ollama/envconfig/config.go

298 lines
10 KiB
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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
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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}
}
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// Origins returns a list of allowed origins. Origins can be configured via the OLLAMA_ORIGINS environment variable.
func Origins() (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://*",
)
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 Bool(k string) func() bool {
return func() 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 false
}
}
var (
// Debug enabled additional debug information.
Debug = Bool("OLLAMA_DEBUG")
// FlashAttention enables the experimental flash attention feature.
FlashAttention = Bool("OLLAMA_FLASH_ATTENTION")
// 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")
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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")
TmpDir = String("OLLAMA_TMPDIR")
CudaVisibleDevices = String("CUDA_VISIBLE_DEVICES")
HipVisibleDevices = String("HIP_VISIBLE_DEVICES")
RocrVisibleDevices = String("ROCR_VISIBLE_DEVICES")
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", 0)
// 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)
// MaxVRAM sets a maximum VRAM override in bytes. MaxVRAM can be configured via the OLLAMA_MAX_VRAM environment variable.
MaxVRAM = Uint("OLLAMA_MAX_VRAM", 0)
)
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{
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"OLLAMA_DEBUG": {"OLLAMA_DEBUG", Debug(), "Show additional debug information (e.g. OLLAMA_DEBUG=1)"},
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"OLLAMA_FLASH_ATTENTION": {"OLLAMA_FLASH_ATTENTION", FlashAttention(), "Enabled flash attention"},
"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"},
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"OLLAMA_ORIGINS": {"OLLAMA_ORIGINS", Origins(), "A comma separated list of allowed origins"},
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"OLLAMA_SCHED_SPREAD": {"OLLAMA_SCHED_SPREAD", SchedSpread(), "Always schedule model across all GPUs"},
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"OLLAMA_TMPDIR": {"OLLAMA_TMPDIR", TmpDir(), "Location for temporary files"},
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"},
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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"}
ret["ROCR_VISIBLE_DEVICES"] = EnvVar{"ROCR_VISIBLE_DEVICES", RocrVisibleDevices(), "Set which AMD devices are visible"}
ret["GPU_DEVICE_ORDINAL"] = EnvVar{"GPU_DEVICE_ORDINAL", GpuDeviceOrdinal(), "Set which AMD devices are visible"}
ret["HSA_OVERRIDE_GFX_VERSION"] = EnvVar{"HSA_OVERRIDE_GFX_VERSION", HsaOverrideGfxVersion(), "Override the gfx used for all detected AMD GPUs"}
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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
}
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// 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)), "\"'")
}
// On windows, we keep the binary at the top directory, but
// other platforms use a "bin" directory, so this returns ".."
func LibRelativeToExe() string {
if runtime.GOOS == "windows" {
return "."
}
return ".."
}