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Rename gpu package discover (#7143)

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Cleaning up go package naming
This commit is contained in:
Daniel Hiltgen 2024-10-16 17:45:00 -07:00 committed by GitHub
parent 7d6eb0d4c3
commit 05cd82ef94
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GPG key ID: B5690EEEBB952194
33 changed files with 94 additions and 94 deletions
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2
gpu/amd_common.go → discover/amd_common.go
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@ -1,6 +1,6 @@
//go:build linux || windows
package gpu
package discover
import (
"errors"

2
gpu/amd_hip_windows.go → discover/amd_hip_windows.go
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@ -1,4 +1,4 @@
package gpu
package discover
import (
"errors"

2
gpu/amd_linux.go → discover/amd_linux.go
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@ -1,4 +1,4 @@
package gpu
package discover
import (
"bufio"

2
gpu/amd_windows.go → discover/amd_windows.go
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@ -1,4 +1,4 @@
package gpu
package discover
import (
"bytes"

2
gpu/cpu_common.go → discover/cpu_common.go
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@ -1,4 +1,4 @@
package gpu
package discover
import (
"os"

2
gpu/cuda_common.go → discover/cuda_common.go
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@ -1,6 +1,6 @@
//go:build linux || windows
package gpu
package discover
import (
"log/slog"

2
gpu/gpu.go → discover/gpu.go
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@ -1,6 +1,6 @@
//go:build linux || windows
package gpu
package discover
/*
#cgo linux LDFLAGS: -lrt -lpthread -ldl -lstdc++ -lm

2
gpu/gpu_darwin.go → discover/gpu_darwin.go
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@ -1,6 +1,6 @@
//go:build darwin
package gpu
package discover
/*
#cgo CFLAGS: -x objective-c

0
gpu/gpu_info.h → discover/gpu_info.h
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0
gpu/gpu_info_cudart.c → discover/gpu_info_cudart.c
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0
gpu/gpu_info_cudart.h → discover/gpu_info_cudart.h
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0
gpu/gpu_info_darwin.h → discover/gpu_info_darwin.h
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0
gpu/gpu_info_darwin.m → discover/gpu_info_darwin.m
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0
gpu/gpu_info_nvcuda.c → discover/gpu_info_nvcuda.c
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0
gpu/gpu_info_nvcuda.h → discover/gpu_info_nvcuda.h
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0
gpu/gpu_info_nvml.c → discover/gpu_info_nvml.c
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0
gpu/gpu_info_nvml.h → discover/gpu_info_nvml.h
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0
gpu/gpu_info_oneapi.c → discover/gpu_info_oneapi.c
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0
gpu/gpu_info_oneapi.h → discover/gpu_info_oneapi.h
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2
gpu/gpu_linux.go → discover/gpu_linux.go
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@ -1,4 +1,4 @@
package gpu
package discover
import (
"bufio"

2
gpu/gpu_oneapi.go → discover/gpu_oneapi.go
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@ -1,6 +1,6 @@
//go:build linux || windows
package gpu
package discover
import (
"log/slog"

2
gpu/gpu_test.go → discover/gpu_test.go
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@ -1,4 +1,4 @@
package gpu
package discover
import (
"runtime"

2
gpu/gpu_windows.go → discover/gpu_windows.go
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@ -1,4 +1,4 @@
package gpu
package discover
import (
"fmt"

2
gpu/gpu_windows_test.go → discover/gpu_windows_test.go
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@ -1,4 +1,4 @@
package gpu
package discover
import "testing"

2
gpu/types.go → discover/types.go
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@ -1,4 +1,4 @@
package gpu
package discover
import (
"fmt"

8
llm/memory.go
View file

@ -7,13 +7,13 @@ import (
"strings"
"github.com/ollama/ollama/api"
"github.com/ollama/ollama/discover"
"github.com/ollama/ollama/envconfig"
"github.com/ollama/ollama/format"
"github.com/ollama/ollama/gpu"
)
// This algorithm looks for a complete fit to determine if we need to unload other models
func PredictServerFit(allGpus gpu.GpuInfoList, ggml *GGML, adapters, projectors []string, opts api.Options) (bool, uint64) {
func PredictServerFit(allGpus discover.GpuInfoList, ggml *GGML, adapters, projectors []string, opts api.Options) (bool, uint64) {
// Split up the GPUs by type and try them
var estimatedVRAM uint64
for _, gpus := range allGpus.ByLibrary() {
@ -67,7 +67,7 @@ type MemoryEstimate struct {
// Given a model and one or more GPU targets, predict how many layers and bytes we can load, and the total size
// The GPUs provided must all be the same Library
func EstimateGPULayers(gpus []gpu.GpuInfo, ggml *GGML, projectors []string, opts api.Options) MemoryEstimate {
func EstimateGPULayers(gpus []discover.GpuInfo, ggml *GGML, projectors []string, opts api.Options) MemoryEstimate {
// Graph size for a partial offload, applies to all GPUs
var graphPartialOffload uint64
@ -157,7 +157,7 @@ func EstimateGPULayers(gpus []gpu.GpuInfo, ggml *GGML, projectors []string, opts
gpuAllocations := make([]uint64, len(gpus))
type gs struct {
i int
g *gpu.GpuInfo
g *discover.GpuInfo
}
gpusWithSpace := []gs{}
for i := range gpus {

6
llm/memory_test.go
View file

@ -10,7 +10,7 @@ import (
"github.com/stretchr/testify/require"
"github.com/ollama/ollama/api"
"github.com/ollama/ollama/gpu"
"github.com/ollama/ollama/discover"
)
func TestEstimateGPULayers(t *testing.T) {
@ -50,7 +50,7 @@ func TestEstimateGPULayers(t *testing.T) {
}
// Simple CPU scenario
gpus := []gpu.GpuInfo{
gpus := []discover.GpuInfo{
{
Library: "cpu",
},
@ -72,7 +72,7 @@ func TestEstimateGPULayers(t *testing.T) {
// Dual CUDA scenario with assymetry
gpuMinimumMemory := uint64(2048)
gpus = []gpu.GpuInfo{
gpus = []discover.GpuInfo{
{
Library: "cuda",
MinimumMemory: gpuMinimumMemory,

14
llm/server.go
View file

@ -26,9 +26,9 @@ import (
"github.com/ollama/ollama/api"
"github.com/ollama/ollama/build"
"github.com/ollama/ollama/discover"
"github.com/ollama/ollama/envconfig"
"github.com/ollama/ollama/format"
"github.com/ollama/ollama/gpu"
"github.com/ollama/ollama/llama"
"github.com/ollama/ollama/runners"
)
@ -61,7 +61,7 @@ type llmServer struct {
estimate MemoryEstimate
totalLayers uint64
// gpuCount int
gpus gpu.GpuInfoList // Recorded just before the model loaded, free space will be incorrect
gpus discover.GpuInfoList // Recorded just before the model loaded, free space will be incorrect
loadDuration time.Duration // Record how long it took the model to load
loadProgress float32
@ -90,7 +90,7 @@ func LoadModel(model string, maxArraySize int) (*GGML, error) {
// NewLlamaServer will run a server for the given GPUs
// The gpu list must be a single family.
func NewLlamaServer(gpus gpu.GpuInfoList, model string, ggml *GGML, adapters, projectors []string, opts api.Options, numParallel int) (LlamaServer, error) {
func NewLlamaServer(gpus discover.GpuInfoList, model string, ggml *GGML, adapters, projectors []string, opts api.Options, numParallel int) (LlamaServer, error) {
var err error
var cpuRunner string
var estimate MemoryEstimate
@ -98,7 +98,7 @@ func NewLlamaServer(gpus gpu.GpuInfoList, model string, ggml *GGML, adapters, pr
var systemFreeMemory uint64
var systemSwapFreeMemory uint64
systemInfo := gpu.GetSystemInfo()
systemInfo := discover.GetSystemInfo()
systemTotalMemory = systemInfo.System.TotalMemory
systemFreeMemory = systemInfo.System.FreeMemory
systemSwapFreeMemory = systemInfo.System.FreeSwap
@ -106,7 +106,7 @@ func NewLlamaServer(gpus gpu.GpuInfoList, model string, ggml *GGML, adapters, pr
// If the user wants zero GPU layers, reset the gpu list to be CPU/system ram info
if opts.NumGPU == 0 {
gpus = gpu.GetCPUInfo()
gpus = discover.GetCPUInfo()
}
if len(gpus) == 1 && gpus[0].Library == "cpu" {
cpuRunner = runners.ServerForCpu()
@ -122,7 +122,7 @@ func NewLlamaServer(gpus gpu.GpuInfoList, model string, ggml *GGML, adapters, pr
case gpus[0].Library != "metal" && estimate.Layers == 0:
// Don't bother loading into the GPU if no layers can fit
cpuRunner = runners.ServerForCpu()
gpus = gpu.GetCPUInfo()
gpus = discover.GetCPUInfo()
case opts.NumGPU < 0 && estimate.Layers > 0 && gpus[0].Library != "cpu":
opts.NumGPU = estimate.Layers
}
@ -281,7 +281,7 @@ func NewLlamaServer(gpus gpu.GpuInfoList, model string, ggml *GGML, adapters, pr
}
if strings.HasPrefix(servers[i], "cpu") {
gpus = gpu.GetCPUInfo()
gpus = discover.GetCPUInfo()
}
// Find an availableServers port, retry on each iteration in case the failure was a port conflict race

14
runners/common.go
View file

@ -18,8 +18,8 @@ import (
"golang.org/x/sync/errgroup"
"github.com/ollama/ollama/discover"
"github.com/ollama/ollama/envconfig"
"github.com/ollama/ollama/gpu"
)
const (
@ -301,11 +301,11 @@ func GetAvailableServers(payloadsDir string) map[string]string {
// serversForGpu returns a list of compatible servers give the provided GPU
// info, ordered by performance. assumes Init() has been called
// TODO - switch to metadata based mapping
func ServersForGpu(info gpu.GpuInfo) []string {
func ServersForGpu(info discover.GpuInfo) []string {
// glob workDir for files that start with ollama_
availableServers := GetAvailableServers(runnersDir)
requested := info.Library
if info.Variant != gpu.CPUCapabilityNone.String() {
if info.Variant != discover.CPUCapabilityNone.String() {
requested += "_" + info.Variant
}
@ -341,12 +341,12 @@ func ServersForGpu(info gpu.GpuInfo) []string {
if !(runtime.GOOS == "darwin" && runtime.GOARCH == "arm64") {
// Load up the best CPU variant if not primary requested
if info.Library != "cpu" {
variant := gpu.GetCPUCapability()
variant := discover.GetCPUCapability()
// If no variant, then we fall back to default
// If we have a variant, try that if we find an exact match
// Attempting to run the wrong CPU instructions will panic the
// process
if variant != gpu.CPUCapabilityNone {
if variant != discover.CPUCapabilityNone {
for cmp := range availableServers {
if cmp == "cpu_"+variant.String() {
servers = append(servers, cmp)
@ -371,9 +371,9 @@ func ServerForCpu() string {
if runtime.GOOS == "darwin" && runtime.GOARCH == "arm64" {
return "metal"
}
variant := gpu.GetCPUCapability()
variant := discover.GetCPUCapability()
availableServers := GetAvailableServers(runnersDir)
if variant != gpu.CPUCapabilityNone {
if variant != discover.CPUCapabilityNone {
for cmp := range availableServers {
if cmp == "cpu_"+variant.String() {
return cmp

4
server/routes.go
View file

@ -27,8 +27,8 @@ import (
"github.com/ollama/ollama/api"
"github.com/ollama/ollama/build"
"github.com/ollama/ollama/discover"
"github.com/ollama/ollama/envconfig"
"github.com/ollama/ollama/gpu"
"github.com/ollama/ollama/llm"
"github.com/ollama/ollama/openai"
"github.com/ollama/ollama/parser"
@ -1235,7 +1235,7 @@ func Serve(ln net.Listener) error {
// At startup we retrieve GPU information so we can get log messages before loading a model
// This will log warnings to the log in case we have problems with detected GPUs
gpus := gpu.GetGPUInfo()
gpus := discover.GetGPUInfo()
gpus.LogDetails()
err = srvr.Serve(ln)

18
server/routes_generate_test.go
View file

@ -15,7 +15,7 @@ import (
"github.com/google/go-cmp/cmp"
"github.com/ollama/ollama/api"
"github.com/ollama/ollama/gpu"
"github.com/ollama/ollama/discover"
"github.com/ollama/ollama/llm"
)
@ -41,8 +41,8 @@ func (mockRunner) Tokenize(_ context.Context, s string) (tokens []int, err error
return
}
func newMockServer(mock *mockRunner) func(gpu.GpuInfoList, string, *llm.GGML, []string, []string, api.Options, int) (llm.LlamaServer, error) {
return func(gpus gpu.GpuInfoList, model string, ggml *llm.GGML, projectors, system []string, opts api.Options, numParallel int) (llm.LlamaServer, error) {
func newMockServer(mock *mockRunner) func(discover.GpuInfoList, string, *llm.GGML, []string, []string, api.Options, int) (llm.LlamaServer, error) {
return func(gpus discover.GpuInfoList, model string, ggml *llm.GGML, projectors, system []string, opts api.Options, numParallel int) (llm.LlamaServer, error) {
return mock, nil
}
}
@ -69,10 +69,10 @@ func TestGenerateChat(t *testing.T) {
unloadedCh: make(chan any, 1),
loaded: make(map[string]*runnerRef),
newServerFn: newMockServer(&mock),
getGpuFn: gpu.GetGPUInfo,
getCpuFn: gpu.GetCPUInfo,
getGpuFn: discover.GetGPUInfo,
getCpuFn: discover.GetCPUInfo,
reschedDelay: 250 * time.Millisecond,
loadFn: func(req *LlmRequest, ggml *llm.GGML, gpus gpu.GpuInfoList, numParallel int) {
loadFn: func(req *LlmRequest, ggml *llm.GGML, gpus discover.GpuInfoList, numParallel int) {
// add small delay to simulate loading
time.Sleep(time.Millisecond)
req.successCh <- &runnerRef{
@ -367,10 +367,10 @@ func TestGenerate(t *testing.T) {
unloadedCh: make(chan any, 1),
loaded: make(map[string]*runnerRef),
newServerFn: newMockServer(&mock),
getGpuFn: gpu.GetGPUInfo,
getCpuFn: gpu.GetCPUInfo,
getGpuFn: discover.GetGPUInfo,
getCpuFn: discover.GetCPUInfo,
reschedDelay: 250 * time.Millisecond,
loadFn: func(req *LlmRequest, ggml *llm.GGML, gpus gpu.GpuInfoList, numParallel int) {
loadFn: func(req *LlmRequest, ggml *llm.GGML, gpus discover.GpuInfoList, numParallel int) {
// add small delay to simulate loading
time.Sleep(time.Millisecond)
req.successCh <- &runnerRef{

44
server/sched.go
View file

@ -15,9 +15,9 @@ import (
"time"
"github.com/ollama/ollama/api"
"github.com/ollama/ollama/discover"
"github.com/ollama/ollama/envconfig"
"github.com/ollama/ollama/format"
"github.com/ollama/ollama/gpu"
"github.com/ollama/ollama/llm"
)
@ -41,10 +41,10 @@ type Scheduler struct {
loaded map[string]*runnerRef
loadedMu sync.Mutex
loadFn func(req *LlmRequest, ggml *llm.GGML, gpus gpu.GpuInfoList, numParallel int)
newServerFn func(gpus gpu.GpuInfoList, model string, ggml *llm.GGML, adapters []string, projectors []string, opts api.Options, numParallel int) (llm.LlamaServer, error)
getGpuFn func() gpu.GpuInfoList
getCpuFn func() gpu.GpuInfoList
loadFn func(req *LlmRequest, ggml *llm.GGML, gpus discover.GpuInfoList, numParallel int)
newServerFn func(gpus discover.GpuInfoList, model string, ggml *llm.GGML, adapters []string, projectors []string, opts api.Options, numParallel int) (llm.LlamaServer, error)
getGpuFn func() discover.GpuInfoList
getCpuFn func() discover.GpuInfoList
reschedDelay time.Duration
}
@ -69,8 +69,8 @@ func InitScheduler(ctx context.Context) *Scheduler {
unloadedCh: make(chan interface{}, maxQueue),
loaded: make(map[string]*runnerRef),
newServerFn: llm.NewLlamaServer,
getGpuFn: gpu.GetGPUInfo,
getCpuFn: gpu.GetCPUInfo,
getGpuFn: discover.GetGPUInfo,
getCpuFn: discover.GetCPUInfo,
reschedDelay: 250 * time.Millisecond,
}
sched.loadFn = sched.load
@ -157,7 +157,7 @@ func (s *Scheduler) processPending(ctx context.Context) {
} else {
// Either no models are loaded or below envconfig.MaxRunners
// Get a refreshed GPU list
var gpus gpu.GpuInfoList
var gpus discover.GpuInfoList
if pending.opts.NumGPU == 0 {
gpus = s.getCpuFn()
} else {
@ -409,7 +409,7 @@ func (pending *LlmRequest) useLoadedRunner(runner *runnerRef, finished chan *Llm
}()
}
func (s *Scheduler) load(req *LlmRequest, ggml *llm.GGML, gpus gpu.GpuInfoList, numParallel int) {
func (s *Scheduler) load(req *LlmRequest, ggml *llm.GGML, gpus discover.GpuInfoList, numParallel int) {
if numParallel < 1 {
numParallel = 1
}
@ -470,7 +470,7 @@ func (s *Scheduler) load(req *LlmRequest, ggml *llm.GGML, gpus gpu.GpuInfoList,
}()
}
func (s *Scheduler) updateFreeSpace(allGpus gpu.GpuInfoList) {
func (s *Scheduler) updateFreeSpace(allGpus discover.GpuInfoList) {
type predKey struct {
Library string
ID string
@ -513,8 +513,8 @@ func (s *Scheduler) updateFreeSpace(allGpus gpu.GpuInfoList) {
// to avoid scheduling another model on the same GPU(s) that haven't stabilized.
// This routine returns the set of GPUs that do not have an active loading model.
// If all GPUs have loading models, an empty list will be returned (not a single CPU entry)
func (s *Scheduler) filterGPUsWithoutLoadingModels(allGpus gpu.GpuInfoList) gpu.GpuInfoList {
ret := append(gpu.GpuInfoList{}, allGpus...)
func (s *Scheduler) filterGPUsWithoutLoadingModels(allGpus discover.GpuInfoList) discover.GpuInfoList {
ret := append(discover.GpuInfoList{}, allGpus...)
s.loadedMu.Lock()
defer s.loadedMu.Unlock()
for _, runner := range s.loaded {
@ -542,7 +542,7 @@ type runnerRef struct {
llama llm.LlamaServer
loading bool // True only during initial load, then false forever
gpus gpu.GpuInfoList // Recorded at time of provisioning
gpus discover.GpuInfoList // Recorded at time of provisioning
estimatedVRAM uint64
estimatedTotal uint64
@ -630,7 +630,7 @@ func (runner *runnerRef) waitForVRAMRecovery() chan interface{} {
start := time.Now()
// Establish a baseline before we unload
gpusBefore := gpu.GetGPUInfo()
gpusBefore := discover.GetGPUInfo()
var totalMemoryBefore, freeMemoryBefore uint64
for _, gpu := range gpusBefore {
totalMemoryBefore += gpu.TotalMemory
@ -648,7 +648,7 @@ func (runner *runnerRef) waitForVRAMRecovery() chan interface{} {
}
// Query GPUs, look for free to go back up
gpusNow := gpu.GetGPUInfo()
gpusNow := discover.GetGPUInfo()
var totalMemoryNow, freeMemoryNow uint64
for _, gpu := range gpusNow {
totalMemoryNow += gpu.TotalMemory
@ -685,7 +685,7 @@ func (a ByDuration) Less(i, j int) bool {
// If the model can not be fit fully within the available GPU(s) nil is returned
// If numParallel is <= 0, this will attempt try to optimize parallism based on available VRAM, and adjust
// opts.NumCtx accordingly
func pickBestFullFitByLibrary(req *LlmRequest, ggml *llm.GGML, gpus gpu.GpuInfoList, numParallel *int) gpu.GpuInfoList {
func pickBestFullFitByLibrary(req *LlmRequest, ggml *llm.GGML, gpus discover.GpuInfoList, numParallel *int) discover.GpuInfoList {
var estimatedVRAM uint64
var numParallelToTry []int
@ -698,22 +698,22 @@ func pickBestFullFitByLibrary(req *LlmRequest, ggml *llm.GGML, gpus gpu.GpuInfoL
for _, gl := range gpus.ByLibrary() {
var ok bool
sgl := append(make(gpu.GpuInfoList, 0, len(gl)), gl...)
sgl := append(make(discover.GpuInfoList, 0, len(gl)), gl...)
// TODO - potentially sort by performance capability, existing models loaded, etc.
// TODO - Eliminate any GPUs that already have envconfig.MaxRunners loaded on them
// Note: at present, this will favor more VRAM over faster GPU speed in mixed setups
sort.Sort(sort.Reverse(gpu.ByFreeMemory(sgl)))
sort.Sort(sort.Reverse(discover.ByFreeMemory(sgl)))
// First attempt to fit the model into a single GPU
for _, p := range numParallelToTry {
req.opts.NumCtx = req.origNumCtx * p
if !envconfig.SchedSpread() {
for _, g := range sgl {
if ok, estimatedVRAM = llm.PredictServerFit([]gpu.GpuInfo{g}, ggml, req.model.AdapterPaths, req.model.ProjectorPaths, req.opts); ok {
if ok, estimatedVRAM = llm.PredictServerFit([]discover.GpuInfo{g}, ggml, req.model.AdapterPaths, req.model.ProjectorPaths, req.opts); ok {
slog.Info("new model will fit in available VRAM in single GPU, loading", "model", req.model.ModelPath, "gpu", g.ID, "parallel", p, "available", g.FreeMemory, "required", format.HumanBytes2(estimatedVRAM))
*numParallel = p
return []gpu.GpuInfo{g}
return []discover.GpuInfo{g}
}
}
}
@ -737,7 +737,7 @@ func pickBestFullFitByLibrary(req *LlmRequest, ggml *llm.GGML, gpus gpu.GpuInfoL
}
// If multiple Libraries are detected, pick the Library which loads the most layers for the model
func pickBestPartialFitByLibrary(req *LlmRequest, ggml *llm.GGML, gpus gpu.GpuInfoList, numParallel *int) gpu.GpuInfoList {
func pickBestPartialFitByLibrary(req *LlmRequest, ggml *llm.GGML, gpus discover.GpuInfoList, numParallel *int) discover.GpuInfoList {
if *numParallel <= 0 {
*numParallel = 1
req.opts.NumCtx = req.origNumCtx
@ -822,7 +822,7 @@ func (s *Scheduler) expireRunner(model *Model) {
// If other runners are loaded, make sure the pending request will fit in system memory
// If not, pick a runner to unload, else return nil and the request can be loaded
func (s *Scheduler) maybeFindCPURunnerToUnload(req *LlmRequest, ggml *llm.GGML, gpus gpu.GpuInfoList) *runnerRef {
func (s *Scheduler) maybeFindCPURunnerToUnload(req *LlmRequest, ggml *llm.GGML, gpus discover.GpuInfoList) *runnerRef {
slog.Debug("evaluating if CPU model load will fit in available system memory")
estimate := llm.EstimateGPULayers(gpus, ggml, req.model.ProjectorPaths, req.opts)
if estimate.TotalSize <= gpus[0].FreeMemory {

48
server/sched_test.go
View file

@ -13,8 +13,8 @@ import (
"github.com/ollama/ollama/api"
"github.com/ollama/ollama/app/lifecycle"
"github.com/ollama/ollama/discover"
"github.com/ollama/ollama/format"
"github.com/ollama/ollama/gpu"
"github.com/ollama/ollama/llm"
)
@ -47,10 +47,10 @@ func TestLoad(t *testing.T) {
sessionDuration: &api.Duration{Duration: 2 * time.Second},
}
// Fail to load model first
s.newServerFn = func(gpus gpu.GpuInfoList, model string, ggml *llm.GGML, adapters []string, projectors []string, opts api.Options, numParallel int) (llm.LlamaServer, error) {
s.newServerFn = func(gpus discover.GpuInfoList, model string, ggml *llm.GGML, adapters []string, projectors []string, opts api.Options, numParallel int) (llm.LlamaServer, error) {
return nil, errors.New("something failed to load model blah")
}
gpus := gpu.GpuInfoList{}
gpus := discover.GpuInfoList{}
s.load(req, ggml, gpus, 0)
require.Empty(t, req.successCh)
require.Len(t, req.errCh, 1)
@ -61,7 +61,7 @@ func TestLoad(t *testing.T) {
require.Contains(t, err.Error(), "this model may be incompatible")
server := &mockLlm{estimatedVRAM: 10, estimatedVRAMByGPU: map[string]uint64{}}
s.newServerFn = func(gpus gpu.GpuInfoList, model string, ggml *llm.GGML, adapters []string, projectors []string, opts api.Options, numParallel int) (llm.LlamaServer, error) {
s.newServerFn = func(gpus discover.GpuInfoList, model string, ggml *llm.GGML, adapters []string, projectors []string, opts api.Options, numParallel int) (llm.LlamaServer, error) {
return server, nil
}
s.load(req, ggml, gpus, 0)
@ -102,7 +102,7 @@ type reqBundle struct {
ggml *llm.GGML
}
func (scenario *reqBundle) newServer(gpus gpu.GpuInfoList, model string, ggml *llm.GGML, adapters []string, projectors []string, opts api.Options, numParallel int) (llm.LlamaServer, error) {
func (scenario *reqBundle) newServer(gpus discover.GpuInfoList, model string, ggml *llm.GGML, adapters []string, projectors []string, opts api.Options, numParallel int) (llm.LlamaServer, error) {
return scenario.srv, nil
}
@ -151,18 +151,18 @@ func newScenarioRequest(t *testing.T, ctx context.Context, modelName string, est
return b
}
func getGpuFn() gpu.GpuInfoList {
g := gpu.GpuInfo{Library: "metal"}
func getGpuFn() discover.GpuInfoList {
g := discover.GpuInfo{Library: "metal"}
g.TotalMemory = 24 * format.GigaByte
g.FreeMemory = 12 * format.GigaByte
return []gpu.GpuInfo{g}
return []discover.GpuInfo{g}
}
func getCpuFn() gpu.GpuInfoList {
g := gpu.GpuInfo{Library: "cpu"}
func getCpuFn() discover.GpuInfoList {
g := discover.GpuInfo{Library: "cpu"}
g.TotalMemory = 32 * format.GigaByte
g.FreeMemory = 26 * format.GigaByte
return []gpu.GpuInfo{g}
return []discover.GpuInfo{g}
}
func TestRequestsSameModelSameRequest(t *testing.T) {
@ -420,9 +420,9 @@ func TestExpireRunner(t *testing.T) {
}
var ggml *llm.GGML
gpus := gpu.GpuInfoList{}
gpus := discover.GpuInfoList{}
server := &mockLlm{estimatedVRAM: 10, estimatedVRAMByGPU: map[string]uint64{}}
s.newServerFn = func(gpus gpu.GpuInfoList, model string, ggml *llm.GGML, adapters []string, projectors []string, opts api.Options, numParallel int) (llm.LlamaServer, error) {
s.newServerFn = func(gpus discover.GpuInfoList, model string, ggml *llm.GGML, adapters []string, projectors []string, opts api.Options, numParallel int) (llm.LlamaServer, error) {
return server, nil
}
s.load(req, ggml, gpus, 0)
@ -460,11 +460,11 @@ func TestPrematureExpired(t *testing.T) {
// Same model, same request
scenario1a := newScenarioRequest(t, ctx, "ollama-model-1a", 10, nil)
s := InitScheduler(ctx)
s.getGpuFn = func() gpu.GpuInfoList {
g := gpu.GpuInfo{Library: "metal"}
s.getGpuFn = func() discover.GpuInfoList {
g := discover.GpuInfo{Library: "metal"}
g.TotalMemory = 24 * format.GigaByte
g.FreeMemory = 12 * format.GigaByte
return []gpu.GpuInfo{g}
return []discover.GpuInfo{g}
}
s.newServerFn = scenario1a.newServer
successCh1a, errCh1a := s.GetRunner(scenario1a.ctx, scenario1a.req.model, scenario1a.req.opts, scenario1a.req.sessionDuration)
@ -530,7 +530,7 @@ func TestUseLoadedRunner(t *testing.T) {
func TestUpdateFreeSpace(t *testing.T) {
ctx, done := context.WithTimeout(context.Background(), 100*time.Millisecond)
defer done()
gpus := gpu.GpuInfoList{
gpus := discover.GpuInfoList{
{
Library: "a",
ID: "1",
@ -563,7 +563,7 @@ func TestUpdateFreeSpace(t *testing.T) {
func TestFilterGPUsWithoutLoadingModels(t *testing.T) {
ctx, done := context.WithTimeout(context.Background(), 100*time.Millisecond)
defer done()
gpus := gpu.GpuInfoList{
gpus := discover.GpuInfoList{
{
Library: "cuda",
ID: "0",
@ -573,7 +573,7 @@ func TestFilterGPUsWithoutLoadingModels(t *testing.T) {
ID: "1",
},
}
r1 := &runnerRef{gpus: gpu.GpuInfoList{gpus[0]}, loading: true}
r1 := &runnerRef{gpus: discover.GpuInfoList{gpus[0]}, loading: true}
s := InitScheduler(ctx)
s.loadedMu.Lock()
@ -584,12 +584,12 @@ func TestFilterGPUsWithoutLoadingModels(t *testing.T) {
require.Len(t, tmp, 1)
require.Equal(t, "1", tmp[0].ID)
r1.gpus = gpu.GpuInfoList{gpus[1]}
r1.gpus = discover.GpuInfoList{gpus[1]}
tmp = s.filterGPUsWithoutLoadingModels(gpus)
require.Len(t, tmp, 1)
require.Equal(t, "0", tmp[0].ID)
r1.gpus = gpu.GpuInfoList{}
r1.gpus = discover.GpuInfoList{}
tmp = s.filterGPUsWithoutLoadingModels(gpus)
require.Len(t, tmp, 2)
}
@ -715,9 +715,9 @@ func TestHomogeneousGPUs(t *testing.T) {
defer done()
s := InitScheduler(ctx)
s.getGpuFn = func() gpu.GpuInfoList {
s.getGpuFn = func() discover.GpuInfoList {
// Set memory values to require the model to be spread
gpus := []gpu.GpuInfo{
gpus := []discover.GpuInfo{
{Library: "cuda"},
{Library: "rocm"},
}
@ -729,7 +729,7 @@ func TestHomogeneousGPUs(t *testing.T) {
}
s.getCpuFn = getCpuFn
a := newScenarioRequest(t, ctx, "ollama-model-1", 10, &api.Duration{Duration: 5 * time.Millisecond})
s.newServerFn = func(gpus gpu.GpuInfoList, model string, ggml *llm.GGML, adapters []string, projectors []string, opts api.Options, numParallel int) (llm.LlamaServer, error) {
s.newServerFn = func(gpus discover.GpuInfoList, model string, ggml *llm.GGML, adapters []string, projectors []string, opts api.Options, numParallel int) (llm.LlamaServer, error) {
require.Len(t, gpus, 1)
return a.newServer(gpus, model, ggml, adapters, projectors, opts, numParallel)
}