- 14 Oct, 2025 5 commits
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Devon Rifkin authored
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Daniel Hiltgen authored
On the llama runner, after the recent GGML bump a new log line reports incorrect 0 MiB free after our patch to remove memory from the props. This adjusts the llama.cpp code to fetch the actual free memory of the active device.
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Thomas Stocker authored
* 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:
😊 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: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 690461a12fd5e93295d174c97edefb2bc33285b1. * 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 8e0624851f5ed7d9f74518f574dfb422e4dd4dc2. * 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:
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:
Masato Nakasaka <masato.nakasaka@intel.com> Co-authored-by:
Daniel Hiltgen <daniel@ollama.com>
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Devon Rifkin authored
add registries for parsers/renderers
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Devon Rifkin authored
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- 13 Oct, 2025 5 commits
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Grace authored
* working (other than tool call is the incorrect order) for tool calls and tools * Tests work, other than image tags (tests do not go through server) and tools (not in the correct order, but contents are the same) * testing for qwen3vl parser - toolparser is working * made changes to JSON tool parser, wraps the TollCallFunction with a TollCall object * Working parser for thinking models - assumes state of thinking, emits unambiguous content in thinking, does not call tool call in thinking * changed the parser to start with collecting content * thinking prefill * add hasThinkingSupport parameter to parser * qwen3-vl -> qwen3-vl-instruct for renderer/parser * Add hasThinkingSupport=false to QwenVLParser --------- Co-authored-by:Devon Rifkin <drifkin@drifkin.net>
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Gabe Goodhart authored
Llama cpp bump (df1b612): granite docling / mamba2 optimizations / multimodal encoding fixes (#12552) * 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:
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Jeffrey Morgan authored
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Michael Yang authored
This reverts commit 3d32249c.
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Michael Yang authored
deepseek's qwen3 distill uses a different rope scheme so support both
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- 11 Oct, 2025 5 commits
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Jeffrey Morgan authored
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Devon Rifkin authored
routes: fix built-in renderers for `api/generate`
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Devon Rifkin authored
Made it so when api/generate builds up a message array and generates the prompt it now goes through the same function as `api/chat` for consistency. This is where we hook the optional built-in renderers to bypass templates, which was missing for `api/generate` before this change. Closes: #12578
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frob authored
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Daniel Hiltgen authored
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- 10 Oct, 2025 10 commits
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Michael Yang authored
hardErrCh will deadlock since forwardBatch is blocked on computeStartedCh which never gets sent. since the response to hardErrCh is to panic, just panic instead
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Daniel Hiltgen authored
* implement nvml for linux * Improve scheduler logging when VRAM doesn't recover
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Michael Yang authored
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Michael Yang authored
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Michael Yang authored
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Michael Yang authored
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Michael Yang authored
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yajianggroup authored
Signed-off-by:yajianggroup <yajianggroup@outlook.com>
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Jeffrey Morgan authored
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Patrick Devine authored
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- 09 Oct, 2025 9 commits
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shengxinjing authored
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shengxinjing authored
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Michael Yang authored
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Michael Yang authored
this change updates how metrics are collected. until now, performance metrics, specifically initial input processing and subsequent generation durations, were collected by taking the timestamp when creating a new sequence, the first token generation, and completing generation. the processing duration is taken as first token generation sub sequence creation while generation is taken as completing generation sub first token generation. while this approach is an accurate end-to-end metric of processing and generation, it's not comparable to other tools which only measure the active, i.e. decode, duration. this change updates the metrics to only capture decode duration so it can be more directly compared to other tools
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Daniel Hiltgen authored
* logs: quiet down context canceled on completion If the client closes the connection before Completion finishes, we were logging at error level implying the runner crashed which was misleading. time=2025-10-08T22:59:20.566-07:00 level=ERROR source=server.go:1490 msg="post predict" error="Post \"http://127.0.0.1:57736/completion\": context canceled" * quiet down scheduler log error on expected case Since we don't hold the lock while performing memory load calculations, other runners can unload in parallel, so finding no runner to unload is a valid scenario which we shouldn't log at error level.
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Parth Sareen authored
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Patrick Devine authored
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Jeffrey Morgan authored
This reverts commit 6a62b894.
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Jeffrey Morgan authored
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- 08 Oct, 2025 4 commits
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Patrick Devine authored
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Jesse Gross authored
Sliding windows models (e.g. gpt-oss, gemma3) remove tokens that are out of the cache's window each time we start a new forward pass. The cache storage needs to handle the window size for each sequence plus the batch size, since the batch needs to attend to the full window size. This means that we have greater than a window size stored while processing the batch. When the next batch comes, we are currently only looking at the sequences in the incoming batch to slide the window forward. However, we also need to clean up the other sequences that might be occupying space in the batch processing buffer to ensure each sequence is only using its window size of storage. Failure to do this can result in "no kv cache slot found" errors. Fixes: #10127
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Jesse Gross authored
GGML picks the wrong kernel and these systems fail with: Sep 28 22:25:39 xavier ollama[48999]: //ml/backend/ggml/ggml/src/ggml-cuda/fattn-wmma-f16.cu:437: ERROR: CUDA kernel flash_attn_ext_f16 has no device code compatible with CUDA arch 720. ggml-cuda.cu was compiled for: __CUDA_ARCH_LIST__ Fixes #12442
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Daniel Hiltgen authored
Remove some flaky scenarios, and switch to chat for better reliability
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- 07 Oct, 2025 2 commits
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Daniel Hiltgen authored
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Daniel Hiltgen authored
* Bring back escape valve for llm libraries If the new discovery logic picks the wrong library, this gives users the ability to force a specific one using the same pattern as before. This can also potentially speed up bootstrap discovery if one of the libraries takes a long time to load and ultimately bind to no devices. For example unsupported AMD iGPUS can sometimes take a while to discover and rule out. * Bypass extra discovery on jetpack systems On at least Jetpack6, cuda_v12 appears to expose the iGPU, but crashes later on in cublasInit so if we detect a Jetpack, short-circuit and use that variant.
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