye <xiaodong.ye@mthreads.com>
Co-authored-by: Daniel Hiltgen <daniel@ollama.com>
* feat: Bump llama.cpp to df1b612
Branch: LlamaCPPBump-GraniteDocling
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix(mtmd): Correctly encode text chunks during mtmd tokenization
There can be text chunks that appear interspersed with the image embeddings
that contain template delimiter tokens for some models. These need to be
correctly translated to text tokens.
Branch: LlamaCPPBump-GraniteDocling
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* tests: Use MtmdChunk in image_test
Branch: LlamaCPPBump-GraniteDocling
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* style: Fix unnecessary conversion linting
Branch: LlamaCPPBump-GraniteDocling
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix(ggml): Revert changes to ggml_hip.cpp
These changes were done largely by our code assistant and are likely wrong
Branch: LlamaCPPBump-GraniteDocling
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix: Revert changes in mem_nvml.cpp
Branch: LlamaCPPBump-GraniteDocling
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* feat: Update sync point to 1deee0
This brings in several more optimization commits and model support for
EmbeddingGemma
Branch: LlamaCPPBump-GraniteDocling
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* feat: Update patches for 1deee0
Branch: LlamaCPPBump-GraniteDocling
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* feat: sync for bump to 1deee0
Branch: LlamaCPPBump-GraniteDocling
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix: Bad patch updates with errant `+`
Branch: LlamaCPPBump-GraniteDocling
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* feat: Bump llama.cpp/ggml to 7049736
Branch: LlamaCPPBump-GraniteDocling
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix: format-patches after latest bump
Branch: LlamaCPPBump-GraniteDocling
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
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Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
Michael Yang
Daniel Hiltgen
Gabe Goodhart
Michael Yang
Jeffrey Morgan
Jesse Gross
Grace
Thomas Stocker
virajwad
Santosh Bhavani