ollama/envconfig/config.go

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package envconfig
import (
"fmt"
"log/slog"
"math"
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"net"
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"net/url"
"os"
"path/filepath"
"runtime"
"strconv"
"strings"
"time"
)
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// Host returns the scheme and host. Host can be configured via the OLLAMA_HOST environment variable.
// Default is scheme "http" and host "127.0.0.1:11434"
func Host() *url.URL {
defaultPort := "11434"
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s := strings.TrimSpace(Var("OLLAMA_HOST"))
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scheme, hostport, ok := strings.Cut(s, "://")
switch {
case !ok:
scheme, hostport = "http", s
if s == "ollama.com" {
scheme, hostport = "https", "ollama.com:443"
}
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case scheme == "http":
defaultPort = "80"
case scheme == "https":
defaultPort = "443"
}
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hostport, path, _ := strings.Cut(hostport, "/")
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host, port, err := net.SplitHostPort(hostport)
if err != nil {
host, port = "127.0.0.1", defaultPort
if ip := net.ParseIP(strings.Trim(hostport, "[]")); ip != nil {
host = ip.String()
} else if hostport != "" {
host = hostport
}
}
if n, err := strconv.ParseInt(port, 10, 32); err != nil || n > 65535 || n < 0 {
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slog.Warn("invalid port, using default", "port", port, "default", defaultPort)
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port = defaultPort
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}
return &url.URL{
Scheme: scheme,
Host: net.JoinHostPort(host, port),
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Path: path,
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}
}
// AllowedOrigins returns a list of allowed origins. AllowedOrigins can be configured via the OLLAMA_ORIGINS environment variable.
func AllowedOrigins() (origins []string) {
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if s := Var("OLLAMA_ORIGINS"); s != "" {
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origins = strings.Split(s, ",")
}
for _, origin := range []string{"localhost", "127.0.0.1", "0.0.0.0"} {
origins = append(origins,
fmt.Sprintf("http://%s", origin),
fmt.Sprintf("https://%s", origin),
fmt.Sprintf("http://%s", net.JoinHostPort(origin, "*")),
fmt.Sprintf("https://%s", net.JoinHostPort(origin, "*")),
)
}
origins = append(origins,
"app://*",
"file://*",
"tauri://*",
"vscode-webview://*",
"vscode-file://*",
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)
return origins
}
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// Models returns the path to the models directory. Models directory can be configured via the OLLAMA_MODELS environment variable.
// Default is $HOME/.ollama/models
func Models() string {
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if s := Var("OLLAMA_MODELS"); s != "" {
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return s
}
home, err := os.UserHomeDir()
if err != nil {
panic(err)
}
return filepath.Join(home, ".ollama", "models")
}
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// KeepAlive returns the duration that models stay loaded in memory. KeepAlive can be configured via the OLLAMA_KEEP_ALIVE environment variable.
// Negative values are treated as infinite. Zero is treated as no keep alive.
// Default is 5 minutes.
func KeepAlive() (keepAlive time.Duration) {
keepAlive = 5 * time.Minute
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if s := Var("OLLAMA_KEEP_ALIVE"); s != "" {
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if d, err := time.ParseDuration(s); err == nil {
keepAlive = d
} else if n, err := strconv.ParseInt(s, 10, 64); err == nil {
keepAlive = time.Duration(n) * time.Second
}
}
if keepAlive < 0 {
return time.Duration(math.MaxInt64)
}
return keepAlive
}
// LoadTimeout returns the duration for stall detection during model loads. LoadTimeout can be configured via the OLLAMA_LOAD_TIMEOUT environment variable.
// Zero or Negative values are treated as infinite.
// Default is 5 minutes.
func LoadTimeout() (loadTimeout time.Duration) {
loadTimeout = 5 * time.Minute
if s := Var("OLLAMA_LOAD_TIMEOUT"); s != "" {
if d, err := time.ParseDuration(s); err == nil {
loadTimeout = d
} else if n, err := strconv.ParseInt(s, 10, 64); err == nil {
loadTimeout = time.Duration(n) * time.Second
}
}
if loadTimeout <= 0 {
return time.Duration(math.MaxInt64)
}
return loadTimeout
}
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func Remotes() []string {
var r []string
raw := strings.TrimSpace(Var("OLLAMA_REMOTES"))
if raw == "" {
r = []string{"ollama.com"}
} else {
r = strings.Split(raw, ",")
}
return r
}
func BoolWithDefault(k string) func(defaultValue bool) bool {
return func(defaultValue bool) bool {
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if s := Var(k); s != "" {
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b, err := strconv.ParseBool(s)
if err != nil {
return true
}
return b
}
return defaultValue
}
}
func Bool(k string) func() bool {
withDefault := BoolWithDefault(k)
return func() bool {
return withDefault(false)
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}
}
// LogLevel returns the log level for the application.
// Values are 0 or false INFO (Default), 1 or true DEBUG, 2 TRACE
func LogLevel() slog.Level {
level := slog.LevelInfo
if s := Var("OLLAMA_DEBUG"); s != "" {
if b, _ := strconv.ParseBool(s); b {
level = slog.LevelDebug
} else if i, _ := strconv.ParseInt(s, 10, 64); i != 0 {
level = slog.Level(i * -4)
}
}
return level
}
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var (
// FlashAttention enables the experimental flash attention feature.
FlashAttention = BoolWithDefault("OLLAMA_FLASH_ATTENTION")
// KvCacheType is the quantization type for the K/V cache.
KvCacheType = String("OLLAMA_KV_CACHE_TYPE")
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// NoHistory disables readline history.
NoHistory = Bool("OLLAMA_NOHISTORY")
// NoPrune disables pruning of model blobs on startup.
NoPrune = Bool("OLLAMA_NOPRUNE")
// SchedSpread allows scheduling models across all GPUs.
SchedSpread = Bool("OLLAMA_SCHED_SPREAD")
// IntelGPU enables experimental Intel GPU detection.
IntelGPU = Bool("OLLAMA_INTEL_GPU")
Re-introduce the `llama` package (#5034) * Re-introduce the llama package This PR brings back the llama package, making it possible to call llama.cpp and ggml APIs from Go directly via CGo. This has a few advantages: - C APIs can be called directly from Go without needing to use the previous "server" REST API - On macOS and for CPU builds on Linux and Windows, Ollama can be built without a go generate ./... step, making it easy to get up and running to hack on parts of Ollama that don't require fast inference - Faster build times for AVX,AVX2,CUDA and ROCM (a full build of all runners takes <5 min on a fast CPU) - No git submodule making it easier to clone and build from source This is a big PR, but much of it is vendor code except for: - llama.go CGo bindings - example/: a simple example of running inference - runner/: a subprocess server designed to replace the llm/ext_server package - Makefile an as minimal as possible Makefile to build the runner package for different targets (cpu, avx, avx2, cuda, rocm) Co-authored-by: Jesse Gross <jesse@ollama.com> Co-authored-by: Daniel Hiltgen <daniel@ollama.com> * cache: Clear old KV cache entries when evicting a slot When forking a cache entry, if no empty slots are available we evict the least recently used one and copy over the KV entries from the closest match. However, this copy does not overwrite existing values but only adds new ones. Therefore, we need to clear the old slot first. This change fixes two issues: - The KV cache fills up and runs out of space even though we think we are managing it correctly - Performance gets worse over time as we use new cache entries that are not hot in the processor caches * doc: explain golang objc linker warning (#6830) * llama: gather transitive dependencies for rocm for dist packaging (#6848) * Refine go server makefiles to be more DRY (#6924) This breaks up the monolithic Makefile for the Go based runners into a set of utility files as well as recursive Makefiles for the runners. Files starting with the name "Makefile" are buildable, while files that end with ".make" are utilities to include in other Makefiles. This reduces the amount of nearly identical targets and helps set a pattern for future community contributions for new GPU runner architectures. When we are ready to switch over to the Go runners, these files should move to the top of the repo, and we should add targets for the main CLI, as well as a helper "install" (put all the built binaries on the local system in a runnable state) and "dist" target (generate the various tar/zip files for distribution) for local developer use. * llama: don't create extraneous directories (#6988) * llama: Exercise the new build in CI (#6989) Wire up some basic sanity testing in CI for the Go runner. GPU runners are not covered yet. * llama: Refine developer docs for Go server (#6842) This enhances the documentation for development focusing on the new Go server. After we complete the transition further doc refinements can remove the "transition" discussion. * runner.go: Allocate batches for all sequences during init We should tell the model that we could have full batches for all sequences. We already do this when we allocate the batches but it was missed during initialization. * llama.go: Don't return nil from Tokenize on zero length input Potentially receiving nil in a non-error condition is surprising to most callers - it's better to return an empty slice. * runner.go: Remove stop tokens from cache If the last token is EOG then we don't return this and it isn't present in the cache (because it was never submitted to Decode). This works well for extending the cache entry with a new sequence. However, for multi-token stop sequences, we won't return any of the tokens but all but the last one will be in the cache. This means when the conversation continues the cache will contain tokens that don't overlap with the new prompt. This works (we will pick up the portion where there is overlap) but it causes unnecessary cache thrashing because we will fork the original cache entry as it is not a perfect match. By trimming the cache to the tokens that we actually return this issue can be avoided. * runner.go: Simplify flushing of pending tokens * runner.go: Update TODOs * runner.go: Don't panic when processing sequences If there is an error processing a sequence, we should return a clean HTTP error back to Ollama rather than panicing. This will make us more resilient to transient failures. Panics can still occur during startup as there is no way to serve requests if that fails. Co-authored-by: jmorganca <jmorganca@gmail.com> * runner.go: More accurately capture timings Currently prompt processing time doesn't capture the that it takes to tokenize the input, only decoding time. We should capture the full process to more accurately reflect reality. This is especially true once we start processing images where the initial processing can take significant time. This is also more consistent with the existing C++ runner. * runner.go: Support for vision models In addition to bringing feature parity with the C++ runner, this also incorporates several improvements: - Cache prompting works with images, avoiding the need to re-decode embeddings for every message in a conversation - Parallelism is supported, avoiding the need to restrict to one sequence at a time. (Though for now Ollama will not schedule them while we might need to fall back to the old runner.) Co-authored-by: jmorganca <jmorganca@gmail.com> * runner.go: Move Unicode checking code and add tests * runner.go: Export external cache members Runner and cache are in the same package so the change doesn't affect anything but it is more internally consistent. * runner.go: Image embedding cache Generating embeddings from images can take significant time (on my machine between 100ms and 8s depending on the model). Although we already cache the result of decoding these images, the embeddings need to be regenerated every time. This is not necessary if we get the same image over and over again, for example, during a conversation. This currently uses a very small cache with a very simple algorithm but it is easy to improve as is warranted. * llama: catch up on patches Carry forward solar-pro and cli-unicode patches * runner.go: Don't re-allocate memory for every batch We can reuse memory allocated from batch to batch since batch size is fixed. This both saves the cost of reallocation as well keeps the cache lines hot. This results in a roughly 1% performance improvement for token generation with Nvidia GPUs on Linux. * runner.go: Default to classic input cache policy The input cache as part of the go runner implemented a cache policy that aims to maximize hit rate in both single and multi- user scenarios. When there is a cache hit, the response is very fast. However, performance is actually slower when there is an input cache miss due to worse GPU VRAM locality. This means that performance is generally better overall for multi-user scenarios (better input cache hit rate, locality was relatively poor already). But worse for single users (input cache hit rate is about the same, locality is now worse). This defaults the policy back to the old one to avoid a regression but keeps the new one available through an environment variable OLLAMA_MULTIUSER_CACHE. This is left undocumented as the goal is to improve this in the future to get the best of both worlds without user configuration. For inputs that result in cache misses, on Nvidia/Linux this change improves performance by 31% for prompt processing and 13% for token generation. * runner.go: Increase size of response channel Generally the CPU can easily keep up with handling reponses that are generated but there's no reason not to let generation continue and handle things in larger batches if needed. * llama: Add CI to verify all vendored changes have patches (#7066) Make sure we don't accidentally merge changes in the vendored code that aren't also reflected in the patches. * llama: adjust clip patch for mingw utf-16 (#7065) * llama: adjust clip patch for mingw utf-16 * llama: ensure static linking of runtime libs Avoid runtime dependencies on non-standard libraries * runner.go: Enable llamafile (all platforms) and BLAS (Mac OS) These are two features that are shown on llama.cpp's system info that are currently different between the two runners. On my test systems the performance difference is very small to negligible but it is probably still good to equalize the features. * llm: Don't add BOS/EOS for tokenize requests This is consistent with what server.cpp currently does. It affects things like token processing counts for embedding requests. * runner.go: Don't cache prompts for embeddings Our integration with server.cpp implicitly disables prompt caching because it is not part of the JSON object being parsed, this makes the Go runner behavior similarly. Prompt caching has been seen to affect the results of text completions on certain hardware. The results are not wrong either way but they are non-deterministic. However, embeddings seem to be affected even on hardware that does not show this behavior for completions. For now, it is best to maintain consistency with the existing behavior. * runner.go: Adjust debug log levels Add system info printed at startup and quiet down noisier logging. * llama: fix compiler flag differences (#7082) Adjust the flags for the new Go server to more closely match the generate flow * llama: refine developer docs (#7121) * llama: doc and example clean up (#7122) * llama: doc and example clean up * llama: Move new dockerfile into llama dir Temporary home until we fully transition to the Go server * llama: runner doc cleanup * llama.go: Add description for Tokenize error case --------- Co-authored-by: Jesse Gross <jesse@ollama.com> Co-authored-by: Daniel Hiltgen <daniel@ollama.com> Co-authored-by: Daniel Hiltgen <dhiltgen@users.noreply.github.com>
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// MultiUserCache optimizes prompt caching for multi-user scenarios
MultiUserCache = Bool("OLLAMA_MULTIUSER_CACHE")
// Enable the new Ollama engine
NewEngine = Bool("OLLAMA_NEW_ENGINE")
// ContextLength sets the default context length
ContextLength = Uint("OLLAMA_CONTEXT_LENGTH", 4096)
// Auth enables authentication between the Ollama client and server
UseAuth = Bool("OLLAMA_AUTH")
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)
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func String(s string) func() string {
return func() string {
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return Var(s)
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}
}
var (
LLMLibrary = String("OLLAMA_LLM_LIBRARY")
CudaVisibleDevices = String("CUDA_VISIBLE_DEVICES")
HipVisibleDevices = String("HIP_VISIBLE_DEVICES")
RocrVisibleDevices = String("ROCR_VISIBLE_DEVICES")
Vulkan based on #9650 (#11835) * 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 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: 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>
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VkVisibleDevices = String("GGML_VK_VISIBLE_DEVICES")
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GpuDeviceOrdinal = String("GPU_DEVICE_ORDINAL")
HsaOverrideGfxVersion = String("HSA_OVERRIDE_GFX_VERSION")
)
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func Uint(key string, defaultValue uint) func() uint {
return func() uint {
if s := Var(key); s != "" {
if n, err := strconv.ParseUint(s, 10, 64); err != nil {
slog.Warn("invalid environment variable, using default", "key", key, "value", s, "default", defaultValue)
} else {
return uint(n)
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}
}
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return defaultValue
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}
}
var (
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// NumParallel sets the number of parallel model requests. NumParallel can be configured via the OLLAMA_NUM_PARALLEL environment variable.
NumParallel = Uint("OLLAMA_NUM_PARALLEL", 1)
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// MaxRunners sets the maximum number of loaded models. MaxRunners can be configured via the OLLAMA_MAX_LOADED_MODELS environment variable.
MaxRunners = Uint("OLLAMA_MAX_LOADED_MODELS", 0)
// MaxQueue sets the maximum number of queued requests. MaxQueue can be configured via the OLLAMA_MAX_QUEUE environment variable.
MaxQueue = Uint("OLLAMA_MAX_QUEUE", 512)
)
func Uint64(key string, defaultValue uint64) func() uint64 {
return func() uint64 {
if s := Var(key); s != "" {
if n, err := strconv.ParseUint(s, 10, 64); err != nil {
slog.Warn("invalid environment variable, using default", "key", key, "value", s, "default", defaultValue)
} else {
return n
}
}
return defaultValue
}
}
// Set aside VRAM per GPU
var GpuOverhead = Uint64("OLLAMA_GPU_OVERHEAD", 0)
type EnvVar struct {
Name string
Value any
Description string
}
func AsMap() map[string]EnvVar {
ret := map[string]EnvVar{
"OLLAMA_DEBUG": {"OLLAMA_DEBUG", LogLevel(), "Show additional debug information (e.g. OLLAMA_DEBUG=1)"},
"OLLAMA_FLASH_ATTENTION": {"OLLAMA_FLASH_ATTENTION", FlashAttention(false), "Enabled flash attention"},
"OLLAMA_KV_CACHE_TYPE": {"OLLAMA_KV_CACHE_TYPE", KvCacheType(), "Quantization type for the K/V cache (default: f16)"},
"OLLAMA_GPU_OVERHEAD": {"OLLAMA_GPU_OVERHEAD", GpuOverhead(), "Reserve a portion of VRAM per GPU (bytes)"},
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"OLLAMA_HOST": {"OLLAMA_HOST", Host(), "IP Address for the ollama server (default 127.0.0.1:11434)"},
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"OLLAMA_KEEP_ALIVE": {"OLLAMA_KEEP_ALIVE", KeepAlive(), "The duration that models stay loaded in memory (default \"5m\")"},
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"OLLAMA_LLM_LIBRARY": {"OLLAMA_LLM_LIBRARY", LLMLibrary(), "Set LLM library to bypass autodetection"},
"OLLAMA_LOAD_TIMEOUT": {"OLLAMA_LOAD_TIMEOUT", LoadTimeout(), "How long to allow model loads to stall before giving up (default \"5m\")"},
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"OLLAMA_MAX_LOADED_MODELS": {"OLLAMA_MAX_LOADED_MODELS", MaxRunners(), "Maximum number of loaded models per GPU"},
"OLLAMA_MAX_QUEUE": {"OLLAMA_MAX_QUEUE", MaxQueue(), "Maximum number of queued requests"},
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"OLLAMA_MODELS": {"OLLAMA_MODELS", Models(), "The path to the models directory"},
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"OLLAMA_NOHISTORY": {"OLLAMA_NOHISTORY", NoHistory(), "Do not preserve readline history"},
"OLLAMA_NOPRUNE": {"OLLAMA_NOPRUNE", NoPrune(), "Do not prune model blobs on startup"},
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"OLLAMA_NUM_PARALLEL": {"OLLAMA_NUM_PARALLEL", NumParallel(), "Maximum number of parallel requests"},
"OLLAMA_ORIGINS": {"OLLAMA_ORIGINS", AllowedOrigins(), "A comma separated list of allowed origins"},
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"OLLAMA_SCHED_SPREAD": {"OLLAMA_SCHED_SPREAD", SchedSpread(), "Always schedule model across all GPUs"},
Re-introduce the `llama` package (#5034) * Re-introduce the llama package This PR brings back the llama package, making it possible to call llama.cpp and ggml APIs from Go directly via CGo. This has a few advantages: - C APIs can be called directly from Go without needing to use the previous "server" REST API - On macOS and for CPU builds on Linux and Windows, Ollama can be built without a go generate ./... step, making it easy to get up and running to hack on parts of Ollama that don't require fast inference - Faster build times for AVX,AVX2,CUDA and ROCM (a full build of all runners takes <5 min on a fast CPU) - No git submodule making it easier to clone and build from source This is a big PR, but much of it is vendor code except for: - llama.go CGo bindings - example/: a simple example of running inference - runner/: a subprocess server designed to replace the llm/ext_server package - Makefile an as minimal as possible Makefile to build the runner package for different targets (cpu, avx, avx2, cuda, rocm) Co-authored-by: Jesse Gross <jesse@ollama.com> Co-authored-by: Daniel Hiltgen <daniel@ollama.com> * cache: Clear old KV cache entries when evicting a slot When forking a cache entry, if no empty slots are available we evict the least recently used one and copy over the KV entries from the closest match. However, this copy does not overwrite existing values but only adds new ones. Therefore, we need to clear the old slot first. This change fixes two issues: - The KV cache fills up and runs out of space even though we think we are managing it correctly - Performance gets worse over time as we use new cache entries that are not hot in the processor caches * doc: explain golang objc linker warning (#6830) * llama: gather transitive dependencies for rocm for dist packaging (#6848) * Refine go server makefiles to be more DRY (#6924) This breaks up the monolithic Makefile for the Go based runners into a set of utility files as well as recursive Makefiles for the runners. Files starting with the name "Makefile" are buildable, while files that end with ".make" are utilities to include in other Makefiles. This reduces the amount of nearly identical targets and helps set a pattern for future community contributions for new GPU runner architectures. When we are ready to switch over to the Go runners, these files should move to the top of the repo, and we should add targets for the main CLI, as well as a helper "install" (put all the built binaries on the local system in a runnable state) and "dist" target (generate the various tar/zip files for distribution) for local developer use. * llama: don't create extraneous directories (#6988) * llama: Exercise the new build in CI (#6989) Wire up some basic sanity testing in CI for the Go runner. GPU runners are not covered yet. * llama: Refine developer docs for Go server (#6842) This enhances the documentation for development focusing on the new Go server. After we complete the transition further doc refinements can remove the "transition" discussion. * runner.go: Allocate batches for all sequences during init We should tell the model that we could have full batches for all sequences. We already do this when we allocate the batches but it was missed during initialization. * llama.go: Don't return nil from Tokenize on zero length input Potentially receiving nil in a non-error condition is surprising to most callers - it's better to return an empty slice. * runner.go: Remove stop tokens from cache If the last token is EOG then we don't return this and it isn't present in the cache (because it was never submitted to Decode). This works well for extending the cache entry with a new sequence. However, for multi-token stop sequences, we won't return any of the tokens but all but the last one will be in the cache. This means when the conversation continues the cache will contain tokens that don't overlap with the new prompt. This works (we will pick up the portion where there is overlap) but it causes unnecessary cache thrashing because we will fork the original cache entry as it is not a perfect match. By trimming the cache to the tokens that we actually return this issue can be avoided. * runner.go: Simplify flushing of pending tokens * runner.go: Update TODOs * runner.go: Don't panic when processing sequences If there is an error processing a sequence, we should return a clean HTTP error back to Ollama rather than panicing. This will make us more resilient to transient failures. Panics can still occur during startup as there is no way to serve requests if that fails. Co-authored-by: jmorganca <jmorganca@gmail.com> * runner.go: More accurately capture timings Currently prompt processing time doesn't capture the that it takes to tokenize the input, only decoding time. We should capture the full process to more accurately reflect reality. This is especially true once we start processing images where the initial processing can take significant time. This is also more consistent with the existing C++ runner. * runner.go: Support for vision models In addition to bringing feature parity with the C++ runner, this also incorporates several improvements: - Cache prompting works with images, avoiding the need to re-decode embeddings for every message in a conversation - Parallelism is supported, avoiding the need to restrict to one sequence at a time. (Though for now Ollama will not schedule them while we might need to fall back to the old runner.) Co-authored-by: jmorganca <jmorganca@gmail.com> * runner.go: Move Unicode checking code and add tests * runner.go: Export external cache members Runner and cache are in the same package so the change doesn't affect anything but it is more internally consistent. * runner.go: Image embedding cache Generating embeddings from images can take significant time (on my machine between 100ms and 8s depending on the model). Although we already cache the result of decoding these images, the embeddings need to be regenerated every time. This is not necessary if we get the same image over and over again, for example, during a conversation. This currently uses a very small cache with a very simple algorithm but it is easy to improve as is warranted. * llama: catch up on patches Carry forward solar-pro and cli-unicode patches * runner.go: Don't re-allocate memory for every batch We can reuse memory allocated from batch to batch since batch size is fixed. This both saves the cost of reallocation as well keeps the cache lines hot. This results in a roughly 1% performance improvement for token generation with Nvidia GPUs on Linux. * runner.go: Default to classic input cache policy The input cache as part of the go runner implemented a cache policy that aims to maximize hit rate in both single and multi- user scenarios. When there is a cache hit, the response is very fast. However, performance is actually slower when there is an input cache miss due to worse GPU VRAM locality. This means that performance is generally better overall for multi-user scenarios (better input cache hit rate, locality was relatively poor already). But worse for single users (input cache hit rate is about the same, locality is now worse). This defaults the policy back to the old one to avoid a regression but keeps the new one available through an environment variable OLLAMA_MULTIUSER_CACHE. This is left undocumented as the goal is to improve this in the future to get the best of both worlds without user configuration. For inputs that result in cache misses, on Nvidia/Linux this change improves performance by 31% for prompt processing and 13% for token generation. * runner.go: Increase size of response channel Generally the CPU can easily keep up with handling reponses that are generated but there's no reason not to let generation continue and handle things in larger batches if needed. * llama: Add CI to verify all vendored changes have patches (#7066) Make sure we don't accidentally merge changes in the vendored code that aren't also reflected in the patches. * llama: adjust clip patch for mingw utf-16 (#7065) * llama: adjust clip patch for mingw utf-16 * llama: ensure static linking of runtime libs Avoid runtime dependencies on non-standard libraries * runner.go: Enable llamafile (all platforms) and BLAS (Mac OS) These are two features that are shown on llama.cpp's system info that are currently different between the two runners. On my test systems the performance difference is very small to negligible but it is probably still good to equalize the features. * llm: Don't add BOS/EOS for tokenize requests This is consistent with what server.cpp currently does. It affects things like token processing counts for embedding requests. * runner.go: Don't cache prompts for embeddings Our integration with server.cpp implicitly disables prompt caching because it is not part of the JSON object being parsed, this makes the Go runner behavior similarly. Prompt caching has been seen to affect the results of text completions on certain hardware. The results are not wrong either way but they are non-deterministic. However, embeddings seem to be affected even on hardware that does not show this behavior for completions. For now, it is best to maintain consistency with the existing behavior. * runner.go: Adjust debug log levels Add system info printed at startup and quiet down noisier logging. * llama: fix compiler flag differences (#7082) Adjust the flags for the new Go server to more closely match the generate flow * llama: refine developer docs (#7121) * llama: doc and example clean up (#7122) * llama: doc and example clean up * llama: Move new dockerfile into llama dir Temporary home until we fully transition to the Go server * llama: runner doc cleanup * llama.go: Add description for Tokenize error case --------- Co-authored-by: Jesse Gross <jesse@ollama.com> Co-authored-by: Daniel Hiltgen <daniel@ollama.com> Co-authored-by: Daniel Hiltgen <dhiltgen@users.noreply.github.com>
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"OLLAMA_MULTIUSER_CACHE": {"OLLAMA_MULTIUSER_CACHE", MultiUserCache(), "Optimize prompt caching for multi-user scenarios"},
"OLLAMA_CONTEXT_LENGTH": {"OLLAMA_CONTEXT_LENGTH", ContextLength(), "Context length to use unless otherwise specified (default: 4096)"},
"OLLAMA_NEW_ENGINE": {"OLLAMA_NEW_ENGINE", NewEngine(), "Enable the new Ollama engine"},
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"OLLAMA_REMOTES": {"OLLAMA_REMOTES", Remotes(), "Allowed hosts for remote models (default \"ollama.com\")"},
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// Informational
"HTTP_PROXY": {"HTTP_PROXY", String("HTTP_PROXY")(), "HTTP proxy"},
"HTTPS_PROXY": {"HTTPS_PROXY", String("HTTPS_PROXY")(), "HTTPS proxy"},
"NO_PROXY": {"NO_PROXY", String("NO_PROXY")(), "No proxy"},
}
if runtime.GOOS != "windows" {
// Windows environment variables are case-insensitive so there's no need to duplicate them
ret["http_proxy"] = EnvVar{"http_proxy", String("http_proxy")(), "HTTP proxy"}
ret["https_proxy"] = EnvVar{"https_proxy", String("https_proxy")(), "HTTPS proxy"}
ret["no_proxy"] = EnvVar{"no_proxy", String("no_proxy")(), "No proxy"}
}
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if runtime.GOOS != "darwin" {
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ret["CUDA_VISIBLE_DEVICES"] = EnvVar{"CUDA_VISIBLE_DEVICES", CudaVisibleDevices(), "Set which NVIDIA devices are visible"}
ret["HIP_VISIBLE_DEVICES"] = EnvVar{"HIP_VISIBLE_DEVICES", HipVisibleDevices(), "Set which AMD devices are visible by numeric ID"}
ret["ROCR_VISIBLE_DEVICES"] = EnvVar{"ROCR_VISIBLE_DEVICES", RocrVisibleDevices(), "Set which AMD devices are visible by UUID or numeric ID"}
Vulkan based on #9650 (#11835) * 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 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: 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>
2025-10-15 01:59:58 +08:00
ret["GGML_VK_VISIBLE_DEVICES"] = EnvVar{"GGML_VK_VISIBLE_DEVICES", VkVisibleDevices(), "Set which Vulkan devices are visible by numeric ID"}
ret["GPU_DEVICE_ORDINAL"] = EnvVar{"GPU_DEVICE_ORDINAL", GpuDeviceOrdinal(), "Set which AMD devices are visible by numeric ID"}
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ret["HSA_OVERRIDE_GFX_VERSION"] = EnvVar{"HSA_OVERRIDE_GFX_VERSION", HsaOverrideGfxVersion(), "Override the gfx used for all detected AMD GPUs"}
2024-07-04 08:22:13 +08:00
ret["OLLAMA_INTEL_GPU"] = EnvVar{"OLLAMA_INTEL_GPU", IntelGPU(), "Enable experimental Intel GPU detection"}
}
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return ret
}
func Values() map[string]string {
vals := make(map[string]string)
for k, v := range AsMap() {
vals[k] = fmt.Sprintf("%v", v.Value)
}
return vals
}
2024-07-09 01:34:12 +08:00
// Var returns an environment variable stripped of leading and trailing quotes or spaces
func Var(key string) string {
return strings.Trim(strings.TrimSpace(os.Getenv(key)), "\"'")
}